Authors,Author(s) ID,Title,Year,Source title,Volume,Issue,Art. No.,Page start,Page end,Page count,Cited by,DOI,Link,Affiliations,Authors with affiliations,Abstract,Author Keywords,Index Keywords,Molecular Sequence Numbers,Chemicals/CAS,Tradenames,Manufacturers,Funding Details,Funding Text 1,Funding Text 2,Funding Text 3,Funding Text 4,Funding Text 5,Funding Text 6,Funding Text 7,Funding Text 8,Funding Text 9,Funding Text 10,References,Correspondence Address,Editors,Sponsors,Publisher,Conference name,Conference date,Conference location,Conference code,ISSN,ISBN,CODEN,PubMed ID,Language of Original Document,Abbreviated Source Title,Document Type,Publication Stage,Open Access,Source,EID
"Sanjeevi N.S.S., Vashista V.","57197797888;53980678300;","Stiffness modulation of a cable-driven leg exoskeleton for effective human-robot interaction",2021,"Robotica","39","12",,"2172","2192",,1,"10.1017/S0263574721000242","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105220613&doi=10.1017%2fS0263574721000242&partnerID=40&md5=fa2e1e71f7daf20df8859fdc0df9f797","Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India","Sanjeevi, N.S.S., Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India; Vashista, V., Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India","With the widespread development of leg exoskeletons to provide external force-based repetitive training for gait rehabilitation, the prospect of undesired movement adaptation due to applied forces and imposed constraints require adequate investigation. A cable-driven leg exoskeleton, CDLE, presents a lightweight, flexible, and redundantly actuated architecture that enables the possibility of system parameters modulation to alter human-robot interaction while applying the desired forces. In this work, multi-joint stiffness performance of CDLE is formulated to systematically analyze human-CDLE interaction. Further, potential alterations in CDLE architecture are presented to tune human-CDLE interaction that favors the desired human leg movement during a gait rehabilitation paradigm. © 2021 The Author(s). Published by Cambridge University Press.","Cable-driven robots; Exoskeleton; Gait rehabilitation; Human-Robot interaction; Stiffness modulation","Agricultural robots; Cables; Exoskeleton (Robotics); Man machine systems; Modulation; Patient rehabilitation; Stiffness; Applied forces; External force; Gait rehabilitation; Leg exoskeleton; Leg movement; Redundantly actuated; Repetitive training; Stiffness modulation; Social robots",,,,,"Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; Science and Engineering Research Board, SERB","This work is partly supported from Inspire Faculty Award, DST, India, and from Early Career Research Award, SERB, India.",,,,,,,,,,"Das, S.K., Who steps stroke surveillance system: Feasibility in India (2009) Indian J. Med. Res., 130 (4), pp. 359-361; Olney, S.J., Richards, C., Hemiparetic gait following stroke. 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Res., 19 (9), pp. 835-847","Vashista, V.; Human Centered Robotics Lab, India; email: vineet.vashista@iitgn.ac.in",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85105220613
"Song J., Zhu A., Tu Y., Wu X., Zhang Y., Zhou X.","57203911021;8876295500;57210522898;57219625510;55972397900;57360377300;","Prediction of Expected Angle of Joints of Lower Limb Assisted Exoskeleton with Human-Robot Interaction [人机交互力对下肢助力外骨骼关节期望角度的预测]",2021,"Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University","55","12",,"55","63 and 69",,,"10.7652/xjtuxb202112007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120482443&doi=10.7652%2fxjtuxb202112007&partnerID=40&md5=e2197df2e135ed0f296654cc624897e5","Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China; Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, 710049, China; Taiyuan Research Institute of China Coal Technology & Engineering Group Co. Ltd., Taiyuan, 030006, China","Song, J., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Zhu, A., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Tu, Y., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Wu, X., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, 710049, China; Zhang, Y., Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, 710049, China; Zhou, X., Taiyuan Research Institute of China Coal Technology & Engineering Group Co. Ltd., Taiyuan, 030006, China","The mapping relationship between the joint angular velocity of the external skeleton and the joint torque generated by the interaction force of the legs is studied to solve the problem of poor human-robot interaction flexibility and discomfort caused by excessive human-robot interaction force in the use of lower limb assisted exoskeleton. A model to simplify the human lower limb swing phase leg into a plane two-bar model with the hip joint as the fulcrum is proposed, and a dynamic model of swing leg of human lower limb is established to obtain the gravity compensation item of human body. A force sensing unit is used to obtain the interaction force between the human body and the exoskeleton, and the leg interaction force between the exoskeleton and the human body is converted into the desired angle of the human hip and knee joint movement at the next moment. A non-linear mapping relationship between the swing phase joint angular velocity of human body and the leg interaction torque is established by means of the S-curve mapping and the admittance control model respectively. Experimental results show that both the expected angle prediction algorithm based on S-curve mapping and the expected angle prediction algorithm based on the admittance control model realize the continuous tracking of human motion intention from the exoskeleton. The expected angle prediction algorithm based on S-curve mapping makes the peak value of the thigh and leg interaction force less than 25 N and the peak value of shank and leg interaction force less than 20 N in the process of the exoskeleton following the human body movement; while the expected angle prediction algorithm based on the admittance control model makes the peak value of thigh-leg interaction force less than 15 N and the peak value of calf-leg interaction force less than 15 N in the process of the exoskeleton following human movement, and the joint angle curve is smooth, which can better identify human movement intention and improve the flexibility of human-robot interaction. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.","Admittance control; Human motion intention; Human-robot interaction force; Lower limb exoskeleton","Angular velocity; Exoskeleton (Robotics); Forecasting; Gravitation; Human robot interaction; Joints (anatomy); Man machine systems; Admittance control; Human bodies; Human motion intention; Human-robot interaction force; Humans-robot interactions; Interaction forces; Low limb exoskeleton; Lower limb; Peak values; Prediction algorithms; Mapping",,,,,,,,,,,,,,,,"ZHAO, Xingang, TAN, Xiaowei, ZHANG, Bi, Development of soft lower extremity exoskeleton and its key technologies: a survey (2020) Robot, 42 (3), pp. 365-384. , 赵新刚, 谈晓伟, 张弼. 柔性下肢外骨骼机器人研究进展及关键技术分析[J]. 机器人, 2020, 42(3): 365-384. 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Zhu, A.; Shaanxi Key Laboratory of Intelligent Robots, China",,,"Xi'an Jiaotong University",,,,,0253987X,,HCTPD,,"Chinese","Hsi An Chiao Tung Ta Hsueh",Article,"Final","",Scopus,2-s2.0-85120482443
"Bisconti P.","57205669543;","How Robots’ Unintentional Metacommunication Affects Human–Robot Interactions. A Systemic Approach",2021,"Minds and Machines","31","4",,"487","504",,,"10.1007/s11023-021-09584-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120626428&doi=10.1007%2fs11023-021-09584-5&partnerID=40&md5=939d48af38a541fb949dd8eae4c731e3","Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Piazza dei Martiri della Libertà, 33, Pisa, Italy","Bisconti, P., Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Piazza dei Martiri della Libertà, 33, Pisa, Italy","In this paper, we theoretically address the relevance of unintentional and inconsistent interactional elements in human–robot interactions. We argue that elements failing, or poorly succeeding, to reproduce a humanlike interaction create significant consequences in human–robot relational patterns and may affect human–human relations. When considering social interactions as systems, the absence of a precise interactional element produces a general reshaping of the interactional pattern, eventually generating new types of interactional settings. As an instance of this dynamic, we study the absence of metacommunicative abilities in social artifacts. Then, we analyze the pragmatic consequences of the aforementioned absence through the lens of Paul Watzlawick’s interactionist theory. We suggest that a fixed complementary interactional setting may be produced because of the asymmetric understanding, between robots and humans, of metacommunication. We highlight the psychological implications of this interactional asymmetry within Jessica Benjamin’s concept of “mutual recognition”. Finally, we point out the possible shift of dysfunctional interactional patterns from human–robot interactions to human–human ones. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Anthropomorphic robots; Behavioral inconsistency; HRI design; HRI social implications; Human–robot interaction; Interactionism; Nonverbal cues; Nonverbal interaction; Robotic metacommunication; Social robots","Human robot interaction; Machine design; Behavioral inconsistency; HRI design; HRI social implication; Humans-robot interactions; Interactionism; Meta-communication; Nonverbal cues; Nonverbal interaction; Nonverbals; Robotic metacommunication; Social implication; Social robots; Anthropomorphic robots",,,,,,,,,,,,,,,,"Baker, A.L., Phillips, E.K., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction: Current research and future directions (2018) ACM Transactions on Interactive Intelligent Systems (TiiS), 8 (4), pp. 1-30; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2020) Human-robot interaction: An introduction, , Cambridge University Press; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., Nonverbal interaction (2020) Human-Robot Interaction, pp. 81-97. , 9781108735; Benjamin, J., (2013) The bonds of love: Psychoanalysis, feminism, and the problem of domination, , Pantheon; Benjamin, J., (2017) Beyond doer and done to: Recognition theory, intersubjectivity and the third, , Routledge; Bisconti Lucidi, P., Nardi, D., Companion robots: The hallucinatory danger of human-robot interactions (2018) Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 17-22. , https://doi.org/10.1145/3278721.3278741; Briggs, G., Scheutz, M., The pragmatic social robot: Toward socially-sensitive utterance generation in human-robot interactions (2016) 2016 AAAI Fall Symposium Series, , In; Brinck, I., Balkenius, C., Mutual recognition in human-robot interaction: A deflationary account (2020) Philosophy & Technology; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may I serve you? 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"Corrales-Paredes A., Malfaz M., Egido-García V., Salichs M.Á.","17433438000;8613265500;57219970132;6603829011;","Waymarking in social robots: Environment signaling using human–robot interaction",2021,"Sensors","21","23","8145","","",,,"10.3390/s21238145","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120622897&doi=10.3390%2fs21238145&partnerID=40&md5=42211fe28f319155bad0911422a54634","Science, Computation and Technology Department, School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain; Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain","Corrales-Paredes, A., Science, Computation and Technology Department, School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain; Malfaz, M., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; Egido-García, V., School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain; Salichs, M.Á., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain","Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environ-ment. In this research, we will define waymarking as the skill of a robot to signal the environment or generate information to facilitate localization and navigation, both for its own use and for other robots as well. We present an automated environment signaling system using human–robot interaction and radio frequency identification (RFID) technology. The goal is for the robot, through human–robot interaction, to obtain information from the environment and use this information to carry out the signaling or waymarking process. HRI will play a key role in the signaling process since this type of communication makes it possible to exchange more specific and enriching information. The robot uses common phrases such as “Where am I?” and “Where can I go?”, just as we humans do when we ask other people for information about the environment. It is also possible to guide the robot and “show” it the environment to carry out the task of writing the signs. The robot will use the information received to create, update, or improve the navigation data in the RFID signals. In this paper, the signaling process will be described, how the robot acquires the information for signals, writing and updating process and finally, the implementation and integration in a real social robot in a real indoor environment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human–robot interaction; Indoor; Navigation; Radio frequency identification; RFID; Signaling; Social robots; Topological; Wayfinding; Waymarking","Human robot interaction; Indoor positioning systems; Information use; Radio frequency identification (RFID); Radio waves; Humans-robot interactions; Indoor; Radio-frequency-identification; Robot environment; Signaling process; Social robots; Topological; Way finding; Waymarking; Signaling",,,,,"CIPI/18/069; Ministerio de Ciencia, Innovación y Universidades, MCIU; Agencia Estatal de Investigación, AEI","This research was (partially) supported by project ?Robots sociales para estimulaci?n f?sica, cognitiva y afectiva de mayores (ROSES)? RTI2018-096338-B-I00 funded by Agencia Estatal de Investigaci?n (AEI), Ministerio de Ciencia, Innovaci?n y Universidades (Spain). 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Aplicación a un Sistema de Navegación Topológica, , Ph.D. Thesis, Universidad Carlos III de Madrid, Madrid, Spain; Egido, V., (2003) Sistema de Navegación Topológica para Robots Móviles autóNomos, , Ph.D. Thesis, Universidad Carlos III de Madrid, Madrid, Spain; Rivas, R., Corrales, A., Barber, R., Salichs, M.A., Robot Skill Abstraction for AD Architecture (2007) Proceedings of the 6th IFAC Symposium on Intelligent Autonomous Vehicles IAV, , Toulouse, France, 3–5 September; Corrales, A., Salichs, M.A., Use of RFID technology on a mobile robot for topological navigation tasks (2011) Proceedings of the 2011 IEEE International Conference on RFID-Technologies and Applications, pp. 408-414. , Sitges, Spain, 15–16 September; (2007) ISO 7001:2007 Graphical Symbols—Public Information Symbols, , International Organization for Standardization: Geneva, Switzerland; (2007) Accesibilidad Universal: Parte 1: Criterios DALCO Para Facilitar la Accesibilidad al Entorno, , Norma Española; UNE 170001-1, AENOR; Instituto Español de Normalización: Madrid, Spain; de Gorostiza, J.F., (2010) Programacion Natural de un Robot Social Mediante Dialogos, , Ph.D. Thesis, Universidad Carlos III de Madrid, Spain; Gorostiza, J.F., Salichs, M.A., End-user programming of a social robot by dialog (2011) Robot. Auton. Syst, 59, pp. 1102-1114; Alonso-Martin, F., Salichs, M., Integration of a Voice Recognition System In A Social Robot (2011) Cybern. Syst, 42, pp. 215-245; (2004) Speech Recognition Grammar Specification, , http://www.w3.org/TR/speech-grammar/, (accessed on 22 November 2021); Crocker, D., Overell, P., Official Internet Protocol Standards. Augmented BNF for Syntax Specifications (2008), ftp://ftp.rfc-editor.org/in-notes/std/std68.txt, ABNF. (accessed on 30 October 2021); Anderson, A., Bader, M., Bard, E., Boyle, E., Doherty, G., Garrod, S., Isard, S., Miller, J., The Hcrc Map Task Corpus (1991) Lang. Speech, 34, pp. 351-366. , 10.1177/002383099103400404; Zwarts, J., Spatial semantics: Modeling the meaning of prepositions (2017) Linguist. Lang. Compass, 11","Corrales-Paredes, A.; Science, Spain; email: anadelvalle.corrales@universidadeuropea.es",,,"MDPI",,,,,14248220,,,,"English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120622897
"Rossi A., Holthaus P., Perugia G., Moros S., Scheunemann M.","57189499587;36699613400;57193757433;57210801232;56549821200;","Trust, Acceptance and Social Cues in Human–Robot Interaction (SCRITA)",2021,"International Journal of Social Robotics","13","8",,"1833","1834",,,"10.1007/s12369-021-00844-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120361559&doi=10.1007%2fs12369-021-00844-z&partnerID=40&md5=7b6a737667edbe443ffc38dddb132c11","University of Naples Federico II, via Pansini, 5, Naples, 80131, Italy; University of Hertfordshire, Hatfield, United Kingdom; Uppsala University, Uppsala, Sweden","Rossi, A., University of Naples Federico II, via Pansini, 5, Naples, 80131, Italy; Holthaus, P., University of Hertfordshire, Hatfield, United Kingdom; Perugia, G., Uppsala University, Uppsala, Sweden; Moros, S., University of Hertfordshire, Hatfield, United Kingdom; Scheunemann, M., University of Hertfordshire, Hatfield, United Kingdom",[No abstract available],,,,,,,,,,,,,,,,,,,"Rossi, A.; University of Naples Federico II, via Pansini, 5, Italy; email: a.rossi@herts.ac.uk",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Editorial,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85120361559
"Hellou M., Gasteiger N., Lim J.Y., Jang M., Ahn H.S.","57216352354;57204357716;57197870753;55682654610;56218250500;","Personalization and localization in human-robot interaction: A review of technical methods",2021,"Robotics","10","4","120","","",,,"10.3390/robotics10040120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119061875&doi=10.3390%2frobotics10040120&partnerID=40&md5=8fc63af8c2725ca2603be25727a4b3b3","Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, 1010, New Zealand; School of Engineering, The University of Manchester, Manchester, M13 9PL, United Kingdom; School of Health Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom; Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea","Hellou, M., Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, 1010, New Zealand, School of Engineering, The University of Manchester, Manchester, M13 9PL, United Kingdom; Gasteiger, N., Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, 1010, New Zealand, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom; Lim, J.Y., Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, 1010, New Zealand; Jang, M., Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea; Ahn, H.S., Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, 1010, New Zealand","Personalization and localization are important when developing social robots for different sectors, including education, industry, healthcare or restaurants. This allows for an adjustment of robot behaviors according to the needs, preferences or personality of an individual when referring to personalization or to the social conventions or the culture of a country when referring to localization. However, there are different models that enable personalization and localization presented in the current literature, each with their advantages and drawbacks. This work aims to help researchers in the field of social robotics by reviewing and analyzing different papers in this domain. We specifically focus our review by exploring different robots that employ distinct models for the adaptation of the robot to its environment. Additionally, we study an array of methods used to adapt the nonverbal and verbal skills of social robots, including state-of-the-art techniques in artificial intelligence. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Adaptation; Human-robot interaction; Localization; Personalization; Social robotics",,,,,,"Ministry of Science, ICT and Future Planning, MSIP; Institute for Information and Communications Technology Promotion, IITP","This research was funded by an Institute for Information Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (No.2020-0-00842, Development of Cloud Robot Intelligence for Continual Adaptation to User Reactions in Real Service Environments). Funders played no role in data collection, interpretation or reporting.",,,,,,,,,,"Spagnuolo, G., Malosio, M., Scano, A., Caimmi, M., Legnani, G., Tosatti, L.M., Passive and active gravity-compensation of LIGHTarm, an exoskeleton for the upper-limb rehabilitation (2015) Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), , Singapore, 11–14 August; Mauri, A., Lettori, J., Fusi, G., Fausti, D., Mor, M., Braghin, F., Legnani, G., Roveda, L., Mechanical and Control Design of an Industrial Exoskeleton for Advanced Human Empowering in Heavy Parts Manipulation Tasks (2019) Robotics, 8, p. 65. , [CrossRef]; Vicentini, F., Pedrocchi, N., Beschi, M., Giussani, M., Iannacci, N., Magnoni, P., Pellegrinelli, S., Askarpour, M., PIROS: Cooperative, Safe and Reconfigurable Robotic Companion for CNC Pallets Load/Unload Stations (2020) Bringing Innovative Robotic Technologies from Research Labs to Industrial End-Users: The Experience of the European Robotics Challenges, pp. 57-96. , Caccavale, F., Ott, C., Winkler, B., Taylor, Z., Eds.; Springer International Publishing: Cham, Switzerland; Land, N., Syberfeldt, A., Almgren, T., Vallhagen, J., A Framework for Realizing Industrial Human-Robot Collaboration through Virtual Simulation (2020) Procedia CIRP, 93, pp. 1194-1199. , [CrossRef]; Tapus, A., Mataric, M.J., Scassellati, B., Socially assistive robotics [Grand Challenges of Robotics] (2007) IEEE Robot. 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"Ye Y., Liu Y., Yin W., Deng J., Zhu X.","57188725742;57189309885;57330602700;7402612791;57330611700;","Improving the measurement accuracy of distance and positioning for capacitive proximity detection in human-robot interaction",2021,"Microsystem Technologies","27","12",,"4305","4317",,,"10.1007/s00542-021-05223-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118913067&doi=10.1007%2fs00542-021-05223-2&partnerID=40&md5=258bfaf09938f2f74077660fdb73028b","School of Electronics and Information Engineering, Anhui University, No. 111 Jiulong Road, Hefei, Anhui Province  230601, China; School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; Institute of High Energy Physics Chinese Academy of Sciences, Beijing, 100049, China; Shenzhen Nuoan Environmental and Safety Inc., Shenzhen, 518055, China; School of Intelligent Manufacturing Engineering, Ma’anshan University, No. 8 Huangchi Road, Dangtu, Ma’anshan City, Anhui Province  243100, China","Ye, Y., School of Electronics and Information Engineering, Anhui University, No. 111 Jiulong Road, Hefei, Anhui Province  230601, China; Liu, Y., School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China, Institute of High Energy Physics Chinese Academy of Sciences, Beijing, 100049, China; Yin, W., Shenzhen Nuoan Environmental and Safety Inc., Shenzhen, 518055, China; Deng, J., School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; Zhu, X., School of Intelligent Manufacturing Engineering, Ma’anshan University, No. 8 Huangchi Road, Dangtu, Ma’anshan City, Anhui Province  243100, China","Capacitive proximity sensors have been widely used in human-robot interaction, but suffer from the problem of the imperfect measurement accuracy in the nonlinear detection range. Meanwhile, sensor calibration makes the measurement tedious, time consuming, and is difficult to be used in 3D positioning. In this paper, we use a hand as the sensing target and propose a new distance estimation and positioning method, which combines with capacitive sensing configurations of a sensor array and machine learning. Fourteen sensing configurations are designed to generate 14 capacitive sensors to detect the target, and the capacitances are used as the input vector of the machine learning models. We compare the four machine learning algorithms (i.e. fitting method, support vector regression (SVR), radial basis function (RBF) network and BP neural network (BPNN)) for hand distance estimation. The experimental results show the RBF offers the best performance that the root mean squared error (RMSE), mean absolute error (MAE) and mean relative error (MRE) are 0.59 cm, 0.45 cm and 5.32%. Due to the lack of a proper 3D positioning device to verify the target positions, all tests for positioning are grounded on the simulation data using COMSOL, which can mimic the real world applications with high accuracy. The testing results are compared regarding the use of a Y-shaped sensor arrangement, and show that our proposed method performs better than the method with a Y-shaped sensor arrangement, the accuracy is improved by more than 98.8% and the BPNN model achieves the highest accuracy. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.",,"Backpropagation; Capacitive sensors; Errors; Learning systems; Man machine systems; Mean square error; Radial basis function networks; Support vector regression; Capacitive proximity sensors; Machine learning models; Mean relative error; Measurement accuracy; Nonlinear detection; Root mean squared errors; Sensing configuration; Support vector regression (SVR); Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61902252; Natural Science Foundation of Guangdong Province: 2018 A030310509","This work was supported by the National Natural Science Foundation of China under grant 61902252, the Natural Science Funding of Guangdong Province No. 2018 A030310509.",,,,,,,,,,"Braun, A., Wichert, R., Kuijper, A., Fellner, D.W., Capacitive proximity sensing in smart environments (2015) J Ambient Intell Smart Environ, 7 (4), pp. 483-510; 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"Bustamante S., Peters J., Scholkopf B., Grosse-Wentrup M., Jayaram V.","57189325365;35248912800;7004460308;11939406800;56626757500;","ArmSym: A Virtual Human-Robot Interaction Laboratory for Assistive Robotics",2021,"IEEE Transactions on Human-Machine Systems","51","6",,"568","577",,,"10.1109/THMS.2021.3106865","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115703911&doi=10.1109%2fTHMS.2021.3106865&partnerID=40&md5=a0a96255d618aea3b7551102c3cb45d5","Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany; Graduate School of Neural Information Processing, University of Tübingen, Tübingen, 72074, Germany; Centre for Robotics and Mechatronics, German Aeroespace Center, Weßling, 82234, Germany; Computer Science Department, Technische Universität, Darmstadt, 01069, Germany; Faculty of Computer Science, University of Vienna, Vienna, 1010, Austria","Bustamante, S., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany, Graduate School of Neural Information Processing, University of Tübingen, Tübingen, 72074, Germany, Centre for Robotics and Mechatronics, German Aeroespace Center, Weßling, 82234, Germany; Peters, J., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany, Computer Science Department, Technische Universität, Darmstadt, 01069, Germany; Scholkopf, B., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany; Grosse-Wentrup, M., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany, Faculty of Computer Science, University of Vienna, Vienna, 1010, Austria; Jayaram, V., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany","Research in human-robot interaction for assistive robotics usually presents many technical challenges for experimenters, forcing researchers to split their time between solving technical problems and conducting experiments. In addition, previous work in virtual reality setups tends to focus on a single assistive robotics application. In order to alleviate these problems, we present ArmSym, a virtual reality laboratory with a fully simulated and developer-friendly robot arm. The system is intended as a testbed to run many sorts of experiments on human control of a robotic arm in a realistic environment, ranging from an upper limb prosthesis to a wheelchair-mounted robotic manipulator. To highlight the possibilities of this system, we perform a study comparing different sorts of prosthetic control types. Looking at nonimpaired subjects, we study different psychological metrics that evaluate the interaction of the user with the robot under different control conditions. Subjects report a perception of embodiment in the absence of realistic cutaneous touch, supporting previous studies in the topic. We also find interesting correlations between control and perceived ease of use. Overall our results confirm that ArmSym can be used to gather data from immersive experiences prosthetics, opening the door to closer collaboration between device engineers and experience designers in the future. © 2013 IEEE.","ArmSym; human-robot interaction; robot arm; simulated laboratory; virtual reality","Human robot interaction; Job analysis; Man machine systems; Manipulators; Prosthetics; Robotics; Virtual reality; Armsym; Assistive robotics; Humans-robot interactions; Psychology; Robot arms; Robot interactions; Shoulder; Simulated laboratory; Task analysis; Tracking; Robotic arms",,,,,,,,,,,,,,,,"Montagnani, F., Controzzi, M., Cipriani, C., Is it finger orwrist dexterity that is missing in current hand prostheses? (2015) IEEE Trans. Neural Syst. Rehabil. Eng., 23 (4), pp. 600-609. , Jul; Mathiowetz, V., Volland, G., Kashman, N., Weber, K., Adult norms for the box and block test of manual dexterity (1985) Amer. J. Occup. Ther., 39 (6), pp. 386-391. , Jun; Hauschild, M., Davoodi, R., Loeb, G.E., A virtual reality environment for designing and fitting neural prosthetic limbs (2007) IEEE Trans. Neural Syst. Rehabil. Eng., 15 (1), pp. 9-15. , Mar; Lambrecht, J.M., Pulliam, C.L., Kirsch, R.F., Virtual reality environment for simulating tasks with a myoelectric prosthesis: An assessment and training tool joris (2011) J. Prosthetics Orthotics, 23 (2), pp. 84-94; Wodlinger, B., Downey, J.E., Tyler-Kabara, E.C., Schwartz, A.B., Boninger, M.L., Collinger, J.L., Ten-dimensional anthropomorphic arm control in a human brain-machine interface: Difficulties, solutions, and limitations (2015) J. Neural Eng., 12 (1). , Feb; Armiger, R.S., A real-time virtual integration environment for neuroprosthetics and rehabilitation (2011) Johns Hopkins APL Tech. Dig. (Applied Phys. Laboratory), 30 (3), pp. 198-206; Resnik, L., Etter, K., Klinger, S.L., Kambe, C., Using virtual reality environment to facilitate training with advanced upper-limb prosthesis (2011) J. Rehabil. Res. Develop., 48 (6), pp. 707-718; Nakamura, G., Ahigh-fidelity virtual training system formyoelectric prostheses using an immersive HMD (2016) Proc. Int. Conv. Rehabil. Eng. Assistive Technology. Singap. Therapeutic, Assistive Rehabil. Technol., p. 18; Muri, F., Carbajal, C., Echenique, A.M., Fernández, H., López, N.M., Virtual reality upper limb model controlled by EMG signals (2013) J. Phys.: Conf. Ser., 477 (1); Pons, J.L., Virtual reality training and EMG control of the MANUS hand prosthesis (2005) Robotica, 23 (3), pp. 311-317; Lai, J., Design of a multi-degree-of-freedom virtual hand bench for myoelectrical prosthesis (2017) Proc. 2nd Int. Conf. Adv. Robot. Mechatronics, pp. 345-350; Simon, A.M., Hargrove, L.J., Lock, B.A., Kuiken, T.A., Target achievement control test: Evaluating real-time myoelectric patternrecognition control of multifunctional upper-limb prostheses (2011) J. Rehabil. Res. Develop., 48 (6), pp. 619-627; Young, A.J., Smith, L.H., Rouse, E.J., Hargrove, L.J., A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements (2014) J. NeuroEngineering Rehabil., 11 (5); Nissler, C., VITA-an everyday virtual reality setup for prosthetics and upper-limb rehabilitation (2019) J. Neural Eng., 16 (2). , Apr; Haidu, A., Beetz, M., Automated models of human everyday activity based on game and virtual reality technology (2019) Proc. Int. Conf. Robot. Autom., pp. 2606-2612; Mandlekar, A., Roboturk: A crowdsourcing platform for robotic skill learning through imitation (2018) Proc. 2nd Conf. Robot Learn., pp. 879-893; Zhang, T., Deep imitation learning for complex manipulation tasks from virtual reality teleoperation (2018) Proc. IEEE Int. Conf. Robot. Autom., pp. 5628-5635; Rakita, D., Mutlu, B., Gleicher, M., A motion retargeting method for effective mimicry-based teleoperation of robot arms (2017) Proc. ACM/IEEE Int. Conf. Hum.-Robot Interact., pp. 361-370; Sagardia, M., VR-OOS: The DLR's virtual reality simulator for telerobotic on-orbit servicingwith haptic feedback (2015) Proc. IEEE Aerosp. Conf., pp. 1-17. , Mar; (2018) Lab Streaming Layer (LSL) Summary, , https://github.com/sccn/labstreaminglayer; Singh, G.K., Claassens, J., An analytical solution for the inverse kinematics of a redundant 7DoF manipulator with link offsets (2010) Proc. IEEE/RSJ Int. Conf. Intell. 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Reality 3D User Interfaces, pp. 636-644; (2015) Vive Hand Controller Latency Is 50 Ms, , https://www.highfidelity.com/blog/vive-handcontroller-latency-is-50ms-874232249377; Kreylos, O., (2016) Lighthouse Tracking Examined, , http://doc-ok.org/?p=1478; Kontson, K., Marcus, I., Myklebust, B., Civillico, E., Targeted box and blocks test: Normative data and comparison to standard tests (2017) PloS One, 12 (5); Rosén, B., Ehrsson, H.H., Antfolk, C., Cipriani, C., Sebelius, F., Lundborg, G., Referral of sensation to an advanced humanoid robotic hand prosthesis (2009) Scand. J. Plastic Reconstructive Surg. Hand Surg., 43 (5), pp. 260-266; Botvinick, M., Cohen, J., Rubber hands 'feel' touch that eyes see (1998) Nature, 391; Venkatesh, V., Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model (2000) Inf. Syst. Res., 11 (4), pp. 342-365; Geoffrey Louie, W.-Y., Mohamed, S., Nejat, G., Human-robot interaction for rehabilitation robots (2017) Robotic Assistive Technologies: Principles and Practice, pp. 25-69. , Berlin, Germany: Springer, , ch. 2; Lesage, F.X., Berjot, S., Deschamps, F., Clinical stress assessment using a visual analogue scale (2012) Occupational Med., 62 (8), pp. 600-605; Miller, L.A., Stubblefield, K.A., Lipschutz, R.D., Lock, B.A., Kuiken, T.A., Improved myoelectric prosthesis control using targeted reinnervation surgery: A case series (2008) IEEE Trans. Neural Syst. Rehabil. Eng., 16 (1), pp. 46-50. , Feb; Haverkate, L., Smit, G., Plettenburg, D.H., Assessment of bodypowered upper limb prostheses by able-bodied subjects, using the box and blocks test and the nine-hole peg test (2016) Prosthetics Orthotics Int., 40, pp. 109-116; Marasco, P.D., Kim, K., Colgate, J.E., Peshkin, M.A., Kuiken, T.A., Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees (2011) Brain, 134 (3), pp. 747-758","Bustamante, S.; Empirical Inference Department, Germany; email: samuel.bustamante@dlr.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","",Scopus,2-s2.0-85115703911
"Shen Z., Elibol A., Chong N.Y.","57200151005;24733903500;7007012034;","Multi-modal feature fusion for better understanding of human personality traits in social human–robot interaction",2021,"Robotics and Autonomous Systems","146",,"103874","","",,,"10.1016/j.robot.2021.103874","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113553122&doi=10.1016%2fj.robot.2021.103874&partnerID=40&md5=0e50194dedd8832beb4cb5de473b53ab","School of Information Science, Japan Advanced Institute of Science and Technology, Japan","Shen, Z., School of Information Science, Japan Advanced Institute of Science and Technology, Japan; Elibol, A., School of Information Science, Japan Advanced Institute of Science and Technology, Japan; Chong, N.Y., School of Information Science, Japan Advanced Institute of Science and Technology, Japan","Since the dynamic nature of human–robot interaction becomes increasingly prevalent in our daily life, there is a great demand for enabling the robot to better understand human personality traits and inspiring humans to be more engaged in the interaction with the robot. Therefore, in this work, as we design the paradigm of human–robot interaction as close to the real situation as possible, the following three main problems are addressed: (1) fusion of visual and audio features of human interaction modalities, (2) integration of variable length feature vectors, and (3) compensation of shaky camera motion caused by movements of the robot's communicative gesture. Specifically, the three most important visual features of humans including head motion, gaze, and body motion were extracted from a camera mounted on the robot performing verbal and body gestures during the interaction. Then, our system was geared to fuse the aforementioned visual features and different types of vocal features, such as voice pitch, voice energy, and Mel-Frequency Cepstral Coefficient, dealing with variable length multiple feature vectors. Lastly, considering unknown patterns and sequential characteristics of human communicative behavior, we proposed a multi-layer Hidden Markov Model that improved the classification accuracy of personality traits and offered notable advantages of fusing the multiple features. The results were thoroughly analyzed and supported by psychological studies. The proposed multi-modal fusion approach is expected to deepen the communicative competence of social robots interacting with humans from different cultures and backgrounds. © 2021 Elsevier B.V.","Human personality traits; Human–robot interaction; Machine learning; Multi-modal feature fusion","Agricultural robots; Behavioral research; Cameras; Hidden Markov models; Machine design; Classification accuracy; Communicative competences; Communicative gestures; Human interactions; Mel frequency cepstral co-efficient; Multi-modal fusion; Personality traits; Robot interactions; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: AFOSR-AOARD / FA2386-19-1-4015","The authors are grateful for financial support from the Air Force Office of Scientific Research, United State under AFOSR-AOARD / FA2386-19-1-4015 and the Shibuya Science, Culture, and Sports Foundation 2019 Grant Program, Japan .",,,,,,,,,,"Allport, G.W., Personality: A Psychological Interpretation (1937), Holt; Vinciarelli, A., Mohammadi, G., A survey of personality computing (2014); Hurtz, G.M., Donovan, J.J., Personality and job performance: The big five revisited (2000); Mõttus, R., McNeill, G., Jia, X., Craig, L.C., Starr, J.M., Deary, I.J., The associations between personality, diet and body mass index in older people (2013) Health Psychol., 32 (4), pp. 353-360. , http://www.ncbi.nlm.nih.gov/pubmed/21928903, URL; Ozer, D.J., Benet-Martínez, V., Personality and the prediction of consequential outcomes (2006) Annu. 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"Zhang D., Shen J., Li S., Gao K., Gu R.","55717539100;57212169668;57248301400;57226885206;36188070700;","I, robot: depression plays different roles in human–human and human–robot interactions",2021,"Translational Psychiatry","11","1","438","","",,,"10.1038/s41398-021-01567-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113257230&doi=10.1038%2fs41398-021-01567-5&partnerID=40&md5=a13b765127526a9328456ea95799f286","School of Psychology, Shenzhen University, Shenzhen, 518060, China; Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, 518060, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China","Zhang, D., School of Psychology, Shenzhen University, Shenzhen, 518060, China, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, 518060, China; Shen, J., School of Psychology, Shenzhen University, Shenzhen, 518060, China; Li, S., School of Psychology, Shenzhen University, Shenzhen, 518060, China; Gao, K., School of Psychology, Shenzhen University, Shenzhen, 518060, China; Gu, R., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China","Socially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different populations. Seeing that depression negatively influences social reward processing in everyday interaction, we investigate this influence during simulated interactions with humans or robots. In this study, 35 participants with mild depression and 35 controls (all from nonclinical populations) finished the social incentive delay task with event-related potential recording, in which they received performance feedback from other persons or from a robot. Compared to the controls, the mild depressive symptom (MDS) group represented abnormalities of social reward processing in the human feedback condition: first, the MDS group showed a lower hit rate and a smaller contingent-negative variation (correlated with each other) during reward anticipation; second, depression level modulated both the early phase (indexed by the feedback-related negativity (FRN)) and the late phase (indexed by the P3) of reward consumption. In contrast, the effect of depression was evident only on FRN amplitude in the robot feedback condition. We suggest that compared to human–human interaction, the rewarding properties of human–robot interaction are less likely to be affected by depression. These findings have implications for the utilization of robot-assisted intervention in clinical practice. © 2021, The Author(s).",,"adult; Article; association; clinical article; comparative study; contingent negative variation; controlled study; depression; event related potential; false discovery rate; feedback system; female; human; male; punishment; reward; social incentive; social interaction; task performance; young adult; depression; electroencephalography; evoked response; robotics; Depression; Electroencephalography; Evoked Potentials; Humans; Reward; Robotics",,,,,"2021SHIBS0003; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, IUSS: 31970980, 32020103008, 32071083; National Natural Science Foundation of China, NSFC: 17ZDA324; Youth Innovation Promotion Association of the Chinese Academy of Sciences: 2019088; Science and Technology Planning Project of Guangdong Province: 2019A050510048","This study was funded by the National Natural Science Foundation of China (31970980, 32071083, 32020103008), Shenzhen-Hong Kong Institute of Brain Science (2021SHIBS0003), Science and Technology Planning Project of Guangdong Province of China (2019A050510048), Major Program of the Chinese National Social Science Foundation (17ZDA324), and Youth Innovation Promotion Association CAS (2019088).",,,,,,,,,,"Hill, C.A., Affiliation motivation: people who need people… but in different ways (1987) J Personal Soc Psychol, 52, pp. 1008-1018. , COI: 1:STN:280:DyaL2s3isFKmsg%3D%3D; Kawamichi, H., Sugawara, S.K., Hamano, Y.H., Makita, K., Kochiyama, T., Sadato, N., Increased frequency of social interaction is associated with enjoyment enhancement and reward system activation (2016) Sci Rep., 6, pp. 1-11. , COI: 1:CAS:528:DC%2BC28XmsFaks7w%3D; Bishop, S.J., Gagne, C., Anxiety, depression, and decision making: a computational perspective (2018) Annu Rev Neurosci, 41, pp. 371-388. , COI: 1:CAS:528:DC%2BC1cXosFyrtLk%3D, PID: 29709209; Kupferberg, A., Bicks, L., Hasler, G., Social functioning in major depressive disorder (2016) Neurosci Biobehav Rev, 69, pp. 313-332. , PID: 27395342; Forbes, E.E., Dahl, R.E., Research review: altered reward function in adolescent depression: what, when and how? 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Gu, R.; CAS Key Laboratory of Behavioral Science, China; email: gurl@psych.ac.cn",,,"Springer Nature",,,,,21583188,,,"34420040","English","Transl. Psychiatry",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113257230
"Kim U., Jo G., Jeong H., Park C.H., Koh J.-S., Park D.I., Do H., Choi T., Kim H.-S., Park C.","54791071900;57193006848;57218766391;57204032511;35491056200;35743433100;24448129400;9133482400;57209188138;57218766795;","A Novel Intrinsic Force Sensing Method for Robot Manipulators during Human-Robot Interaction",2021,"IEEE Transactions on Robotics","37","6",,"2218","2225",,2,"10.1109/TRO.2021.3072736","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105882679&doi=10.1109%2fTRO.2021.3072736&partnerID=40&md5=99fcc4fcc711e5cc4fa65dfc49c2e966","Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Department of Mathematics, Kunsan National University, Gunsan, South Korea; Department of Mechanical Engineering, Ajou University, Suwon, South Korea","Kim, U., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Jo, G., Department of Mathematics, Kunsan National University, Gunsan, South Korea; Jeong, H., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Park, C.H., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Koh, J.-S., Department of Mechanical Engineering, Ajou University, Suwon, South Korea; Park, D.I., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Do, H., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Choi, T., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Kim, H.-S., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Park, C., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea","Robotic manipulators require contact force sensing capabilities to sense the contact force between the manipulator and an object. Specifically, for humans and robots to coexist in the work environment, the robot must be able to detect an external force applied by a human. This study presents a new intrinsic force sensing method for robot manipulators that can obtain accurate information of the external force applied by a human during human-robot interaction. The method employs a robot cover, which is typically utilized in robot manipulators. Unlike conventional force sensing methods, a six-axis force/torque sensor is placed between the cover and the link of the robot manipulator. As a result, the proposed method provides information of the three-axis contact force applied to the cover surface and its contact location. Therefore, the cover itself becomes a sensorized cover based on the intrinsic force sensing method. To evaluate its sensing performance, the sensorized cover is experimentally validated using reference sensors. Finally, an experiment is performed in which the robot successfully recognizes letters written on the cover, indicating a high level of contact force sensing performance. © 2004-2012 IEEE.","Contact location; human-robot interaction (HRI); intrinsic force sensing; six-axis force/torque (F/T) sensor; three-axis force","Agricultural robots; Flexible manipulators; Industrial manipulators; Modular robots; Robot applications; Contact location; Reference sensors; Robot interactions; Robot manipulator; Robotic manipulators; Sensing performance; Six-axis force/torque sensor; Work environments; Human robot interaction",,,,,"Korea Institute of Machinery and Materials, KIMM: NK224C, NK232F","This work was supported by the Basic Research Program funded by the Korea Institute of Machinery and Materials under Grant NK224C and Grant NK232F.",,,,,,,,,,"Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. Theory, 43, pp. 253-270; Bartolozzi, C., Natale, L., Nori, F., Metta, G., Robots with a sense of touch (2016) Nature Mater., 15, pp. 921-925; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100. , Oct; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans. Robot., 31 (4), pp. 850-863. , Aug; Lecours, A., Otis, M.J., Gosselin, C., Modeling of physical humanrobot interaction: Admittance controllers applied to intelligent assist devices with large payload (2016) Int. J. Adv. Robot. Syst., 13 (5), pp. 1-12; Park, Y.-L., Ryu, S.C., Black, R.J., Chau, K.K., Moslehi, B., Cutkosky, M.R., Exoskeletal force-sensing end-effectors with embedded optical fiber-Bragg-grating sensors (2009) IEEE Trans. Robot., 25 (6), pp. 1319-1331. , Dec; Hong, M.B., Park, H., Yoon, Y.H., Kim, D.J., A novel elastic structure for three-axis force/torque sensor:Kinematic design and feasibility study (2018) IEEE Sens. J., 18 (17), pp. 6969-6977. , Sep; Kim, U., Kim, Y.B., Seok, D.-Y., So, J., Choi, H.R., Asurgical palpation probe with 6-axis force/torque sensing capability for minimally invasive surgery (2018) IEEE Trans. Ind. Electron., 65 (3), pp. 2755-2765. , Mar; Kim, U., Lee, D.-H., Yoon, W.J., Hannaford, B., Choi, H.R., Force sensor integrated surgical forceps for minimally invasive robotic surgery (2015) IEEE Trans. Robot., 31 (5), pp. 1214-1224. , Oct; Almassri, A.M., Pressure sensor: State of the art, design, and application for robotic hand (2015) J. Sens., 2015, pp. 1-12; Dahiya, R.S., Metta, G., Valle, M., Sandini, G., Tactile sensing-from humans to humanoids (2010) IEEE Trans. Robot., 26 (1), pp. 1-20. , Feb; Jung, B., Kim, B., Koo, J.C., Choi, H.R., Moon, H., Joint torque sensor embedded in harmonic drive using order tracking method for robotic application (2017) IEEE/ASME Trans. Mechatronics, 22 (4), pp. 1594-1599. , Aug; Kim, Y.B., Kim, U., Seok, D.-Y., So, J., Lee, Y.H., Choi, H.R., Torque sensor embedded actuator module for robotic applications (2018) IEEE/ASME Trans. Mechatronics, 23 (4), pp. 1662-1672. , Aug; Zhang, H.-X., Ryoo, Y.-J., Byun, K.-S., Development of torque sensor with high sensitivity for joint of robot manipulator using 4-Bar linkage shape (2016) Sensors, 16, pp. 991-1002; Luca, A.D., Mattone, R., Sensorless robot collision detection and hybrid force/motion control (2005) Proc. IEEE Int. Conf. Robot. Automat., pp. 999-1004; Boutry, C.M., A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics (2018) Sci. Robot., 3; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human-robot interaction (2016) IEEE Trans. Robot. Autom., 1 (1), pp. 41-48. , Jan; Wu, Y., A skin-inspired tactile sensor for smart prosthetics (2018) Sci. Robot., 3; Kim, J., Stretchable silicon nanoribbon electronics for skin prosthesis (2014) Nature Commun., 5; Schmitz, A., Maiolino, P., Maggiali, M., Natale, L., Cannata, G., Metta, G., Methods and technologies for the implementation of large-scale robot tactile sensors (2011) IEEE Trans. Robot., 27 (3), pp. 389-400. , Jun; Xu, K., Simaan, N., An investigation of the intrinsic force sensing capabilities of continuum robots (2008) IEEE Trans. Robot., 24 (3), pp. 576-587. , Jun; Bicchi, A., Salisbury, J.K., Brock, D.L., Contact sensing from force measurement (1993) Int. J. Robot. Res., 12, pp. 249-262; Boucher, G., Laliberte, T., Gosselin, C., A parallel low-impedance sensing approach for highly responsive physical human-robot interaction (2019) Proc. IEEE Int. Conf. Robot. Automat., pp. 3754-3760; Kim, U., Lee, D.-H., Kim, Y.B., Seok, D.-Y., Choi, H.R., A novel 6-axis force/torque sensor for robotic applications (2017) IEEE/ASME Trans. Mechatronics, 22 (3), pp. 1381-1391. , Jun; (2017), https://www.robotous.com/forcetorquesensor, Robotous RFT60-HA. Accessed: Jul. 20; Kim, U., Jeong, H., Do, H., Park, J., Park, C., Six-axis force/torque fingertip sensor for an anthropomorphic robot hand (2020) IEEE Trans. Robot. Autom., 5 (4), pp. 5566-5572. , Oct","Kim, U.; Department of Robotics and Mechatronics, South Korea; email: ukkim@kimm.re.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85105882679
"Cominelli L., Feri F., Garofalo R., Giannetti C., Meléndez-Jiménez M.A., Greco A., Nardelli M., Scilingo E.P., Kirchkamp O.","56303460500;16549737500;56959998600;55366692200;23985752900;56854902700;55734951300;6601971636;6507213896;","Promises and trust in human–robot interaction",2021,"Scientific Reports","11","1","9687","","",,,"10.1038/s41598-021-88622-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105440198&doi=10.1038%2fs41598-021-88622-9&partnerID=40&md5=8c892c097d8e210e475cacdab9bad379","Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy; Department of Economics, Royal Holloway University of London, London, United Kingdom; Department of Economics and Management, University of Pisa, Pisa, Italy; Department of Economic Theory and Economic History, University of Málaga, Malaga, Spain; Chair of Behavioural and Experimental Economics, Friedrich-Schiller University Jena, Jena, Germany","Cominelli, L., Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy; Feri, F., Department of Economics, Royal Holloway University of London, London, United Kingdom; Garofalo, R., Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy; Giannetti, C., Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy, Department of Economics and Management, University of Pisa, Pisa, Italy; Meléndez-Jiménez, M.A., Department of Economic Theory and Economic History, University of Málaga, Malaga, Spain; Greco, A., Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy; Nardelli, M., Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy; Scilingo, E.P., Department of Information Engineering and Center E. Piaggio, University of Pisa, Pisa, Italy; Kirchkamp, O., Chair of Behavioural and Experimental Economics, Friedrich-Schiller University Jena, Jena, Germany","Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and contexts. The aim of this paper is to investigate whether human-beings trust a social robot—i.e. a human-like robot that embodies emotional states, empathy, and non-verbal communication—differently than other types of agents. To do so, we adapt the well-known economic trust-game proposed by Charness and Dufwenberg (2006) to assess whether receiving a promise from a robot increases human-trust in it. We find that receiving a promise from the robot increases the trust of the human in it, but only for individuals who perceive the robot very similar to a human-being. Importantly, we observe a similar pattern in choices when we replace the humanoid counterpart with a real human but not when it is replaced by a computer-box. Additionally, we investigate participants’ psychophysiological reaction in terms of cardiovascular and electrodermal activity. Our results highlight an increased psychophysiological arousal when the game is played with the social robot compared to the computer-box. Taken all together, these results strongly support the development of technologies enhancing the humanity of robots. © 2021, The Author(s).",,"artificial intelligence; human; man machine interaction; robotics; social interaction; trust; Artificial Intelligence; Humans; Man-Machine Systems; Robotics; Social Interaction; Trust",,,,,"Friedrich-Schiller-Universität Jena, FSU; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Università di Pisa, UniPi: PRA_2018_12","Open Access funding enabled and organized by Projekt DEAL. The project received also funding from the University of Pisa under the PRA project ""Istituzioni, Mercati imperfetti e problemi di Policy"". The research leading to these results has also received partial funding from the ItalianMinistry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).",,,,,,,,,,"Lange, P.A.M.V., Generalized trust: four lessons from genetics and culture (2015) Curr. Dir. Psychol. 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Neurosci., 10, pp. 192-194. , COI: 1:STN:280:DC%2BD3MvmvVarsA%3D%3D, PID: 11512429; Vaa, T., (2001); Fox, A.S., Lapate, R.C., Shackman, A.J., Davidson, R.J., (2018) The Nature of Emotion: Fundamental Questions, , Oxford University Press; Arkin, R.C., Ulam, P., Wagner, A.R., Moral decision making in autonomous systems: enforcement, moral emotions, dignity, trust, and deception (2011) Proc. IEEE, 100, pp. 571-589; Tortosa, M.I., Strizhko, T., Capizzi, M., Ruz, M., Interpersonal effects of emotion in a multi-round trust game (2013) Psicologica Int. J. Methodol. Exp. Psychol., 34, pp. 179-198; Campellone, T.R., Kring, A.M., Who do you trust? The impact of facial emotion and behaviour on decision making (2013) Cogn. Emot., 27, pp. 603-620. , PID: 23017055; Hancock, P.A., A meta-analysis of factors affecting trust in human–robot interaction (2011) Hum. Factors, 53, pp. 517-527. , PID: 22046724; Crandall, J.W., Cooperating with machines (2018) Nat. Commun., 9, pp. 1-12. , COI: 1:CAS:528:DC%2BC1cXhtFSntrzN; Nitsch, V., Glassen, T., Investigating the effects of robot behavior and attitude towards technology on social human–robot interactions (2015) 2015 24Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) 535–540 (IEEE; Waytz, A., Heafner, J., Epley, N., The mind in the machine: anthropomorphism increases trust in an autonomous vehicle (2014) J. Exp. Soc. Psychol., 52, pp. 113-117; Nass, C., Moon, Y., Machines and mindlessness: social responses to computers (2000) J. Soc. Issues, 56, pp. 81-103; March, C., (2019) The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players; Mori, M., The uncanny valley: The original essay by Masahiro Mori (2017) IEEE Robots&; Charness, G., Dufwenberg, M., Promises and partnership (2006) Econometrica, 74, pp. 1579-1601; Berg, J., Dickhaut, J., McCabe, K., Trust, reciprocity, and social history (1995) Games Econom. 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Comput., 44, pp. 1031-1051; Ghiasi, S.E.A., A new sympathovagal balance index from electrodermal activity and instantaneous vagal dynamics: A preliminary cold pressor study (2018) 2018 40Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3068-3071. , IEEE; Ghiasi, S., Greco, A., Barbieri, R., Scilingo, E.P., Valenza, G., Assessing autonomic function from electrodermal activity and heart rate variability during cold-pressor test and emotional challenge (2020) Sci. Rep., 10, pp. 1-13; Franke, T., Attig, C., Wessel, D., A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) scale (2019) Int. J. Hum. Comput. Interact., 35, pp. 456-467; Rieger, M.O., Wang, M., Hens, T., Risk preferences around the world (2015) Manag. Sci., 61, pp. 637-648; Gneezy, U., Kajackaite, A., Sobel, J., Lying aversion and the size of the lie (2018) Am. Econ. 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Control, 51, pp. 42-49; Greco, A., Valenza, G., Scilingo, E.P., (2016) Advances in Electrodermal Activity Processing with Applications for Mental Health, , Springer; Greco, A., Valenza, G., Lanata, A., Scilingo, E.P., Citi, L., cvxeda: a convex optimization approach to electrodermal activity processing (2015) IEEE Trans. Biomed. Eng., 63, pp. 797-804. , PID: 26336110; Strigo, I.A., Craig, A.D., Interoception, homeostatic emotions and sympathovagal balance (2016) Philos. Trans. R. Soc. B Biol. Sci., 371, p. 20160010; Sleight, P., Bernardi, L., Sympathovagal balance (1998) Circulation, 98, p. 2640. , COI: 1:STN:280:DyaK1M%2FmtVWkuw%3D%3D, PID: 9843476","Giannetti, C.; Department of Economics and Management, Italy; email: caterina.giannetti@unipi.it",,,"Nature Research",,,,,20452322,,,"33958624","English","Sci. Rep.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105440198
"Ince G., Yorganci R., Ozkul A., Duman T.B., Köse H.","56040248000;54929920000;56577886900;57208839640;25031883600;","An audiovisual interface-based drumming system for multimodal human–robot interaction",2021,"Journal on Multimodal User Interfaces","15","4",,"413","428",,,"10.1007/s12193-020-00352-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096018839&doi=10.1007%2fs12193-020-00352-w&partnerID=40&md5=f3819f1c7a14cdf317b67e5c5e1bfe3b","Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey","Ince, G., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey, Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Yorganci, R., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Ozkul, A., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Duman, T.B., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Köse, H., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey, Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey","This paper presents a study of an audiovisual interface-based drumming system for multimodal human–robot interaction. The interactive multimodal drumming game is used in conjunction with humanoid robots to establish an audiovisual interactive interface. This study is part of a project to design robot and avatar assistants for education and therapy, especially for children with special needs. It specifically focuses on evaluating robot/virtual avatar tutors, tangible interaction devices, and mobile multimedia devices within a simple drumming-based interactive music tutoring game scenario. Several parameters, including the effect of the embodiment of the tutor/interface and the presence of feedback and training mechanisms, were the focus of interest. For that purpose, we created an interactive drumming game relying on turn-taking and imitation principles, in which a human user is able to play drums with a humanoid robot (Robotic Drum Mate). Three interactive scenarios with different experimental setups for humanoid robots and mobile devices were developed and tested. As a part of those scenarios, a system that enables drum strokes to be automatically detected and recognized using auditory cues was implemented and incorporated into the experimental framework. We verified the applicability and effectiveness of the proposed system in a drum-playing game with adult human test subjects by evaluating it both objectively and subjectively. The results showed that the physical robot tutor, the feedback and training mechanisms had a positive effect on the subjects’ performance and, as expected, although the physical medium is preferred, the virtual medium for drumming caused less failure. © 2020, Springer Nature Switzerland AG.","Audio processing; Audiovisual interaction; Drum game; Human–computer-interaction (HCI); Human–robot-interaction (HRI); Multimodality; Non-verbal communication","Anthropomorphic robots; Audiovisual; Educational robots; Machine design; Audiovisual interfaces; Interactive interfaces; Interactive music; Mobile multimedia device; Physical medium; Physical robots; Robot interactions; Tangible interaction; Social robots",,,,,"Istanbul Teknik Üniversitesi, IT: MOA-2019-42321","This work is supported by the Scientific Research Project Unit (BAP) of Istanbul Technical University, Project Number: MOA-2019-42321.",,,,,,,,,,"Walus, B.P., Pauletto, S., Mason-Jones, A., Sonification and music as support to the communication of alcohol-related health risks to young people (2016) J Multimodal User Interfaces, 10, p. 235246; Kose-Bagci, H., Ferrari, E., Dautenhahn, K., Syrdal, D., Nehaniv, C.L., Effects of embodiment and gestures on social interaction in drumming games with a humanoid robot (2009) Adv Robot, 24 (14), pp. 1951-1996; Robins, B., Dickerson, P., Stribling, P., Dautenhahn, K., Robot-mediated joint attention in children with autism: a case study in robot–human interaction (2004) Interact Stud, 5 (2), pp. 161-198; Kose-Bagci, H., Dautenhahn, K., Nehaniv, C.L., Emergent dynamics of turn-taking interaction in drumming games with a humanoid robot (2008) Proceedings of IEEE International Symposium on Robot and Human Interactive Communication, pp. 346-353. , RO-MAN; Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Robins, B., Kose-Bagci, H., Mirza, N.A., Blow, M., KASPAR: a minimally expressive humanoid robot for human–robot interaction research (2009) Appl Bionics Biomech, 6 (3), pp. 369-397; Ince, G., Duman, T.B., Yorganci, R., Kose, H., Towards a robust drum stroke recognition system for human–robot interaction (2015) Proceedings of the IEEE/SICE International Symposium on System Integration (SII), pp. 744-749; Gurpinar, C., Uluer, P., Akalin, N., Kose, H., Sign recognition system for an assistive robot sign tutor for children (2020) Int J Soc Robot, 12, p. 355369; Towards an affective robot companion for audiology rehabilitation: How does pepper feel today?. 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Ince, G., Nakadai, K., Rodemann, T., Tsujino, H., Imura, J., Ego-motion noise cancellation of a robot using missing feature masks (2011) Appl Intell, 34 (3), pp. 360-371","Ince, G.; Faculty of Computer and Informatics Engineering, Turkey; email: gokhan.ince@itu.edu.tr",,,"Springer Science and Business Media Deutschland GmbH",,,,,17837677,,,,"English","J. Multimodal User Interfaces",Article,"Final","",Scopus,2-s2.0-85096018839
"Horii T., Nagai Y.","55842337200;57226523020;","Active Inference Through Energy Minimization in Multimodal Affective Human–Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"684401","","",,,"10.3389/frobt.2021.684401","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120980016&doi=10.3389%2ffrobt.2021.684401&partnerID=40&md5=7af3d2571d515abdfb2a5edbfcdf60e6","Graduate School of Engineering Science, Osaka University, Osaka, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan","Horii, T., Graduate School of Engineering Science, Osaka University, Osaka, Japan, International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan; Nagai, Y., International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan, Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan","During communication, humans express their emotional states using various modalities (e.g., facial expressions and gestures), and they estimate the emotional states of others by paying attention to multimodal signals. To ensure that a communication robot with limited resources can pay attention to such multimodal signals, the main challenge involves selecting the most effective modalities among those expressed. In this study, we propose an active perception method that involves selecting the most informative modalities using a criterion based on energy minimization. This energy-based model can learn the probability of the network state using energy values, whereby a lower energy value represents a higher probability of the state. A multimodal deep belief network, which is an energy-based model, was employed to represent the relationships between the emotional states and multimodal sensory signals. Compared to other active perception methods, the proposed approach demonstrated improved accuracy using limited information in several contexts associated with affective human–robot interaction. We present the differences and advantages of our method compared to other methods through mathematical formulations using, for example, information gain as a criterion. Further, we evaluate performance of our method, as pertains to active inference, which is based on the free energy principle. Consequently, we establish that our method demonstrated superior performance in tasks associated with mutually correlated multimodal information. Copyright © 2021 Horii and Nagai.","active inference; emotion; energy based models; human-robot interaction; multimodal perception",,,,,,"Ministry of Education, Culture, Sports, Science and Technology, MEXT; Japan Science and Technology Agency, JST: JPMJCR16E2; University of Tokyo","This research was supported by JST CREST ?Cognitive Mirroring? (Grant Number: JPMJCR16E2), Institute for AI and Beyond, the University of Tokyo, and World Premier International Research Centre Initiative (WPI), MEXT, Japan.",,,,,,,,,,"Barrett, L.F., The Theory of Constructed Emotion: an Active Inference Account of Interoception and Categorization (2017) Soc. Cogn. Affect Neurosci, 12, pp. 1-23; Barros, P., Weber, C., Wermter, S., Emotional Expression Recognition with a Cross-Channel Convolutional Neural Network for Human-Robot Interaction (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) (IEEE), pp. 582-587. , IEEE; Barros, P., Wermter, S., Developing Crossmodal Expression Recognition Based on a Deep Neural Model (2016) Adaptive Behav, 24, pp. 373-396; Breazeal, C., Aryananda, L., Recognition of Affective Communicative Intent in Robot-Directed Speech (2002) Autonomous Robots, 12, pp. 83-104; Breazeal, C., Emotion and Sociable Humanoid Robots (2003) Int. J. 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Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120980016
"Merkouris A., Garneli V., Chorianopoulos K.","57147763100;56414878600;20336592900;","Programming human-robot interactions for teaching robotics within a collaborative learning open space: Robots playing capture the flag game: Programming human-robot interactions within a collaborative learning open space",2021,"ACM International Conference Proceeding Series",,,"3489422","","",,,"10.1145/3489410.3489422","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120905743&doi=10.1145%2f3489410.3489422&partnerID=40&md5=bf582c731dd75f278a4010c4a03bd21e","Department of Informatics, Ionian University, Greece","Merkouris, A., Department of Informatics, Ionian University, Greece; Garneli, V., Department of Informatics, Ionian University, Greece; Chorianopoulos, K., Department of Informatics, Ionian University, Greece","Game-based competitive or cooperative robotics activities constitute an effective approach to exploit the child-robot interaction perspective. However, in most game-based robotics activities robots act autonomously to achieve the goal. In this work, we aim to promote the child-robot interaction aspect through a multiplayer game where one team of robots and humans collaborates to compete with another team of humans and cobots. We describe the design of an open space that will allow children to gain access, locally and remotely, and program robotic agents to play the traditional ""Capture the Flag""game in a physical stadium-arena. Through this space, we intend to teach robotics, while programming human-robot interfaces, within a computer-supported game-based learning environment. We give insights on the initial design of such an open space and the educational benefits of its use in the comprehension of abstract computational and STEM concepts. © 2021 ACM.","Child-robot interaction; children; computational thinking; educational robotics; embodied learning; game design; STEM","Computer aided instruction; Computer games; Educational robots; Game design; Human computer interaction; Machine design; Man machine systems; Robot programming; Robotics; Capture the flag; Child; Child-robot interactions; Collaborative learning; Computational thinkings; Educational robotics; Embodied learning; Game design; Humans-robot interactions; STEM; Human robot interaction",,,,,,,,,,,,,,,,"Alimisis, D., Educational robotics: Open questions and new challenges (2013) Themes in Science and Technology Education, 6 (1), pp. 63-71; Asrifan, A., (2020) Pandemic, Humanity and Education, , https://doi.org/10.31219/osf.io/q2gpk, December 30; Beer, J.M., Fisk, A.D., Rogers, W.A., Toward a framework for levels of robot autonomy in human-robot interaction (2014) Journal of Human-robot Interaction, 3 (2), pp. 74-99; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954; Benitti, F.B.V., Exploring the educational potential of robotics in schools: A systematic review (2012) Computers & Education, 58 (3), pp. 978-988; Bers, M.U., The tangiblek robotics program: Applied computational thinking for young children (2010) Early Childhood Research & Practice, 12 (2), p. n2; Brennan, K., Resnick, M., New frameworks for studying and assessing the development of computational thinking (2012) Proceedings of the 2012 Annual Meeting of the American Educational Research Association, 1, p. 25. , April, Vancouver, Canada; Colgate, J.E., Edward, J., Peshkin, M.A., Wannasuphoprasit, W., (1996) Cobots: Robots for Collaboration with Human Operators; Danowitz, A., Benson, B., Edmonds, J., Teaching systems and robotics in a four-week summer short course (2017) 2017 American Society for Engineering Education Conference & Exposition Proceedings; Eguchi, A., Robocupjunior for promoting stem education, 21st century skills, and technological advancement through robotics competition (2016) Robotics and Autonomous Systems, 75, pp. 692-699; Fernandez, J., Marin, R., Wirz, R., Online competitions: An open space to improve the learning process (2007) IEEE Transactions on Industrial Electronics, 54 (6), pp. 3086-3093; Ford, V., Siraj, A., Haynes, A., Brown, E., Capture the flag unplugged: An offline cyber competition (2017) Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pp. 225-230. , March; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Foundations and Trends in Human-computer Interaction, 1 (3), pp. 203-275; Guzdial, M., (2015) Learner-centered Design of Computing Education: Research on Computing for Everyone, , Morgan & Claypool Publishers; Hauge, J.B., Stefan, I.A., Stefan, A., Cazzaniga, M., Yanez, P., Skupinski, T., Mohier, F., Exploring context-aware activities to enhance the learning experience (2017) International Conference on Games and Learning Alliance, pp. 238-247. , December. 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Wiley-Interscience; Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Kafai, Y., Scratch: Programming for all (2009) Communications of the ACM, 52 (11), pp. 60-67; Vella, K., Koren, C.J., Johnson, D., The impact of agency and familiarity in cooperative multiplayer games (2017) Proceedings of the Annual Symposium on Computer-Human Interaction in Play, pp. 423-434. , October; Victor, B., Seeing spaces (2014) Vimeo, , https://vimeo.com/97903574, Retrieved September 27, 2021 from; Werner, L., Denner, J., Campe, S., Torres, D.M., Computational sophistication of games programmed by children: A model for its measurement (2020) ACM Transactions on Computing Education (TOCE), 20 (2), pp. 1-23; Yadollahi, E., Couto, M., Dillenbourg, P., Paiva, A., Can you guide me?. supporting children's spatial perspective taking through games with robots (2020) Proceedings of the 2020 ACM Interaction Design and Children Conference: Extended Abstracts, pp. 217-222. , June; Zagal, J.P., Rick, J., Hsi, I., Collaborative games: Lessons learned from board games (2006) Simulation & Gaming, 37 (1), pp. 24-40",,,,"Association for Computing Machinery","1st International Conference of the ACM Greek SIGCHI Chapter, CHI Greece 2021","25 November 2021 through 27 November 2021",,174677,,9781450385787,,,"English","ACM Int. Conf. Proc. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85120905743
"Tsfasman M., Saravanan A., Viner D., Goslinga D., De Wolf S., Raman C., Jonker C.M., Oertel C.","57378020800;57377399200;57377399300;57376934700;57377708700;57211681203;7006702150;50561763400;","Towards a Real-time Measure of the Perception of Anthropomorphism in Human-robot Interaction",2021,"MuCAI 2021 - Proceedings of the 2nd ACM Multimedia Workshop on Multimodal Conversational AI, co-located with ACM MM 2021",,,,"13","18",,1,"10.1145/3475959.3485394","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119377600&doi=10.1145%2f3475959.3485394&partnerID=40&md5=f7b4de2e2a07d2423c89cd419ff82ebc","Delft University of Technology, Delft, Netherlands","Tsfasman, M., Delft University of Technology, Delft, Netherlands; Saravanan, A., Delft University of Technology, Delft, Netherlands; Viner, D., Delft University of Technology, Delft, Netherlands; Goslinga, D., Delft University of Technology, Delft, Netherlands; De Wolf, S., Delft University of Technology, Delft, Netherlands; Raman, C., Delft University of Technology, Delft, Netherlands; Jonker, C.M., Delft University of Technology, Delft, Netherlands; Oertel, C., Delft University of Technology, Delft, Netherlands","How human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversational partner's engagement and emotions. Prosodically, this can be achieved through (dis)entrainment. While speech-synthesis has been a limiting factor for many years, restrictions in this regard are increasingly mitigated. These advancements now emphasise the importance of studying the effect of robot embodiment on human entrainment. In this study, we conducted a between-subjects online human-robot interaction experiment in an educational use-case scenario where a tutor was either embodied through a human or a robot face. 43 English-speaking participants took part in the study for whom we analysed the degree of acoustic-prosodic entrainment to the human or robot face, respectively. We found that the degree of subjective and objective perception of anthropomorphism positively correlates with acoustic-prosodic entrainment. © 2021 Owner/Author.","acoustic-prosodic entrainment; human-robot interaction; multi-modal; prosody",,,,,,,,,,,,,,,,,"Akata, Zeynep, Balliet, Dan, de Rijke, Maarten, Dignum, Frank, Dignum, Virginia, Eiben, Guszti, Fokkens, Antske, Welling, Max, A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artifcial Intelligence (2020) Computer, 53 (8), pp. 18-28. , https://doi.org/10.1109/MC.2020.2996587, [1] (2020); Moubayed, Samer Al, Beskow, Jonas, Skantze, Gabriel, Granström, Björn, Furhat: A Back-Projected Human-Like Robot Head for Multiparty Human-Machine Interaction (2012) Cognitive Behavioural Systems, pp. 114-130. , [2] Anna Esposito, Antoni-etta M. Esposito, Alessandro Vinciarelli, Rüdiger Hofmann, and Vincent C. Müller (Eds). 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K., Dickens, L., Jeong, Sooyeon, Harris, P., DeSteno, D., Breazeal, C., (2015) A Comparison of Children Learning New Words from Robots, , [36] Tablets, People; Yuan, Jiahong, Liberman, Mark, Cieri, Christopher, (2007) Towardsan integrated understanding of speech overlaps in conversation, , [37] (01 2007)","Tsfasman, M.; Delft University of TechnologyNetherlands; email: M.Tsfasman@tudelft.nl
Saravanan, A.; Delft University of TechnologyNetherlands; email: A.Saravanan@student.tudelft.nl",,"ACM SIGMM","Association for Computing Machinery, Inc","2nd ACM Multimedia Workshop on Multimodal Conversational AI, MuCAI 2021, co-located with ACM MM 2021","24 October 2021",,174214,,9781450386791,,,"English","MuCAI - Proc. ACM Multimed. Workshop Multimodal Conversational AI, co-located ACM MM",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85119377600
"Liberman-Pincu E., David A., Sarne-Fleischmann V., Edan Y., Oron-Gilad T.","57221689984;57354238300;55078262300;7004434501;16042913400;","Comply with me: Using design manipulations to affect human–robot interaction in a covid-19 officer robot use case",2021,"Multimodal Technologies and Interaction","5","11","71","","",,,"10.3390/mti5110071","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120007952&doi=10.3390%2fmti5110071&partnerID=40&md5=9cc2022294dec01915b013beeea2b31a","Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel","Liberman-Pincu, E., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; David, A., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; Sarne-Fleischmann, V., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; Edan, Y., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; Oron-Gilad, T., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel","This study examines the effect of a COVID-19 Officer Robot (COR) on passersby compliance and the effects of its minor design manipulations on human–robot interaction. A robotic application was developed to ensure participants entering a public building comply with COVID restrictions of a green pass and wearing a face mask. The participants’ attitudes toward the robot and their perception of its authoritativeness were explored with video and questionnaires data. Thematic analysis was used to define unique behaviors related to human–COR interaction. Direct and extended interactions with minor design manipulation of the COR were evaluated in a public scenario setting. The results demonstrate that even minor design manipulations may influence users’ attitudes toward officer robots. The outcomes of this research can support manufacturers in rapidly adjusting their robots to new domains and tasks and guide future designs of authoritative socially assistive robots (SARs). © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Compliance; COVID-19; Design manipulation; Human–robot interaction; Robot authority; Service robot; Social assistive robot; Thematic analysis; Visual qualities",,,,,,"3-15625; Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","Funding: This research was supported by Ministry of Innovation, Science and Technology, Israel (grant 3-15625), and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the W. Gunther Plaut Chair in Manufacturing Engineering and by the George Shrut Chair in Human performance Management.",,,,,,,,,,"Marques, L., Murphy, R., Althoefer, K., Tadokoro, S., Laschi, C., Robotics Responds to the COVID-19 Outbreak [From the Guest Editors] (2021) IEEE Robot. Autom. 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Applications and Case Studies, Proceedings of the 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, pp. 63-78. , Orlando, FL, USA, 26–31 July Springer: Cham, Switzerland, 2019","Liberman-Pincu, E.; Department of Industrial Engineering and Management, Israel; email: elapin@post.bgu.ac.il",,,"MDPI",,,,,24144088,,,,"English","Multimodal Tech. Inter.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120007952
"Scheutz C., Law T., Scheutz M.","57325224500;57212389268;6603548841;","Envirobots: How human–robot interaction can facilitate sustainable behavior",2021,"Sustainability (Switzerland)","13","21","12283","","",,,"10.3390/su132112283","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118545630&doi=10.3390%2fsu132112283&partnerID=40&md5=425b738a2eb1e430b478e597e6579564","Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States","Scheutz, C., Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States; Law, T., Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States; Scheutz, M., Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States","Environmental psychology aims to study human behavior with regard to the environment and how psychological techniques can be used to motivate behavior change. We argue that these concepts can be applied to interactive robots designed for other tasks, which then enables them to encourage sustainability behaviors in humans. We first present a literature review on the current state of social robots that are used to encourage sustainable behaviors. We next present eight hypothetical scenarios which are informed by the progress that has already been made in social robots in sustainability, as well as notable gaps where further environmental psychological concepts could be utilized. These scenarios encompass possible robots that range from limited sensing and no manipulation capabilities, to more sophisticated sensing and no manipulation, to sophisticated sensing and manipulation capabilities. We present these scenarios in which human–robot interaction could potentially result in pro-environmental behavioral changes in humans as recommendations for robot designers interested in helping design social robots for sustainability. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Ecological psychology; Human–robot interaction; Sustainability robots","design method; human behavior; psychology; robotics; sustainability",,,,,,,,,,,,,,,,"Nations, U., The Sustainable Development Goals Report 2020, , https://unstats.un.org/sdgs/report/2020/, (accessed on 11 June 2021); Turesinin, M., Kabir, A.M.H., Mollah, T., Sarwar, S., Hosain, M.S., Aquatic Iguana: A Floating Waste Collecting Robot with IoT Based Water Monitoring System Proceedings of the 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI), pp. 21-25. , Yogyakarta, Indonesia, 1–2 October 2020; Davidson, E., The Design of an Autonomous Recycling Robot (2008) Grace Allen Scholars Theses, , https://digitalcommons.usf.edu/cgi/viewcontent.cgi?article=1001&context=honors_gast, (accessed on 11 June 2021); Rahmawati, E., Sucahyo, I., Asnawi, A., Faris, M., Taqwim, M., Mahendra, D., A water surface cleaning robot (2019) J. Phys. Conf. 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Behav, 25, pp. 275-321; Short, E.S., Swift-Spong, K., Shim, H., Wisniewski, K.M., Zak, D.K., Wu, S., Zelinski, E., Matarić, M.J., Understanding social interactions with socially assistive robotics in intergenerational family groups Proceedings of the 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 236-241. , Lisbon, Portugal, 28 August–1 September 2017; Nakagawa, K., Shinozawa, K., Ishiguro, H., Akimoto, T., Hagita, N., Motion modification method to control affective nuances for robots Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5003-5008. , St. Louis, MO, USA, 10–15 October 2009","Scheutz, M.; Human–Robot Interaction Laboratory, United States; email: matthias.scheutz@tufts.edu",,,"MDPI",,,,,20711050,,,,"English","Sustainability",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85118545630
"van Maris A., Zook N., Dogramadzi S., Studley M., Winfield A., Caleb-Solly P.","57193757921;57224238437;6507900733;8922780300;7006158694;16244225300;","A new perspective on robot ethics through investigating human–robot interactions with older adults",2021,"Applied Sciences (Switzerland)","11","21","10136","","",,,"10.3390/app112110136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118167866&doi=10.3390%2fapp112110136&partnerID=40&md5=b5c8c57fdab4fb4c6576d0ea26799ff2","Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Faculty of Health and Applied Sciences, University of the West of England, Bristol, BS16 1QY, United Kingdom; Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, S10 2TN, United Kingdom; School of Computer Science, University of Nottingham, Nottingham, NG7 2RD, United Kingdom","van Maris, A., Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Zook, N., Faculty of Health and Applied Sciences, University of the West of England, Bristol, BS16 1QY, United Kingdom; Dogramadzi, S., Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, S10 2TN, United Kingdom; Studley, M., Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Winfield, A., Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Caleb-Solly, P., School of Computer Science, University of Nottingham, Nottingham, NG7 2RD, United Kingdom","This work explored the use of human–robot interaction research to investigate robot ethics. A longitudinal human–robot interaction study was conducted with self-reported healthy older adults to determine whether expression of artificial emotions by a social robot could result in emotional deception and emotional attachment. The findings from this study have highlighted that currently there appears to be no adequate tools, or the means, to determine the ethical impact and concerns ensuing from long-term interactions between social robots and older adults. This raises the question whether we should continue the fundamental development of social robots if we cannot determine their potential negative impact and whether we should shift our focus to the development of human–robot interaction assessment tools that provide more objective measures of ethical impact. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Attachment; Deception; Ethics; Older adults; Social robot",,,,,,"Horizon 2020 Framework Programme, H2020: 721619","Funding: This research and the APC were funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 721619 for the SOCRATES project.",,,,,,,,,,"Roveda, L., Haghshenas, S., Caimmi, M., Pedrocchi, N., Molinari Tosatti, L., Assisting operators in heavy industrial tasks: On the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules (2019) Front. Robot. AI, 6, p. 75. , [CrossRef]; Yang, G., Lv, H., Zhang, Z., Yang, L., Deng, J., You, S., Du, J., Yang, H., Keep healthcare workers safe: Application of teleoperated robot in isolation ward for COVID-19 prevention and control (2020) Chin. J. Mech. 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Technol, 108, pp. 841-865. , [CrossRef]","van Maris, A.; Bristol Robotics Laboratory, United Kingdom; email: anouk.vanmaris@brl.ac.uk",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85118167866
"Scholl C., Tobola A., Ludwig K., Zanca D., Eskofier B.M.","57315343900;56786073700;12769118900;57202056859;26428080900;","A smart capacitive sensor skin with embedded data quality indication for enhanced safety in human–robot interaction",2021,"Sensors","21","21","7210","","",,,"10.3390/s21217210","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118117065&doi=10.3390%2fs21217210&partnerID=40&md5=678eac87f80a2783b27ec7a51235aed4","Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany; Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany; Institute of Electronics Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstr. 9, Erlangen, 91054, Germany; Faculty of Electrical Engineering, Precision Engineering, Information Technology, Nuremberg Institute of Technology, Wassertorstraße 10, Nürnberg, 90489, Germany","Scholl, C., Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany, Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany; Tobola, A., Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany, Institute of Electronics Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstr. 9, Erlangen, 91054, Germany, Faculty of Electrical Engineering, Precision Engineering, Information Technology, Nuremberg Institute of Technology, Wassertorstraße 10, Nürnberg, 90489, Germany; Ludwig, K., Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany; Zanca, D., Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany; Eskofier, B.M., Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany","Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human– robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6 mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5 mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Capacitive sensor system; Data quality; Embedded AI; Human–robot interaction; Signal processing; Smart sensors","Accident prevention; Data reduction; Human robot interaction; Learning algorithms; Learning systems; Man machine systems; Monitoring; Signal processing; Smart sensors; Capacitive sensor system; Data quality; Embedded AI; Humans-robot interactions; Integral part; Machine learning methods; Safety measures; Sensor skins; Sensor systems; Signal-processing; Capacitive sensors",,,,,"Deutsche Forschungsgemeinschaft, DFG: ES 434/8-1; Bundesministerium für Wirtschaft und Energie, BMWi: 01MT20001A, 12ES0497","This work was supported in part by the Federal Ministry for Economic Affairs and Energy, Germany, within the projects ?GEMiMEG-II?Sichere und robuste kalibrierte Messsysteme f?r die digitale Transformation? (grant number 01MT20001A) and ?Verbundprojekt: Transfer-und Qualifizierungsmethodik h?chstintegrierter Elektronikkomponenten f?r die Automobil-und Automatisierungsindustrie?TRACE? (grant number 12ES0497). Bjoern Eskofier gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship program (grant number ES 434/8-1).",,,,,,,,,,"Arents, J., Abolins, V., Judvaitis, J., Vismanis, O., Oraby, A., Ozols, K., Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review (2021) J. Sens. 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(accessed on 21 October 2021); Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C., Estimating the support of a high-dimensional distribution (2001) Neural Comput, 13, pp. 1443-1471; Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J., Support Vector Method for Novelty Detection (1999) Proceedings of the Advances in Neural Information Processing Systems 12 (NIPS 1999), 12, pp. 582-588. , Denver, CO, USA, 29 November–4 December","Scholl, C.; Siemens AG, Guenther-Scharowsky-Str. 1, Germany; email: christoph.scholl@siemens.com",,,"MDPI",,,,,14248220,,,,"English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85118117065
"Li S., Han K., Li X., Zhang S., Xiong Y., Xie Z.","35484295400;57211891584;57300021600;57205673318;57222561100;57224848111;","Hybrid Trajectory Replanning-Based Dynamic Obstacle Avoidance for Physical Human-Robot Interaction",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","103","3","41","","",,,"10.1007/s10846-021-01510-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117271171&doi=10.1007%2fs10846-021-01510-2&partnerID=40&md5=79a515d60dc6a4a430bc50e4f6711794","School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Ubtech Robotics Corp Ltd, Shenzhen, 518000, China","Li, S., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Han, K., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Li, X., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Zhang, S., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Xiong, Y., Ubtech Robotics Corp Ltd, Shenzhen, 518000, China; Xie, Z., Ubtech Robotics Corp Ltd, Shenzhen, 518000, China","This paper presents a hierarchical replanning framework that rapidly modulates the ongoing trajectory to help 7-DoF redundant manipulators avoid dynamic obstacles in physical human-robot interaction. In this method, the safety requirements of the end-effector and links are met without adding additional control burden. Firstly, an improved rapidly-exploring random trees planner is utilized to initialize the path in the joint space. To prevent excessive trimming, a heuristic method based on geometric path descriptors is proposed for finding and removing redundant nodes. Secondly, a hybrid scheme combining local path rewiring and redundancy-based node self-motion is used to identify and replan the part of the pre-planned path affected by dynamic obstacles. Lastly, an adaptive online trajectory generator is designed to adjust the trajectory in real time and generate smooth motion primitives for actuators. Simulations and real-world experiments validate the effectiveness and robustness of the proposed framework for whole-arm obstacle avoidance in dynamic scenarios. Compared to other methods, our approach has a 35% higher success rate and a 47.2% reduction in replanning time. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Dynamic obstacle avoidance; Online trajectory generation; Physical human-robot interaction; Redundant manipulator; Trajectory replanning","Dynamics; Heuristic methods; Human robot interaction; Industrial manipulators; Man machine systems; Redundant manipulators; Additional control; Dynamic obstacle avoidance; Dynamic obstacles; On-line trajectory generations; Physical humanrobot interaction (phri); Rapidly-exploring random trees; Re-planning; Redundant manipulator; Safety requirements; Trajectory replanning; Trajectories",,,,,"Special Project for Research and Development in Key areas of Guangdong Province: 2019B010154003","This study was funded by the Key-Area Research and Development Program of Guangdong Province, China (grant number 2019B010154003).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. 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Cybern., 49 (7), pp. 2568-2579","Han, K.; School of Mechanical Science and Engineering, China; email: hanke@hust.edu.cn",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85117271171
"Paliga M., Pollak A.","57194699368;57194705523;","Development and validation of the fluency in human-robot interaction scale. A two-wave study on three perspectives of fluency",2021,"International Journal of Human Computer Studies","155",,"102698","","",,1,"10.1016/j.ijhcs.2021.102698","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112669498&doi=10.1016%2fj.ijhcs.2021.102698&partnerID=40&md5=d611995f0b05cd8616749ce8183ccac4","Uniwersytet Śląski w Katowicach, Instytut Psychologii/ Institute of Psychology, Katowice, 40-126, Poland","Paliga, M., Uniwersytet Śląski w Katowicach, Instytut Psychologii/ Institute of Psychology, Katowice, 40-126, Poland; Pollak, A., Uniwersytet Śląski w Katowicach, Instytut Psychologii/ Institute of Psychology, Katowice, 40-126, Poland","Contemporary work offers many humans the possibility to collaborate with robots. In fact, in industrial settings, human-robot interaction has become a necessity. To improve the quality of human collaboration with their robotic counterparts, practitioners and researchers turn their attention to the concept of fluency. It is crucial for human-robot interaction yet difficult to investigate. Moreover, we argue that the notion of fluency being a unidimensional construct is insufficient to understand the complex interaction in a human-robot team. Therefore, we aimed to develop and validate a new measure to study subjective fluency from three different perspectives, i.e., the human-oriented, the robot-oriented, and the team-oriented. The scale can serve as a tool to better understand the dynamics of the relationship between the operator and the robot. In this paper, we presented the factor analyses and psychometric properties of the Fluency in Human-Robot Interaction Scale. Furthermore, we tested the validity of the scale by investigating the relationship between three perspectives of fluency and the individual performance as well as fluency change over 6 weeks. The results showed positive relationships among all perspectives of fluency and performance. However, no change over time was observed. The findings were discussed with regard to the possible use of the scale in practical and academic contexts. © 2021","Factor analysis; Human-robot fluency; Human-robot interaction; Industry 4.0; Job performance; Scale development","End effectors; Industrial robots; Man machine systems; Human-robot-team; Individual performance; Industrial settings; Psychometric properties; Two waves; Social robots",,,,,,,,,,,,,,,,"Aly, A., Griffiths, S.S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics (2017) Cogn. Syst. 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"he Z., Liu T.","57195555620;56352343300;","Design of a three-dimensional capacitor-based six-axis force sensor for human-robot interaction",2021,"Sensors and Actuators, A: Physical","331",,"112939","","",,,"10.1016/j.sna.2021.112939","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111040221&doi=10.1016%2fj.sna.2021.112939&partnerID=40&md5=1a9ac8f28d3d441e709748cf4a9ae1ef","The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","he, Z., The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Liu, T., The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","Multi-dimensional force sensing capability of the robot plays a critical role in its interaction with the human and environment. The six-axis force sensor as a typical representative of the multi-axis force sensor, however, has not been extensively used in the field of human-robot interaction due to its high price. A low cost, a good sensing accuracy, and a high integration level six-axis force sensor is highly desirable to create. In this paper, to weaken the crosstalk phenomenon between the six-axis and optimize the sensitivity of overall system, a 3D capacitor structure with a cross-shape configuration of the shear force sensing cell was proposed. Eight electrodes were arranged on three perpendicular planes of the Cartesian coordinate system independently. The cross-shape differential capacitor was designed to achieve the improvement of sensitivity along the shear force direction. With Polyoxymethylene (POM) selected as the substrate material, a monolithic spatial structure was manufactured using CNC technology, and then conductive copper paint was sprayed on the specific surfaces of this 3D structure. The cost-effective prototype sensor has been manufactured without the manual work and experimentally validated by comparing it with a commercial six-axis force sensor. The characteristics of the prototype were analyzed in terms of its linearity, interference error, hysteresis, time domain response, SNR, offset repeatability and time drift. Finally, to verify the instrumented six-axis force sensor further, a controlled lab test was performed to measure the interaction forces with the Baxter Research Robot in a simulated scenario. © 2021 Elsevier B.V.","Human-robot interaction; Manufacturing process; Six-axis force sensor; Three-dimensional capacitor structure","Cost effectiveness; Costs; Man machine systems; Substrates; Force sensing; Humans-robot interactions; Manufacturing process; Multi dimensional; Multi-Axis; Multi-Axis-; Shear force; Six axis force sensor; Three dimensional capacitors; Three-dimensional capacitor structure; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51775485; Natural Science Foundation of Zhejiang Province, ZJNSF: LZ20E050002","This work was supported in part by the NSFC Grant No. 51775485 , and the Zhejiang Provincial Natural Science Foundation of China under Grant No, LZ20E050002 .",,,,,,,,,,"Azocar, A.F., Mooney, L.M., Duval, J.-F., Simon, A.M., Hargrove, L.J., Rouse, E.J., Design and clinical implementation of an open-source bionic leg (2020) Nat. 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J., 13, pp. 3472-3482; Wang, Z.J., Li, Z.X., He, J., Yao, J.T., Zhao, Y.S., Optimal design and experiment research of a fully pre-stressed six-axis force/torque sensor (2013) Measurement, 46, pp. 2013-2021; Jia, Z.-Y., Lin, S., Liu, W., Measurement method of six-axis load sharing based on the Stewart platform (2010) Measurement, 43, pp. 329-335; Bekhti, R., Duchaine, V., Cardou, P., Ieee, miniature capacitive three-axis force sensor (2014) 2014 Ieee/Rsj International Conference on Intelligent Robots and Systems, pp. 3939-3946; Kim, U., Lee, D.H., Kim, Y.B., Seok, D.Y., Choi, H.R., A novel six-axis force/torque sensor for robotic applications (2017) IEEE. ASME, 22, pp. 1381-1391; Kim, Y.B., Kim, U., Seok, D.Y., So, J., Lee, Y.H., Choi, H.R., Torque sensor embedded actuator module for robotic applications (2018) IEEE-ASME Trans. Mechatron., 23, pp. 1662-1672; Oh, H.S., Kim, U., Kang, G., Seo, J.K., Choi, H.R., Multi-axial force/torque sensor calibration method based on deep-learning (2018) IEEE Sens. J., 18, pp. 5485-5496; WACOH-TECH Inc, DynPick: WEF-6A1000-30 Series https://wacoh-tech.com/products/dynpick/; Bicchi, A., A criterion for optimal design of multi-axis force sensors (1992) Rob. Auton. Syst., 10, pp. 269-286; Uchiyama, M., Nakamura, Y., Hakomori, K., Evaluation of the robot force sensor structure using singular value decomposition (1990) Adv. Robot., 5, pp. 39-52; Kim, D., Lee, C.H., Kim, B.C., Lee, D.H., Lee, H.S., Canh Toan, N., Six-axis capacitive force/torque sensor based on dielectric elastomer (2013) Electroactive Polymer Actuators and Devices, , Y. BarCohen","Liu, T.; The State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: liutao@zju.edu.cn",,,"Elsevier B.V.",,,,,09244247,,SAAPE,,"English","Sens Actuators A Phys",Article,"Final","",Scopus,2-s2.0-85111040221
"Jiang Y., Zhao M., Wang C., Wei F., Wang K., Qi H.","8503974900;57201289964;57201294365;57193667386;57192679123;55167086600;","Diver’s hand gesture recognition and segmentation for human–robot interaction on AUV",2021,"Signal, Image and Video Processing","15","8",,"1899","1906",,,"10.1007/s11760-021-01930-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107190460&doi=10.1007%2fs11760-021-01930-5&partnerID=40&md5=d2c94f937b42beb67bbbfc0e487146d8","College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Jiang, Y., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Zhao, M., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Wang, C., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Wei, F., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Wang, K., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Qi, H., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","For the interaction between marine robots and divers in the underwater environment, a method of diver’s gesture recognition and segmentation is proposed. This method first uses the progressive growing training method to optimize the generative adversarial networks, generating high-resolution images with complex content. Then, we use the generative adversarial network model as a data augmentation method and generate high-resolution images. We make the masks of gestures in the new dataset and use the mask R-CNN algorithm for gesture recognition and gesture segmentation. The experimental results show that the generating data improves the accuracy of several object recognition algorithms but cannot completely replace the original data and the mean average precision of gesture recognition is 0.85. The visualization shows the validity and weakness of segmentation. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","Autonomous underwater vehicle; Gesture recognition; Human–robot interaction; Underwater object recognition","Object recognition; Social robots; Adversarial networks; Data augmentation; Gesture segmentation; Hand-gesture recognition; High resolution image; Object recognition algorithm; Robot interactions; Underwater environments; Gesture recognition",,,,,"20170101081JC, 20190302107GX, 20190303006SF; National Natural Science Foundation of China, NSFC: 51679105, 51809112, 62072211","This work was supported by the National Natural Science Foundation of China under Grant 62072211, Grant 51809112, and Grant 51679105. This work was also supported by the Science-Technology Development Plan Project of Jilin Province of China through Grants 20170101081JC, 20190303006SF and 20190302107GX.",,,,,,,,,,"Wu, N., Wang, M., Ge, T., Experiments on high-performance maneuvers control for a work-class 3000-m remote operated vehicle (2019) Proc. Inst. Mech. Eng., 233 (5), pp. 558-569; Kim, Y.J., Kim, H.T., Cho, Y.J., Development of a power control system for AUVs probing for underwater mineral resources (2009) J. Mar. Sci. Appl., 8 (4), p. 259; Palomeras, N., Vallicrosa, G., Mallios, A., Bosch, J., Vidal, E., Hurtos, N., AUV homing and docking for remote operations (2018) Ocean Eng., 154, pp. 106-120; Flammang, B.E., Tangorra, J.L., Mignano, A.P., Building a fish: the biology and engineering behind a bioinspired autonomous underwater vehicle (2017) Mar. Technol. Soc. 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Intell., 43 (1), pp. 172-186; Liu, J., Liu, Y., Wang, Y., Decoupled representation learning for skeleton-based gesture recognition (2020) IEEE Conference on Computer Vision and Pattern Recognition; Yang, J., Wilson, J.P., Gupta, S., Diver gesture recognition using deep learning for underwater human–robot interaction (2019) OCEANS 2019 MTS/IEEE SEATTLE; Jiang, Y., Peng, X., Xue, M., An underwater human–robot interaction using hand gestures for fuzzy control (2020) Int. J. Fuzzy Syst., 3, pp. 1-11; Mišković, N., Pascoal, A., Bibuli, M., Caccia, M., Neasham, J., Birk, A., (2017) CADDY project, year 3: The final validation trials. In: Oceans 2017-aberdeen, pp. 1-5; Stilinovic, N., Nad, D., Mišković, N., AUV for diver assistance and safety–design and implementation (2015) Oceans 2015-Genova. IEEE, pp. 1-4; Gomez Chavez, A., Ranieri, A., Chiarella, D., CADDY underwater stereo-vision dataset for human–robot interaction (HRI) in the context of diver activities (2019) J. Mar. Sci. Eng., 7 (1), p. 16; Odetti, A., Bibuli, M., Bruzzone, G., e-URoPe: a reconfgurable AUV/ROV for man–robot underwater cooperation (2017) IFAC-PapersOnLine, 50 (1), pp. 11203-11208; Chiarella, D., Bibuli, M., Bruzzone, G., A novel gesture-based language for underwater human–robot interaction (2018) J. Mar. Sci. Eng., 6 (3), p. 91; Chiarella, D., Bibuli, M., Bruzzone, G., Caccia, M., Ranieri, A., Zereik, E., (2015) Gesture-Based Language for diver–robot Underwater Interaction. in Oceans 2015-Genova, pp. 1-9; Radford, A., Metz, L., Chintala, S., (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks; Karras, T., Aila, T., Laine, S., (2017) Progressive Growing of Gans for Improved Quality, Stability, and Variation; Vuola, A.O., Akram, S.U., Kannala, J., Mask-RCNN and U-net ensembled for nuclei segmentation (2019) IEEE 16Th International Symposium on Biomedical Imaging (ISBI 2019), pp. 208-212","Qi, H.; College of Computer Science and Technology, China; email: qihong@jlu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,18631703,,,,"English","Signal Image Video Process.",Article,"Final","",Scopus,2-s2.0-85107190460
"Spatola N., Kühnlenz B., Cheng G.","57193889944;55909854300;10243873000;","Perception and Evaluation in Human–Robot Interaction: The Human–Robot Interaction Evaluation Scale (HRIES)—A Multicomponent Approach of Anthropomorphism",2021,"International Journal of Social Robotics","13","7",,"1517","1539",,6,"10.1007/s12369-020-00667-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099425734&doi=10.1007%2fs12369-020-00667-4&partnerID=40&md5=7e7dc65e978eebc84791d218eade0758","Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Via Morego, 30, Genova, GE  16163, Italy; Department of Education, University of Potsdam, Am Neuen Palais 10, Potsdam, 14469, Germany; Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany","Spatola, N., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Via Morego, 30, Genova, GE  16163, Italy, Department of Education, University of Potsdam, Am Neuen Palais 10, Potsdam, 14469, Germany; Kühnlenz, B., Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany; Cheng, G., Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany","The evaluation of how (human) individuals perceive robots is a central issue to better understand human–robot interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of HRI. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots. © 2021, Springer Nature B.V.","Anthropomorphism; Human–robot interaction; Questionnaire; Robot evaluation; Robot perception; Scale","Agricultural robots; Social robots; Animacy; Intentional agents; Multicomponents; Robot interactions; Social robotics; Surveys",,,,,,,,,,,,,,,,"Anderson, M.L., Why is AI so scary? 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Russell Sage foundation series on trust, 2, pp. 121-147. , Cook KS, (ed), Russell Sage Foundation, New York, NY; Yang, G.Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., The grand challenges of science robotics (2018) Sci Robot, 3 (14), p. eaar7650; Yarkin, K.L., Harvey, J.H., Bloxom, B.M., Cognitive sets, attribution, and social interaction (1981) J Pers Soc Psychol","Spatola, N.; Social Cognition in Human-Robot Interaction, Via Morego, 30, Italy; email: NicolasSpatola@hotmail.fr",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85099425734
"Gunson N., Hernandez Garcia D., Part J.L., Yu Y., Sieińska W., Dondrup C., Lemon O.","36188108600;56520258500;57193651604;56991624700;57215089194;56086338900;6603015795;","Combining Visual and Social Dialogue for Human-Robot Interaction",2021,"ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction",,,,"841","842",,,"10.1145/3462244.3481303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118990012&doi=10.1145%2f3462244.3481303&partnerID=40&md5=4726ea8c0485b4e63b94d1abe156343c","Heriot-Watt University, Edinburgh, United Kingdom","Gunson, N., Heriot-Watt University, Edinburgh, United Kingdom; Hernandez Garcia, D., Heriot-Watt University, Edinburgh, United Kingdom; Part, J.L., Heriot-Watt University, Edinburgh, United Kingdom; Yu, Y., Heriot-Watt University, Edinburgh, United Kingdom; Sieińska, W., Heriot-Watt University, Edinburgh, United Kingdom; Dondrup, C., Heriot-Watt University, Edinburgh, United Kingdom; Lemon, O., Heriot-Watt University, Edinburgh, United Kingdom","We will demonstrate a prototype multimodal conversational AI system that will act as a receptionist in a hospital waiting room, combining visually-grounded dialogue with social conversation. The system supports visual object conversation in the waiting room (e.g. looking for available seats or personal belongings), task-based dialogues regarding navigation and check-in procedures in the hospital, as well as access to the latest news, and a quiz game about coronavirus. The prototype system therefore demonstrates how to weave together a wide range of natural, daily conversations with end users that vary in complexity; from complex visual dialogue to chitchat and quiz games, to task-oriented domain-specific conversations. We are currently able to demonstrate the system via a web-based interface. It will soon be deployed on the ARI robot in a hospital waiting room. © 2021 Owner/Author.","Social Dialogue; Visual Dialogue","Coronavirus; Human robot interaction; AI systems; Humans-robot interactions; Multi-modal; Social conversations; Social dialogue; Systems support; Task-based; Visual dialog; Visual objects; Waiting room; Hospitals",,,,,"871245","This research has been funded by the EU H2020 program under grant agreement no. 871245 (http://spring-h2020.eu/).",,,,,,,,,,"Curry, A.C., Papaioannou, I., Suglia, A., Agarwal, S., Shalyminov, I., Xu, X., Dušek, O., Rieser, V., Alana v2: Entertaining and informative open-domain social dialogue using ontologies and entity linking (2018) Alexa Prize Proceedings (2018); Dondrup, C., Papaioannou, I., Lemon, O., (2019) Petri Net Machines for Human-Agent Interaction, , arXiv:1909.06174; Lemon, O., Gruenstein, A., Battle, A., Peters, S., Multi- tasking and collaborative activities in dialogue systems (2002) Proceedings of the 3rd SIGdial Workshop on Discourse and Dialogue, pp. 113-124; Mostafazadeh, N., Brockett, C., Dolan, B., Galley, M., Gao, J., Spithourakis, G.P., Vanderwende, L., (2017) Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation, pp. 462-472. , http://arxiv.org/abs/1701.08251, (2017), arXiv:1701.08251; Papaioannou, I., Dondrup, C., Lemon, O., Human-robot interaction requires more than slot filling - Multi-threaded dialogue for collaborative tasks and social conversation (2018) Proceedings of the FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, pp. 61-64; Zhou, L., Gao, J., Li, D., Shum, H.Y., The design and implementation of xiaoice, an empathetic social chatbot (2020) Computational Linguistics, 46 (1), pp. 53-93. , https://doi.org/10.1162/COLI_a_00368arXiv:1812.08989, 2020",,,"ACM SIGCHI","Association for Computing Machinery, Inc","23rd ACM International Conference on Multimodal Interaction, ICMI 2021","18 October 2021 through 22 October 2021",,172814,,9781450384810,,,"English","ICMI - Proc. Int. Conf. Multimodal Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85118990012
"Fiorini L., Loizzo F.G.C., Sorrentino A., Kim J., Rovini E., Di Nuovo A., Cavallo F.","56902334800;57238508000;57194094756;57202611686;55516869500;56962754700;14628324200;","Daily Gesture Recognition during Human-Robot Interaction Combining Vision and Wearable Systems",2021,"IEEE Sensors Journal","21","20",,"23568","23577",,,"10.1109/JSEN.2021.3108011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113886192&doi=10.1109%2fJSEN.2021.3108011&partnerID=40&md5=6fc765b34005efab449666c13441dc15","Department of Industrial Engineering, University of Florence, Florence, 50139, Italy; BioRobotics Institute, Scuola Superiore sant'Anna, Pisa, 56025, Italy; Department of Excellence in Robotics and AI, Scuola Superiore sant'Anna, Pisa, 56127, Italy; Sheffield Robotics, Sheffield, S1 1WB, United Kingdom; Department of Computing, Sheffield Hallam University, Sheffield, S1 1WB, United Kingdom","Fiorini, L., Department of Industrial Engineering, University of Florence, Florence, 50139, Italy; Loizzo, F.G.C., BioRobotics Institute, Scuola Superiore sant'Anna, Pisa, 56025, Italy, Department of Excellence in Robotics and AI, Scuola Superiore sant'Anna, Pisa, 56127, Italy; Sorrentino, A., BioRobotics Institute, Scuola Superiore sant'Anna, Pisa, 56025, Italy, Department of Excellence in Robotics and AI, Scuola Superiore sant'Anna, Pisa, 56127, Italy; Kim, J., BioRobotics Institute, Scuola Superiore sant'Anna, Pisa, 56025, Italy, Department of Excellence in Robotics and AI, Scuola Superiore sant'Anna, Pisa, 56127, Italy; Rovini, E., Department of Industrial Engineering, University of Florence, Florence, 50139, Italy; Di Nuovo, A., Sheffield Robotics, Sheffield, S1 1WB, United Kingdom, Department of Computing, Sheffield Hallam University, Sheffield, S1 1WB, United Kingdom; Cavallo, F., Department of Industrial Engineering, University of Florence, Florence, 50139, Italy, BioRobotics Institute, Scuola Superiore sant'Anna, Pisa, 56025, Italy, Department of Excellence in Robotics and AI, Scuola Superiore sant'Anna, Pisa, 56127, Italy","The recognition of human gestures is crucial for improving the quality of human-robot cooperation. This article presents a system composed of a Pepper robot that mounts an RGB-D camera and an inertial device called SensHand. The system acquired data from twenty people who performed five daily living activities (i.e., Having Lunch, Personal Hygiene, Working, House Cleaning, Relax). The activities were composed of at least two 'basic' gestures for a total of 10 gestures. The data acquisition was performed by two cameras positioned laterally and frontally to mimic the real conditions. The acquired data were off-line classified considering different combinations of sensors to evaluate how the sensor fusion approach improves the recognition abilities. Specifically, the article presents an experimental study that evaluated four algorithms often used in computer vision, i.e., three classical machine learning and one belonging to the field of deep learning, namely Support Vector Machine, Random Forest, K-Nearest Neighbor and Long Short-Term Memory Recurrent Neural Network. The comparative analysis of the results shows a significant improvement of the accuracy when fusing camera and sensors data, i.e., 0.81 for the whole system configuration when the robot is in a frontal position with respect to the user (0.79 if we consider only the index finger sensors) and equal to 0.75 when the robot is in a lateral position. Interestingly, the system performs well in recognising the transitions between gestures when these are presented one after the other, a common event in the real-life that was often neglected in the previous studies. © 2001-2012 IEEE.","Gesture recognition; Human-robot interaction; Inertial sensor; Social robot","Cameras; Computer vision; Data acquisition; Decision trees; Gesture recognition; Nearest neighbor search; Recurrent neural networks; Sensor data fusion; Support vector machines; Wearable sensors; Comparative analysis; Daily living activities; Human-robot cooperation; K-nearest neighbors; Lateral positions; Recognition abilities; System configurations; Wearable systems; Social robots",,,,,"Faculty of Science and Engineering, University of Manchester, FSE: ARS01_01120","This work was supported by the Co-Founding of European Union-Fondo Europeo di Sviluppo Regionale (FESR) o FSE, Programma Operativo Nazionale (PON) Research and Innovation 2014-2020 Project through SI-ROBOTICS-Healthy and active ageing through SocIal ROBOTICS under Grant ARS01_01120",,,,,,,,,,"Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., Cavallo, F., A survey of behavioral models for social robots (2019) Robotics, 8 (3), p. 54. , Jul; Akkaladevi, S.C., Heindl, C., Action recognition for human robot interaction in industrial applications (2015) Proc. 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Technol., 29 (4), pp. 565-570. , https://www.sciencedirect.com/science/article/pii/S2095268619302812, Jul","Fiorini, L.; Department of Industrial Engineering, Italy; email: laura.fiorini@unifi.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85113886192
"Korayem M.H., Azargoshasb S., Korayem A.H., Tabibian S.","6603758099;55387405500;57209727796;27467912200;","Design and Implementation of the Voice Command Recognition and the Sound Source Localization System for Human-Robot Interaction",2021,"Robotica","39","10",,"1779","1790",,,"10.1017/S0263574720001496","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102718393&doi=10.1017%2fS0263574720001496&partnerID=40&md5=b25e27619f5c877899ccd097a40d7602","Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran; Mechanical and Mechatronics Engineering Department, University of WaterlooON  N2L 3G1, Canada; Department of Mechanical and Mechatronics Engineering, Cyberspace Research Institute, Shahid Beheshti University, Tehran, Iran","Korayem, M.H., Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran; Azargoshasb, S., Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran; Korayem, A.H., Mechanical and Mechatronics Engineering Department, University of WaterlooON  N2L 3G1, Canada; Tabibian, S., Department of Mechanical and Mechatronics Engineering, Cyberspace Research Institute, Shahid Beheshti University, Tehran, Iran","Human-robot interaction (HRI) is becoming more important nowadays. In this paper, a low-cost communication system for HRI is designed and implemented on the Scout robot and a robotic face. A hidden Markov model-based voice command detection system is proposed and a non-native database has been collected by Persian speakers, which contains 10 desired English commands. The experimental results confirm that the proposed system is capable to recognize the voice commands, and properly performs the task or expresses the right answer. Comparing the system with a trained system on the Julius native database shows a better true detection (about 10%). © Iran University of Science and Technology, 2021.","Hidden Markov model; Human-robot interaction; Robotic face; Social robotic; Voice command","Agricultural robots; Hidden Markov models; Machine design; Man machine systems; Speech recognition; Design and implementations; Detection system; Human robot Interaction (HRI); Robotic faces; Scout robots; Sound source localization; Voice command; Voice command recognition; Social robots",,,,,,,,,,,,,,,,"Lo, S.-Y., Huang, H.-P., Realization of sign language motion using a dual-Arm/hand humanoid robot (2016) Intell. Serv. Rob., 9 (4), pp. 333-345; Halawani, A., Ur Réhman, S., Li, H., Anani, A., Active vision for controlling an electric wheelchair (2012) Intell. Service Rob., 5 (2), pp. 89-98; Leica, P., Roberti, F., Monllor, M., Toibero, J.M., Carelli, R., Control of bidirectional physical human-robot interaction based on the human intention (2017) Intell. Serv. Rob., 10 (1), pp. 31-40; Kennedy, J., Lemaignan, S., Montassier, C., Lavalade, P., Irfan, B., Papadopoulos, F., Senft, E., Belpaeme, T., Child speech recognition in human-robot interaction: Evaluations and recommendations (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction; Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E., Towards engagement models that consider individual factors in hri: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task (2017) Int. J. Soc. Rob., 9 (1), pp. 63-86; Varrasi, S., Lucas, A., Soranzo, A., McNamara, J., Di Nuovo, A., IBM cloud services enhance automatic cognitive assessment via human-robot interaction (2019) New Trends in Medical and Service Robotics, pp. 169-176. , Springer; Grigore, E.C., Pereira, A., Zhou, I., Wang, D., Scassellati, B., Talk to me: Verbal communication improves perceptions of friendship and social presence in human-robot interaction (2016) International Conference on Intelligent Virtual Agents, , Springer; Fardana, A., Jain, S., Jovancevíc, I., Suri, Y., Morand, C., Robertson, N., Controlling a mobile robot with natural commands based on voice and gesture (2013) Proceedings of the Ieee International Conference on Robotics and Automation (ICRA); Gómez, J.-B., Ceballos, A., Prieto, F., Redarce, T., Mouth gesture and voice command based robot command interface (2009) International Conference on Robotics and Automation; Mustofa, T., Implementation speech recognition for robot control using mfcc and ANFIS (2017) J. Telematics Inf. (JTI), 5 (2), pp. 47-55; Zinchenko, K., Wu, C.-Y., Song, K.-T., A study on speech recognition control for a surgical robot (2016) Ieee Trans. Ind. Inf., 13 (2), pp. 607-615; Burger, B., Ferrané, I., Lerasle, F., Infantes, G., Two-handed gesture recognition and fusion with speech to command a robot (2012) Auto. Robots., 32 (2), pp. 129-147; Abdullahi, Z.H., Muhammad, N.A., Kazaure, J.S., Amuda, F.A., Mobile robot voice recognition in control movements (2015) Int. J. Comput. Sci. Electron. Eng. (IJCSEE)., 3 (1), pp. 11-16; Abed, A.A., Jasim, A.A., Design and implementation of wireless voice controlled mobile robot (2016) Al-Qadisiyah J. Eng. Sci., 9 (2), pp. 135-147; Okuno, H.G., Nakadai, K., Takahashi, T., Takeda, R., Nakamura, K., Mizumoto, T., Yoshida, T., Itohara, T., Hark Document Version 2.0.0.(Revision: 6357; Tabibian, S., A voice command detection system for aerospace applications (2017) Int. J. 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Rob., 10 (2), pp. 179-198; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Human-robot interaction in autism treatment: Acase study on three pairs of autistic children as twins, siblings, and classmates (2018) Int. J. Soc. Rob., 10 (1), pp. 93-113; Bennett, C., Šabanovic, S., Perceptions of affective expression in a minimalist robotic face (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI); Bennett, C.C., Šabanoviæ, S., Deriving minimal features for human-like facial expressions in robotic faces (2014) Int. J. Soc. 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Control, 92 (9), pp. 2136-2149; Korayem, A.H., Nekoo, S.R., Korayem, M.H., Optimal sliding mode control design based on the state-dependent riccati equation for cooperative manipulators to increase dynamic load carrying capacity (2019) Robotica, 37 (2), pp. 321-337; Davouddoust, A., (2018) Upgrading the Scout Mobile Robot, by Creating Social Interactions with Humans, Using Audio and Video Channels in a Dynamic Environment, , Master's Thesis Islamic Azad University of Tehran Research Sciences; Tanha, S., Dehkordi, S., Korayem, A., Control a mobile robot in social environments by considering human as a moving obstacle (2018) 2018 6th Rsi International Conference on Robotics and Mechatronics (IcRoM)","Korayem, M.H.; Robotic Research Laboratory, Iran; email: hkorayem@iust.ac.ir",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85102718393
"Kim K.","57208161791;","Post-plant3: The Third Type of a Series of Non-humanoid Robots with an Embedded Physical Interaction: The development of non-verbal human-robot interaction framework and interactive robot prototypes",2021,"Adjunct Publication of the 34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021",,,,"140","141",,,"10.1145/3474349.3480206","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117908852&doi=10.1145%2f3474349.3480206&partnerID=40&md5=a2853291cd739d9b5049ee98afb64378","Department of Design / Communication, Seoul National University, South Korea","Kim, K., Department of Design / Communication, Seoul National University, South Korea","Post-plant is a series of plant-like robots which communicate nonverbally through physical movements. Most of the social robots look like humans or animals. communicating with us by mimicking human speech and gestures. Inspired by plants, post-plant respond to touch instead of language. With post-plant as a starting point, robots of the future will communicate with us in their own way, without having to mimic human behaviors. Post-plant 3 is the third type of post-plant robot series. © 2021 Owner/Author.","integrated motor interface; Physical human-robot interaction; Tangible user interface","Agricultural robots; Anthropomorphic robots; Behavioral research; Man machine systems; Social robots; Humanoid robot; Integrated motor interface; Interaction framework; Interactive robot; Non-verbal human; Physical humanrobot interaction (phri); Physical interactions; Robot prototypes; Tangible user interfaces; Verbal human-robot interactions; User interfaces",,,,,"Seoul National University, SNU","The background research was funded by Undergraduate Research Program and Student Directed Education course from Faculty of Liberal Education, Seoul National University. The motor interface system of post plant 1 and 2 is engineered by Jaeyeon Park and Taeyun Li.",,,,,,,,,,"Kim, K., Park, J., Li, T., (2021) Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1-3. , May. Post-plant: A Series of Non-humanoid Robots with Embedded Physical Non-verbal Interaction: The Development of Non-Verbal Human-Robot Interaction Framework and Input/Output Integrated Motor Interface","Kim, K.; Department of Design / Communication, South Korea; email: kwkim15@snu.ac.kr",,"ACM Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)","Association for Computing Machinery, Inc","34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021","10 October 2021 through 14 October 2021",,172491,,9781450386555,,,"English","Adjun. Publ. Annu. ACM Symp. User Interface Softw. Technol., UIST",Conference Paper,"Final","",Scopus,2-s2.0-85117908852
"She Y., Song S., Su H.-J., Wang J.","56545216500;57200117296;8379272100;7701333092;","A Parametric Study of Compliant Link Design for Safe Physical Human-Robot Interaction",2021,"Robotica","39","10",,"1739","1759",,,"10.1017/S0263574720001472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114724896&doi=10.1017%2fS0263574720001472&partnerID=40&md5=d1236e9929eab8e916abb647e4bce203","Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA  02139, United States; Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","She, Y., Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA  02139, United States, Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","Robots of next-generation physically interact with the world rather than be caged in a controlled area, and they need to make contact with the open-ended environment to perform their task. Compliant robot links offer intrinsic mechanical compliance for addressing the safety issue for physical human-robot interactions (pHRI). However, many important research questions are yet to be answered. For instance, how do system parameters, for example, mechanical compliance, motor torque, impact velocities, and so on, affect the impact force? how to formulate system impact dynamics of compliant robots, and how to size their geometric dimensions to maximize impact force reduction. In this paper, we present a parametric study of compliant link (CL) design for safe pHRI. We first present a theoretical model of the pHRI system that is comprised of robot dynamics, an impact contact model, and dummy head dynamics. After experimentally validating the theoretical model, we then systematically study the effects of CL parameters on the impact force in more detail. Specifically, we explore how the design and actuation parameters affect the impact force of pHRI system. Based on the parametric studies of the CL design, we propose a step-by-step process and a list of concrete guidelines for designing CL with safety constraints in pHRI. We further conduct a simulation case study to validate this design process and design guidelines. © The Author(s) 2021.","Compliant links; Design; Modeling; Physical human-robot interaction; Variable stiffness links","Agricultural robots; Dynamics; Machine design; Man machine systems; Geometric dimensions; Impact velocities; Mechanical compliance; Physical human-robot interactions; Physical humanrobot interaction (phri); Research questions; Safety constraint; Theoretical modeling; Human robot interaction",,,,,"National Science Foundation, NSF: CMMI-1637656","This material is based on work supported by the National Science Foundation under Grant No: CMMI-1637656.",,,,,,,,,,"Versace, J., (1971) A Review of the Severity Index, , SAE Technical Paper 710881; Gao, D., Wampler, C.W., Head injury criterion (2009) Ieee Robot. 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"Vyas Y., Allenspach M., Lanegger C., Siegwart R., Tognon M.","57247982900;57193098989;57216322074;57337917400;56742951400;","Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction",2021,"AIRPHARO 2021 - 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment",,,,"","",,,"10.1109/AIRPHARO52252.2021.9571032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119087667&doi=10.1109%2fAIRPHARO52252.2021.9571032&partnerID=40&md5=c14f83f70b539c5ffd836aa91bb6fed6","ETH Zurich, Autonomous Systems Lab8092, Switzerland","Vyas, Y., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Allenspach, M., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Lanegger, C., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Siegwart, R., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Tognon, M., ETH Zurich, Autonomous Systems Lab8092, Switzerland","An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be extracted. Quality and robustness of the estimation are improved by opportune filtering based on heuristics. The approach is successfully evaluated on a real dataset of walking humans, including complex trajectories and changing step frequency over time. © 2021 IEEE.",,"Extended Kalman filters; Human robot interaction; Man machine systems; Biased sinusoidal signals; Direction of motion; Extended Kalman filter algorithm; Frequency bias; Human gait; Human walking; Physical humanrobot interaction (phri); Real- time; Vertical direction; Walking gait; Kinematics",,,,,"Eidgenössische Technische Hochschule Zürich, ETH: SEED-04 20-2","This work was supported by ETH Career Seed Grant SEED-04 20-2 (AEROGUIDE)",,,,,,,,,,"Lasota, P.A., Fong, T., Shah, J.A., (2017) A Survey of Methods for Safe Human-robot Interaction, , Now Publishers; Losey, D.P., McDonald, C.G., Battaglia, E., O'Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction (2018) Applied Mechanics Reviews, 70 (1); Brescianini, D., Jung, J.-Y., Jang, I.-H., Park, H.S., Riener, R., Ins/ekf-based stride length, height and direction intent detection for walking assistance robots (2011) 2011 IEEE International Conference on Rehabilitation Robotics. IEEE, pp. 1-5; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2013) IEEE/ASME Transactions on Mechatronics, 19 (3), pp. 1007-1014; Pehlivan, A.U., Losey, D.P., O'Malley, M.K., Minimal assistas-needed controller for upper limb robotic rehabilitation (2015) IEEE Transactions on Robotics, 32 (1), pp. 113-124; Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robotic exoskeleton by admittance control (2018) IEEE Transactions on Industrial Electronics, 65 (12), pp. 9614-9624; Acharya, U., Bevins, A., Duncan, B.A., Investigation of humanrobot comfort with a small unmanned aerial vehicle compared to a ground robot (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)., pp. 2758-2765; Augugliaro, F., D'Andrea, R., Admittance control for physical human-quadrocopter interaction (2013) 2013 European Control Conference (ECC), pp. 1805-1810; Tognon, M., Alami, R., Siciliano, B., Physical human-robot interaction with a tethered aerial vehicle: Application to a force-based human guiding problem (2021) IEEE Transactions on Robotics, 37 (3), pp. 723-734; Carpentier, J., Benallegue, M., Laumond, J.-P., On the centre of mass motion in human walking (2017) International Journal of Automation and Computing, 14 (5), pp. 542-551; Yazdanian, M., Mojiri, M., Sheikholeslam, F., An extended kalman filter for identification of biased sinusoidal signals (2012) 20th Iranian Conference on Electrical Engineering (ICEE2012)., pp. 990-993",,,,"Institute of Electrical and Electronics Engineers Inc.","1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment, AIRPHARO 2021","4 October 2021 through 5 October 2021",,173180,,9781665433891,,,"English","AIRPHARO - AIRPHARO Workshop Aer. Robot. Syst. Phys. Interact. Environ.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119087667
"Oliveira R., Arriaga P., Paiva A.","57201501871;12793958200;7102683774;","Human-robot interaction in groups: Methodological and research practices",2021,"Multimodal Technologies and Interaction","5","10","59","","",,,"10.3390/mti5100059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116608293&doi=10.3390%2fmti5100059&partnerID=40&md5=a9118395389f1337b790c23c5a7d2295","Centro de Intervenção e Investigação Social (CIS-Iscte), Iscte-Instituto Universitário de Lisboa (CIS-IUL), Lisbon, 1100, Portugal; INESC-ID (GAIPS), Lisbon, 1100, Portugal; Instituto Superior Técnico, University of Lisbon, Lisbon, 1100, Portugal","Oliveira, R., Centro de Intervenção e Investigação Social (CIS-Iscte), Iscte-Instituto Universitário de Lisboa (CIS-IUL), Lisbon, 1100, Portugal, INESC-ID (GAIPS), Lisbon, 1100, Portugal; Arriaga, P., Centro de Intervenção e Investigação Social (CIS-Iscte), Iscte-Instituto Universitário de Lisboa (CIS-IUL), Lisbon, 1100, Portugal; Paiva, A., INESC-ID (GAIPS), Lisbon, 1100, Portugal, Instituto Superior Técnico, University of Lisbon, Lisbon, 1100, Portugal","Understanding the behavioral dynamics that underline human–robot interactions in groups remains one of the core challenges in social robotics research. However, despite a growing interest in this topic, there is still a lack of established and validated measures that allow researchers to analyze human–robot interactions in group scenarios; and very few that have been developed and tested specifically for research conducted in-the-wild. This is a problem because it hinders the development of general models of human–robot interaction, and makes the comprehension of the inner workings of the relational dynamics between humans and robots, in group contexts, significantly more difficult. In this paper, we aim to provide a reflection on the current state of research on human–robot interaction in small groups, as well as to outline directions for future research with an emphasis on methodological and transversal issues. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).","Group interactions; Human–robot interaction; Research methodology; Social robotics",,,,,,"PD/BD/150570/2020","Funding: R.O. acknowledges a PhD grant awarded by Fundação para a Ciência e Tecnologia (ref: PD/BD/150570/2020).","R.O. acknowledges a PhD grant awarded by Funda??o para a Ci?ncia e Tecnologia (ref: PD/BD/150570/2020).",,,,,,,,,"Alemi, M., Meghdari, A., Ghazisaedy, M., Employing humanoid robots for teaching English language in Iranian junior high-schools (2014) Int. J. Hum. 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"Alonso R., Concas E., Recupero D.R.","56188189100;57212245253;57206674454;","An abstraction layer exploiting voice assistant technologies for effective human—robot interaction",2021,"Applied Sciences (Switzerland)","11","19","9165","","",,1,"10.3390/app11199165","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116318035&doi=10.3390%2fapp11199165&partnerID=40&md5=c880ea5c4eb9eb473170a3792e9b4824","ICT Division, R2M Solution s.r.l., Pavia, 27100, Italy; Department of Mathematics and Computer Science and Human-Robot Interaction Lab, University of Cagliari, Cagliari, 09124, Italy","Alonso, R., ICT Division, R2M Solution s.r.l., Pavia, 27100, Italy; Concas, E., Department of Mathematics and Computer Science and Human-Robot Interaction Lab, University of Cagliari, Cagliari, 09124, Italy; Recupero, D.R., ICT Division, R2M Solution s.r.l., Pavia, 27100, Italy, Department of Mathematics and Computer Science and Human-Robot Interaction Lab, University of Cagliari, Cagliari, 09124, Italy","A lot of people have neuromuscular problems that affect their lives leading them to lose an important degree of autonomy in their daily activities. When their disabilities do not involve speech disorders, robotic wheelchairs with voice assistant technologies may provide appropriate human–robot interaction for them. Given the wide improvement and diffusion of Google Assistant, Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, etc., such voice assistant technologies can be fully integrated and exploited in robotic wheelchairs to improve the quality of life of affected people. As such, in this paper, we propose an abstraction layer capable of providing appropriate human– robot interaction. It allows use of voice assistant tools that may trigger different kinds of applications for the interaction between the robot and the user. Furthermore, we propose a use case as a possible instance of the considered abstraction layer. Within the use case, we chose existing tools for each component of the proposed abstraction layer. For example, Google Assistant was employed as a voice assistant tool; its functions and APIs were leveraged for some of the applications we deployed. On top of the use case thus defined, we created several applications that we detail and discuss. The benefit of the resulting Human–Computer Interaction is therefore two-fold: on the one hand, the user may interact with any of the developed applications; on the other hand, the user can also rely on voice assistant tools to receive answers in the open domain when the statement of the user does not enable any of the applications of the robot. An evaluation of the presented instance was carried out using the Software Architecture Analysis Method, whereas the user experience was evaluated through ad-hoc questionnaires. Our proposed abstraction layer is general and can be instantiated on any robotic platform including robotic wheelchairs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Cloud robotics; Human–robot dialogue; Language understanding; Robotic wheelchairs; Voice assistant technology",,,,,,"Horizon 2020 Framework Programme, H2020: 956573","Funding: This research was partially funded by the H2020 project STAR—Novel AI technology for dynamic and unpredictable manufacturing environments (grant number 956573).",,,,,,,,,,"Feil-Seifer, D., Mataric, M., Robot-assisted therapy for children with autism spectrum disorders Proceedings of the 7th International Conference on Interaction Design and Children, pp. 49-52. , London, UK, 17–24 June 2008; [CrossRef]; D’Onofrio, G., Sancarlo, D., Raciti, M., Recupero, D.R., Mangiacotti, A., Russo, A., Ricciardi, F., Presutti, V., MARIO Project: Experimentation in the Hospital Setting Proceedings of the Ambient Assisted Living—Italian Forum 2017, Eighth Italian on Ambient Assisted Living Forum, ForItAAL 2017, pp. 289-303. , Genoa, Italy, 14–15 June 2017; [CrossRef]; (2018) Health at a Glance: Europe 2018, p. 212. , Organisation for Economic Co-operation and Development. 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J. Mach. Learn. Cybern, 10, pp. 2045-2055. , [CrossRef]; Atzeni, M., Dridi, A., Recupero, D.R., Using frame-based resources for sentiment analysis within the financial domain (2018) Prog. AI, 7, pp. 273-294. , [CrossRef]; Atzeni, M., Recupero, D.R., Fine-Tuning of Word Embeddings for Semantic Sentiment Analysis (2018) Proceedings of the Semantic Web Challenges—5th SemWebEval Challenge at ESWC 2018, pp. 140-150. , Heraklion, Greece, 3–7 June Revised Selected Papers, [CrossRef]; Atzeni, M., Dridi, A., Recupero, D.R., Fine-Grained Sentiment Analysis on Financial Microblogs and News Headlines (2017) Proceedings of the Semantic Web Challenges—4th SemWebEval Challenge at ESWC 2017, pp. 124-128. , Portoroz, Slovenia, 28 May–1 June Revised Selected Papers, [CrossRef]; Gangemi, A., Presutti, V., Recupero, D.R., Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool (2014) IEEE Comp. Int. Mag, 9, pp. 20-30. , [CrossRef]; Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G., Sentilo: Frame-Based Sentiment Analysis (2015) Cogn. Comput, 7, pp. 211-225. , [CrossRef]; Dridi, A., Atzeni, M., Recupero, D.R., Bearish-Bullish Sentiment Analysis on Financial Microblogs (2017) Proceedings of the 3rd International Workshop at ESWC on Emotions, Modality, Sentiment Analysis and the Semantic Web Co-Located with 14th ESWC 2017, , Portroz, Slovenia, 28 May; Atzeni, M., Reforgiato, Â., Recupero, D., Multi-domain sentiment analysis with mimicked and polarized word embeddings for humanoid robot interaction (2020) Future Gener. Comput. Syst, 110, pp. 984-999. , [CrossRef]; Reforgiato Recupero, D., Spiga, F., Knowledge acquisition from parsing natural language expressions for humanoid robot action commands (2020) Inf. Process. Manag, 57, p. 102094. , [CrossRef]; Reforgiato Recupero, D., Technology Enhanced Learning Using Humanoid Robots (2021) Future Internet, 13, p. 32. , [CrossRef]; Bass, L., Clements, P., Kazman, R., (2003) Software Architecture in Practice, , https://books.google.ro/books?hl=en&lr=&id=ZY6UZTjBnGQC&oi=fnd&pg=PA1&dq=Bass,+L.%3B+Clements,+P.%3B+Kazman,+R.+%5Cemph%7BSoftware+Architecture+in+Practice%7D%3B++%5Chl%7B2003.%7D&ots=gsptTsR_rh&sig=YhRFJCTVL4ldBqRggttYO-SIBck&redir_esc=y#v=onepage&q&f=false, (accessed on 30 June 2021); Kazman, R., Klein, M., Barbacci, M., Longstaff, T., Lipson, H., Carrière, S., The Architecture Tradeoff Analysis Method (2002) Proceedings of the Fourth IEEE International Conference on Engineering of Complex Computer Systems (Cat. No.98EX193), pp. 68-78. , Monterey, CA, USA, 6 August [CrossRef]; Kazman, R., Bass, L., Abowd, G., Webb, M., SAAM: A method for analyzing the properties of software architectures (1994) Proceedings of the 16th International Conference on Software Engineering, pp. 81-90. , Sorrento, Italy, 16–21 May; Clements, P., (2000) Active Reviews for Intermediate Designs, p. 26. , Software Engineering Institute, Carnegie Mellon University: Pittsburgh, PA, USA; Ali Babar, M., Gorton, I., Comparison of Scenario-Based Software Architecture Evaluation Methods (2004) Proceedings of the 11th Asia-Pacific Software Engineering Conference, pp. 600-607. , Busan, Korea, 30 November–3 December [CrossRef]","Recupero, D.R.; ICT Division, Italy; email: diego.reforgiato@unica.it",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85116318035
"Filippini C., Perpetuini D., Cardone D., Merla A.","57205533040;57196390915;54788843200;24169413000;","Improving human–robot interaction by enhancing nao robot awareness of human facial expression",2021,"Sensors","21","19","6438","","",,,"10.3390/s21196438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115757400&doi=10.3390%2fs21196438&partnerID=40&md5=5d799c68ae8cf9d677b4fa60dddd159f","Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy","Filippini, C., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy; Perpetuini, D., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy; Cardone, D., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy; Merla, A., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy","An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Affective computing; Emotion recognition; Facial expression recognition; Human–robot interaction; Machine learning","Agricultural robots; Anthropomorphic robots; Convolutional neural networks; Face recognition; Human robot interaction; Intelligent robots; Machine design; Robot programming; Affective Computing; Emotion recognition; Emotional intelligence; Facial expression recognition; Facial Expressions; Human emotion; Human facial expressions; Humanoid robot; Humans-robot interactions; Interaction fields; Machine learning; 10-N-nonylacridinium orange; aminoacridine derivative; emotion; facial expression; facial recognition; human; robotics; Aminoacridines; Emotions; Facial Expression; Facial Recognition; Humans; Robotics",,"10-N-nonylacridinium orange; Aminoacridines",,,,"Funding: This research was funded by the following grants: PON FESR MIUR R&I 2014-2020-ADAS+, grant number ARS01_00459, and PON MIUR SI-ROBOTICS, grant number ARS01_01120.",,,,,,,,,,"Scassellati, B., Admoni, H., Matarić, M., Robots for Use in Autism Research (2012) Annu. 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Image Underst, 140, pp. 93-114. , [CrossRef]; Liu, K., Zhang, M., Pan, Z., Facial Expression Recognition with CNN Ensemble Proceedings of the 2016 International Conference on Cyberworlds (CW), pp. 163-166. , Chongqing, China, 28–30 September 2016; Zahara, L., Musa, P., Wibowo, E.P., Karim, I., Musa, S.B., The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm Based Raspberry Pi Proceedings of the 2020 5th International Conference on Informatics and Computing (ICIC), pp. 1-9. , Shenzhen, China, 12–15 August 2020; Knyazev, B., Shvetsov, R., Efremova, N., Kuharenko, A., (2017) Convolutional Neural Networks Pretrained on Large Face Recognition Datasets for Emotion Classification from Video, , arXiv arXiv:1711.04598; Melinte, D.O., Vladareanu, L., Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer (2020) Sensors, 20, p. 2393. , [CrossRef] [PubMed]","Merla, A.; Department of Neurosciences, Italy; email: arcangelo.merla@unich.it",,,"MDPI",,,,,14248220,,,"34640758","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85115757400
"Moon S.J., Kim J., Yim H., Kim Y., Choi H.R.","57240504900;57240193100;57240193200;57219162424;7404339840;","Real-time obstacle avoidance using dual-type proximity sensor for safe human-robot interaction",2021,"IEEE Robotics and Automation Letters","6","4","9508896","8021","8028",,,"10.1109/LRA.2021.3102318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113994583&doi=10.1109%2fLRA.2021.3102318&partnerID=40&md5=0982c41667507493737e0effb0f7c2c4","School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea","Moon, S.J., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Kim, J., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Yim, H., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Kim, Y., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Choi, H.R., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea","This letter introduces a dual-type proximity sensor and a control strategy for a robot manipulator to realize safe human-robot interactions (HRI) by using the sensor. Safety is an essential condition for HRI in practical scenarios. To achieve this condition, information about the relationship between an external objects and the robot is required. To obtain this information, we employ a dual-type proximity sensor, which consists of capacitive and inductive transducers and can detect the distance between a robot and external objects. Further, we propose a real-time trajectory planning method to deal with obstacles by using admittance control and distance measurements. To update the motion of the manipulator according to our control strategy, a Weight-Prioritized solution based on a QP (quadratic programming) formalism was applied. Further, the problem of self-sensing is solved via machine learning using a training dataset consisting of data corresponding to random joint positions. The proposed method was implemented on a collaborate robot (UR10). Experiments were conducted considering realistic human-robot interactions, and safety improvement was validated. © 2016 IEEE.","admittance control; human-robot interaction; Obstacle avoidance; proximity sensor","Agricultural robots; Capacitive sensors; Inductive sensors; Industrial manipulators; Man machine systems; Manipulators; Proximity sensors; Quadratic programming; Robot applications; Robot programming; Admittance control; Control strategies; Inductive transducers; Real time obstacle avoidance; Real-time trajectories; Robot manipulator; Safe human-robot interaction; Safety improvement; Human robot interaction",,,,,"Ministry of Trade, Industry and Energy, MOTIE","Manuscript received February 24, 2021; accepted July 12, 2021. Date of publication August 6, 2021; date of current version August 23, 2021. This letter was recommended for publication by Editor Gentiane Venture upon evaluation of the Associate Editor and reviewers’ comments. This work was supported by under Grant 20000944 from Robot Industry Core Technology Development Program funded by the Ministry of Trade, Industry and Energy (MI, Korea). (Corresponding author: Hyouk Ryeol Choi.) The authors are with the School of Mechanical Engineering, Sungkyunkwan University, Seoburo 2066, Jangan-gu, Suwon, South Korea (e-mail: msj19@skku.edu; sol5100@skku.edu; yimhongsik@skku.edu; ye.kim@skku.edu; choihyoukryeol@gmail.com). Digital Object Identifier 10.1109/LRA.2021.3102318",,,,,,,,,,"ISO 10218 robots and robotic devices-safety requirements for industrial robots, ISO, Geneva, CH, Tech. Rep., 2011; Reddy, A., Bright, G., Padayachee, J., A review of safety methods for human-robot collaboration and a proposed novel approach (2019) Int. Conf. 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Intell. Syst. Comput., p. 540; M'Colo, K.E., Luong, B., Crosnier, A., Neel, C., Fraisse, P., Obstacle avoidance using a capacitive skin for safe human-robot interaction (2019) IROS; (2020), https://www.aidinrobotics.com/, Accessed: Jul. 20; Bouyarmane, K., Khdeear, A., On weight-prioritized multitask control of humanoid robots (2018) IEEE Trans. Autom. Control, 63 (6), pp. 1632-1647; Hogan, N., Impedance control: An approach to manipulation (1984) Proc. IEEE Amer. Control Conf. 107, pp. 304-313; (2015) ISO 13849 Safety of Machinery-safety-related Parts of Control Systems, , ISO, Geneva, CH, Tech. Rep","Choi, H.R.; School of Mechanical Engineering, Seoburo, Jangan-gu, South Korea; email: choihyoukryeol@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85113994583
"Selvaggio M., Cognetti M., Nikolaidis S., Ivaldi S., Siciliano B.","57188639260;54792819500;55635433000;26643860200;7006263661;","Autonomy in physical human-robot interaction: A brief survey",2021,"IEEE Robotics and Automation Letters","6","4","9501975","7989","7996",,,"10.1109/LRA.2021.3100603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112635301&doi=10.1109%2fLRA.2021.3100603&partnerID=40&md5=ccc50c75cc5f15c2efb93ae757e1ceb0","Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80138, Italy; Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, W23 F2K8, Ireland; Department of Computer Science, University of Southern California, Los Angeles, CA  90007, United States; Inria, Loria, Université de Lorraine, CNRS, Nancy, 54000, France","Selvaggio, M., Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80138, Italy; Cognetti, M., Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, W23 F2K8, Ireland; Nikolaidis, S., Department of Computer Science, University of Southern California, Los Angeles, CA  90007, United States; Ivaldi, S., Inria, Loria, Université de Lorraine, CNRS, Nancy, 54000, France; Siciliano, B., Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80138, Italy","Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system. This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the letter, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter. © 2016 IEEE.","Human-centered robotics; Human-robot collaboration; Physical human-robot interaction","Agricultural robots; Control theory; Learning systems; Man machine systems; Robotics; Autonomy levels; Design paradigm; Learning techniques; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical workloads; Research communities; Shared autonomy; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 101017008; Horizon 2020","Manuscript received February 24, 2021; accepted July 19, 2021. Date of publication July 29, 2021; date of current version August 20, 2021. This letter was recommended for publication by Associate Editor K. Hashtrudi-Zaad and Editor J.-H. Ryu upon evaluation of the reviewers’ comments. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 101017008. (Corresponding author: Mario Selvaggio.) Mario Selvaggio and Bruno Siciliano are with the Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples 80138, Italy (e-mail: mar.selvaggio@gmail.com; siciliano@ieee.org).","This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 101017008.",,,,,,,,,"Ferrell, W.R., Sheridan, T.B., Supervisory control of remote manipulation (1967) IEEE Spectr., 4 (10), pp. 81-88. , Oct; Sheridan, T., Human and Computer Control of Undersea Teleoperators (1978) Massachusetts Inst of Tech Cambridge Man-Machine Systems Lab, , Dec; Thrun, S., Toward robotic cars (2010) Commun. 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"Jiang Y., Zhao M., Wang C., Wei F., Qi H.","8503974900;57201289964;57201294365;57193667386;55167086600;","A Method for Underwater Human–Robot Interaction Based on Gestures Tracking with Fuzzy Control",2021,"International Journal of Fuzzy Systems","23","7",,"2170","2181",,,"10.1007/s40815-021-01086-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108278504&doi=10.1007%2fs40815-021-01086-x&partnerID=40&md5=57a1028b47ff47d5a3e5da41c2c851cb","College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Jiang, Y., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Zhao, M., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Wang, C., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Wei, F., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Qi, H., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Working in collaboration with an Autonomous Underwater Vehicle is a new working method for divers. Using gestures to give instructions for the diver is a simple and effective mode of underwater human–robot interaction (HRI). In this paper, a gestures tracking method for under human–robot interaction based on fuzzy control is proposed. Firstly, four object recognition algorithms in terms of gesture recognition are compared. YOLO V4-tiny was an extremely high performance, as the gesture area recognition algorithm. We propose a model based on Siamese Network for gesture classification. A gesture tracking method based on fuzzy control is proposed, analyzing the image from AUV front camera to establish a 3D fuzzy rule set. This method can realize the self-regulation of AUV and keep the diver's gestures in the camera view. The experiment result shows the efficiency of the proposed method. © 2021, Taiwan Fuzzy Systems Association.","Autonomous underwater vehicle; Fuzzy control; Gesture recognition; Human–robot interaction","Autonomous underwater vehicles; Cameras; Fuzzy control; Fuzzy inference; Human robot interaction; Object recognition; Gesture classifications; Gesture tracking; Object recognition algorithm; Recognition algorithm; Robot interactions; Self regulation; Tracking method; Working methods; Gesture recognition",,,,,"20170101081JC, 20190302107GX, 20190303006SF; National Natural Science Foundation of China, NSFC: 51679105, 51809112, 51939003","This work was supported by the National Natural Science Foundation of China under Grant 51679105, Grant 51809112, and Grant 51939003. 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IEEE; Islam, M.J., Sattar, J., Mixed-domain biological motion tracking for underwater human-robot interaction (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA)., pp. 4457-4464. , IEEE; Chavez, A.G., Mueller, C.A., Birk, A., Babic, A., Miskovic, N., Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance (2017) OCEANS 2017-Aberdeen, pp. 1-7; Fulton, M., Edge, C., Sattar, J., Robot communication via motion: Closing the underwater human-robot interaction loop (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 4660-4666; Sun, K., Qiu, J., Karimi, H.R., Gao, H., A novel finite-time control for Nonstrict feedback saturated nonlinear systems with tracking error constraint (2019) IEEE Transactions on Syst. Man Cyberneti. Syst.","Qi, H.; College of Computer Science and Technology, China; email: qihong@jlu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,15622479,,,,"English","Int. J. Fuzzy Syst.",Article,"Final","",Scopus,2-s2.0-85108278504
"Birch B., Griffiths C.A., Morgan A.","57223398635;57219637654;57224184276;","Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction",2021,"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","235","12",,"1939","1948",,,"10.1177/09544054211014492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105786317&doi=10.1177%2f09544054211014492&partnerID=40&md5=7779fddaf3206f0d2a533723bf494501","College of Engineering, Swansea University, Swansea, United Kingdom","Birch, B., College of Engineering, Swansea University, Swansea, United Kingdom; Griffiths, C.A., College of Engineering, Swansea University, Swansea, United Kingdom; Morgan, A., College of Engineering, Swansea University, Swansea, United Kingdom","Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high. © IMechE 2021.","Cobots; dynamic time warping; human-robot-interaction; industrial robots; MEL frequency cepstral coefficients; speech recognition; voice control","Drilling machines (machine tools); Industrial robots; Man machine systems; Manufacture; Social robots; Advanced manufacturing; Collaborative robots; Dynamic time warping; Environmental factors; Feature extraction algorithms; Human-Robot Interface; Manufacturing environments; Mel frequency cepstral co-efficient; Speech recognition",,,,,,,,,,,,,,,,"Chen, C.H., (1976) Pattern recognition and artificial intelligence: proceedings of the joint workshop on pattern recognition and artificial intelligence, Held at Hyannis, Massachusetts, June 1–3, 1976, , New York, NY, Academic Press, Academic Press Rapid Manuscript Reproduction; Juang, B.H., Rabiner, L.R., Automatic speech recognition–a brief his- tory of the technology development (2005) Georgia Institute of Technology Atlanta Rutgers University and the University of California Santa Barbara, 1, p. 67. , p; Mazhar, O., Navarro, B., Ramdani, S., A real-time human-robot interaction framework with robust background invariant hand gesture detection (2019) Robot Comput Integr Manuf, 60, pp. 34-48; (2016), https://www.kuka.com/en-gb/future-production/human-robot-collaboration, accessed 8 February 2019, KUKA AG; Rahman, S.M., Wang, Y., Mutual trust-based subtask allocation for human–robot collaboration in flexible lightweight assembly in manufacturing (2018) Mechatronics, 54, pp. 94-109; Gustavsson, P., Syberfeldt, A., Brewster, R., Human-robot collaboration demonstrator combining speech recognition and haptic control (2017) Procedia CIRP, 63, pp. 396-401; Rogowski, A., Web-based remote voice control of robotized cells (2013) Robot Comput Integr Manuf, 29 (4), pp. 77-89; Ittichaichareon, C., Suksri, S., Yingthawornsuk, T., Speech recognition using MFCC (2012) International conference on computer graphics, simulation and modeling (ICGSM’2012), pp. 28-29. , July, Pattaya, Thailand, Planetary Scientific Research Centre, In; Muda, L., Begam, M., Elamvazuthi, I., (2010) Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques, , arXiv preprint; Berg, J., Lottermoser, A., Richter, C., Human-robot- interaction for mobile industrial robot teams (2019) Procedia CIRP, 79, pp. 614-619; Kulyukin, V., Human-robot interaction through gesture-free spoken dialogue (2004) Auton Robot, 16 (3), pp. 239-257; Rogowski, A., Industrially oriented voice control system (2012) Robot Comput Integr Manuf, 28 (3), pp. 303-315; Kulyukin, V.A., Robotic NLP vs. Human subset language acquisition (2007) AAAI spring symposium: multidisciplinary collaboration for socially assistive robotics, pp. 33-35. , Menlo Park, CA, The AAAI Press, In; Moore, R.C., Dowding, J., Bratt, H., (1997) CommandTalk: a spoken-language interface for battlefield simulations, pp. 1-7. , Proceedings of the fifth conference on alied natural language processing, Washington, DC, Association for Computational Linguistics, In; Bugmann, G., Klein, E., Lauria, S., (2004) Corpus-based robotics: a route instruction example, pp. 96-103. , Proceedings of Intelligent Autonomous Systems, Citeseer, Germany, Springer, In; Pires, J.N., Semi-autonomous manufacturing systems: the role of the human–machine interface software and of the manufacturing tracking software (2005) Mechatronics, 15 (10), pp. 1191-1205; Wang, Y.Q., Bao, Y.J., Gao, H., Studies on disfigurement-fee technology of drilling carbon fiber reinforced plastics (2007) Advanced materials research, 24, pp. 125-132. , Gao H., Jin Z., Rui Y., (eds), Trans Tech Publication, In:, (eds; http://www.boeing.com/news/frontiers/archive/2014/september/pubData/source/Frontiers-SEPT14_COMPLETE.pdf, accessed 27 April 2021, 109380-Leslie Nichols; Bringmann, E., Kramer, A., Model-based testing of automotive systems (2008) 2008 1st international conference on software testing, verification, and validation, pp. 485-493. , New York, IEEE, In; Durham, B.J., (2014) Determining appropriate levels of robotic automation in commercial aircraft nacelle assembly, , Cambridge, MA, Massachusetts Institute of Technology; Sahidullah, M., Saha, G., Design, analysis and experimental evaluation of block-based transformation in MFCC computation for speaker recognition (2012) Speech Commun, 54 (4), pp. 543-565; Bauters, K., Cottyn, J., Claeys, D., Automated work cycle classification and performance measurement for manual work stations (2018) Robot Comput Integr Manuf, 51, pp. 139-157; Lau, H.Y., A hidden Markov model-based assembly contact recognition system (2003) Mechatronics, 13 (8-9), pp. 1001-1023; Hinton, G., Dahl, G., Mohamed, A.-R., Deep neural networks for acoustic modeling in speech recognition (2012) Signal Processing Magazine, , November; Koening, W., A new frequency scala for acoustic measurements (1949) Bell Lab Rec, 1, pp. 299-301; Rabiner, L.R., Schafer, R.W., (2011) Theory and applications of digital speech processing, 64. , Uer Saddle River, NJ, Pearson; Keogh, E.J., Pazzani, M.J., (2001) Derivative dynamic time warping, pp. 1-11. , Proceedings of the 2001 SIAM international conference on data mining, Chicago, IL, SIAM, In","Griffiths, C.A.; College of Engineering, United Kingdom; email: c.a.griffiths@swansea.ac.uk",,,"SAGE Publications Ltd",,,,,09544054,,PIBME,,"English","Proc Inst Mech Eng Part B J Eng Manuf",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85105786317
"Cheng R.-Z., Huang P.-F., Liu Z.-X., Lu Z.-Y.","57202004309;8608333800;24450548800;56411276300;","A Human-robot Interaction Method of On-orbit Service-oriented Space Teleoperation [一种面向在轨服务的空间遥操作人机交互方法]",2021,"Yuhang Xuebao/Journal of Astronautics","42","9",,"1187","1196",,,"10.3873/j.issn.1000-1328.2021.09.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118934816&doi=10.3873%2fj.issn.1000-1328.2021.09.014&partnerID=40&md5=78e6a0a677d6a6d7008be3a41d156d7c","Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi'an, 710072, China; National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, 710072, China","Cheng, R.-Z., Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi'an, 710072, China, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, 710072, China; Huang, P.-F., Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi'an, 710072, China, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, 710072, China; Liu, Z.-X., Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi'an, 710072, China, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, 710072, China; Lu, Z.-Y., Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi'an, 710072, China, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, 710072, China","In order to solve the practical problems of spatial-temporal isolation between the operator and target, lack of environmental information and large time delay from Earth to space in the process of teleoperation, as well as the disadvantages including the lack of autonomous intelligence of the space robots and the operator's hands tremble, a method of layered assisted teleoperation (LAT) is proposed based on the augmented virtuality security channel (AVSC) for the operation interaction of space on-orbit services. Firstly, the AVSC with composite unit configuration should be designed to impose a virtual guiding force on the master operator to assist the operating duty. The operation takes less time with higher stability compared with an unarmed one. Secondly, the LAT system should be designed in a way so that different virtual force fields can be divided within the security channel to help the operator move more flexibly by obtaining the distance between the end-effector of the space robot and the nearest point on the desired trajectory. The demonstration experiments show that the method, using force haptic and visual information to guide the operator to control the end-effector of the space robot to the desired position, can effectively improve the operation accuracy and reduce the operator's mental stress and operating time. © 2021, Editorial Dept. of JA. All right reserved.","Augmented virtuality; Human-robot interaction; Space robot; Space teleoperation; Virtual force haptic feedback","Earth (planet); End effectors; Feedback; Intelligent robots; Orbits; Remote control; Virtual reality; Augmented virtualities; Haptic feedbacks; Humans-robot interactions; Interaction methods; On-orbit services; Service Oriented; Space robots; Space teleoperation; Virtual force haptic feedback; Virtual forces; Human robot interaction",,,,,,,,,,,,,,,,"Meng, Zhong-jie, Huang, Pan-feng, Lu, ying-bo, Applications and development of space tether in on-orbit servicing (2019) Journal of Astronautics, 40 (10), pp. 1134-1145. , 孟中杰, 黄攀峰, 鲁迎波, 等. 在轨服务中空间系绳的应用及发展[J]. 宇航学报, 2019, 40(10): 1134-1145.[[J]]; Li, Cheng, Liang, Bin, Teleoperation of space robotics (2001) Journal of Astronautics, 22 (1), pp. 95-98. , 李成, 梁斌. 空间机器人的遥操作[J]. 宇航学报, 2001, 22(1): 95-98.[[J]]; Wang, Yong, Xie, Yuan, Zhou, Jian-liang, A research survey on teleoperation of space robot through time delay (2010) Journal of Astronautics, 31 (2), pp. 299-306. , 王永, 谢圆, 周建亮. 空间机器人大时延遥操作技术研究综述[J]. 宇航学报, 2010, 31(2): 299-306.[[J]]; (2015), pp. 6-20. , 黄攀峰, 刘正雄. 空间遥操作技术[M]. 北京: 国防工业出版社; Liu, Dong-yu, Liu, Hong, Li, Zhi-qi, Calibration strategy of space manipulator system on-orbit servicing fine operation (2017) Journal of Astronautics, 38 (6), pp. 630-637. , 刘冬雨, 刘宏, 李志奇. 空间机械臂手系统在轨精细维修操作的标定策略[J]. 宇航学报, 2017, 38(6): 630-637.[[J]]; Chen, Z, Huang, F, Yang, C, Adaptive fuzzy backstepping control for stable nonlinear bilateral teleoperation manipulators with enhanced transparency performance (2020) IEEE Transactions on Industrial Electronics, 67 (1), pp. 746-756. , [J]; Sun, D, Liao, Q, Gu, X, Multilateral teleoperation with new cooperative structure based on reconfigurable robots and type-2 fuzzy logic (2018) IEEE transactions on cybernetics, 49 (8), pp. 2845-2859. , [J]; Vagvolgyi, B P, Pryor, W, Reedy, R, Scene modeling and augmented virtuality interface for telerobotic satellite servicing (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 4241-4248. , [J]; Sakow, M, Parus, A, Pajor, M, Unilateral hydraulic telemanipulation system for operation in machining work area (2018) Advances in Manufacturing, pp. 415-425. , [M]. 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Seattle, USA, September 18-22; Zhang, Bin, Huang, Pan-feng, Liu, Zheng-xiong, An interactive space robot teleoperation experiment based on Virtual Fixtures (2011) Journal of Astronautics, 32 (2), pp. 446-450. , 张斌, 黄攀峰, 刘正雄, 等. 基于虚拟夹具的交互式空间机器人遥操作实验[J]. 宇航学报, 2011, 32(2): 446-450.[[J]]; Tian, Zhi-yu, Huang, Pan-feng, Liu, Zheng-xiong, Design and implementation of Virtual Tube as an aid for space teleoperation (2014) Journal of Astronautics, 35 (7), pp. 834-842. , 田志宇, 黄攀峰, 刘正雄. 辅助空间遥操作的虚拟管道设计与实现[J]. 宇航学报, 2014, 35(7): 834-842.[[J]]; Selvaggio, M, Fontanelli, G A, Ficuciello, F, Passive virtual fixtures adaptation in minimally invasive robotic surgery (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3129-3136. , [J]; Vitrani, M A, Poquet, C, Morel, G., Applying virtual fixtures to the distal end of a minimally invasive surgery instrument (2016) IEEE Transactions on Robotics, 33 (1), pp. 114-123. , [J]; Panzirsch, M, Singh, H, Stelzer, M, Extended predictive model-mediated teleoperation of mobile robots through multilateral control (2018) 2018 IEEE Intelligent Vehicles Symposium (IV), , [C]. Suzhou, China, June 26-30; Bowyer, S A, Davies, B L, Baena, F R., Active constraints/virtual fixtures: A survey (2013) IEEE Transactions on Robotics, 30 (1), pp. 138-157. , [J]","Liu, Z.-X.; Research Center for Intelligent Robotics, China; email: liuzhengxiong@nwpu.edu.cn
Liu, Z.-X.; National Key Laboratory of Aerospace Flight Dynamics, China; email: liuzhengxiong@nwpu.edu.cn",,,"Chinese Society of Astronautics",,,,,10001328,,YUXUD,,"Chinese","Yuhang Xuebao",Article,"Final","",Scopus,2-s2.0-85118934816
"Veselic S., Zito C., Farina D.","57208599560;55556640700;7004908025;","Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control",2021,"Frontiers in Neurorobotics","15",,"695022","","",,,"10.3389/fnbot.2021.695022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117135870&doi=10.3389%2ffnbot.2021.695022&partnerID=40&md5=86ba8ba660f376ca6dbd94c887561272","Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; School of Computer Science, University of Birmingham, Birmingham, United Kingdom; Autonomous Robotics Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab Emirates; Department of Bioengineering, Imperial College London, London, United Kingdom","Veselic, S., Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom, School of Computer Science, University of Birmingham, Birmingham, United Kingdom; Zito, C., School of Computer Science, University of Birmingham, Birmingham, United Kingdom, Autonomous Robotics Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab Emirates; Farina, D., Department of Bioengineering, Imperial College London, London, United Kingdom","Physical human-robot interaction (pHRI) enables a user to interact with a physical robotic device to advance beyond the current capabilities of high-payload and high-precision industrial robots. This paradigm opens up novel applications where a the cognitive capability of a user is combined with the precision and strength of robots. Yet, current pHRI interfaces suffer from low take-up and a high cognitive burden for the user. We propose a novel framework that robustly and efficiently assists users by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision-making on how to assist the user. Context-awareness is achieved by inferring the candidate objects to be grasped in a task or scene and automatically computing plans for reaching them. User-awareness is implemented by facilitating the motion toward the most likely object that the user wants to grasp, as well as dynamically recovering from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting user intention, the proposed controller allows subjects to achieve superhuman performance in terms of accuracy and, thereby, usability. © Copyright © 2021 Veselic, Zito and Farina.","AI assistance; motion intention estimation; motion prediction; physical human robot interaction; reach and grasp","Controllers; Decision making; Degrees of freedom (mechanics); Forecasting; Human robot interaction; Industrial robots; Man machine systems; AI assistance; Current capability; Humans-robot interactions; Motion intention estimations; Motion prediction; Movement intentions; Physical humanrobot interaction (phri); Reach and grasp; Robotic devices; Robust predictions; Motion estimation",,,,,"810346; H2020 European Research Council, ERC; es:CEI; fr:CER; pl:ERBN","This study was supported by the European Research Council Synergy Project Natural BionicS (contract # 810346 to DF). All data needed to evaluate the conclusions in the study are present in the study and/or the Supplementary Materials. Additional data related to this study may be requested from the authors.",,,,,,,,,,"Benedikt Frey, C., Osborne, M.A., The future of employment: how susceptible are jobs to computerisation?* (2017) Technol. Forecast. Soc. Change, 114, pp. 254-280; Boessenkool, H., Heemskerk, C., Heemskerk, C., A task-specific analysis of the benefit of haptic shared control during telemanipulation (2013) IEEE Trans. Haptics, 6, pp. 2-12. , 24808263; Borner, H., Endo, S., Frisoli, A., Hirche, S., “Effects of vibrotactile feedback on human control performance in a dynamical stabilization task,” (2015) IEEE World Haptics Conference, WHC 2015, pp. 415-420. , Evanston, IL, IEEE; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mech. Mach. 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Healthc Eng, 2017, pp. 1-11. , 29093805; Van Den Berg, J., Miller, S., Duckworth, D., Hu, H., Wan, A., Fu, X.Y., “Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations,” (2010) Proceedings IEEE International Conference on Robotics and Automation, pp. 2074-2081. , Anchorage, AK, IEEE; Zito, C., Ortienzi, V., Adjigble, M., Kopicki, M.S., Stolkin, R., Wyatt, Hypothesis-based belief planning for dexterous grasping (2019) arXiv, , [cs.RO] (cs.AI","Zito, C.; School of Computer Science, United Kingdom; email: claudio.zito@tii.ae",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85117135870
"Kragic D., Sandamirskaya Y.","6701873248;24173904200;","Effective and natural human-robot interaction requires multidisciplinary research",2021,"Science Robotics","6","58","eabl7022","","",,,"10.1126/scirobotics.abl7022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117044459&doi=10.1126%2fscirobotics.abl7022&partnerID=40&md5=6c387e63a433a722894575b1201a7928","School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, Sweden; Neuromorphic Computing Lab, Intel Labs, Intel Corporation, Munich, Germany","Kragic, D., School of Electrical Engineering and Computer Science, Royal Institute of Technology KTH, Stockholm, Sweden; Sandamirskaya, Y., Neuromorphic Computing Lab, Intel Labs, Intel Corporation, Munich, Germany",[No abstract available],,,,,,,,,,,,,,,,,,"Yang, G.-Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Wood, R., The grand challenges of Science Robotics (2018) Sci. Robot, 3, p. eaar7650; Yang, G.-Z., Nelson, B. J., Murphy, R. R., Choset, H., Christensen, H., Collins, S. H., Dario, P., Nutt, M. M., Combating COVID-19- The role of robotics in managing public health and infectious diseases (2020) Sci. Robot, 5, p. eabb5589; Deng, E., Mutlu, B., Mataric, M. J., (2019) Embodiment in Socially Interactive Robots (Now Foundations and Trends, 7 (4), pp. 251-356. , http://dx.doi.org/10.1561/2300000056",,,,"American Association for the Advancement of Science",,,,,24709476,,,"34586838","English","Sci. Robotics",Editorial,"Final","",Scopus,2-s2.0-85117044459
"Saunderson S.P., Nejat G.","16029308800;12789569200;","Persuasive robots should avoid authority: The effects of formal and real authority on persuasion in human-robot interaction",2021,"Science Robotics","6","58","eabd5186","","",,1,"10.1126/scirobotics.abd5186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116816421&doi=10.1126%2fscirobotics.abd5186&partnerID=40&md5=78cf72ef88c295eb4fa20c9926d683e9","Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON  M5S 3G8, Canada","Saunderson, S.P., Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON  M5S 3G8, Canada; Nejat, G., Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON  M5S 3G8, Canada","Social robots must take on many roles when interacting with people in everyday settings, some of which may be authoritative, such as a nurse, teacher, or guard. It is important to investigate whether and how authoritative robots can influence people in applications ranging from health care and education to security and in the home. Here, we present a human-robot interaction study that directly investigates the effect of a robot's peer or authority role (formal authority) and control of monetary rewards and penalties (real authority) on its persuasive influence. The study consisted of a social robot attempting to persuade people to change their answers to the robot's suggestion in a series of challenging attention and memory tasks. Our results show that the robot in a peer role was more persuasive than when in an authority role, contrary to expectations from human-human interactions. The robot was also more persuasive when it offered rewards over penalties, suggesting that participants perceived the robot's suggestions as a less risky option than their own estimates, in line with prospect theory. In general, the results show an aversion to the persuasive influence of authoritative robots, potentially due to the robot's legitimacy as an authority figure, its behavior being perceived as dominant, or participant feelings of threatened autonomy. This paper explores the importance of persuasion for robots in different social roles while providing critical insight into the perception of robots in these roles, people's behavior around these robots, and the development of human-robot relationships. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Home health care; Man machine systems; Human-human interactions; Humans-robot interactions; Interaction studies; Monetary rewards; Peer roles; Persuasive robots; Prospect theory; Reward and penalty; Social robots; Teachers'; Human robot interaction",,,,,,,,,,,,,,,,"Olson, J., Maio, G., Attitudes in social behavior (2003) Handbook of Social Psychology, Personality and Social Psychology, 5, pp. 299-325. , T. 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H., Ward, T., Ridgeway, V., Nimmo-Smith, I., The structure ofñormal human attention: The test of everyday attention (1996) J. Int. Neuropsychol. Soc, 2, pp. 525-534; Brickenkamp, R., (1962) Test d2: Aufmerksamkeits-Belastungs-Test, , Verlag Psychol; Wilkins, A. J., Shallice, T., McCarthy, R., Frontal lesions and sustained attention (1987) Neuropsychologia, 25, pp. 359-365; Nachar, N., The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution (2008) Tutor. Quant. Methods Psychol, 4, pp. 13-20","Saunderson, S.P.; Autonomous Systems and Biomechatronics Lab, 5 King's College Road, Canada; email: shane.saunderson@mail.utoronto.ca",,,"American Association for the Advancement of Science",,,,,24709476,,,"34550717","English","Sci. Robotics",Article,"Final","",Scopus,2-s2.0-85116816421
"Plooij M., Apte S., Keller U., Baines P., Sterke B., Asboth L., Courtine G., von Zitzewitz J., Vallery H.","55556616400;57202579289;55578353800;57290143200;57207943251;54584914000;6602616445;21935318200;24172820000;","Neglected physical human-robot interaction may explain variable outcomes in gait neurorehabilitation research",2021,"Science Robotics","6","58","eabf1888","","",,,"10.1126/scirobotics.abf1888","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116740088&doi=10.1126%2fscirobotics.abf1888&partnerID=40&md5=dd4ec3f5be90fe3977740dee1dfec265","Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands; Demcon Advanced Mechatronics, Delfttechpark 23, Delft, Netherlands; Motek, a DIH brand, Hogehilweg 18-C, Amsterdam, 1101 CD, Netherlands; Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; ONWARD, EPFL Innovation Park, Lausanne, Switzerland; Center for Neuroprosthetics (CNP) Valais, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Swiss Children’s Rehab, University Children’s Hospital Zurich, Affoltern am Albis, Switzerland; Department of Rehabilitation Medicine, Erasmus MC, Postbus 2040, Rotterdam, 3000 CA, Netherlands; Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland","Plooij, M., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands, Demcon Advanced Mechatronics, Delfttechpark 23, Delft, Netherlands, Motek, a DIH brand, Hogehilweg 18-C, Amsterdam, 1101 CD, Netherlands; Apte, S., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands, Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Keller, U., ONWARD, EPFL Innovation Park, Lausanne, Switzerland, Center for Neuroprosthetics (CNP) Valais, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, Swiss Children’s Rehab, University Children’s Hospital Zurich, Affoltern am Albis, Switzerland; Baines, P., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands; Sterke, B., Motek, a DIH brand, Hogehilweg 18-C, Amsterdam, 1101 CD, Netherlands, Department of Rehabilitation Medicine, Erasmus MC, Postbus 2040, Rotterdam, 3000 CA, Netherlands; Asboth, L., Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland, Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland; Courtine, G., ONWARD, EPFL Innovation Park, Lausanne, Switzerland, Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland, Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland; von Zitzewitz, J., ONWARD, EPFL Innovation Park, Lausanne, Switzerland, Center for Neuroprosthetics (CNP) Valais, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Vallery, H., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands, Department of Rehabilitation Medicine, Erasmus MC, Postbus 2040, Rotterdam, 3000 CA, Netherlands","During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation. Copyright © 2021 The Authors, some rights reserved;",,"Energy efficiency; Human robot interaction; Man machine systems; Neuromuscular rehabilitation; Robotics; Body weight supports; Force vectors; Gait pattern; Gait rehabilitation; Neurological disorders; Neurorehabilitation []; Physical humanrobot interaction (phri); Quality of gait; Support devices; Support forces; Gait analysis",,,,,"nccr – on the move; Eurostars: 10152, PCIG13-GA-2013-618899; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF","Funding: The work in this paper was partially funded by Eurostars project 10152, by a Marie-Curie career integration grant PCIG13-GA-2013-618899, by Wings for Life accelerated translation program, and by the National Center of Competence in Research (NCCR) Robotics of the Swiss National Foundation.",,,,,,,,,,"Pennycott, A., Wyss, D., Vallery, H., Klamroth-Marganska, V., Riener, R., Towards more effective robotic gait training for stroke rehabilitation: A review (2012) J. 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Press)","Vallery, H.; Department of Biomechanical Engineering, Mekelweg 2, Netherlands; email: h.vallery@tudelft.nl",,,"American Association for the Advancement of Science",,,,,24709476,,,"34550719","English","Sci. Robotics",Article,"Final","",Scopus,2-s2.0-85116740088
"Esterwood C., Robert L.P.","57216331965;8247734500;","A Systematic Review of Human and Robot Personality in Health Care Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"748246","","",,,"10.3389/frobt.2021.748246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116360922&doi=10.3389%2ffrobt.2021.748246&partnerID=40&md5=b8921e408b252b525b11336502577d43","School of Information, University of Michigan, Ann ArborMI, United States; Robotics Institute, University of Michigan, Ann ArborMI, United States","Esterwood, C., School of Information, University of Michigan, Ann ArborMI, United States; Robert, L.P., School of Information, University of Michigan, Ann ArborMI, United States, Robotics Institute, University of Michigan, Ann ArborMI, United States","Robots have become vital to the delivery of health care and their personalities are often important to understanding their effectiveness as health care providers. Despite this, there is a lack of a systematic overarching understanding of personality in health care human-robot interaction. This makes it difficult to understand what we know and do not know about the impact of personality in health care human-robot interaction (H-HRI). As a result, our understanding of personality in H-HRI has not kept pace with the deployment of robots in various health care environments. To address this, the authors conducted a literature review that identified 18 studies on personality in H-HRI. This paper expands, refines, and further explicates the systematic review done in a conference proceedings [see: Esterwood (Proceedings of the 8th International Conference on Human-Agent Interaction, 2020, 87–95)]. Review results: 1) highlight major thematic research areas, 2) derive and present major conclusions from the literature, 3) identify gaps in the literature, and 4) offer guidance for future H-HRI researchers. Overall, this paper represents a reflection on the existing literature and provides an important starting point for future research on personality in H-HRI. © Copyright © 2021 Esterwood and Robert.","big five personality; health care HRI; healthcare; healthcare robots; HRI (human robot interaction); personality; systematic review",,,,,,,,,,,,,,,,,"Andrist, S., Mutlu, B., Tapus, A., (2015) Look like Me: Matching Robot Personality via Gaze to Increase Motivation, pp. 3603-3612. , New York, NY, USA, ACM; Asch, S.E., (1961) Forming Impressions of Personality, , University of California Press; Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G., Borkenau, P., Integrating Personality Structure, Personality Process, and Personality Development (2017) Eur. J. Pers, 31, pp. 503-528; Bem, S.L., (1974) Bem Sex Role Inventory [database Record], p. 10. , Retrieved from PsycTests; Bhandari, G., (2020) Robotics Industry Market Report. Tech. 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Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116360922
"Thorsten H., Marc-Andre F., Aly K., Ayoub A.-H., Laslo D.","57295010500;57295666100;57295230900;57295780100;57295010600;","Semantic-aware environment perception for mobile human-robot interaction",2021,"International Symposium on Image and Signal Processing and Analysis, ISPA","2021-September",,,"200","203",,,"10.1109/ISPA52656.2021.9552148","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117002652&doi=10.1109%2fISPA52656.2021.9552148&partnerID=40&md5=bc49f4df704bdd8445746ce53040d1e1","Neuro-Information Technology, Otto-von-Guericke-University, Magdeburg, Germany","Thorsten, H., Neuro-Information Technology, Otto-von-Guericke-University, Magdeburg, Germany; Marc-Andre, F., Neuro-Information Technology, Otto-von-Guericke-University, Magdeburg, Germany; Aly, K., Neuro-Information Technology, Otto-von-Guericke-University, Magdeburg, Germany; Ayoub, A.-H., Neuro-Information Technology, Otto-von-Guericke-University, Magdeburg, Germany; Laslo, D., Neuro-Information Technology, Otto-von-Guericke-University, Magdeburg, Germany","Current technological advances open up new opportunities for bringing human-machine interaction to a new level of human-centered cooperation. In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging tasks for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications. © 2021 IEEE.","Human-robot interaction; Interaction willingness; Semantic objects","Agricultural robots; Anthropomorphic robots; Man machine systems; Mobile robots; Semantics; 'current; Aware environments; Environment perceptions; Human machine interaction; Humans-robot interactions; Interaction willingness; Key Issues; Semantic objects; Semantic-aware; Technological advances; Human robot interaction",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448L, 03ZZ0470","This work is funded by the Federal Ministry of Education and Research of Germany (BMBF) (RoboAssist no. 03ZZ0448L, HuBa no. 03ZZ0470, and Robo-Lab no 03zz04x02b) within the Zwanzig20 Alliance 3Dsensation.",,,,,,,,,,"Bajones, M., Fischinger, D., Weiss, A., Wolf, D., Vincze, M., De La Puente, P., Koertner, T., Frennert, S., Hobbit: Providing fall detection and prevention for the elderly in the real world (2018) Journal of Robotics, 2018, pp. 1-20. , 06; Martínez, E., Del Pobil, A.P., Object detection and recognition for assistive robots (2017) IEEE Robotics & Automation Magazine, PP, pp. 1-1. , 03; Lee, S., Lee, S., Lee, J., Moon, D., Kim, E., Seo, J., Robust recognition and pose estimation of 3d objects based on evidence fusion in a sequence of images (2007) Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 3773-3779; Girshick, R., Donahue, J., Darrell, T., Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation (2013) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , 11; Ren, S., He, K., Girshick, R.B., Sun, J., Faster r-cnn: Towards realtime object detection with region proposal networks (2015) NIPS, pp. 91-99. , C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Eds; Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A., (2015) You only Look Once: Unified, Real-time Object Detection, , http://arxiv.org/abs/1506.02640, CoRR, vol. abs/1506.02640; Redmon, J., Farhadi, A., (2016) Yolo9000: Better, Faster, Stronger, , http://arxiv.org/abs/1612.08242, cite; Bochkovskiy, A., Wang, C., Liao, H.M., (2020) Yolov4: Optimal Speed and Accuracy of Object Detection, , https://arxiv.org/abs/2004.10934, CoRR, vol. abs/2004.10934; Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., Microsoft coco: Common objects in context (2014) Computer Vision - ECCV 2014, pp. 740-755. , https://doi.org/10.1007/978-3-319-10602-148, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing; Bochinski, E., Eiselein, V., Sikora, T., (2017) High-speed Tracking-bydetection without Using Image Information [Challenge Winner iwot4s], , 08; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C., Ssd: Single shot multibox detector (2016) Computer Vision - ECCV 2016, pp. 21-37. , B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874; Werner, P., Saxen, F., Al-Hamadi, A., Landmark based head pose estimation benchmark and method (2017) 2017 IEEE International Conference on Image Processing (ICIP), pp. 3909-3913; Hempel, T., Al-Hamadi, A., Slam-based multistate tracking system for mobile human-robot interaction (2020) Image Analysis and Recognition - 17th International Conference, ICIAR 2020, pp. 368-376. , vol. 12131. Springer, Póvoa de Varzim, Portugal, June 24-26, 2020, Proceedings, Part I, ser. Lecture Notes in Computer Science, A. Campilho, F. Karray, and Z. Wang, Eds; Labbé, M., Michaud, F., Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and longterm online operation: LabbE and michaud (2018) Journal of Field Robotics, 36. , 10",,"Petkovic T.Petrinovic D.Loncaric S.",,"IEEE Computer Society","12th International Symposium on Image and Signal Processing and Analysis, ISPA 2021","13 September 2021 through 15 September 2021",,172371,18455921,9781665426398,,,"English","Int. Symp. Image Signal Process. Anal., ISPA",Conference Paper,"Final","",Scopus,2-s2.0-85117002652
"Roesler E., Manzey D., Onnasch L.","57217215081;6604041254;35111634600;","A meta-Analysis on the effectiveness of anthropomorphism in human-robot interaction",2021,"Science Robotics","6","58","5425","","",,1,"10.1126/scirobotics.abj5425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114805229&doi=10.1126%2fscirobotics.abj5425&partnerID=40&md5=0f0a83cafb8c6f24e3ca381c7961b104","Technische Universitat Berlin, Berlin, Germany; Humboldt-Universitat zu Berlin, Berlin, Germany","Roesler, E., Technische Universitat Berlin, Berlin, Germany; Manzey, D., Technische Universitat Berlin, Berlin, Germany; Onnasch, L., Humboldt-Universitat zu Berlin, Berlin, Germany","The application of anthropomorphic design features is widely assumed to facilitate human-robot interaction (HRI). However, a considerable number of study results point in the opposite direction. There is currently no comprehensive common ground on the circumstances under which anthropomorphism promotes interaction with robots. Our meta-Analysis aims to close this gap. A total of 4856 abstracts were scanned. After an extensive evaluation, 78 studies involving around 6000 participants and 187 effect sizes were included in this meta-Analysis. The majority of the studies addressed effects on perceptual aspects of robots. In addition, effects on attitudinal, affective, and behavioral aspects were also investigated. Overall, a medium positive effect size was found, indicating a beneficial effect of anthropomorphic design features on human-related outcomes. However, closer scrutiny of the lowest variable level revealed no positive effect for perceived safety, empathy, and task performance. Moreover, the analysis suggests that positive effects of anthropomorphism depend heavily on various moderators. For example, anthropomorphism was in contrast to other fields of application, constantly facilitating social HRI. The results of this analysis provide insights into how design features can be used to improve the quality of HRI. Moreover, they reveal areas in which more research is needed before any clear conclusions about the effects of anthropomorphic robot design can be drawn. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Agricultural robots; Anthropomorphic robots; Machine design; Man machine systems; Quality control; Anthropomorphic design; Beneficial effects; Common ground; Design features; Human robot Interaction (HRI); Meta analysis; Perceptual aspects; Task performance; Human robot interaction",,,,,,,,,,,,,,,,"(2020) World Robotics 2020: Industrial Robots, , International Federation of Robotics, in 13 16; (2020) World Robotics 2020: Service Robots, , International Federation of Robotics, in 11 12; Onnasch, L., Roesler, E., A taxonomy to structure and analyze human robot interaction (2021) Int. J. Soc. Robot, 13, p. 833849; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Rob. Auton. Syst, 42, p. 143166; Breazeal, C., Toward sociable robots (2003) Rob. Auton. 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"Ge R., Zheng Z., Tian X., Liao L.","36135213200;57297660600;37011501600;57203993651;","Human-robot interaction: When investors adjust the usage of robo-advisors in peer-to-peer lending",2021,"Information Systems Research","32","3",,"774","785",,1,"10.1287/ISRE.2021.1009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117251988&doi=10.1287%2fISRE.2021.1009&partnerID=40&md5=96aa74994f1ea24d9e8a5c0a9408fd43","Department of Electronic Commerce, Shanghai Business School, Shanghai, 200235, China; Jindal School of Management, University of Texas at Dallas, Richardson, TX  75080, United States; PBC School of Finance, Tsinghua University, Beijing, 100083, China","Ge, R., Department of Electronic Commerce, Shanghai Business School, Shanghai, 200235, China; Zheng, Z., Jindal School of Management, University of Texas at Dallas, Richardson, TX  75080, United States; Tian, X., PBC School of Finance, Tsinghua University, Beijing, 100083, China; Liao, L., PBC School of Finance, Tsinghua University, Beijing, 100083, China","We study the human-robot interaction of financial-advising services in peer-topeer lending (P2P). Many crowdfunding platforms have started using robo-advisors to help lenders augment their intelligence in P2P loan investments. Collaborating with one of the leading P2P companies, we examine how investors use robo-advisors and how the human adjustment of robo-advisor usage affects investment performance. Our analyses show that, somewhat surprisingly, investors who need more help from robo-advisors- that is, those encountered more defaults in their manual investing-are less likely to adopt such services. Investors tend to adjust their usage of the service in reaction to recent robo-advisor performance. However, interestingly, these human-in-the-loop interferences often lead to inferior performance. Copyright: © 2021 INFORMS.","Augmented intelligence; Human-in-the-loop; Peer-to-peer lending; Robo-advisor","Crowdsourcing; Finance; Intelligent robots; Man machine systems; Augmented intelligence; Human-in-the-loop; Humans-robot interactions; Investment performance; Peer-to-peer lending; Performance; Robo-advisor; Human robot interaction",,,,,"19YJC630041; National Natural Science Foundation of China, NSFC: 71532004, 71790591, 71825002, 71850013","Funding: R. Ge received financial support from the Ministry of Education in China Research Program for Humanities and Social Sciences [Grant No. 19YJC630041]. E. Zheng received financial support from the National Natural Science Foundation of China (NSFC) [Grants 71850013 and 71532004]. T. Xuan received financial support from the NSFC [Grants 71825002 and 71790591].",,,,,,,,,,"Arnold, V, Collier, P, Leech, S, Sutton, S, The effect of experience and complexity on order and recency bias in decision making by professional accountants (2000) Accounting Finance, 40 (2), pp. 109-134; Attenberg, J, Ipeirotis, P, Provost, F, Beat the machine: Challenging humans to find a predictive model's ""unknown unknowns (2015) J. Data Inform. 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"Pollmann K., Ziegler D.","57128090300;56236807100;","A pattern approach to comprehensible and pleasant human–robot interaction",2021,"Multimodal Technologies and Interaction","5","9","49","","",,,"10.3390/mti5090049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114261840&doi=10.3390%2fmti5090049&partnerID=40&md5=ccdc2b4cb5ab81898503d9897dc79068","Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany","Pollmann, K., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Ziegler, D., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany","HRI designers are faced with the task of creating robots that are easy and pleasant to use for the users. The growing body of research in human–robot interaction (HRI) is still mainly focused on technical aspects of the interaction. It lacks defined guidelines that describe how behavioral expressions for social robots need to be designed to promote high usability and positive user experi-ence. To achieve this goal, we propose to apply the concept of design patterns to HRI. We present a design process that provides step-by-step guidance and methods for HRI designers to generate high quality behavioral patterns for social robots that can be used for different robots and use cases. To document the resulting patterns, we developed a documentation format that provides a clear, standardized structure to note down all relevant aspects of a pattern so that others can understand its design recommendations and apply them to their own robot and use cases. In the present paper, we demonstrate our pattern approach based on an example and describe how we arrived at a pattern language of 40 behavioral patterns that found the basis for future social robot design and related research activities. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Behavioral design patterns; Human-centered design; Human–robot interaction; Pattern language; User experience",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7941","Funding: This research was conducted as part of the NIKA project and funded by the German Federal Ministry of Education and Research (BMBF 16SV7941).",,,,,,,,,,"Forlizzi, J., How robotic products become social products: An ethnographic study of cleaning in the home Proceedings of the 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 129-136. , Arlington, VA, USA, 10–12 March 2007; Tanaka, F., Ghosh, M., The implementation of care-receiving robot at an English learning school for children Proceedings of the 6th International Conference on Human-Robot Interaction (HRI), pp. 265-266. , Lausanne, Switzerland, 6–9 March 2011; Wada, K., Shibata, T., Living with seal robots—Its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. 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Artif. Intell, 13, pp. 519-538","Pollmann, K.; Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Germany; email: kathrin.pollmann@iao.fraunhofer.de",,,"MDPI",,,,,24144088,,,,"English","Multimodal Tech. Inter.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85114261840
"Fiedler M.-A., Werner P., Khalifa A., Al-Hamadi A.","57218453987;54685435400;57242588900;6603014610;","Sfpd: Simultaneous face and person detection in real-time for human–robot interaction",2021,"Sensors","21","17","5918","","",,,"10.3390/s21175918","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114114244&doi=10.3390%2fs21175918&partnerID=40&md5=03e656ae3f20e33b5d12ff4325b28be8","Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany","Fiedler, M.-A., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany; Werner, P., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany; Khalifa, A., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany; Al-Hamadi, A., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany","Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. In order to combine face and person detection in one network, we applied multi-task learning. The difficulty lies in the fact that no datasets are available that contain both face as well as person annotations. Since we did not have the resources to manually annotate the datasets, as it is very time-consuming and automatic generation of ground truths results in annotations of poor quality, we solve this issue algorithmically by applying a special training procedure and network architecture without the need of creating new labels. Our newly developed method called Simultaneous Face and Person Detection (SFPD) is able to detect persons and faces with 40 frames per second. Because of this good trade-off between detection performance and inference time, SFPD represents a useful and valuable real-time framework especially for a multitude of real-world applications such as, e.g., human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Face detection; Multi-task learning; Person detection; Real-time detection","Economic and social effects; Human robot interaction; Multi-task learning; Network architecture; Attribute detections; Automatic Generation; Body pose estimation; Detection performance; Facial expression analysis; Human-action recognition; Real-time processing speed; Training procedures; Face recognition; facial expression; facial recognition; human; image processing; robotics; Facial Expression; Facial Recognition; Humans; Image Processing, Computer-Assisted; Robotics",,,,,"03ZZ04X02B; Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448L, 03ZZ0470","Funding: This work was funded by the German Federal Ministry of Education and Research (BMBF) under Grant Nos. 03ZZ0448L (RoboAssist), 03ZZ0470 (HuBA), and 03ZZ04X02B (RoboLab) within the Zwanzig20 Alliance 3Dsensation. We acknowledge support for the Article Processing Charge by the Open Access Publication Fund of Magdeburg University. 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On the limits of boosted trees for object detection Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 3350-3355. , Cancun, Mexico, 4–8 December 2016; Zhang, C., Xu, X., Tu, D., (2018) Face detection using improved Faster RCNN, , arXiv arXiv:1802.02142","Fiedler, M.-A.; Neuro-Information Technology Group, Germany; email: marc-andre.fiedler@ovgu.de",,,"MDPI",,,,,14248220,,,"34502809","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114114244
"Kress-Gazit H., Eder K., Hoffman G., Admoni H., Argall B., Ehlers R., Heckman C., Jansen N., Knepper R., KÅ™etínský J., Levy-Tzedek S., Li J., Murphey T., Riek L., Sadigh D.","15762557900;23391989300;35726707500;35180933000;57265524200;57219751588;35976340800;36646082100;16549885400;56406498200;42061792400;57238171100;57265773800;27868000800;55052871500;","Formalizing and guaranteeing human-robot interaction",2021,"Communications of the ACM","64","9",,"78","84",,,"10.1145/3433637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113859888&doi=10.1145%2f3433637&partnerID=40&md5=935683a3da12dbbe8af162cd1ecb071e","Cornell University, Ithaca, NY, United States; University of Bristol, United Kingdom; Cornell University, Ithaca, NY, United States; Carnegie Mellon University, Pittsburgh, PA, United States; Northwestern University, Evanston, IL, United States; Clausthal University of Technology in Clausthal-Zellerfeld, Germany; University of Colorado, Boulder, CO, United States; Radboud University Nijmegen, Netherlands; Technical University of Munich, Germany; Ben-Gurion University, Beersheba, Israel; Ryerson University in Toronto, Canada; Northwestern University, Evanston, IL, United States; University of California San Diego, United States; Stanford University, Stanford, CA, United States","Kress-Gazit, H., Cornell University, Ithaca, NY, United States; Eder, K., University of Bristol, United Kingdom; Hoffman, G., Cornell University, Ithaca, NY, United States; Admoni, H., Carnegie Mellon University, Pittsburgh, PA, United States; Argall, B., Northwestern University, Evanston, IL, United States; Ehlers, R., Clausthal University of Technology in Clausthal-Zellerfeld, Germany; Heckman, C., University of Colorado, Boulder, CO, United States; Jansen, N., Radboud University Nijmegen, Netherlands; Knepper, R., Cornell University, Ithaca, NY, United States; KÅ™etínský, J., Technical University of Munich, Germany; Levy-Tzedek, S., Ben-Gurion University, Beersheba, Israel; Li, J., Ryerson University in Toronto, Canada; Murphey, T., Northwestern University, Evanston, IL, United States; Riek, L., University of California San Diego, United States; Sadigh, D., Stanford University, Stanford, CA, United States","Robot capabilities are maturing across domains, from self-driving cars to bipeds to drones. The growing field of human-robot interaction (HRI) studies various aspects of this scenario, ranging from social norms to collaborative manipulation to human-robot teaming. Researchers in HRI have made great efforts in developing models, methods, and algorithms for robots acting with and around humans. Demonstrating trustworthiness in various forms of automation through formal guarantees has been the focus of validation, verification, and synthesis efforts. Formalizing HRI can enable the creation of trustworthy systems and, just as importantly, support explicit reasoning about the context of guarantees.",,"Man machine systems; Human robot Interaction (HRI); Human robots; Social norm; Trustworthy systems; Social robots",,,,,,,,,,,,,,,,"Alami, R., Verification and synthesis of human-robot interaction (Dagstuhl Seminar 19081) (2019) Dagstuhl Reports, 9 (2), pp. 91-110. , https://doi.org/10.4230/DagRep.9.2.91; Araiza-Illan, D., Coverage-driven verification-An approach to verify code for robots that directly interact with humans (2015) Hardware and Software: Verification and Testing, pp. 69-84; Araiza-Illan, D., Systematic and realistic testing in simulation of control code for robots in collaborative human-robot interactions (2016) Towards Autonomous Robotic Systems Conference, pp. 20-32; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176. , https://doi.org/10.1016/j.robot.2010.07.002, Oct; Breazeal, C., Social robotics (2016) Springer Handbook of Robotics, Springer, pp. 1935-1972; Calinescu, R., Socio-cyber-physical systems: Models, opportunities, open challenges (2019) 2019 IEEE/ACM 5th Intern. 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On Informatics and Psychology: Mental Models and Human-Computer Interaction 1 (NLD, pp. 41-69; Riek, L.D., Healthcare robotics (2017) Commun Acm, 60 (11), pp. 68-78. , https://doi.org/10.1145/3127874; Sadigh, D., Planning for cars that coordinate with people: Leveraging effects on human actions for planning and active information gathering over human internal state Autonomous Robots (AURO, 42 (7), pp. 1405-1426. , Oct. 2018; Thomaz, A., Computational human-robot interaction (2016) Found. Trends Robotics, 4 (2-3), pp. 105-223. , https://doi.org/10.1561/2300000049, Dec; Tretmans, G.J., Test generation with inputs, outputs and repetitive quiescence Centre for Telematics and Information Technology (CTIT; Webster, M., A corroborative approach to verification and validation of human-robot teams (2020) The Intern. J. Robotics Research, 39 (1), pp. 73-99. , https://doi.org/10.1177/0278364919883338; Wilkes-Gibbs, D., Clark, H.H., Coordinating beliefs in conversation (1992) J. Memory and Language, 31 (2), pp. 183-194",,,,"Association for Computing Machinery",,,,,00010782,,CACMA,,"English","Commun ACM",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85113859888
"Wang J., Liu Y., Yue T., Wang C., Mao J., Wang Y., You F.","57203558641;57219442494;57226787478;57227169900;57228079300;57218648909;56192500500;","Robot transparency and anthropomorphic attribute effects on human–robot interactions",2021,"Sensors","21","17","5722","","",,,"10.3390/s21175722","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113345256&doi=10.3390%2fs21175722&partnerID=40&md5=2c32eade53714a44b31fbe2ad84f8b13","Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Shenzhen Research Institute, Sun Yat‐Sen University, Shenzhen, 518057, China; College of Design and Innovation, Tongji University, Shanghai, 200092, China","Wang, J., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China, Shenzhen Research Institute, Sun Yat‐Sen University, Shenzhen, 518057, China; Liu, Y., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China, College of Design and Innovation, Tongji University, Shanghai, 200092, China; Yue, T., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Wang, C., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Mao, J., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Wang, Y., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; You, F., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China, Shenzhen Research Institute, Sun Yat‐Sen University, Shenzhen, 518057, China","Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human–robot performances and pos-itive human experiences. Previous research has shown that the characteristics of robot communication positively affect human–robot interaction outcomes such as usability, trust, workload, and per-formance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual‐channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future di-rections are discussed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Anthropomorphism; Human–robot interactions; On‐board robot; Robot transparency","Anthropomorphic robots; Transparency; Amount of information; Channel communication; Robot communication; Robot interactions; Robot performance; Textual information; Visual channels; Voice channels; Social robots; human; interpersonal communication; robotics; trust; Communication; Humans; Robotics; Trust",,,,,"2020‐1509; 1717; National Office for Philosophy and Social Sciences, NPOPSS: 19FYSB040, 202002JG26; International Science and Technology Cooperation Programme, ISTCP: GHZ20190823164803756","This research was supported by the National Social Science Fund of China (No. 19FYSB040), CES?Kingfar Excellent Young Scholar Joint Research Funding (No. 202002JG26), China Scholarship Council Foundation (2020?1509), Shanghai Automotive Industry Science and Technology Development Foundation (No. 1717), and Shenzhen Collaborative Innovation Project: International Science and Technology Cooperation (No. GHZ20190823164803756).","Funding: This research was supported by the National Social Science Fund of China (No. 19FYSB040), CES‐Kingfar Excellent Young Scholar Joint Research Funding (No. 202002JG26), China Scholarship Council Foundation (2020‐1509), Shanghai Automotive Industry Science and Technol‐ ogy Development Foundation (No. 1717), and Shenzhen Collaborative Innovation Project: Interna‐ tional Science and Technology Cooperation (No. GHZ20190823164803756).",,,,,,,,,"Mavridis, N., A review of verbal and non‐verbal human–robot interactive communication (2015) Robot. 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You, F.; Car Interaction Design Lab, China; email: youfang@tongji.edu.cn",,,"MDPI AG",,,,,14248220,,,"34502613","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113345256
"Vega J.","55339446100;","Basic human–robot interaction system running on an embedded platform",2021,"Microprocessors and Microsystems","85",,"104316","","",,,"10.1016/j.micpro.2021.104316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111162691&doi=10.1016%2fj.micpro.2021.104316&partnerID=40&md5=f44f0279b89cfb326e129299603f11cb","Universidad Rey Juan Carlos, Spain","Vega, J., Universidad Rey Juan Carlos, Spain","Robotics will be a dominant area in society throughout future generations. Its presence is currently increasing in most daily life settings, with devices and mechanisms that facilitate the accomplishment of diverse tasks, as well as in work scenarios, where machines perform more and more jobs. This increase in the presence of autonomous robotic systems in society is due to their great efficiency and security compared to human capacity, which is thanks mainly to the enormous precision of their sensor and actuator systems. Among these, vision sensors are of the utmost importance. Humans and many animals naturally enjoy powerful perception systems, but, in robotics, this constitutes a constant line of research. In addition to having a high capacity for reasoning and decision-making, these robots incorporate important advances in their perceptual systems, allowing them to interact effectively in the working environments of this new industrial revolution. Drawing on the most basic interaction between humans, looking at the face, an innovative system is presented in this paper, which was developed for an autonomous and DIY robot. This system is composed of three modules. First, the face detection component, which detects human faces in the current image. Second, the scene representation algorithm, which offers a wider field of view than that of the single camera used, mounted on a servo-pan unit. Third, the active memory component, which was designed and implemented according to two competing dynamics: life and salience. The algorithm intelligently moves the servo-pan unit with the aim of finding new faces, follow existing ones and forgetting those that no longer appear on the scene. The system was developed and validated using a low-cost platform based on a Raspberry Pi3 board. © 2021 The Author(s)","Face tracking; Human–robot interaction; Low-cost; Python; Raspberry Pi; Visual attention","Behavioral research; Decision making; Face recognition; Human robot interaction; Robotics; Autonomous robotic systems; Daily lives; Embedded platforms; Face Tracking; Future generations; Humans-robot interactions; Interaction systems; Low-costs; Raspberry pi; Visual Attention; Costs",,,,,"Comunidad de Madrid: F664; Universidad Rey Juan Carlos, URJC","This work was partially funded by the Community of Madrid through the Jóvenes Investigadores project, Spain (ref. F664 ) of the Rey Juan Carlos University.",,,,,,,,,,"Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J., Interaction primitives for human-robot cooperation tasks (2014); Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G., Learning multiple collaborative tasks with a mixture of interaction primitives (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1535-1542; Faraj, Z., Selamet, M., Morales, C., Torres, P., Hossain, M., Lipson, H., Facially expressive humanoid robotic face (2020) HardwareX; Bakar, M., Mohamad Amran, M.F., A study on techniques of person following robot (2015) Int. J. Comput. Appl., 125, pp. 27-30; Parks, D., Borji, A., Itti, L., Augmented saliency model using automatic 3d head pose detection and learned gaze following in natural scenes (2014) J. Vis. 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"Xiao H., Hu Z.","56585172600;56225778100;","Feature-similarity network via soft-label training for infrared facial emotional classification in human-robot interaction",2021,"Infrared Physics and Technology","117",,"103823","","",,1,"10.1016/j.infrared.2021.103823","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110194765&doi=10.1016%2fj.infrared.2021.103823&partnerID=40&md5=fe42146e2ad842e1422a5c85c735e0c6","School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan, Hubei  442002, China","Xiao, H., School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan, Hubei  442002, China; Hu, Z., School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan, Hubei  442002, China","Facial emotional classification (FEC) is a challenging issue in human-robot interaction. In this study, we proposed a novel FEC network with feature similarity-based soft label training. The proposed model aims at addressing the problems of occlusion and label ambiguity. It argues that the mouth region is very important for FEC task. To this end, all the facial emotional images are firstly used to extract the infrared facial features by the trained ResNet networks. The facial features similarities are computed by the cosine function. The emotional labels can be constructed by the similarities values. Then, Kullback-Leibler distance is employed to measure the difference between the groundtruth label and predicted label. Extension experimental results on two public datasets demonstrate that the proposed model can obtain state-of-the-art performance compared with the existing studies. Furthermore, we have applied proposed model on the assisted driving scene and human robot interaction environment. © 2021 Elsevier B.V.","Deep neural network; Emotional understanding; Facial emotional classification; Infrared images; Robot vision","Computer vision; Cosine transforms; Deep neural networks; Human robot interaction; Man machine systems; Classification networks; Classification tasks; Emotional classification; Emotional understanding; Facial emotional classification; Facial feature; Humans-robot interactions; Neural-networks; Similarity network; Soft labels; Infrared imaging",,,,,,,,,,,,,,,,"Liu, X., Jin, L., Han, X., You, J., Mutual Information Regularized Identity-aware Facial Expression Recognition in Compressed Video (2021) Pattern Recognit., , 108105; Liu, H., Wang, X., Zhang, W., Zhang, Z., Li, Y.-F., Infrared head pose estimation with multi-scales feature fusion on the IRHP database for human attention recognition (2020) Neurocomputing, 411, pp. 510-520; Huang, Z., Fang, H., Li, Q., Li, Z., Zhang, T., Sang, N., Li, Y., Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction (2018) Infrared Phys. 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"Freire-Obregón D., Rosales-Santana K., Marín-Reyes P.A., Penate-Sanchez A., Lorenzo-Navarro J., Castrillón-Santana M.","23396618800;57225990265;57191274555;26421312300;15042453800;57218418238;","Improving user verification in human-robot interaction from audio or image inputs through sample quality assessment",2021,"Pattern Recognition Letters","149",,,"179","184",,,"10.1016/j.patrec.2021.06.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110097878&doi=10.1016%2fj.patrec.2021.06.014&partnerID=40&md5=3b45362b9809aa098d6e2d1dd14f6f90","SIANI, Universidad de Las Palmas de Gran Canaria, Spain","Freire-Obregón, D., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Rosales-Santana, K., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Marín-Reyes, P.A., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Penate-Sanchez, A., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Lorenzo-Navarro, J., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Castrillón-Santana, M., SIANI, Universidad de Las Palmas de Gran Canaria, Spain","In this paper, we tackle the task of improving biometric verification in the context of Human-Robot Interaction (HRI). A robot that wants to identify a specific person to provide a service can do so by either image verification or, if light conditions are not favourable, through voice verification. In our approach, we will take advantage of the possibility a robot has of recovering further data until it is sure of the identity of the person. The key contribution is that we select from both image and audio signals the parts that are of higher confidence. For images we use a system that looks at the face of each person and selects frames in which the confidence is high while keeping those frames separate in time to avoid using very similar facial appearance. For audio our approach tries to find the parts of the signal that contain a person talking, avoiding those in which noise is present by segmenting the signal. Once the parts of interest are found, each input is described with an independent deep learning architecture that obtains a descriptor for each kind of input (face/voice). We also present in this paper fusion methods that improve performance by combining the features from both face and voice, results to validate this are shown for each independent input and for the fusion methods. © 2021","Audiovisual verification; Biometric verification; Human robot interaction","Biometrics; Deep learning; Image enhancement; Man machine systems; Audio input; Audiovisual verification; Biometric verification; Fusion methods; Humans-robot interactions; Image inputs; Image verification; Quality assessment; Sample quality; User verification; Human robot interaction",,,,,"Ministerio de Economía y Competitividad, MINECO: RTI2018-093337-B-I00; Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI: CEI-2018-4; European Regional Development Fund, ERDF: ProID2020010024","This work is partially funded by the ULPGC under project ULPGC2018-08, by ACIISI and ULPGC under project CEI-2018-4, the Spanish Ministry of Economy and Competitiveness (MINECO) under project RTI2018-093337-B-I00, and by FEDER funds under project ProID2020010024. Adrian Penate-Sanchez is funded by a Beatriz Galindo grant from the Ministy of Education of Spain.",,,,,,,,,,"McInnes, L., Healy, J., Melville, J., (2018), UMAP: uniform manifold approximation and projection for dimension reduction arXiv:; Halko, N., Martinsson, P.-G., Tropp, J.A., (2009), Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions arXiv:; Al-Qaderi, M.K., Rad, A.B., A multi-modal person recognition system for social robots (2018) Appl. 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"Gomez Chavez A., Ranieri A., Chiarella D., Birk A.","57104723100;56512556800;25930765400;7005194050;","Underwater Vision-Based Gesture Recognition: A Robustness Validation for Safe Human-Robot Interaction",2021,"IEEE Robotics and Automation Magazine","28","3",,"67","78",,,"10.1109/MRA.2021.3075560","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107221665&doi=10.1109%2fMRA.2021.3075560&partnerID=40&md5=46d8fd1bf89828311e68ea7d47b0853c","Robotics, Computer Science and Electrical Engineering, Jacobs University Bremen GGmbH, Bremen, 28759, Germany; CNR - National Research Council of Italy, Italy; Institute for Computational Linguistics Antonio Zampolli, National Research Council, Genoa, 16149, Italy; Computer Science and Electrical Engineering, Jacobs University, Bremen, 25725, Germany","Gomez Chavez, A., Robotics, Computer Science and Electrical Engineering, Jacobs University Bremen GGmbH, Bremen, 28759, Germany; Ranieri, A., CNR - National Research Council of Italy, Italy; Chiarella, D., Institute for Computational Linguistics Antonio Zampolli, National Research Council, Genoa, 16149, Italy; Birk, A., Computer Science and Electrical Engineering, Jacobs University, Bremen, 25725, Germany","Underwater robotics requires very reliable and safe operations. This holds especially true for missions in cooperation with divers who are-despite the significant advancements of marine robotics in recent years-still essential for many underwater operations. 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"Laubscher C.A., Sawicki J.T.","57197800670;7102846145;","A robust impedance controller for improved safety in human-robot interaction",2021,"Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME","143","9","091004","","",,,"10.1115/1.4050504","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104421939&doi=10.1115%2f1.4050504&partnerID=40&md5=9e72ab0418162054ce1c24de8628691f","Center for Rotating Machinery Dynamics and Control (RoMaDyC), Cleveland State University, Cleveland, OH  44115, United States","Laubscher, C.A., Center for Rotating Machinery Dynamics and Control (RoMaDyC), Cleveland State University, Cleveland, OH  44115, United States; Sawicki, J.T., Center for Rotating Machinery Dynamics and Control (RoMaDyC), Cleveland State University, Cleveland, OH  44115, United States","This paper presents a novel impedance controller modified with a switching strategy for the purpose of improving safety in human-robot interactions. Under normal operating conditions, an impedance controller is enabled when adequate tracking performance is maintained in the presence of bounded disturbances. However, if disturbances are greater than anticipated such that tracking performance is degraded, the proposed controller temporarily switches modes to a control strategy better apt to limit control inputs. With disturbances returning to the prescribed bounds, tracking performance will be restored and the impedance controller will resume for nominal operation. The control parameters are constrained by a few conditions necessary for smooth operation. First, a pair of equality constraints is required for the control signal to be continuous when switching control modes. Second, a Lyapunov analysis is performed to formulate an equality constraint on the control parameters to ensure only a single switch occurs when changing control modes to avert control chatter. Third, a matrix inequality constraint is necessary to ensure a robust positive invariant set is formed for when impedance control is active. Numerical simulations are provided to illustrate the controller and conditions. The simulation results successfully validate the presented theory, demonstrating how the constraints yield a continuous control signal, eliminate switching chatter, and permit robustness to disturbances. © 2021 by ASME","Impedance control; Robotics; Robust control; Sliding mode control; Switching control","Constraint theory; Control system analysis; Human robot interaction; Man machine systems; Bounded disturbances; Continuous control; Equality constraints; Impedance controllers; Normal operating conditions; Positive invariant set; Switching strategies; Tracking performance; Controllers",,,,,,,,,,,,,,,,"Hogan, N., Impedance Control: An Approach to Manipulation: Part I-Theory (1985) ASME J. Dyn. Syst., Meas., Control, 107 (1), pp. 1-7; Asada, H., Slotine, J.-J. E., (1986) Robot Analysis and Control, , Wiley, New York; Ibarra, J. C. P., Siqueira, A. A. G., Impedance Control of Rehabilitation Robots for Lower Limbs, Review (2014) Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol, pp. 235-240. , IEEE, Sao Carlos, Sao Paulo, Brazil, Oct. 18-23; Raiola, G., Cardenas, C. A., Tadele, T. S., de Vries, T., Stramigioli, S., Development of a Safety- and Energy-Aware Impedance Controller for Collaborative Robots (2018) IEEE Rob. Autom. Lett, 3 (2), pp. 1237-1244; Mohammadi, H., Richter, H., Robust Tracking/Impedance Control: Application to Prosthetics (2015) American Control Conference, pp. 2673-2678. , IEEE, Chicago, IL, July 1-3; Edwards, C., Spurgeon, S., (1998) Sliding Mode Control: Theory and Applications, , CRC Press, London, UK; Kurfess, T. R., (2005) Robotics and Automation Handbook, , CRC Press, Boca Raton, FL; Zheng, E.-H., Xiong, J.-J., Luo, J.-L., Second Order Sliding Mode Control for a Quadrotor UAV (2014) ISA Trans, 53 (4), pp. 1350-1356; Altintas, Y., Erkorkmaz, K., Zhu, W.-H., Sliding Mode Controller Design for High Speed Feed Drives (2000) CIRP Ann, 49 (1), pp. 265-270; Madani, T., Daachi, B., Djouani, K., Non-Singular Terminal Sliding Mode Controller: Application to an Actuated Exoskeleton (2016) Mechatronics, 33, pp. 136-145; Utkin, V., Guldner, J., Shijun, M., (1999) Sliding Mode Control in Electromechanical Systems, , CRC Press, London, UK; Bartolini, G., Ferrara, A., Usai, E., Chattering Avoidance by Second-Order Sliding Mode Control (1998) IEEE Trans. Autom. Control, 43 (2), pp. 241-246; Hung, J. Y., Gao, W., Hung, J. C., Variable Structure Control: A Survey (1993) IEEE Trans. Ind. Electron, 40 (1), pp. 2-22; Piltan, F., Sulaiman, N. B., Review of Sliding Mode Control of Robotic Manipulator (2012) World Appl. Sci. J, 18 (12), pp. 1855-1869; Kachroo, P., Tomizuka, M., Chattering Reduction and Error Convergence in the Sliding-Mode Control of a Class of Nonlinear Systems (1996) IEEE Trans. Autom. Control, 41 (7), pp. 1063-1068; Garcıa-Valdovinos, L.-G., Parra-Vega, V., Arteaga, M. A., Observer-Based Sliding Mode Impedance Control of Bilateral Teleoperation Under Constant Unknown Time Delay (2007) Rob. Auton. Syst, 55 (8), pp. 609-617; Cho, H. C., Park, J. H., Kyunghwan, K., Park, J.-O., Sliding-Mode-Based Impedance Controller for Bilateral Teleoperation Under Varying Time-Delay (2001) International Conference on Robotics & Automation, pp. 1025-1030. , Seoul, South Korea, May 21-26; Xu, Q., Adaptive Discrete-Time Sliding Mode Impedance Control of a Piezoelectric Microgripper (2013) IEEE Trans. Rob, 29 (3), pp. 663-673; Azimi, V., Abolfazl Fakoorian, S., Tien Nguyen, T., Simon, D., Robust Adaptive Impedance Control With Application to a Transfemoral Prosthesis and Test Robot (2018) ASME J. Dyn. Syst., Meas., Control, 140 (12), p. 121002; Mohammadi, A., Marquez, H. J., Tavakoli, M., Nonlinear Disturbance Observers: Design and Applications to Euler-Lagrange Systems (2017) IEEE Control Syst. Mag, 37 (4), pp. 50-72; Radke, A., Zhiqiang, G., A Survey of State and Disturbance Observers for Practitioners (2006) American Control Conference, pp. 5183-5188. , Minneapolis, MN, June 14-16; Bernstein, D. S., Matrix Mathematics: Theory (2009) Facts, and Formulas, , Princeton University Press, Princeton, NJ; Gardiner, J. D., Stabilizing Control for Second-Order Models and Positive Real Systems (1992) J. Guid., Control, Dyn, 15 (1), pp. 280-282; Blanchini, F., Set Invariance in Control (1999) Automatica, 35 (11), pp. 1747-1767; Horn, R. A., Mathias, R., Block-Matrix Generalizations of Schur's Basic Theorems on Hadamard Products (1992) Linear Algebra Appl, 172, pp. 337-346; Slotine, J.-J. E., Coetsee, J. A., Adaptive Sliding Controller Synthesis for Non-Linear Systems (1986) Int. J. Control, 43 (6), pp. 1631-1651","Sawicki, J.T.; Center for Rotating Machinery Dynamics and Control (RoMaDyC), United States; email: j.sawicki@csuohio.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,00220434,,JDSMA,,"English","J Dyn Syst Meas Control Trans ASME",Article,"Final","",Scopus,2-s2.0-85104421939
"Amarillo A., Sanchez E., Caceres J., Oñativia J.","57207832580;57196876065;57221383358;24765137000;","Collaborative Human–Robot Interaction Interface: Development for a Spinal Surgery Robotic Assistant",2021,"International Journal of Social Robotics","13","6",,"1473","1484",,,"10.1007/s12369-020-00733-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098964746&doi=10.1007%2fs12369-020-00733-x&partnerID=40&md5=625743404e47d978bbae6bf36fdc4909","Universidad de Navarra, Tecnun, Manuel Lardizabal 13, San Sebastian, 20018, Spain; Ceit, Manuel Lardizabal 15, San Sebastian, 20018, Spain; Cyber Surgery S.L., Paseo Mikeletegui 71, San Sebastian, 20009, Spain","Amarillo, A., Universidad de Navarra, Tecnun, Manuel Lardizabal 13, San Sebastian, 20018, Spain, Cyber Surgery S.L., Paseo Mikeletegui 71, San Sebastian, 20009, Spain; Sanchez, E., Universidad de Navarra, Tecnun, Manuel Lardizabal 13, San Sebastian, 20018, Spain, Ceit, Manuel Lardizabal 15, San Sebastian, 20018, Spain; Caceres, J., Ceit, Manuel Lardizabal 15, San Sebastian, 20018, Spain; Oñativia, J., Cyber Surgery S.L., Paseo Mikeletegui 71, San Sebastian, 20009, Spain","The growing introduction of robotics in non-industrial applications where the environment is unstructured and changing, has led to the need of development of safer and more intuitive, human–robot interfaces. In such environments, the use of collaborative robots has potential benefits, due to the combination of user experience, knowledge and flexibility with the robot’s accuracy, stiffness and repeatability. Nevertheless, in order to guarantee a functional collaboration in these environments, the interaction between user and robot must be intuitive, natural, fast and easy to use. On one hand, commercial collaborative robots are less accurate and less stiff than the traditional industrial ones, on the other hand, the later have not intuitive interaction interfaces. There are tasks in which the stiffness of industrial robots and the intuitive interaction interfaces of collaborative commercial robots, are desirable. This is the case of some robotic assisted surgical procedures, such as robotic assisted spine surgery, with high accuracy demands and with the need of intuitive surgeon–robot interaction. This paper presents a hand guiding methodology for functional human–robot collaboration and the introduction of novel algorithms to enhance its behavior. Also its implementation on a robotic surgical assistant for spine procedures is presented. It is emphasized how a traditional industrial robot can be used as a collaborative one when the available commercial collaborative robots do not have the required accuracy and stiffness for the task. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.","Collaborative surgical robotics; Hand-guiding; Human robot interface; Transpedicular fixation technology","Agricultural robots; Industrial robots; Robotics; Social robots; Stiffness; Surgical equipment; Transplantation (surgical); User experience; Collaborative robots; Commercial robots; Intuitive interaction; Potential benefits; Robot interactions; Robotic assistants; Spine procedures; Surgical procedures; Robotic surgery",,,,,,"The present research work was possible thanks to the ROBOTRACKER(Basque government, GAITEK IG-2015/0000782), ELCANO (Spanish goverment founding, INNPACTO IPT-2012-0508-300000) and MAXILARIS (Spanish goverment, RETOS RTC-2015-3871-1) projects, and the people who have worked on them, they deserve sincere thanks for the contributions that have provided. 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"Lim V., Rooksby M., Cross E.S.","57219908339;16646936300;56497637800;","Social Robots on a Global Stage: Establishing a Role for Culture During Human–Robot Interaction",2021,"International Journal of Social Robotics","13","6",,"1307","1333",,6,"10.1007/s12369-020-00710-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095988448&doi=10.1007%2fs12369-020-00710-4&partnerID=40&md5=9c8365a1c5ddb6ad22552963596d7adf","School of Management, University College London, London, United Kingdom; Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Cognitive Science, Macquarie University, Sydney, Australia","Lim, V., School of Management, University College London, London, United Kingdom; Rooksby, M., Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Cross, E.S., Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom, Department of Cognitive Science, Macquarie University, Sydney, Australia","Robotic agents designed to assist people across a variety of social and service settings are becoming increasingly prevalent across the world. Here we synthesise two decades of empirical evidence from human–robot interaction (HRI) research to focus on cultural influences on expectations towards and responses to social robots, as well as the utility of robots displaying culturally specific social cues for improving human engagement. Findings suggest complex and intricate relationships between culture and human cognition in the context of HRI. The studies reviewed here transcend the often-studied and prototypical east–west dichotomy of cultures, and explore how people’s perceptions of robots are informed by their national culture as well as their experiences with robots. Many of the findings presented in this review raise intriguing questions concerning future directions for robotics designers and cultural psychologists, in terms of conceptualising and delivering culturally sensitive robots. We point out that such development is currently limited by heterogenous methods and low statistical power, which contribute to a concerning lack of generalisability. We also propose several avenues through which future work may begin to address these shortcomings. In sum, we highlight the critical role of culture in mediating efforts to develop robots aligned with human users’ cultural backgrounds, and argue for further research into the role of culturally-informed robotic development in facilitating human–robot interaction. © 2020, The Author(s).","Culture; Embodied social interaction; Human cognition; Human–robot interaction; Individualism–collectivism; Social robotics","Agricultural robots; Robotics; Cultural backgrounds; Cultural influences; Human cognition; Intriguing questions; National cultures; Robot interactions; Robotic agents; Statistical power; Social robots",,,,,"Leverhulme Trust: PLP-2018-152; European Research Council, ERC; Horizon 2020: 677270","M.R. and E.S.C.’s contributions to this piece were supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.). 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ICSR 2015. Lecture notes in computer science, vol 9388, , Tapus A, André E, Martin JC, Ferland F, Ammi M, (eds), Springer, New York; Spatola, N., Anier, N., Redersdorff, S., Ferrand, L., Belletier, C., Normand, A., Huguet, P., National stereotypes and robots’ perception: the “made in” effect (2019) Front Robot AI; Eyssel, F., Kuchenbrandt, D., Social categorisation of social robots: anthropomorphism as a function of robot group membership (2012) Br J Soc Psychol, 51 (4), pp. 724-731; Buolamwini, J., Artificial intelligence has a problem with gender and racial bias. 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(2019) Philos Trans R Soc B Biol Sci, 374, p. 20180039; Székely, M., Powell, H., Vannucci, F., Rea, F., Sciutti, A., Michael, J., The perception of a robot partner’s effort elicits a sense of commitment to human-robot interaction (2019) Interact Stud; Cross, E.S., Riddoch, K.A., Pratts, J., Titone, S., Chaudhury, B., Hortensius, R., A neurocognitive investigation of the impact of socializing with a robot on empathy for pain (2019) Philos Trans R Soc B, 374, p. 20180034; Giuliani, M., Mirnig, N., Stollnberger, G., Stadler, S., Buchner, R., Tscheligi, M., Systematic analysis of video data from different human-robot interaction studies: a categorization of social signals during error situations (2015) Front Psychol; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Front Psychol; Cross, E.S., Liepelt, R., Hamilton, A.F.C., Parkinson, J., Ramsey, R., Stadler, W., Prinz, W., Robotic movement preferentially engages the action observation network (2012) Hum Brain Mapp, 33 (9), pp. 2238-2254; Mori, M., The uncanny valley (1970) Energy, 7, pp. 33-35; Henschel, A., Hortensius, R., Cross, E.S., Social cognition in the age of human-robot interaction (2020) Trends Neurosci, 43 (6), pp. 373-384; Ng, T.K., Wang, K.W.C., Chan, W., Acculturation and cross-cultural adaptation: The moderating role of social support (2017) Int J Intercult Relat, 59, pp. 19-30; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Long-term interactions with empathic robots: evaluating perceived support in children (2012) Social robotics, vol 7621, pp. 298-307. , Ge S, Khatib O, Cabibihan J-J, Simmons R, Williams M-A, (eds), Springer, Berlin; Jones, A., Castellano, G., Adaptive robotic tutors that support self-regulated learning: a longer-term investigation with primary school children (2018) Int J Soc Robot, 10, pp. 357-370; Ramge, T., (2019) Who’s afraid of AI? Fear and promise in the age of thinking machines, , Springer, South Melbourne; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is human-like? Decomposing robots' human-like appearance using the anthropomorphic roBOT (ABOT) database (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 105-113","Cross, E.S.; Institute of Neuroscience and Psychology, United Kingdom; email: Emily.cross@mq.edu.au",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Review,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85095988448
"Jiang Y., Peng X., Xue M., Wang C., Qi H.","8503974900;57218802103;57218799682;57201294365;55167086600;","An Underwater Human–Robot Interaction Using Hand Gestures for Fuzzy Control",2021,"International Journal of Fuzzy Systems","23","6",,"1879","1889",,,"10.1007/s40815-020-00946-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091317442&doi=10.1007%2fs40815-020-00946-2&partnerID=40&md5=196378047c9ebfcb89bf5b007afa15b9","College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Jiang, Y., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Peng, X., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Xue, M., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Wang, C., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Qi, H., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Autonomous underwater vehicle (AUV) plays an important role in ocean research and exploration. The underwater environment has a great influence on AUV control and human–robot interaction, since underwater environment is highly dynamic with unpredictable fluctuation of water flow, high pressure and light attenuation. The traditional control model contains a large number of parameters, which is not effective and produces errors frequently. The proposal of fuzzy control addressed this issue to a certain extent. It applies fuzzy variables to the controller, which replace the values in an interval. In addition to the controller, underwater human–robot interaction is also difficult. Divers cannot speak or show any facial expressions underwater. The buttons on the AUV also need to overcome the huge water pressure. In this paper, we proposed a method to recognize the gesture instructions and apply it to the fuzzy control of AUV. Our contribution is the gesture recognition framework for the human–robot interaction, including the gesture detection network and the algorithm for the control of AUV. The experiment result shows the efficiency of the proposed method. © 2020, Taiwan Fuzzy Systems Association.","AUV; Fuzzy control; Gesture recognition; Human–robot communication","Controllers; Flow of water; Fuzzy control; Gesture recognition; High pressure effects; Social robots; Autonomous underwater vehicles (AUV); Facial Expressions; Gesture detections; Light attenuation; Ocean research; Robot interactions; Underwater environments; Water pressures; Autonomous underwater vehicles",,,,,"National Natural Science Foundation of China, NSFC: 51679105, 51809112, 51939003","This work was supported by the National Natural Science Foundation of China under Grant 51679105, Grant 51809112, and Grant 51939003.",,,,,,,,,,"Papanikolopoulos, N.P., Khosla, P.K., Adaptive robotic visual tracking: theory and experiments (1993) IEEE Trans. Autom. Control, 38 (3), pp. 429-445; Jiang, Y., Zhao, M., Hu, C., He, L., Bai, H., Wang, J., A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU (2019) J Supercomput, 75 (2), pp. 732-745; Qin, H., Wang, C., Jiang, Y., Deng, Z., Zhang, W., Trend prediction of the 3D thermocline’s lateral boundary based on the SVR method (2018) EURASIP J Wirel Commun Netw., 2018, p. 252; Dudek, G., A visually guided swimming robot (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3604-3609; Son-Cheol, Y., Ura, T., Fujii, T., Kondo, H., Navigation of autonomous underwater vehicles based on artificial underwater landmarks (2001) Oceans IEEE, pp. 409-416; Sattar, J., Dudek, G., Robust servo-control for underwater robots using banks of visual filters (2009) IEEE International Conference on Robotics and Automation, pp. 3583-3588; Neverova, N., Wolf, C., Taylor, G.W., Nebout, F., Multi-scale deep learning for gesture detection and localization (2015) Workshop at the European Conference on Computer Vision, pp. 474-490. , Springer; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1137-1149; Fiala, M., ARTag, a fiducial marker system using digital techniques (2005) IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 590-596; Olson, E., AprilTag: A robust and flexible visual fiducial system (2011) IEEE International Conference on Robotics and Automation, pp. 3400-3407; Dudek, G., Sattar, J., Xu, A., A visual language for robot control and programming: A human-interface study (2007) IEEE International Conference on Robotics and Automation, pp. 2507-2513; Molchanov, P., Gupta, S., Kim, K., Kautz, J., Hand gesture recognition with 3D convolutional neural networks (2015) IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1-7; Girshick, R., Fast R-CNN (2015) IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448; He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask R-CNN (2017) IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Berg, A.C.S.S.D., Single Shot MultiBox Detector (2015) European Conference on Computer Vision (ECCV, p. 2016; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: unified, real-time object detection (2016) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788; Jeen-Shing, W., Lee, C.S.G., Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle (2002) IEEE International Conference on Robotics and Automation, pp. 1095-1100; Zhang, L., Pang, Y., Su, Y., HPSO-based fuzzy neural network control for AUV (2008) J. Control Theory Appl., 6, pp. 322-326; Huang, W., Fang, H., Liu, L., Obstacle avoiding policy of multi-AUV formation based on virtual AUV (2009) Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 131-135; Li, Y., Pang, Y., Wan, L., Tang, X., A fuzzy motion control of AUV based on apery intelligence (2009) Chinese Control and Decision Conference, pp. 1316-1321; Li, Q., Shi, X.H., Kang, Z.Q., The research of Fuzzy-PID control based on grey prediction for AUV (2012) Appl. Mech. Mater., 246-247, pp. 888-892; Liang, X., Qu, X., Wan, L., Ma, Q., Three-dimensional path following of an underactuated AUV based on Fuzzy backstepping sliding mode control (2018) Int. J. Fuzzy Syst., 20, pp. 640-649; Yu, C., Xiang, X., Wilson, P.A., Guidance-Error-Based Robust Fuzzy Adaptive Control for Bottom Following of a Flight-Style AUV With Saturated Actuator Dynamics (2019) IEEE Transactions on Cybernetics, pp. 1-13; Li, H., He, B., Yin, Q., Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control (2019) Electronics., 8 (6), p. 608; Gomez Chavez, A., Ranieri, A., Chiarella, D., Zereik, E., Babić, A., Birk, A., Caddy underwater stereo-vision dataset for human–robot interaction (HRI) in the context of diver activities (2019) J. Marine Sci. Eng., 7 (1), p. 16","Qi, H.; College of Computer Science and Technology, China; email: qihong@jlu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,15622479,,,,"English","Int. J. Fuzzy Syst.",Article,"Final","",Scopus,2-s2.0-85091317442
"Niiyama R., Seong Y.A., Kawahara Y., Kuniyoshi Y.","57218118140;26658670700;7202786793;7005371269;","Blower-Powered Soft Inflatable Joints for Physical Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"720683","","",,,"10.3389/frobt.2021.720683","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114429831&doi=10.3389%2ffrobt.2021.720683&partnerID=40&md5=ac8aa3fb011b14591160562affafce9e","Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","Niiyama, R., Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Seong, Y.A., Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Kawahara, Y., Graduate School of Engineering, The University of Tokyo, Tokyo, Japan; Kuniyoshi, Y., Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","Inflatables are safe and lightweight structures even at the human scale. Inflatable robots are expected to be applied to physical human-robot interaction (pHRI). Although active joint mechanisms are essential for developing inflatable robots, the existing mechanisms are complex in structure and it is difficult to integrate actuators, which diminish the advantages of inflatables. This study proposes blower-powered soft inflatable joints that are easy to fabricate and contain enough space for an actuation inside. The joints are driven by tendon wires pulled by linear actuators. We derived a theoretical model for both unilateral and bilateral joints and demonstrated a hugging robot with multiple joints as an application of the proposed joint mechanism. The novelty of the proposed joint mechanism and the inflatable robot is that rigid parts have been thoroughly eliminated and the tendons for actuation have been successfully hidden inside. Moreover, the active control of the internal pressure makes inflatables resistant to punctures. We expect that the contact safety of inflatable robots will facilitate advancement of the pHRI field. © Copyright © 2021 Niiyama, Seong, Kawahara and Kuniyoshi.","inflatable robot; physical human-robot interaction; soft mechanism; soft robotics; tendon wire",,,,,,"Japan Science and Technology Agency, JST: JPMJMS 2013; Exploratory Research for Advanced Technology, ERATO: JPMJER1501","This work was supported by JST ERATO Grant Number JPMJER1501 and JST Moonshot R&D Grant Number JPMJMS 2013. The ERATO supported development of inflatable robots. The Moonshot R&D supported experiments in human-robot interaction.","We would like to thank Babot Inc. (Tokyo, Japan) for the advice and assistance with the manufacturing of the inflatables.",,,,,,,,,"Alspach, A., Kim, J., Yamane, K., Design and Fabrication of a Soft Robotic Hand and Arm System (2018) IEEE International Conference on Soft Robotics (RoboSoft), pp. 369-375. , Livorno, Italy, April 25–27, 2018; Annan, M.M., Mark, R.C., Martin, B., (2016) Inflatable Robots, Robotic Components and Assemblies and Methods Including Same, , US Patent, US20140109560A1, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Best, C.M., Wilson, J.P., Killpack, M.D., Control of a Pneumatically Actuated, Fully Inflatable, Fabric-Based, Humanoid Robot (2015) IEEE-RAS International Conference on Humanoid Robots, pp. 1133-1140. , Seoul, South Korea, November 3–5, 2015; Garzotto, F., Gelsomini, M., Kinoe, Y., Puffy: A mobile Inflatable Interactive Companion for Children with Neurodevelopmental Disorder (2017) IFIP Conference on Human-Computer Interaction, pp. 467-492. , Bombay, India, September 25–29, 2017, INTERACT; Gazit, D., Dranger, A.L., (2001) Apparatus and Method for Providing Inflated Undulating Figures, , US Patent, US6186857B1, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Hofer, M., D’Andrea, R., Design, Fabrication, Modeling and Control of a Fabric-Based Spherical Robotic Arm (2020) Mechatronics, 68, p. 102369; Kim, H.-J., Kawamura, A., Nishioka, Y., Kawamura, S., Mechanical Design and Control of Inflatable Robotic Arms for High Positioning Accuracy (2018) Adv. Robotics, 32, pp. 89-104. , a; Kim, T., Yoon, S.J., Park, Y.-L., Soft Inflatable Sensing Modules for Safe and Interactive Robots (2018) IEEE Robot. Autom. Lett, 3, pp. 3216-3223. , b; Koren, Y., Weinstein, Y., (1991) Inflatable Structure, , US Patent, US5065640A, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Liang, X., Cheong, H., Sun, Y., Guo, J., Chui, C.K., Yeow, C.-H., Design, Characterization, and Implementation of a Two-Dof Fabric-Based Soft Robotic Arm (2018) IEEE Robot. Autom. Lett, 3, pp. 2702-2709; McMahan, W., Jones, B.A., Walker, I.D., Design and Implementation of a Multi-Section Continuum Robot: Air-Octor (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3345-3352. , Edmonton, Canada, August 2–6, 2005, IROS; Ohta, P., Valle, L., King, J., Low, K., Yi, J., Atkeson, C.G., Design of a Lightweight Soft Robotic Arm Using Pneumatic Artificial Muscles and Inflatable Sleeves (2017) Soft Robot, 5, pp. 204-215; Sanan, S., Lynn, P.S., Griffith, S.T., Pneumatic Torsional Actuators for Inflatable Robots (2014) J. Mech. Robotics, 6, p. 031003; Sanan, S., Ornstein, M.H., Atkeson, C.G., Physical Human Interaction for an Inflatable Manipulator (2011) International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7401-7404. , Boston, MA, United States, August 30–September 4, 2011, EMBC; Seong, Y.A., Niiyama, R., Kawahara, Y., Kuniyoshi, Y., Low-pressure Soft Inflatable Joint Driven by Inner Tendon (2019) EEE International Conference on Soft Robotics, pp. 37-42. , Seoul, South Korea, April 14–18, 2019, RoboSoft; Takahashi, S., (1987) Pneumatic Structure, , US Patent, US4765079, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Takeichi, M., Suzumori, K., Endo, G., Nabae, H., Development of a 20-M-Long Giacometti Arm with Balloon Body Based on Kinematic Model with Air Resistance (2017) IEEE International Conference on Intelligent Robots and Systems, pp. 2710-2716. , Vancouver, Canada, September 24–28, 2017, IROS; Voisembert, S., Mechbal, N., Riwan, A., Aoussat, A., Design of a Novel Long-Range Inflatable Robotic Arm: Manufacturing and Numerical Evaluation of the Joints and Actuation (2013) J. Mech. Robotics, 5, p. 045001","Niiyama, R.; Graduate School of Information Science and Technology, Japan; email: niiyama@isi.imi.i.u-tokyo.ac.jp",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114429831
"Sutjipto S., Woolfrey J., Carmichael M.G., Paul G.","57202214637;57190128692;36801255200;7202627449;","Cartesian Inertia Optimization via Redundancy Resolution for Physical Human-Robot Interaction",2021,"IEEE International Conference on Automation Science and Engineering","2021-August",,,"570","575",,,"10.1109/CASE49439.2021.9551663","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117006375&doi=10.1109%2fCASE49439.2021.9551663&partnerID=40&md5=dc62f70037ca4d94050fd89bb8bb0fde","University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia","Sutjipto, S., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia; Woolfrey, J., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia; Carmichael, M.G., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia; Paul, G., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia","The objective of introducing robotic manipulators into human-centric domains is to improve the efficacy of tasks in a safe and practical manner. The shift toward collaborative manipulator platforms has facilitated physical human-robot interaction (pHRI) in such environments. Often, these platforms are kinematically redundant and possess more degrees of freedom (DOF) than needed to complete a desired task. When no additional task is defined, it is possible for the manipulator to converge upon joint configurations that are unfavourable for the collaborative task. Consequently, there is potential for the posture of the manipulator to affect the interaction experienced. This paper investigates an inertia-based optimization control method for redundant manipulators interacting with an active agent. The inertia-based reconfiguration is evaluated through simulations and quantified with real-life experiments conducted with a robot-robot dyad. It was found that resolving redundancy to reconfigure the Cartesian inertia reduced the energy expenditure of the active agent during the interaction. © 2021 IEEE.",,"Degrees of freedom (mechanics); Human robot interaction; Man machine systems; Manipulators; Active agents; Cartesians; Collaborative tasks; Control methods; Human-centric; Joint configuration; Optimisations; Optimization control; Physical humanrobot interaction (phri); Redundancy resolution; Redundancy",,,,,,"Sheila Sutjipto is supported by an Australian Government Research Training Program Scholarship. The authors would like to thank Richardo Khonasty and Dinh Tung Le for helping setup the experiments, and to Yujun Lai for giving detailed feedback on the manuscript.",,,,,,,,,,"Hogan, N., Impedance control: An approach to manipulation: Part i-theory (1985) Journal of Dynamic Systems, Measurement, and Control, 107 (1), pp. 1-7; Ikeura, R., Inooka, H., Variable impedance control of a robot for cooperation with a human (1995) Proceedings-IEEE International Conference on Robotics and Automation, 3, pp. 3097-3102; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control with virtual stiffness for human-robot cooperative peg-in-hole task (2002) IEEE International Conference on Intelligent Robots and Systems, 2, pp. 1075-1081; Erden, M.S., Billard, A., Robotic assistance by impedance compensation for hand movements while manual welding (2016) IEEE Transactions on Cybernetics, 46 (11), pp. 2459-2472. , 11; Ajoudani, A., Tsagarakis, N.G., Bicchi, A., Choosing poses for force and stiffness control (2017) IEEE Transactions on Robotics, 33 (6), pp. 1483-1490. , 12; Woolfrey, J., Lu, W., Liu, D., A control method for joint torque minimization of redundant manipulators handling large external forces (2019) Journal of Intelligent and Robotic Systems: Theory and Applications, 96 (1), pp. 3-16. , 10; Lai, Y., Sutjipto, S., Clout, M.D., Carmichael, M.G., Paul, G., GAVRe 2 : Towards data-driven upper-limb rehabilitation with adaptive-feedback gamification (2019) 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018, pp. 164-169. , Institute of Electrical and Electronics Engineers Inc., 3; Sutjipto, S., Lai, Y., Carmichael, M.G., Paul, G., Fitts' law in the presence of interface inertia (2020) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020, pp. 4749-4752. , July. Institute of Electrical and Electronics Engineers Inc., 7; Ott, C., (2008) Cartesian Impedance Control of Redundant and Flexible-Joint Robots, , Springer Berlin Heidelberg; Asada, H., Dynamic analysis and design of robot manipulators using inertia ellipsoids (1984) Proceedings-IEEE International Conference on Robotics and Automation, pp. 94-102. , Institute of Electrical and Electronics Engineers Inc; Walker, I.D., Impact configurations and measures for kinematically redundant and multiple armed robot systems (1994) IEEE Transactions on Robotics and Automation, 10 (5), pp. 670-683; Sharma, S., Suomalainen, M., Kyrki, V., Compliant manipulation of free-floating objects (2018) Proceedings-IEEE International Conference on Robotics and Automation, pp. 865-872. , Institute of Electrical and Electronics Engineers Inc., 9; Vigni, F., Knoop, E., Prattichizzo, D., Malvezzi, M., The role of closed-loop hand control in handshaking interactions (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 878-885. , 4; Carmichael, M.G., Khonasty, R., Aldini, S., Liu, D., Human preferences in using damping to manage singularities during physical human-robot collaboration (2020) Proceedings-IEEE International Conference on Robotics and Automation, pp. 10184-10190. , Institute of Electrical and Electronics Engineers Inc., 5; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863. , 8; Gil, J.J., Rubio, Á., Savall, J., Decreasing the apparent inertia of an impedance haptic device by using force feedforward (2009) IEEE Transactions on Control Systems Technology, 17 (4), pp. 833-838; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) IEEE Journal on Robotics and Automation, 3 (1), pp. 43-53; Yoshikawa, T., Manipulability of robotic mechanisms (1985) The International Journal of Robotics Research, 4 (2), pp. 3-9. , 6","Sutjipto, S.; University of Technology Sydney (UTS), Australia; email: sheila.sutjipto@uts.edu.au",,,"IEEE Computer Society","17th IEEE International Conference on Automation Science and Engineering, CASE 2021","23 August 2021 through 27 August 2021",,172370,21618070,9781665418737,,,"English","IEEE Int. Conf. Autom. Sci. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85117006375
"Paplu S.H., Arif M.N.I., Berns K.","57204053864;57223303742;7006119626;","Utilizing Semantic and Contextual Information during Human-Robot Interaction",2021,"IEEE International Conference on Development and Learning, ICDL 2021",,,,"","",,,"10.1109/ICDL49984.2021.9515611","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114553612&doi=10.1109%2fICDL49984.2021.9515611&partnerID=40&md5=39057b64a7021ac60f25858042e50f1a","Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany","Paplu, S.H., Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany; Arif, M.N.I., Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany; Berns, K., Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany","Humans are aware of the context and the environment while communicating with an interaction partner. Although equipping a robot with such cognitive skills is still a far cry, integrating contextual information into a robot is a step towards making Human-Robot Interaction (HRI) more meaningful, realistic or 'human-like'. This paper focuses on establishing HRI based on semantic and contextual information in the conversation. The humanoid robot called ROBIN recognizes the user utterances using a speech recognizer called Julius. The approach proposed extracts named entities, contextual information and the sentiment of each user utterance, with robot's responses triggered by a chatbot while gestures and facial expressions invoked by the sentiment labels of the user speech. The human-centered evaluation results based on the experimental scenarios show that the approach ensures more fluid and natural interaction. © 2021 IEEE.","Binary Relationships; Context-based Interaction; Human-Robot Interaction; Named Entity Recognition; Speech Recognition","Anthropomorphic robots; Man machine systems; Semantics; Cognitive skill; Contextual information; Facial Expressions; Human robot Interaction (HRI); Human-centered evaluation; Named entities; Natural interactions; Speech recognizer; Human robot interaction",,,,,,,,,,,,,,,,"Nardi, B.A., (1996) Context and Consciousness: Activity Theory and Humancomputer Interaction, , mit Press; Quintas, J., Menezes, P., Dias, J., Context-based perception and understanding of human intentions (2013) 2013 IEEE RO-MAN. IEEE, pp. 346-347; Potter, J., Cognition as context (whose cognition?) (1998) Research on Language and Social Interaction, 31 (1), pp. 29-44; Menezes, P., Quintas, J., Dias, J., The role of context information in human-robot interaction (2014) 23rd IEEE International Symposium on Robot and Human Interactive Communication Workshop on Interactive Robots for Aging And/or Impaired People.; Al-Darraji, S., (2016) Perception of Nonverbal Cues for Human-Robot Interaction, , Germany: Verlag Dr. Hut; Zafar, Z., Paplu, S.H., Berns, K., Automatic assessment of human personality traits: A step towards intelligent human-robot interaction (2018) 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1-9; Maas, J.F., Spexard, T., Fritsch, J., Wrede, B., Sagerer, G., Biron, what's the topic? a multi-modal topic tracker for improved human-robot interaction (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication. 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IEEE, pp. 1-6; http://www.acapela-group.com/, Voice synthesis- Text to Speech; Reichardt, M., F¨ohst, T., Berns, K., (2012) Introducing Finroc: A Convenient Real-time Framework for Robotics Based on A Systematic Design Approach, , Robotics Research Lab, Department of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany, Technical Report; Al-Darraji, S., Berns, K., Rodíc, A., Action unit based facial expression recognition using deep learning (2016) International Conference on Robotics in Alpe-Adria Danube Region, pp. 413-420. , Springer",,,"et al.;IEEE;IEEE Computational Intelligence Society;Microsoft;NSFC;Peking University","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Development and Learning, ICDL 2021","23 August 2021 through 26 August 2021",,171395,,9781728162423,,,"English","IEEE Int. Conf. Dev. Learn., ICDL",Conference Paper,"Final","",Scopus,2-s2.0-85114553612
"Del Vecchio A., Castellini C., Beckerle P.","57195475054;8957778700;36964076200;","Peripheral Neuroergonomics – An Elegant Way to Improve Human-Robot Interaction?",2021,"Frontiers in Neurorobotics","15",,"691508","","",,,"10.3389/fnbot.2021.691508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114356555&doi=10.3389%2ffnbot.2021.691508&partnerID=40&md5=7612dadacbb33a0e9d610587db310479","Department of Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany; Institute of Robotics and Mechatronics, DLR German Aerospace Center, Weßling, Germany; Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering and Department of Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany; Institute for Mechatronic Systems, Mechanical Engineering, Technical University of Darmstadt, Darmstadt, Germany","Del Vecchio, A., Department of Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany; Castellini, C., Institute of Robotics and Mechatronics, DLR German Aerospace Center, Weßling, Germany; Beckerle, P., Chair of Autonomous Systems and Mechatronics, Department of Electrical Engineering and Department of Artificial Intelligence in Biomedical Engineering, Faculty of Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany, Institute for Mechatronic Systems, Mechanical Engineering, Technical University of Darmstadt, Darmstadt, Germany",[No abstract available],"human-robot interaction; myography; neuroergonomics; peripheral interfaces; user experience",,,,,,"Deutsche Forschungsgemeinschaft, DFG: BE 5729/16, CA1389/1-2","This work received support from the German Research Foundation (DFG) through the projects Active transfer learning with neural networks through human-robot interactions (TRAIN) (no. 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"Tuan H.M., Sanfilippo F., Hao N.V.","57247838000;55388355200;57248055100;","Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"679304","","",,,"10.3389/frobt.2021.679304","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114341093&doi=10.3389%2ffrobt.2021.679304&partnerID=40&md5=bde8869688311f77ca07bda9245d38d3","Department of Control Engineering and Automation, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam; Department of Engineering Sciences, University of Agder (UiA), Grimstad, Norway","Tuan, H.M., Department of Control Engineering and Automation, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam; Sanfilippo, F., Department of Engineering Sciences, University of Agder (UiA), Grimstad, Norway; Hao, N.V., Department of Control Engineering and Automation, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam","Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype. © Copyright © 2021 Tuan, Sanfilippo and Hao.","collaborative robot; human-robot interaction; robot arm; robotics; series elastic actuator",,,,,,"Viet Nam National University Ho Chi Minh City, VNU-HCM; Universitetet i Agder, UiA","We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM and from the Top Research Centre Mechatronics (TRCM), University of Agder, Norway, for this study.",,,,,,,,,,"Al-Odienat, A.I., Al-Lawama, A.A., The Advantages of Pid fuzzy controllers over the conventional types (2008) Am. J. Appl. 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"Jackson R.B., Williams T.","57205545565;7404172627;","A Theory of Social Agency for Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"687726","","",,1,"10.3389/frobt.2021.687726","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114355141&doi=10.3389%2ffrobt.2021.687726&partnerID=40&md5=a9df8b22907f1f6b305f3648f67852b0","MIRRORLab, Department of Computer Science, Colorado School of Mines, Golden, CO, United States","Jackson, R.B., MIRRORLab, Department of Computer Science, Colorado School of Mines, Golden, CO, United States; Williams, T., MIRRORLab, Department of Computer Science, Colorado School of Mines, Golden, CO, United States","Motivated by inconsistent, underspecified, or otherwise problematic theories and usages of social agency in the HRI literature, and leveraging philosophical work on moral agency, we present a theory of social agency wherein a social agent (a thing with social agency) is any agent capable of social action at some level of abstraction. Like previous theorists, we conceptualize agency as determined by the criteria of interactivity, autonomy, and adaptability. We use the concept of face from politeness theory to define social action as any action that threatens or affirms the face of a social patient. With these definitions in mind, we specify and examine the levels of abstraction most relevant to HRI research, compare notions of social agency and the surrounding concepts at each, and suggest new conventions for discussing social agency in our field. © Copyright © 2021 Jackson and Williams.","human-robot interaction; levels of abstraction; moral agency; politeness theory; social agency",,,,,,"19RT0497","This work was funded in part by Air Force Young Investigator Award 19RT0497.",,,,,,,,,,"Alač, M., Social Robots: Things or Agents? (2016) AI Soc, 31, pp. 519-535; Atkinson, R.K., Mayer, R.E., Merrill, M.M., Fostering Social agency in Multimedia Learning: Examining the Impact of an Animated Agent's Voice (2005) Contemp. 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Psychol, 8, p. 1962; Thellman, S., Ziemke, T., The Intentional Stance toward Robots: Conceptual and Methodological Considerations (2019) The 41st Annual Conference of the Cognitive Science Society, pp. 1097-1103. , Montreal, Canada, July 24-26; Ullman, D., Leite, L., Phillips, J., Kim-Cohen, J., Scassellati, B., Smart Human, Smarter Robot: How Cheating Affects Perceptions of Social agency (2014) Proceedings of the Annual Meeting of the Cognitive Science Society, , Quebec, Canada, in; Verbeek, P.-P., (2011) Moralizing Technology: Understanding and Designing the Morality of Things, , University of Chicago Press; Westlund, J.M.K., Martinez, M., Archie, M., Das, M., Breazeal, C., Effects of Framing a Robot as a Social Agent or as a Machine on Children’s Social Behavior (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 688-693. , New York, 26-31 Aug. 2016, IEEE","Jackson, R.B.; MIRRORLab, United States; email: rbjackso@mines.edu",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114355141
"Patrona F., Mademlis I., Pitas I.","57090843300;56436769200;35569279700;","Self-Supervised Convolutional Neural Networks for Fast Gesture Recognition in Human-Robot Interaction",2021,"2021 10th International Conference on Information and Automation for Sustainability, ICIAfS 2021",,,,"88","93",,,"10.1109/ICIAfS52090.2021.9605915","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119958973&doi=10.1109%2fICIAfS52090.2021.9605915&partnerID=40&md5=ad9bbfb3d199fb60f06f0b3af019369d","Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece","Patrona, F., Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece; Mademlis, I., Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece; Pitas, I., Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece","Current autonomous systems (e.g., self-driving cars, autonomous drones, consumer robots, etc.) can already perform a wide variety of tasks and are predicted to be able to collaboratively assist humans in the near future. Thus, the need for efficient human-robot interaction (HRI) methods is greatly increasing. Gesture recognition is an effective HRI approach, since many robots are equipped with cameras and computer vision algorithms have progressed significantly in recent years, with advanced Deep Neural Networks (DNNs) being able to be executed on-board an autonomous system. However, computational/memory limitations are still significant for embedded AI methods, rendering the increase of DNN accuracy without imposing a penalty on runtime requirements a very important research priority. This paper investigates self-supervised DNN pretraining for a novel pretext task, relying on spatiotemporal video frame compression via tensor decomposition and low-rank approximation, as a means to augment gesture recognition performance, without inducing any runtime overhead during the inference stage. Thus, the method permits the use of less complex and much faster neural architectures that are well-suited to robotics applications and HRI. Quantitative evaluation on a gesture recognition dataset for autonomous Unmanned Aerial Vehicle (UAV) handling demonstrates the effectiveness and real-time performance of the proposed method on embedded AI compute hardware. © 2021 IEEE.","Autonomous Unmanned Aerial Vehicles; Convolutional Neural Networks; Gesture Recognition; Human-Robot Interaction; Low-Rank Approximation; Self-Supervised Learning; Tensor Decomposition","Aircraft detection; Antennas; Autonomous vehicles; Convolution; Convolutional neural networks; Deep neural networks; Drones; Gesture recognition; Man machine systems; Tensors; 'current; Autonomous unmanned aerial vehicles; Computer vision algorithms; Convolutional neural network; Gestures recognition; Humans-robot interactions; Interaction methods; Low rank approximations; Self-supervised learning; Tensor decomposition; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No871479 (AERIAL-CORE).",,,,,,,,,,"Ahsan, U., Madhok, R., Essa, I., Video jigsaw: Unsupervised learning of spatiotemporal context for video action recognition (2019) Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). 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Wang, J., Jiao, J., Liu, Y.-H., Self-supervised video representation learning by pace prediction (2020) Proceedings of the European Conference on Computer Vision (ECCV)., , Springer; Wang, X., Gupta, A., Unsupervised learning of visual representations using videos (2015) Proceedings of the IEEE International Conference on Computer Vision (ICCV); Wu, Z., Xiong, Y., Yu, S.X., Lin, D., Unsupervised feature learning via non-parametric instance discrimination (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Xu, D., Xiao, J., Zhao, Z., Shao, J., Xie, D., Zhuang, Y., Selfsupervised spatiotemporal learning via video clip order prediction (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Zhai, X., Oliver, A., Kolesnikov, A., Beyer, L., S4l: Self-supervised semi-supervised learning (2019) Proceedings of the IEEE International Conference on Computer Vision (ICCV); Zhang, R., Isola, P., Efros, A.A., Colorful image colorization (2016) Proceedings of the European Conference on Computer Vision (ECCV)., , Springer; Zhang, R., Isola, P., Efros, A.A., Split-brain autoencoders: Unsupervised learning by cross-channel prediction (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",,,,"Institute of Electrical and Electronics Engineers Inc.","10th International Conference on Information and Automation for Sustainability, ICIAfS 2021","11 August 2021 through 13 August 2021",,173956,,9781665444293,,,"English","Int. Conf. Inf. Autom. Sustain., ICIAfS",Conference Paper,"Final","",Scopus,2-s2.0-85119958973
"Khiabani H., Ahmadi M.","57212452322;7103163180;","A classical machine learning approach for Emg-based lower limb intention detection for human-robot interaction systems",2021,"ICAS 2021 - 2021 IEEE International Conference on Autonomous Systems, Proceedings",,,,"","",,,"10.1109/ICAS49788.2021.9551190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117447938&doi=10.1109%2fICAS49788.2021.9551190&partnerID=40&md5=e4b6cc677f1cdf4581c031ef9c7736c3","Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, ON, Canada","Khiabani, H., Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, ON, Canada; Ahmadi, M., Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, ON, Canada","Surface Electromyography (sEMG)-based intention-detection systems of lower limb can intelligently augment human- robot interaction (HRI) systems to detect subject's walking direction prior-to or during walking. Ten Subject-Exclusive (Subj-Ex) and Generalized (Gen) Classical Machine Learning (C-ML)-based models are employed to detect direction intentions and evaluate inter-subject robustness in one knee/foot- gesture and three walking-related scenarios. In each, sEMG signals are collected from eight muscles of nine subjects during at least nine distinct gestures/activities. Linear Discriminant Analysis (LDA) and Random Forest (RF) classifiers, applied to the Time-Domain (TD) feature set (of the four input sets), provided the best accuracy. Subj-Ex approach achieves the highest prediction accuracy, facing occasional competition from the Gen approach. In knee/foot gesture scenario, LDA reaches an accuracy of 91.67%, signifying its applicability to robotic-assisted walking, prosthetics, and orthotics. The overall prediction accuracy among walking- related scenarios, though not as remarkably high as in the knee/foot gesture recognition scenario, can reach up to 75%. © 2021 IEEE.","Assisted Walking; Classical Machine Learning; Direction Detection; Intelligent HRI; Intention Detection; Robotic; Surface EMG","Decision trees; Discriminant analysis; Intelligent robots; Machine learning; Man machine systems; Robotics; Time domain analysis; Walking aids; Assisted walking; Classical machine learning; Direction detections; Humans-robot interactions; Intelligent human- robot interaction; Intention detection; Lower limb; Machine-learning; Robotic; Surface EMG; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC: 497303","This research was supported by the Discovery Grant, from Natural Sciences and Engineering Council Canada (NSERC) held by Mojtaba Ahmadi. Support for Hasti Khiabani was provided by the NSERC CREATE Grant 497303.",,,,,,,,,,"Meyer-Heim, A., Van Hedel, H.J.A., Robot-assisted and computer-enhanced therapies for children with cerebral palsy: Current state and clinical implementation (2013) Seminars in Pediatric Neurology, 20, pp. 139-145. , Elsevier; Bi, L., Guan, C., A review on emgbased motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomedical Signal Processing and Control, 51, pp. 113-127; Hudgins, B., Parker, P., Scott, R.N., A new strategy for multifunction myoelectric control (1993) IEEE Transactions on Biomedical Engineering, 40 (1), pp. 82-94; Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M., Classification of the myoelectric signal using time-frequency based representations (1999) Medical Engineering & Physics, 21 (6-7), pp. 431-438; Englehart, K., Hudgin, B., Parker, P.A., A wavelet-based continuous classification scheme for multifunction myoelectric control (2001) IEEE Transactions on Biomedical Engineering, 48 (3), pp. 302-311; Englehart, K., Hudgins, B., A robust, realtime control scheme for multifunction myoelectric control (2003) IEEE Transactions on Biomedical Engineering, 50 (7), pp. 848-854; Kuzborskij, I., Gijsberts, A., Caputo, B., On the challenge of classifying 52 hand movements from surface electromyography (2012) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4931-4937; Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Mittaz Hager, A., Deriaz, O., Castellini, C., Caputo, B., Characterization of a benchmark database for myoelectric movement classification (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (1), pp. 73-83; Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Mittaz Hager, A., Elsig, S., Giatsidis, G., Müller, H., Electromyography data for non-invasive naturally-controlled robotic hand prostheses (2014) Scientific Data, 1 (1), pp. 1-13; Gijsberts, A., Atzori, M., Castellini, C., Müller, H., Caputo, B., Movement error rate for evaluation of machine learning methods for semg-based hand movement classification (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22 (4), pp. 735-744; Janet Rechy-Ramirez, E., Hu, H., Biosignal based control in assistive robots: A survey (2015) Digital Communications and Networks, 1 (2), pp. 85-101; Mohebbi, A., Human-robot interaction in rehabilitation and assistance: A review (2020) Current Robotics Reports, pp. 1-14; Zhang, F., Huang, H., Decoding movement intent of patient with multiple sclerosis for the powered lower extremity exoskeleton (2013) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4957-4960; Nazmi, N., Azizi Abdul Rahman, M., Yamamoto, S., Anom Ahmad, S., Zamzuri, H., Amri Mazlan, S., A review of classification techniques of emg signals during isotonic and isometric contractions (2016) Sensors, 16 (8), p. 1304; Lyons, K.R., Joshi, S.S., A case study on classification of foot gestures via surface electromyography (2015) Annu. Conf. Rehabil. Eng. Assist. Technol. Soc. Amer., pp. 1-5; Thi Thu Vu, H., Dong, D., Cao, H., Verstraten, T., Lefeber, D., Vanderborght, B., Geeroms, J., A review of gait phase detection algorithms for lower limb prostheses (2020) Sensors, 20 (14), p. 3972; Jochumsen, M., Khan Niazi, I., Detection and classification of single-trial movement-related cortical potentials associated with functional lower limb movements (2020) Journal of Neural Engineering, 17 (3), p. 035009; Hu, B., Rouse, E., Hargrove, L., Fusion of bilateral lower-limb neuromechanical signals improves prediction of locomotor activities (2018) Frontiers in Robotics and AI, 5, p. 78; Surface Electromyography for the Non-invasive Assessment of Muscles, , http://www.seniam.org/; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for emg signal classification (2012) Expert Systems with Applications, 39 (8), pp. 7420-7431",,,"ASI;IEEE;IEEE Signal Processing Society","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Autonomous Systems, ICAS 2021","11 August 2021 through 13 August 2021",,172357,,9781728172897,,,"English","ICAS - IEEE Int. Conf. Auton. Syst., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85117447938
"Gurung N., Grant J.B., Herath D.","57222468411;18036903700;8366358100;","What's in a face? The effect of faces in human robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1024","1030",,,"10.1109/RO-MAN50785.2021.9515378","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115101122&doi=10.1109%2fRO-MAN50785.2021.9515378&partnerID=40&md5=a11b2f87d426adf7b38efdf516f79087","University of Canberra, Faculty of Science and Technology, Canberra, ACT  2617, Australia; University of Canberra, Faculty of Health, Canberra, ACT  2617, Australia","Gurung, N., University of Canberra, Faculty of Science and Technology, Canberra, ACT  2617, Australia; Grant, J.B., University of Canberra, Faculty of Health, Canberra, ACT  2617, Australia; Herath, D., University of Canberra, Faculty of Science and Technology, Canberra, ACT  2617, Australia","The face is the most influential feature in any interaction. This study investigated the effect of robot faces in embodied interactions on a range of subjective and objective factors. The platform used to answer the research question was an interactive robotic art installation, incorporating a robot arm that could be presented with or without a face displayed on an attached screen. Participants were exposed to one of three conditions - the robot arm only, the robot arm with a static face displayed, and the robot arm with a dynamic face displayed. We used the Godspeed Questionnaire to determine whether the different embodiments would be perceived differently on measures of likeability, animacy, and safety before and after the interaction. We also measured how close participants stood to the robot and how much time they spent interacting with the robot. We found that the three embodiments did not significantly differ in time spent, distance, animacy, likeability, or safety before and after the interaction. This surprising result hints at other possible reasons that influence the success of a robot interaction and advances the understanding of the effect of faces in human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Man machine systems; Robotic arms; Embodied interaction; Interactive robotics; Likeability; Research questions; Robot arms; Robot face; Robot interactions; Time spent; Social robots",,,,,,,,,,,,,,,,"Jack, R.E., Schyns, P.G., The human face as a dynamic tool for social communication (2015) Current Biology, 25 (14), pp. 621-634; Todorov, A., Pakrashi, M., Oosterhof, N.N., Evaluating faces on trustworthiness after minimal time exposure (2009) Social Cognition, 27 (6), pp. 813-833; Zebrowitz, L.A., Montepare, J.M., Social psychological face perception: Why appearance matters (2008) Social and Personality Psychology Compass, 2 (3), pp. 1497-1517; Frith, C., Role of facial expressions in social interactions (2009) Philosophical Transactions of the Royal Society B: Biological Sciences, 364 (1535), pp. 3453-3458; Thornhill, R., Gangestad, S.W., Facial attractiveness (1999) Trends in Cognitive Sciences, 3 (12), pp. 452-460; Norman, D., (2013) The Design of Everyday Things: Revised and Expanded Edition, , Basic books; Blow, M., Dautenhahn, K., Appleby, A., Nehaniv, C.L., Lee, D., The art of designing robot faces: Dimensions for human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction, pp. 331-332; Breazeal, C., Social interactions in hri: The robot view (2004) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 181-186; Mirnig, N., Strasser, E., Weiss, A., Kühnlenz, B., Wollherr, D., Tscheligi, M., Can you read my face? (2015) International Journal of Social Robotics, 7 (1), pp. 63-76; Mori, M., The uncanny valley (1970) Energy, 7 (4), pp. 33-35; McCloud, S., (1993) Understanding Comics: The Invisible Art, , Northampton, Mass; Kalegina, A., Schroeder, G., Allchin, A., Berlin, K., Cakmak, M., Characterizing the design space of rendered robot faces (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 96-104; Rothstein, N., Kounios, J., Ayaz, H., De Visser, E.J., Assessment of human-likeness and anthropomorphism of robots: A literature review (2020) International Conference on Applied Human Factors and Ergonomics, pp. 190-196. , Springer; Reeves, B., Nass, C., (1996) The Media Equation: How People Treat Computers, Television, and New Media like Real People, , Cambridge university press Cambridge, UK; Broadbent, E., Kumar, V., Li, X., Sollers, J., III, Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: A robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PloS One, 8 (8); Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is humanlike? decomposing robots' human-like appearance using the anthropomorphic robot (abot) database (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 105-113; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., Is the uncanny valley an uncanny cliff? (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 368-373; Schwind, V., Wolf, K., Henze, N., Korn, O., Determining the characteristics of preferred virtual faces using an avatar generator (2015) Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, pp. 221-230; Benitez, J., Wyman, A.B., Carpinella, C.M., Stroessner, S.J., The authority of appearance: How robot features influence trait inferences and evaluative responses (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 397-404; Bruce, A., Nourbakhsh, I., Simmons, R., The role of expressiveness and attention in human-robot interaction (2002) Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 4, pp. 4138-4142; Cavedon, L., Kroos, C., Herath, D., Burnham, D., Bishop, L., Leung, Y., Stevens, C.J., C'mon dude!"": Users adapt their behaviour to a robotic agent with an attention model (2015) International Journal of Human-Computer Studies, 80, pp. 14-23; Argyle, M., Dean, J., Eye-contact, distance and affiliation (1965) Sociometry, American Sociological Association, pp. 289-304; Zibrek, K., Martin, S., McDonnell, R., Is photorealism important for perception of expressive virtual humans in virtual reality? (2019) ACM Transactions on Applied Perception (TAP), 16 (3), pp. 1-19; Zibrek, K., Kokkinara, E., McDonnell, R., The effect of realistic appearance of virtual characters in immersive environments-does the character's personality play a role? (2018) IEEE Transactions on Visualization and Computer Graphics, 24 (4), pp. 1681-1690; Gurung, N., Herath, D., Grant, J.B., Feeling safe: A study on trust with an interactive robotic art installation (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 447-451; Rajamohan, V., Scully-Allison, C., Dascalu, S., Feil-Seifer, D., Factors influencing the human preferred interaction distance (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-7; Koay, K.L., Syrdal, D.S., Walters, M.L., Dautenhahn, K., Living with robots: Investigating the habituation effect in participants' preferences during a longitudinal human-robot interaction study (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 564-569; Siebert, F.W., Klein, J., Rötting, M., Roesler, E., (2020) The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception; Siebert, F.W., Pickl, J., Klein, J., Rötting, M., Roesler, E., Let's not get too personal-distance regulation for follow me robots (2020) International Conference on Human-Computer Interaction, pp. 459-467. , Springer; Shanee, H.S., Dror, K., Tal, O.-G., Yael, E., The influence of following angle on performance metrics of a human-following robot (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 593-598; Patterson, M.L., An arousal model of interpersonal intimacy (1976) Psychological Review, 83 (3), p. 235; Mumm, J., Mutlu, B., Human-robot proxemics: Physical and psychological distancing in human-robot interaction (2011) Proceedings of the 6th International Conference on Human-robot Interaction, pp. 331-338; Herath, D.C., Kroos, C., Stevens, C., Burnham, D., Adopt-a-robot: A story of attachment (or the lack thereof) (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 135-136; Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M., Mundell, A., Rosenthal, S., Sellner, B., Schultz, A.C., Designing robots for long-term social interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1338-1343; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Kroos, C., Herath, D.C., Evoking agency: Attention model and behavior control in a robotic art installation (2012) Leonardo, 45 (5), pp. 401-407; Clarke, J., (2021) View of Stelarc's Prosthetic Head | Ctheory, , https://journals.uvic.ca/index.php/ctheory/article/view/14458/5300; Kroos, C., Herath, D., Stelarc ""the articulated head: An intelligent interactive agent as an artistic installation (2009) Proceedings of International Conference on Intelligent Robots and Systems; (2020) Innovation in Hardware, Design and Interfaces for Social Robots and Hri, , https://sites.psu.edu/icsr2020/awards/, I. Conference; Lazaroo, S., (2021) Talk to the (Articulated) Head 2. 0, , https://www.canberra.edu.au/uncover/newsarchive/2019/september/talk-to-the-articulated-head-2.0; McDonnell, R., Breidt, M., Bülthoff, H.H., Render me real? investigating the effect of render style on the perception of animated virtual humans (2012) ACM Transactions on Graphics (TOG), 31 (4), pp. 1-11",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115101122
"Mogster J., Stoelen M., Kyrkjebo E.","57262066200;36175043400;15925412000;","Estimating robot body torque for two-handed cooperative physical human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"279","284",,,"10.1109/RO-MAN50785.2021.9515470","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115099840&doi=10.1109%2fRO-MAN50785.2021.9515470&partnerID=40&md5=6f937e53a7528d0d7951fe70175c6b03","Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway; Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, United Kingdom","Mogster, J., Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway; Stoelen, M., Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway, Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, United Kingdom; Kyrkjebo, E., Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway","Cooperative physical Human Robot Interaction (pHRI) aims to combine the best of human problem-solving skills with the strength, speed and accuracy of a robot. When humans and robots physically interact, there will be interaction Forces and Torques (F/Ts) that must be within safe limits to avoid threats to human life and unacceptable damage to equipment. When measured, these F/Ts can be limited by safety rated emergency stops, and one can design a compliant robot behavior to reduce interaction F/Ts, and avoid unnecessary emergency stops. Several recent collaborative robots offer measurements of interaction F/Ts by utilizing torque sensors in joints or observers for joint torque, and the classical end-effector F/T sensor can provide measurements of interaction at the working end of a robot. The end-effector wrench can be calculated from joint torques if and only if there is no interaction on the robot body. Typically, safety limits are evaluated around a single point of contact - on the end-effector or elsewhere on the robot body. This approach fails when a human uses both hands to interact with a robot, e.g. when hand guiding or otherwise cooperating with the robot placing one hand on the robot end-effector and the other hand on the robot elbow. Having two points of contact that are evaluated as one will limit the allowed F/Ts of the sum of the contacts rather than individually. In this paper, we introduce the body torque as the interaction on the body that is not the result of interactions on the end-effector. We then use this body torque, which is a more accurate representation of the forces applied to the robot body, to limit the body interaction F/Ts to ensure safe human-robot interaction. Furthermore, the body torque can be used to design null-space compliance for a redundant robot. Distinguishing body torque is a step towards safe cooperative pHRI, where body torque, unknown end-effector loads, and end-effector interaction F/T are all important measurements for safety, control and compliance. © 2021 IEEE.",,"Agricultural robots; Compliance control; End effectors; Machine design; Man machine systems; Torque; Collaborative robots; Human problem solving; Interaction forces; Measurements of; Physical human-robot interactions; Physical humanrobot interaction (phri); Robot end effector; Safe human-robot interaction; Social robots",,,,,"Norges Forskningsråd: 28077","*This work was funded by the Research Council of Norway through grant number 28077. 1Department of Computer science, Electrical engineering and Mathematical sciences, Faculty of Engineering and Science, Western Norway University of Applied Sciences, Førde, Norway jmog@hvl.no 2Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, UK Fig. 1. Example of hand guiding a robot using both hands. Joint torque measurements alone will not allow distinguishing interaction on end-effector from interaction on the body, nor will this scenario allow calculating the end-effector wrench from joint torque.",,,,,,,,,,"Haddadin, S., Croft, E., (2016) Physical Human-Robot Interaction, pp. 1835-1874. , ch. 69. Springer International Publishing; Robots and robotic devices-collaborative robots (2016) ISO, , https://www.iso.org/standard/62996.html, ISO/TS 15066, Tech. Rep. 15066; Robots and robotic devices-vocabulary (2012) ISO, , https://www.iso.org/standard/55890.html, ISO 8373, Tech. Rep. 8373; (2017) KUKA Sunrise. OS 1. 14 Operating and Programming Instructions for System Integrators, KUKA, , Aug; Haddadin, S., (2014) Towards Safe Robots, , Springer Berlin Heidelberg; Sadeghian, H., Ficuciello, F., Villani, L., Keshmiri, M., Global impedance control of dual-arm manipulation for safe interaction (2012) IFAC Proceedings Volumes, 45 (22), pp. 767-772; Sadeghian, H., Villani, L., Keshmiri, M., Siciliano, B., Task-space control of robot manipulators with null-space compliance (2014) IEEE Transactions on Robotics, 30 (2), pp. 493-506. , apr; Vigoriti, F., Ruggiero, F., Lippiello, V., Villani, L., Control of redundant robot arms with null-space compliance and singularity-free orientation representation (2018) Robotics and Autonomous Systems, 100, pp. 186-193. , feb; Likar, N., Zlajpah, L., External joint torque-based estimation of contact information (2014) International Journal of Advanced Robotic Systems, 11 (7), p. 107. , jan; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863. , aug; Ficuciello, F., Villani, L., Siciliano, B., Impedance control of redundant manipulators for safe humanrobot collaboration (2016) Acta Polytechnica Hungarica, 13 (1). , jan; Spong, M.W., Hutchinson, S., Vidyasagar, M., (2005) Robot Modeling and Control, , John Wiley & Sons Inc; Corke, P., (2017) Robotics, Vision and Control, , Springer International Publishing; Hogan, N., Impedance control: An approach to manipulation (1984) 1984 American Control Conference. IEEE, , https://ieeexplore.ieee.org/document/4788393/citations#citations, jul",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115099840
"Park U., Kim M., Jang Y., Lee G., Kim K., Kim I.-J., Choi J.","57261767000;57262208200;57261621500;57219850523;35105292400;14009916700;55994218300;","Robot facial expression framework for enhancing empathy in human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"832","838",,,"10.1109/RO-MAN50785.2021.9515533","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115094305&doi=10.1109%2fRO-MAN50785.2021.9515533&partnerID=40&md5=6d3d0e413d59881f4f22a73eace6f855","Korea Institute of Science and Technology, Seoul, 02792, South Korea; Dept. of Industrial Design, Seoultech, Seoul, 01811, South Korea","Park, U., Dept. of Industrial Design, Seoultech, Seoul, 01811, South Korea; Kim, M., Dept. of Industrial Design, Seoultech, Seoul, 01811, South Korea; Jang, Y., Dept. of Industrial Design, Seoultech, Seoul, 01811, South Korea; Lee, G., Korea Institute of Science and Technology, Seoul, 02792, South Korea; Kim, K., Korea Institute of Science and Technology, Seoul, 02792, South Korea; Kim, I.-J., Korea Institute of Science and Technology, Seoul, 02792, South Korea; Choi, J., Korea Institute of Science and Technology, Seoul, 02792, South Korea","A social robot interacts with humans based on social intelligence, for which related applications are being developed across diverse fields to be increasingly integrated in modern society. In this regard, social intelligence and interaction are the keywords of a social robot. Social intelligence refers to the ability to control interactions or thoughts and feelings of relationships with other people; primal empathy, which is the ability to empathize by perceiving emotional signals, among the components of social intelligence was applied to the robot in this study. We proposed that the empathic ability of a social robot can be improved if the social robot can create facial expressions based on the emotional state of a user. Moreover, we suggested a framework of facial expressions for robots. These facial expressions can be repeatedly used in various social robot platforms to achieve such a strategy. © 2021 IEEE.",,"Agricultural robots; Intelligent robots; Diverse fields; Emotional state; Facial Expressions; Robot platform; Social intelligence; Social robots",,,,,"Korea Institute of Science and Technology, KIST","*Research supported by Korea Institute of Science and Technology. 1Korea Institute of Science and Technology, Seoul 02792, Republic of Korea. {oorrppp, danny, drjay, cjs}@kist.re.kr 2M.A., Dept. of Industrial Design, Seoultech, Seoul 01811, Republic of Korea. {zzangyoungeuni}@gmail.com *Corresponding Author, University of Science and Technology, Daejeon 34113, Republic of Korea. {wpark, kim1102}@kist.re.kr Ung Park, Minsoo Kim and Youngeun Jang contributed equally.",,,,,,,,,,"Breazeal, C., (2004) Designing Sociable Robots, , Cambridge MA: MIT Press; Choi, Y., Theoretical concept of interactivity and application of terms to television media (2001) 2001 Korean Journal of Journalism and Communication Studies Seminar; Wawra, D., Social intelligence: The key intercultural communication (2009) European Journal of English Studies, 13, pp. 163-177; Barrett-Lennard, G., The empathy cycle: Refinement of a nuclear concept (1981) J Counseling Psych.; Shamay-Tsoory, S., Aharon-Peretz, J., Perry, D., Characterization of empathy deficits following prefrontal brain damage: The role of the right ventromedial prefrontal cortex (2009) J Cogn Neurosci.; Bosnjakovic, J., Radionov, T., Empathy: Concepts, theories and neuroscientific basis (2018) Alcoholism and Psychiatry Research; Brothers, L., A biological perspective on empathy (1989) American Journal of Psychiatry, 146 (1), pp. 10-19; Anderson, C., Keltner, D., The role of empathy in the formation and maintenance of social bonds (2002) Behavioural and Brain Sciences, 25 (1), pp. 21-22; De Vignemont, F., Singer, T., The empathic brain: How, when and why? 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Kim, M.; University of Science and TechnologySouth Korea; email: kim1102@kist.re.kr",,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115094305
"Rossi A., Raiano M., Rossi S.","57189499587;57215275463;55619320887;","Affective, cognitive and behavioural engagement detection for human-robot interaction in a bartending scenario",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"208","213",,,"10.1109/RO-MAN50785.2021.9515435","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115087121&doi=10.1109%2fRO-MAN50785.2021.9515435&partnerID=40&md5=2cb2f4c3854549683cd7dcd34cc81e6b","University of Naples Federico II, Interdepartmental Center for Advances in Robotic Surgery, Italy; University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Italy","Rossi, A., University of Naples Federico II, Interdepartmental Center for Advances in Robotic Surgery, Italy; Raiano, M., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Italy; Rossi, S., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Italy","Guaranteeing people's engagement during an interaction is very important to elicit positive and effective emotions in public service scenarios. A robot should be able to detect its interlocutor's level and mode of engagement to accordingly modulate its behaviours. However, there is not a generally accepted model to annotate and classify engagement during an interaction. In this work, we consider engagement as a multidimensional construct with three relevant dimensions: affective, cognitive and behavioural. To be automatically evaluated by a robot, such a complex construct requires the selection of the proper interaction features among a large set of possibilities. Moreover, manually collecting and annotating large datasets of real interactions are extremely time-consuming and costly. In this study, we collected the recordings of human-robot interactions in a bartending scenario, and we compared different feature selection and regression models to find the features that characterise a user's engagement in the interaction, and the model that can efficiently detect them. Results showed that the characterisation of each dimension separately in terms of features and regression obtains better results with respect to a model that directly combines the three dimensions. © 2021 IEEE.",,"Agricultural robots; Feature extraction; Large dataset; Man machine systems; Regression analysis; Bartending; Interaction features; Large datasets; Public services; Real interactions; Regression model; Three dimensions; Social robots",,,,,,"ACKNOWLEDGMENT This work has been supported by Italian PON I&C 2014-2020 within the BRILLO research project “Bartending Robot for Interactive Long-Lasting Operations”, no. F/190066/01-02/X44.",,,,,,,,,,"Berezina, K., Ciftci, O., Cobanoglu, C., (2019) Robots, Artificial Intelligence, and Service Automation in Restaurants, 10, pp. 185-219; Masuda, T., Misaki, D., Development of Japanese green tea serving robot ""t-bartender"" (2005) IEEE International Conference Mechatronics and Automation, 2, pp. 1069-1074. , 2005; Rossi, A., Staffa, M., Origlia, A., Di Maro, M., Rossi, S., Brillo: A robotic architecture for personalised long-lasting interactions in a bartending domain (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '21 Companion, pp. 426-429. , New York, NY, USA: Association for Computing Machinery; Stikic, M., Berka, C., Levendowski, D.J., Rubio, R.F., Tan, V., Korszen, S., Barba, D., Wurzer, D., Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics (2014) Frontiers in Neuroscience, 8, p. 342; Ben-Eliyahu, A., Moore, D., Dorph, R., Schunn, C.D., Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts (2018) Contemporary Educational Psychology, 53, pp. 87-105. , https://www.sciencedirect.com/science/article/pii/S0361476X17300334; Dell'Aquila, E., Maggi, G., Conti, D., Rossi, S., A preparatory study for measuring engagement in pediatric virtual and robotics rehabilitation settings (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '20, pp. 183-185. , New York, NY, USA: Association for Computing Machinery; Rossi, S., Dell'Aquila, E., Maggi, G., Russo, D., What would you like to drink? engagement and interaction styles in hri (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '20, pp. 415-417. , New York, NY, USA: Association for Computing Machinery; Staffa, M., Rossi, S., Recommender interfaces: The more humanlike, the more humans like (2016) Social Robotics, pp. 200-210. , Cham: Springer International Publishing; Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., Peters, C., Engagement in human-agent interaction: An overview (2020) Frontiers in Robotics and AI, 7, p. 92; Sidner, C.L., Lee, C., Lesh, N., Engagement when looking: Behaviors for robots when collaborating with people (2003) Diabruck: Proceedings of the 7th Workshop on the Semantic and Pragmatics of Dialogue, pp. 123-130. , University of Saarland; Bosch, N., Detecting student engagement: Human versus machine (2016) Ser. UMAP '16, pp. 317-320. , New York, NY, USA: Association for Computing Machinery; Sharma, P., Joshi, S., Gautam, S., Maharjan, S., Filipe, V., Reis, M.J.C.S., (2020) Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning; Foster, M.E., Keizer, S., Lemon, O., Towards action selection under uncertainty for a socially aware robot bartender (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '14, pp. 158-159. , New York, NY, USA: Association for Computing Machinery; Giuliani, M., Petrick, R.P., Foster, M.E., Gaschler, A., Isard, A., Pateraki, M., Sigalas, M., Comparing task-based and socially intelligent behaviour in a robot bartender (2013) Proceedings of the 15th ACM on International Conference on Multimodal Interaction, Ser. ICMI '13, pp. 263-270. , New York, NY, USA: Association for Computing Machinery; Petrick, R.P.A., Foster, M.E., Planning for social interaction in a robot bartender domain (2013) Proceedings of the Twenty-Third International Conference on International Conference on Automated Planning and Scheduling, Ser. ICAPS'13, pp. 389-397. , AAAI Press; Ishii, R., Nakano, Y.I., Nishida, T., Gaze awareness in conversational agents: Estimating a user's conversational engagement from eye gaze (2013) ACM Trans. Interact. Intell. Syst., 3 (2). , Aug; Brown, L., Kerwin, R., Howard, A.M., Applying behavioral strategies for student engagement using a robotic educational agent (2013) 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4360-4365; Rich, C., Ponsleur, B., Holroyd, A., Sidner, C., (2010) Recognizing Engagement in Human-robot Interaction, 3, pp. 375-382; Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., Mcowan, P.W., Context-sensitive affect recognition for a robotic game companion (2014) ACM Trans. Interact. Intell. Syst., 4 (2). , Jun; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9, p. 11; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) International Journal of Social Robotics, 7 (4), pp. 465-478; Niculescu, A., Dijk, B., Nijholt, A., Li, H., Lan, S.S., Making social robots more attractive: The effects of voice pitch, humor and empathy (2013) International Journal of Social Robotics, 5, pp. 171-191; Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A., Openpose: Realtime multi-person 2D pose estimation using part affinity fields (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence; Subramainan, L., Mahmoud, M., Ahmad, M., Yusoff, M., An emotion-based model for improving students' engagement using agentbased social simulator (2016) International Journal on Advanced Science, Engineering and Information Technology, 6 (12), p. 952",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115087121
"Zhang Z., Zheng J., Thalmann N.M.","57261577100;57194827755;55218502200;","Engagement intention estimation in multiparty human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"117","122",,,"10.1109/RO-MAN50785.2021.9515373","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115079686&doi=10.1109%2fRO-MAN50785.2021.9515373&partnerID=40&md5=91b137988b6e187540ef561fd6b5f6bd","Nanyang Technological University, School of Computer Science and Engineering, Singapore; Nanyang Technological University, Institute of Media Innovation, Singapore","Zhang, Z., Nanyang Technological University, School of Computer Science and Engineering, Singapore, Nanyang Technological University, Institute of Media Innovation, Singapore; Zheng, J., Nanyang Technological University, School of Computer Science and Engineering, Singapore; Thalmann, N.M., Nanyang Technological University, Institute of Media Innovation, Singapore","As the applications of intelligent agents (IAs) are gradually increasing in daily life, they are expected to have reasonable social intelligence to interact with people by appropriately interpreting human behavior and intention. This paper presents a method to estimate whether people have willingness to join in a conversation, which helps to endow IAs with the capability of detecting potential participants. The method is built on the CNN-LSTM network, which takes image features and social signals as input, making use of general information conveyed in images, semantic social cues proven by social psychology studies, and temporal information in the sequence of inputs. The network is designed to have a multi-branch structure with the flexibility of accommodating different types of inputs. We also discuss the signal transition in multiparty human-robot interaction scenarios. The method is evaluated on three datasets with social signals and/or images as inputs. The results show that the proposed method can infer human engagement intention well. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Intelligent agents; Long short-term memory; Man machine systems; Semantics; General information; Human behaviors; Intention estimation; Multi-branch structures; Signal transition; Social intelligence; Social psychology; Temporal information; Social robots",,,,,"National Research Foundation Singapore, NRF","This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.",,,,,,,,,,"Murphy, J., Gretzel, U., Pesonen, J., Marketing robot services in hospitality and tourism: The role of anthropomorphism (2019) Journal of Travel & Tourism Marketing, 36 (7), pp. 784-795. , Feb; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21); Tahir, Y., Dauwels, J., Thalmann, D., Thalmann, N.M., A user study of a humanoid robot as a social mediator for two-person conversations (2018) International Journal of Social Robotics, pp. 1-14; Wan, S., Gu, Z., Ni, Q., Cognitive computing and wireless communications on the edge for healthcare service robots (2020) Computer Communications, 149, pp. 99-106. , Jan; Breazeal, C.L., (2002) Designing Sociable Robots, , MIT press; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1480), pp. 679-704. , Feb; Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., Peters, C., Engagement in human-agent interaction: An overview (2020) Frontiers in Robotics and AI, 7, p. 92; Michalowski, M.P., Sabanovic, S., Simmons, R., A spatial model of engagement for a social robot (2006) 9th IEEE International Workshop on Advanced Motion Control. IEEE, pp. 762-767; Bohus, D., Horvitz, E., Models for multiparty engagement in openworld dialog (2009) Proc. Of the SIGDIAL 2009 Conference. ACL, pp. 225-234; Vaufreydaz, D., Johal, W., Combe, C., Starting engagement detection towards a companion robot using multimodal features (2016) Robotics and Autonomous Systems, 75, pp. 4-16; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9 (5), pp. 659-674; Ozaki, Y., Ishihara, T., Matsumura, N., Nunobiki, T., Yamada, T., Decision-making prediction for human-robot engagement between pedestrian and robot receptionist (2018) 27th IEEE Intl Symp on Robot and Human Interactive Communication. 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ACM, pp. 464-472",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115079686
"Asavanant C., Umemuro H.","57261916000;8617314100;","Personal space violation by a robot: An application of expectation violation theory in human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1181","1188",,,"10.1109/RO-MAN50785.2021.9515472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115069626&doi=10.1109%2fRO-MAN50785.2021.9515472&partnerID=40&md5=3626c23d12d01723126b3b179e46615a","Tokyo Institute of Technology, School of Engineering, Department of Industrial Engineering and Economics, Tokyo, 1528552, Japan","Asavanant, C., Tokyo Institute of Technology, School of Engineering, Department of Industrial Engineering and Economics, Tokyo, 1528552, Japan; Umemuro, H., Tokyo Institute of Technology, School of Engineering, Department of Industrial Engineering and Economics, Tokyo, 1528552, Japan","Studies of personal space between human and robot have been growing. However, little is known about the effects of personal space violation by a robot. Expectation Violation Theory (EVT) explains how people respond to personal space invasion by another person, depending on if the person is perceived as rewarding or punishing. This study aims to extend EVT to HRI and to examine the effects of personal space violation using EVT as an analytical framework. A 2 × 5 within-subject experimental design was employed. The participants were approached by two robots with different reward value (rewarding and punishing) at varying distances (threat, near, norm, slightly far, and far distances). The participants were asked to rate the robots' perceived warmth, competence, and comfort scores after each approach to evaluate the communication outcomes after the personal space violation occurred. The results suggested that approaching distances had influences on the communication outcomes. As predicted by EVT, how each distance affected the communication outcomes differ according on robot's reward value. The findings indicated that EVT was partially successful in predicting the effects of personal space violations depending on the reward value and approaching distance of a robot. This study has provided a partial support that EVT can be applied to HRI and contributed to understanding the effects of personal space violation by a robot. When considering personal space in HRI, perceived reward value of the robot plays an important role as it can influence the effects of the personal space violation greatly. © 2021 IEEE.",,"Agricultural robots; Personal space invasion; Personal spaces; Social robots",,,,,,,,,,,,,,,,"Hall, E.T., (1966) The Hidden Dimension, , Garden City, N. Y.: Doubleday; Hayduk, L.A., Personal space: Where we now stand (1983) Psychol. Bull., 94 (2), pp. 293-335; Walters, M.L., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005) 5th IEEE-RAS International Conference on Humanoid Robots, pp. 450-455; Syrdal, D.S., Dautenhahn, K., Woods, S., Walters, M.L., Koay, K.L., 'doing the right thing wrong': Personality and tolerance to uncomfortable robot approaches (2006) The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 183-188; Walters, M.L., Syrdal, D.S., Koay, K.L., Dautenhahn, K., Te Boekhorst, R., Human approach distances to a mechanical-looking robot with different robot voice styles (2008) Proceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2008, pp. 707-712; Lauckner, M., Kobiela, F., Manzey, D., 'hey robot, please step back!': Exploration of a spatial threshold of comfort for humanmechanoid spatial interaction in a hallway scenario (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 780-787; Rossi, S., Staffa, M., Bove, L., Capasso, R., Ercolano, G., User's personality and activity influence on hri comfortable distances (2017) Social Robotics, pp. 167-177. , 10652 November, A. Kheddar, E. Yoshida, S. S. Ge, K. Suzuki, J.-J. Cabibihan, F. Eyssel, and H. He, Eds. Cham: Springer International Publishing; Burgoon, J.K., Jones, S.B., Toward a theory of personal space expectations and their violations (1976) Hum. Commun. Res., 2 (2), pp. 131-146; Burgoon, J.K., A communication model of personal space violations: Explication and an initial test (1978) Hum. Commun. Res., 4 (2), pp. 129-142; Burgoon, J.K., Hale, J.L., Nonverbal expectancy violations: Model elaboration and application to immediacy behaviors (1988) Commun. Monogr., 55 (1), pp. 58-79; Burgoon, J.K., Interpersonal expectations, expectancy violations, and emotional communication (1993) J. Lang. Soc. Psychol., 12, pp. 30-48; Lannutti, P.J., Laliker, M., Hale, J.L., Violations of expectations and social-sexual communication in student/professor interactions (2001) Commun. Educ., 50 (1), pp. 69-82; Campo, S., Cameron, K.A., Brossard, D., Frazer, M.S., Social norms and expectancy violation theories: Assessing the effectiveness of health communication campaigns (2004) Commun. Monogr., 71 (4), pp. 448-470; McLaughlin, C., Vitak, J., Norm evolution and violation on facebook (2012) New Media Soc., 14 (2), pp. 299-315; Burgoon, J.K., Application of expectancy violations theory to communication with and judgments about embodied agents during a decision-making task (2016) Int. J. Hum. Comput. Stud., 91, pp. 24-36; Leichtmann, B., Nitsch, V., How much distance do humans keep toward robots? Literature review, meta-analysis, and theoretical considerations on personal space in human-robot interaction (2020) J. Environ. Psychol., 68, p. 101386. , November 2019; Sardar, A., Joosse, M., Weiss, A., Evers, V., Don't stand so close to me: Users' attitudinal and behavioral responses to personal space invasion by robots (2012) HRI'12-Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 229-230; (2021) LOVOT Powered by Love, , https://lovot.life/en/, GROOVE X. [Accessed: 08-Jan-2021]; (2020) SoftBank Robotics, , https://www.softbankrobotics.com/emea/en/pepper, [Accessed: 08-Jan-2021]; Nicholls, S.B., Rice, R.E., A dual-identity model of responses to deviance in online groups: Integrating social identity theory and expectancy violations theory (2017) Commun. Theory, 27 (3), pp. 243-268; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas) (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 254-262",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115069626
"Hannibal G., Weiss A., Charisi V.","56512012200;23089739100;56766259400;","""The robot may not notice my discomfort""- Examining the experience of vulnerability for trust in human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"704","711",,,"10.1109/RO-MAN50785.2021.9515513","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115062842&doi=10.1109%2fRO-MAN50785.2021.9515513&partnerID=40&md5=1aa6620f7b38ece3857672daeb5edbe0","TU Wien, Institute of Visual Computing and Human-Centered Technology, Austria; EU Commission, Joint Research Center (JRC)","Hannibal, G., TU Wien, Institute of Visual Computing and Human-Centered Technology, Austria; Weiss, A., TU Wien, Institute of Visual Computing and Human-Centered Technology, Austria; Charisi, V., EU Commission, Joint Research Center (JRC)","Ensuring trust in human-robot interaction (HRI) is considered essential for widespread use of robots in society and everyday life. While the majority of studies use game-based and high-risk scenarios with low familiarity to gain a deeper understanding of human trust in robots, scenarios with more subtle trust violations that could happen in everyday life situations are less often considered. In this paper, we present a theory-driven approach to studying the situated trust in HRI by focusing on the experience of vulnerability. Focusing on vulnerability not only challenges previous work on trust in HRI from a theoretical perspective, but is also useful for guiding empirical investigations. As a first proof-of-concept study, we conducted an interactive online survey that demonstrates that it is possible to measure human experience of vulnerability in the ordinary, mundane, and familiar situation of clothes shopping. We conclude that the inclusion of subtle trust violation scenarios occurring in the everyday life situation of clothes shopping enables a better understanding of situated trust in HRI, which is of special importance when considering more near-future applications of robots. © 2021 IEEE.",,"Agricultural robots; Clothes; Man machine systems; Risk perception; Empirical investigation; Future applications; Game-Based; Human robot Interaction (HRI); Online surveys; Proof of concept; Risk scenarios; Theory driven approaches; Social robots",,,,,"E056-05; Technische Universitat Wien; European Commission, EC","This work has been supported by the Trust Robot Doctoral College (E056-05) at TU Wien and the Human Behaviour and Machine Intelligence (HUMAINT) project at the EU Commission.",,,,,,,,,,"Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors: The Journal of the Human Factors and Ergonomics Society, 46 (1), pp. 50-80; Hoffman, R.R., Cognition, M., Lee, J.D., Madison, W., Trust in automation (2013) IEEE Intelligent Systems, 28 (1), pp. 84-88; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations of Trusted Autonomy (Studies in Systems, Decision and Control, 177, pp. 135-159. , H. 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(2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3701-3708. , 10; Zörner, S., Arts, E., Vasiljevic, B., Srivastava, A., Schmalzl, F., Mir, G., Bhatia, K., Wermter, S., An immersive investment game to study human-robot trust (2021) Frontiers in Robotics and AI, 8, pp. 1-16. , June; Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., Paiva, A., Just follow the suit! trust in human-robot interactions during card game playing (2016) 25th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), 8, pp. 507-512. , New York, USA: IEEE; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction (CHII), pp. 141-148. , Portland, USA: ACM; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., Holthaus, P., Evaluating people's perceptions of trust in a robot in a repeated interactions study (2020) 12th International Conference on Social Robotics. Golden (CO), 11, pp. 453-465. , USA: Springer, Cham; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Towards safe and trustworthy social robots: Ethical challenges and practical issues (2015) 7th International Conference on Social Robotics (ICSR). Lecture Notes in Artificial Intelligence, 9388, pp. 584-593. , A. Tapus, E. André, J. Martin, F. Ferland, and M. Ammi, Eds. Paris, France: Springer, Cham; Luhmann, N., Familiarity, confidence, trust: Problems and alternatives (1988) Trust: Making and Breaking Cooperative Relations, pp. 94-107. , D. Gambetta, Ed. Oxford, UK: Basil Blackwell",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115062842
"Obo T., Hase R., Takizawa K., Masuda R.","35243395400;57219555158;57297859900;57297652000;","Effect of human-robot interaction for safety driving on driver's self-efficacy",2021,"World Automation Congress Proceedings","2021-August",,,"137","141",,,"10.23919/WAC50355.2021.9559556","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117252977&doi=10.23919%2fWAC50355.2021.9559556&partnerID=40&md5=f14bb7c81b383fd025e85698e5b2b71f","Tokyo Polytechnic University, Graduate School of Engineering, Kanagawa, Atsugi, 243-0297, Japan; Tokyo Polytechnic University, Department of Applied Computer Science, Kanagawa, Atsugi, 243-0297, Japan","Obo, T., Tokyo Polytechnic University, Graduate School of Engineering, Kanagawa, Atsugi, 243-0297, Japan; Hase, R., Tokyo Polytechnic University, Graduate School of Engineering, Kanagawa, Atsugi, 243-0297, Japan; Takizawa, K., Tokyo Polytechnic University, Graduate School of Engineering, Kanagawa, Atsugi, 243-0297, Japan; Masuda, R., Tokyo Polytechnic University, Department of Applied Computer Science, Kanagawa, Atsugi, 243-0297, Japan","This paper presents a robot communication system for promoting driver's motivation toward safe driving. Various navigation and control systems for safety driving have been proposed in related works. However, these don't lead to raise drivers' awareness of traffic safety. Without the feedback of own driving performance, it can be difficult for drivers to build self-confidence to achieve safe driving. In this study, we used a communication robot as a guide to navigate and score the level of safety driving. Moreover, we proposed a communication model to prevent a lack of self-efficacy to achieve safety driving, based on the theory of self-serving bias. To verify the effect of the proposed approach, we conducted some experiments and questionnaire investigation to discuss the applicability for motivating safety driving. From the experimental results, we found that the effect on driver's self-efficacy varies with not only the scoring result but also the frequency of car use in daily life and driving experience. © 2021 TSI Enterprises.","Communication robot; Human-robot interaction; Self-efficacy; Self-serving bias","Automobile drivers; Man machine systems; Motivation; Raise driving; Communication robot; Communications systems; Humans-robot interactions; Navigation and control; Related works; Robot communication; Safe driving; Self efficacy; Self-serving bias; Traffic safety; Human robot interaction",,,,,,,,,,,,,,,,"Cabinet Office, White Paper on Traffic Safety in Japan 2020 2020; (2016) Challenges in Realizing A Super Smart Society Supported by the IoT, Big Data, and Artificial Intelligence-Japan As A Global Frontrunner-, , Ministry of Education, Culture, Sports, Science and Technology White Paper on Science and Technology 2016; Haramak, T., Nishino, H., An improved safe driving training system based on learning of driving behaviors (2019) Proceedings of 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW); Von Sawitzky, T., Increasing trust in fully automated driving: Route indication on an augmented reality head-up display (2019) Proceedings of the 8th ACM International Symposium on Pervasive Displays; Lee, S.C., Kim, Y.W., Ji, Y.G., Effects of visual complexity of invehicle information display: Age-related differences in visual search task in the driving context (2019) Applied Ergonomics, 81. , November; Park, H.S., Park, M.W., Won, K.H., Kim, K.-H., Jung, S.K., In vehicle arhud system to provide drivingsafety information (2013) ETRI Journal, 35 (6); Tanaka, T., Fujikake, K., Yonekawa, T., Yamagishi, M., Inagami, M., Kinoshita, F., Aoki, H., Kanamori, H., Driver agent for encouraging safe driving behavior for the elderly (2017) Proceedings of the 5th International Conference on Human Agent Interaction (HAI '17), , Association for Computing Machinery; Obo, T., Human-robot communication based on self-serving bias for daily exercise support (2017) International Journal of Artificial Life Research, 7 (1), pp. 24-37; Obo, T., Kasuya, C., Sun, S., Kubota, N., Human-robot interaction based on cognitive bias to increase motivation for daily exercise (2017) Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017); Bandura, A., Self-efficacy mechanism in human agency (1982) American Psychologist, 37, pp. 122-147; Maslow, A.H., A theory of human motivation (1943) Originally Published, Psychological Review, 50, pp. 370-396; Miller, D.T., Ross, M., Self-serving biases in the attribution of causality: Fact or fiction? (1975) Psychological Bulletin, 82, pp. 213-225",,,,"IEEE Computer Society","2021 World Automation Congress, WAC 2021","1 August 2021 through 5 August 2021",,172461,21544824,9781685241117,,,"English","World Autom. Congress Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85117252977
"Du Q., Zhang T., Yang G., Chen C.-Y., Wang W., Zhang C.","57268716500;57268859400;7405752170;55597280800;35241350900;56428199200;","A Review of Powered Backdrivability of Robot Actuators for Human-Robot Interaction",2021,"Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021",,,,"1115","1120",,,"10.1109/ICIEA51954.2021.9516160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115441877&doi=10.1109%2fICIEA51954.2021.9516160&partnerID=40&md5=d43dbbafd8aaa8fa8e176f9c72026084","Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; University of Chinese Academy of Sciences, Beijing, China","Du, Q., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Zhang, T., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Yang, G., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Chen, C.-Y., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Wang, W., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Zhang, C., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China","To achieve effective and safe Human-Robot Interaction (HRI), much attention is paid on the backdrivability of robot actuators. According to the realization approaches, the backdrivability of actuators are classified into two categories, i.e., powered and unpowered backdrivabilities. This paper focuses on the powered backdrivability realized through controlling actuator reversely based on the sensor feedback. A thorough literature review is conducted to obtain an in-depth understanding about the research advances on powered backdrivability of actuators. The definition, classification and evaluation methods of the actuator backdrivability are discussed firstly. Powered backdrivabilities with force sensors and encoders are reviewed. The research direction of powered backdrivabilities is proposed based on the discussion. © 2021 IEEE.","human-robot interaction; powered backdrivability; robot actuator","Human robot interaction; Man machine systems; Backdrivability; Classifieds; Humans-robot interactions; In-depth understanding; Literature reviews; Powered backdrivability; Realization approaches; Robot actuators; Safe human-robot interaction; Sensor feedback; Actuators",,,,,"National Natural Science Foundation of China, NSFC: 2018B10058, 92048201; National Key Research and Development Program of China, NKRDPC: 2018YFB1308900","This work was supported in part by National Key R&D Program of China (Grant No.2018YFB1308900), in part by the National Natural Science Foundation of China (Grant No.92048201) and in part by the Major Special Projects of the Plan “Science and Technology Innovation 2025” in Ningbo (Grant No.2018B10058)",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schaffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Robla-Gomez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, PP (99), p. 1; Kaminaga, H., Odanaka, K., Ando, Y., Otsuki, S., Nakamura, Y., Evaluations on contribution of backdrivability and force measurement performance on force sensitivity of actuators Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference On, Conference Proceedings; Xuan, W.L., Zhou, L.L., Li, Y.B., Chai, H., Wang, H.Y., (2017) Controller Design and Experimental Validation Ofa Robot Joint with Active Compliance, Ser. 2017 Chinese Automation Congress, , New York: Ieee; Tadele, T.S., Vries T, D., Stramigioli, S., The safety of domestic robotics: A survey of various safety-related publications (2014) IEEE Robotics & Automation Magazine, 21 (3), pp. 134-142; Kaminaga, H., Amari, T., Katayama, Y., Ono, J., Shimoyama, Y., Nakamura, Y., Backdrivability analysis of electro-hydrostatic actuator and series dissipative actuation model 2010 IEEE International Conference on Robotics and Automation. IEEE, Conference Proceedings, pp. 4204-4211; Wang, A., Kim, S., (2015) Directional Efficiency in Geared Transmissions: Characterization of Backdrivability towards Improved Proprioceptive Control; Ishida, T., Takanishi, A., A robot actuator development with high backdrivability 2006 IEEE Conference on Robotics, Automation and Mechatronics, Conference Proceedings, pp. 1-6; Yoo, S., Lee, W., Chung, W.K., Intrinsically backdrivable hydraulic servovalve for interactive robot control 2017 IEEE International Conference on Robotics and Automation (ICRA), Conference Proceedings, pp. 51-57; Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2016) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 613-624; Yue, S.Y., Huang, T.H., Wang, D.P., Lynn, B., Sayd, D., Silivanov, V., Park, Y.S., Su, H., Design and control of a high-torque and highly backdrivable hybrid soft exoskeleton for knee injury prevention during squatting (2019) Ieee Robotics and Automation Letters, 4 (4), pp. 4579-4586; Wang, J., Li, X., Huang, T., Yu, S., Li, Y., Chen, T., Carriero, A., Su, H., Comfort-centered design of a lightweight and backdrivable knee exoskeleton (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 4265-4272; Kim, H.M., Yang, S.U., Choi, Y.S., Mun, H.M., Park, C.M., Choi, H.R., (2015) Design OfBack-drivable Joint Mechanism for In-pipe Robot, Ser. IEEE International Conference on Intelligent Robots and Systems, pp. 3779-3784; Yu, S.Y., Huang, T.H., Yang, X.L., Jiao, C.H., Yang, J.F., Chen, Y., Yi, J.G., Su, H., Quasi-direct drive actuation for a lightweight hip exoskeleton with high backdrivability and high bandwidth (2020) Ieee-Asme Transactions on Mechatronics, 25 (4), pp. 1794-1802; Kaminaga, H., Otsuki, S., Nakamura, Y., Development of high-power and backdrivable linear electro-hydrostatic actuator 2014 IEEE-RAS International Conference on Humanoid Robots, Conference Proceedings, pp. 973-978; Heo, U., Kim, S.J., Kim, J., Backdrivable and fully-portable pneumatic back support exoskeleton for lifting assistance (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 2047-2053; Veronneau, C., Bigue, J.P.L., Plante, J.S., Desbiens, A.L.J.I.R., A high-bandwidth back-drivable hydrostatic power distribution system for exoskeletons based on magneto-rheological clutches (2018) IEEE Robotics Automation Letters, PP (99), p. 1; Kaminaga, H., Amari, T., Niwa, Y., Nakamura, Y., Development of knee power assist using backdrivable electro-hydrostatic actuator IEEE/RSJ International Conference on Intelligent Robots & Systems, Conference Proceedings; Goo, A., Laubscher, C.A., Farris, R.J., Sawicki, J.T., Design and evaluation of a pediatric lower-limb exoskeleton joint actuator (2020) Actuators, 9 (4); Wensing, P.M., Wang, A., Seok, S., Otten, D., Lang, J., Kim, S., Proprioceptive actuator design in the mit cheetah: Impact mitigation and high-bandwidth physical interaction for dynamic legged robots (2017) IEEE Transactions on Robotics, 33 (3), pp. 509-522; Kawai, Y., Yokokura, Y., Ohishi, K., Miyazaki, T., Forward-back and back-forward drivable controls for human-robot interaction 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC), Conference Proceedings; Kazerooni, H., Dynamics and control of instrumented harmonic drives (1995) J Dyn Syst Meas Control, 117, pp. 15-19; Sanchez Manchola, M.D., Arciniegas Maya, L., Munera, M., Ci-Fuentes, C., (2019) Impedance-based Backdrivability Recovery Ofa Lower-limb Exoskeleton for Knee Rehabilitation; Yusuke, K., Yuki, Y., Kiyoshi, O., Pattawan, B.-W., Analysis and realization of robot actuator based on bidirectional drivability matrix (2017) Ifac Papersonline; Kawai, Y., Yokokura, Y., Ohishi, K., Saito, K., Shimamoto, A., High back-drivable pseudo i-pd torque control using load-side torque observer with torsion torque sensor 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC). IEEE, Conference Proceedings, pp. 167-172; Kapsalyamov, A., Hussain, S., Sharipov, A., Jamwal, P., Brain-computer interface and assist-as-needed model for upper limb robotic arm (2019) Advances in Mechanical Engineering, 11 (9), p. 9; Kawai, Y., Yokokura, Y., Ohishi, K., Miyazaki, T., Analysis of estimation performance of load-side torque and load-side velocity observers for human interaction control based on torsion torque control 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Conference Proceedings, pp. 652-657; Kawai, Y., Yokokura, Y., Ohishi, K., Miyazaki, T.J.I.J.O.I.A., (2019) Smooth Human Interaction Control Using Torsion Torque Controller and Motor-side Normalization Compensator Focusing on Back-forward Drivability, 8 (2), pp. 322-333; Kawai, Y., Nagao, S., Yokokura, Y., Ohishi, K., Miyazaki, T., Quick torsion torque control based on model error compensator and disturbance observer with torsion torque sensor 2021 IEEE/SICE International Symposium on System Integration (SII), Conference Proceedings, pp. 511-516; Yamada, S., Fujimoto, H., Precise external torque estimation for two-inertia system considering modeling errors 2018 Annual American Control Conference (ACC), Conference Proceedings, pp. 5238-5243; Katsura, S., Matsumoto, Y., Ohnishi, K.J.I.T.O.I.E., (2007) Modeling of Force Sensing and Validation of Disturbance Observer for Force Control, 54 (1), pp. 530-538; Xu, S., Yokoyama, M., Shimono, T., Back-drivability improvement of geared system based on disturbance observer and load-side disturbance observer (2020) IEEJ Journal of Industry Applications, 9 (5), p. 475485; Nagano, K., Shimono, T., Fujimoto, Y., Backdrivability improvement method based on angular transmission error in a high reduction gear (2019) IEEJ Journal of Industry Applications, 8 (5), pp. 779-786; Zhang, H., Liu, G., Torque estimation for robotic joint with harmonic drive transmission based on position measurements (2015) Robotics, IEEE Transactions on, 31, pp. 322-330; Ismail, M.A.A., Windelberg, J., Liu, G., Simplified sensorless torque estimation method for harmonic drive based electro-mechanical actuator (2021) IEEE Robotics and Automation Letters, 6 (2), p. 835840; Yamada, S., Inukai, K., Fujimoto, H., Omata, K., Takeda, Y., Maki-Nouchi, S., Joint torque control for two-inertia system with encoders on drive and load sides 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), Conference Proceedings, pp. 396-401; Kanai, Y., Fujimoto, Y., (2018) Torque-sensorless Control for A Powered Exoskeleton Using Highly Back-drivable Actuators, Ser. IEEE Industrial Electronics Society. New York: Ieee, pp. 5116-5121; Nef, T., Lum, P., Improving backdrivability in geared rehabilitation robots (2009) Med Biol Eng Comput, 47 (4), pp. 441-447; Weiss, P., Zenker, P., Maehle, E., Feed-forward friction and inertia compensation for improving backdrivability of motors 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), Conference Proceedings, pp. 288-293; Schabowsky, C.N., Godfrey, S.B., Holley, R.J., Lum, P.S., Development and pilot testing of hexorr: Hand exoskeleton rehabilitation robot (2010) Journal OfNeuroengineering and Rehabilitation, 7, p. 16; Ugartemendia, A., Rosquete, D., Gil, J.J., Diaz, I., Borro, D., Machine learning for active gravity compensation in robotics: Application to neurological rehabilitation systems (2020) Ieee Robotics & Automation Magazine, 27 (2), pp. 78-86; Yamada, S., Fujimoto, H., (2016) Proposal of High Backdrivable Control Using Load-Side Encoder and Backlash, Ser. IEEE Industrial Electronics Society. New York: Ieee, pp. 6429-6434; Kawazawa, M., Sakaino, S., Tsuji, T., High backdrivability control based on estimation of shaft torsion using load side angle sensor IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Conference Proceedings, pp. 5457-5462; Yamada, S., Fujimoto, H.J.I.J.O.I.A., (2020) Position-based High Back-drivable Control Using Load-side Encoder and Backlash, p. 20004583; Yamada, S., Fujimoto, H., Human-machine interactive control for geared mechatronic systems by using load-side encoder Proc. The 3rd IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON), Conference Proceedings",,,,"Institute of Electrical and Electronics Engineers Inc.","16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021","1 August 2021 through 4 August 2021",,171511,,9781665422482,,,"English","Proc. IEEE Conf. Ind. Electron. Appl. ICIEA",Conference Paper,"Final","",Scopus,2-s2.0-85115441877
"Zakia U., Menon C.","57190259490;23989261600;","Toward Long-Term FMG Model-Based Estimation of Applied Hand Force in Dynamic Motion during Human-Robot Interactions",2021,"IEEE Transactions on Human-Machine Systems","51","4","9475880","310","323",,,"10.1109/THMS.2021.3087902","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111661338&doi=10.1109%2fTHMS.2021.3087902&partnerID=40&md5=ba38032238dfae22ef8d4435e95434bd","Faculty of Applied Science, Menrva Research Group, School of Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada; Biomedical and Mobile Health Technology Laboratory, Eth Zurich, Zürich, 8092, Switzerland; Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada","Zakia, U., Faculty of Applied Science, Menrva Research Group, School of Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada; Menon, C., Biomedical and Mobile Health Technology Laboratory, Eth Zurich, Zürich, 8092, Switzerland, Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada","Physical human-robot interaction (pHRI) is reliant on human actions and can be addressed by studying human upper-limb motions during interactions. Use of force myography (FMG) signals, which detect muscle contractions, can be useful in developing machine learning algorithms as controls. In this paper, a novel long-term calibrated FMG-based trained model is presented to estimate applied force in dynamic motion during real-time interactions between a human and a linear robot. The proposed FMG-based pHRI framework was investigated in new, unseen, real-time scenarios for the first time. Initially, a long-term reference dataset (multiple source distributions) of upper-limb FMG data was generated as five participants interacted with the robot applying force in five different dynamic motions. Ten other participants interacted with the robot in two intended motions to evaluate the out-of-distribution (OOD) target data (new, unlearned), which was different than the population data. Two practical scenarios were considered for assessment: i) a participant applied force in a new, unlearned motion (scenario 1), and ii) a new, unlearned participant applied force in an intended motion (scenario 2). In each scenario, few long-term FMG-based models were trained using a baseline dataset [reference dataset (scenario 1, 2) and/or a learnt participant dataset (scenario 1)] and a calibration dataset (collected during evaluation). Real-time evaluation showed that the proposed long-term calibrated FMG-based models (LCFMG) could achieve estimation accuracies of 80%-94% in all scenarios. These results are useful towards integrating and generalizing human activity data in a robot control scheme by avoiding extensive HRI training phase in regular applications. © 2013 IEEE.","Admittance control; calibration data; force myography (FMG); generalized zero-shot learning (GZSL); human-robot interaction (HRI); long-term calibrated FMG (LCFMG) model; multiple source adaptation; out-of-distribution (OOD) samples","Educational robots; Learning algorithms; Machine learning; Man machine systems; Muscle; Population statistics; Human activities; Model-based estimation; Muscle contractions; Physical humanrobot interaction (phri); Population data; Real time evaluation; Real time interactions; Upper limb motion; Human robot interaction",,,,,"Canadian Institutes of Health Research, CIHR; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs","Manuscript received August 5, 2020; revised January 18, 2021 and March 19, 2021; accepted May 23, 2021. Date of publication July 7, 2021; date of current version July 19, 2021. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by the Canadian Institutes of Health Research (CIHR), and in part by the Canada Research Chair (CRC) program. This article was recommended by Associate Editor B. Hu. (Corresponding author: Umme Zakia.) Umme Zakia is with the Menrva Research Group, School of Engineering Science, Faculty of Applied Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada (e-mail: umme_zakia@sfu.ca).",,,,,,,,,,"Xiao, Z.G., Menon, C., Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities (2014) J. Neuroeng. Rehabil., 11 (2), pp. 1-13. , Jan; Oskoei, M.A., Hu, H., Myoelectric control systems-A survey (2007) Biomed. Signal Process. Control, 2 (4), pp. 275-294. , Oct; Duan, F., Dai, L., Chang, W., Chen, Z., Zhu, C., Li, W., SEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform (2016) IEEE Trans. Ind. Electron., 63 (3), pp. 1923-1934. , Mar; Allard, U.C., A convolutional neural network for robotic arm guidance using sEMG based frequency-features (2016) Proc. IEEE/RSJ Int. Conf. IROS, pp. 2464-2470; Meattini, R., Benatti, S., Scarcia, U., De Gregorio, D., Benini, L., Melchiorri, C., An sEMG-based human-robot interface for robotic hands using machine learning and synergies (2018) IEEE Trans. Compon., Pack. Manuf. Tech., 8 (7), pp. 1149-1158. , Jul; Radmand, A., High-density forcemyography: A possible alternative for upper-limb prosthetic control (2016) J. Rehab. Res. Develop., 53 (4), pp. 443-456; Godiyal, A.K., Verma, H.K., Khanna, N., Joshi, D., Aforcemyographybased system for gait event detection in overground and ramp walking (2018) IEEE Trans. Inst. Measure, 67 (10), pp. 2314-2323. , Oct; Godiyal, A.K., Mondal, M., Joshi, S.D., Joshi, D., Force myography based novel strategy for locomotion classification (2018) IEEE Trans. Hum.-Mach. Syst., 48 (6), pp. 648-657. , Dec; Xiao, Z.G., Menon, C., A review of force myography research and development (2019) Sensors, 19 (20). , Oct; Belyea, A., Englehart, K., Scheme, E., FMG versus EMG: A comparison of usability for real-time motion recognition based control (2019) IEEE Trans. Biomed. Eng., 66 (11), pp. 3098-3104. , Nov; Islam, M.R.U., A comparative study of motion detection with FMG and sEMGmethods for assistive applications (2020) J. Rehab. Assist. Tech. Eng., , Dec; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2014) IEEE Trans. Mechatronics, 19 (3), pp. 1007-1014. , Jun; Gaglio, S., Re, G.L., Morana, M., Human activity recognition process using 3D posture data (2015) IEEE Trans. Hum.-Mach. 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Soc., pp. 1-4; Prahm, C., Counteracting electrode shifts in upper-limb prosthesis control via transfer learning (2019) IEEE Trans. Neural Syst. Rehabil. Eng., 27 (5), pp. 956-962. , May; Du, Y., Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation (2017) Sensors, 17; Prahm, C., Transfer learning for rapid Re-calibration of a myoelectric prosthesis after electrode shift (2017) Converging Clinicical and Engineering Research on Neurorehabilitation II, 15. , https://doi.org/10.1007/978-3-319-46669-9_28, New York, NY, USA: Springer; Côté-Allard, U., Unsupervised domain adversarial self-calibration for electromyography-based gesture recognition (2020) IEEE Access, 8, pp. 177941-177955; Field, A., (2017) Discovering Statistics Using IBM SPSS, , 5th ed., Newbury Park, CA, USA: Sage; Côté-Allard, U., (2020) Virtual Reality to Study the Gap between Offline and Real-time EMG-based Gesture Recognition, , Dec","Zakia, U.; Faculty of Applied Science, Canada; email: umme_zakia@sfu.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85111661338
"Lopes J.M., Figueiredo J., Pinheiro C., Reis L.P., Santos C.P.","57208665200;57195993391;57208665955;13907511600;57211929847;","Biomechanical Assessment of Adapting Trajectory and Human-Robot Interaction Stiffness in Impedance-Controlled Ankle Orthosis",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","102","4","76","","",,,"10.1007/s10846-021-01423-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109701139&doi=10.1007%2fs10846-021-01423-0&partnerID=40&md5=54c6f871f0d2ba121c9ecd2dba635dee","Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Artificial Intelligence and Computer Science Laboratory (LIACC), University of Porto, Porto, 4200-465, Portugal","Lopes, J.M., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Figueiredo, J., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Pinheiro, C., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Reis, L.P., Artificial Intelligence and Computer Science Laboratory (LIACC), University of Porto, Porto, 4200-465, Portugal; Santos, C.P., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal","Gait disabilities empowered intensive research on the field of human-robot interaction to promote effective gait rehabilitation. Assist-as-needed strategies are becoming prominent, appealing to the users’ participation in their rehabilitation therapy. This study proposes and assesses the biomechanical effects of an adaptive impedance control strategy that innovatively allows adaptability in interaction-based stiffness and gait trajectory towards a fully assist-as-needed therapy. By modulating the interaction-based stiffness per gait phase, we hypothesize that the strategy appeals to a symbiotic human-orthotic cooperation, augmenting the user’s muscular activity. The interaction stiffness was estimated by modelling the human-orthosis interaction torque vs angle curve with a linear regression model. The strategy also allows for real-time trajectory adaptations at different gait phases to fulfil the users’ needs. The biomechanical assessment of the impedance-controlled ankle orthosis involved eight healthy volunteers walking at 1.0 and 1.6 km/h. The results revealed a stronger muscular activation regarding the non-assisted leg for the gastrocnemius lateralis (increment ratio ≥ 1.0 for both gait speeds) and for the tibialis anterior muscle (increment ratio ≥ 1.0 for 1.6 km/h). The strategy guided users successfully on a healthy gait pattern while allowing deviations (median error < 5.0°) given the users’ intention weighted by interaction stiffness. Findings showed the relevance for adapting gait trajectory as users prefer higher trajectories as the speed increases. No significant temporal variations or neither knee angular compensations were observed (p value ≥0.11). Overall results support that this strategy may be applied for intensity-adapted gait training, allowing different human-robot compliant levels. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Adaptive assistive strategies; Human-orthosis interaction; Impedance control; Locomotion and actuation systems; Robotic rehabilitation","Agricultural robots; Biomechanics; Educational robots; Man machine systems; Regression analysis; Stiffness; Trajectories; Biomechanical assessment; Biomechanical effects; Impedance control strategy; Linear regression models; Muscular activation; Muscular activities; Real-time trajectories; Rehabilitation therapy; Human robot interaction",,,,,"NORTE-01-0145-FEDER-030386; Fundação para a Ciência e a Tecnologia, FCT: UIDB/04436/2020, UIDP/04436/2020","This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the SmartOs project under Grant NORTE-01-0145-FEDER-030386, and under the national support to R&D units grant through the reference project UIDB/04436/2020 and UIDP/04436/2020.",,,,,,,,,,"Mikolajczyk, T., Ciobanu, I., Badea, D.I., Iliescu, A., Pizzamiglio, S., Schauer, T., Seel, T., Berteanu, M., Advanced technology for gait rehabilitation: an overview (2018) Adv. 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ICARSC, 2020, pp. 16-21","Lopes, J.M.; Centre for MicroElectroMechanical Systems (CMEMS), Portugal; email: a74982@alunos.uminho.pt",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85109701139
"Spatola N., Wudarczyk O.A.","57193889944;57253733800;","Implicit Attitudes Towards Robots Predict Explicit Attitudes, Semantic Distance Between Robots and Humans, Anthropomorphism, and Prosocial Behavior: From Attitudes to Human–Robot Interaction",2021,"International Journal of Social Robotics","13","5",,"1149","1159",,5,"10.1007/s12369-020-00701-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108882852&doi=10.1007%2fs12369-020-00701-5&partnerID=40&md5=e0f761658f15992869f51a96a229fba1","Center for Human Technologies, Istituto Italiano di Tecnologia, Via Morego, 30, Genoa, 16163, Italy; Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany","Spatola, N., Center for Human Technologies, Istituto Italiano di Tecnologia, Via Morego, 30, Genoa, 16163, Italy; Wudarczyk, O.A., Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany","How people behave towards others relies, to a large extent, on the prior attitudes that they hold towards them. In Human–Robot Interactions, individual attitudes towards robots have mostly been investigated via explicit reports that can be biased by various conscious processes. In the present study, we introduce an implicit measure of attitudes towards robots. The task utilizes the measure of semantic priming to evaluate whether participants consider humans and robots as similar or different. Our results demonstrate a link between implicit semantic distance between humans and robots and explicit attitudes towards robots, explicit semantic distance between robots and humans, perceived robot anthropomorphism, and pro/anti-social behavior towards a robot in a real life, interactive scenario. Specifically, attenuated semantic distance between humans and robots in the implicit task predicted more positive explicit attitudes towards robots, attenuated explicit semantic distance between humans and robots, attribution of an anthropomorphic characteristic, and consequently a future prosocial behavior towards a robot. Crucially, the implicit measure of attitudes towards robots (implicit semantic distance) was a better predictor of a future behavior towards the robot than explicit measure of attitudes towards robots (self-reported attitudes). Cumulatively, the current results emphasize a new approach to measure implicit attitudes towards robots, and offer a starting point for further investigations of implicit processing of robots. © 2020, The Author(s).","Anthropomorphism; Human–robot interaction; Implicit attitude; Prosocial behaviour; Semantic priming","Agricultural robots; Semantics; Social robots; Conscious process; Explicit semantics; Implicit attitudes; Implicit measures; Implicit semantics; Robot interactions; Semantic distance; Semantic primings; Anthropomorphic robots",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390523135, EXC 2002/1; Istituto Italiano di Tecnologia, IIT","Open access funding provided by Istituto Italiano di Tecnologia within the CRUI-CARE Agreement. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2002/1 “Science of Intelligence” - project number 390523135.",,,,,,,,,,"Ajzen, I., Fishbein, M., Attitude-behavior relations: a theoretical analysis and review of empirical research (1977) Psychol Bull, 84, pp. 888-918; MacDorman, K.F., Vasudevan, S.K., Ho, C.C., Does Japan really have robot mania? 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J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85108882852
"Raptopoulou A., Komnidis A., Bamidis P.D., Astaras A.","57223639683;36537172500;6603398831;6507977235;","Human–robot interaction for social skill development in children with ASD: A literature review",2021,"Healthcare Technology Letters","8","4",,"90","96",,,"10.1049/htl2.12013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105923742&doi=10.1049%2fhtl2.12013&partnerID=40&md5=7f80782c2da06775dea6eb4cb132c160","Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Computer Science, American College of Thessaloniki, Thessaloniki, Greece","Raptopoulou, A., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Komnidis, A., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Bamidis, P.D., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Astaras, A., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece, Computer Science, American College of Thessaloniki, Thessaloniki, Greece","Human–robot interaction has been demonstrated to be a promising methodology for developing socio-communicational skills of children and adolescents with autism spectrum disorder (ASD). This paper systematically reviews studies that report experimental results on this topic published in scientific journals between the years 2010 and2018. A total of 1805 articles from various literature were filtered based on relevance and transparency. In the first set of criteria, article titles are screened and in the second both titles and abstracts. The final number of articles which were subsequently thoroughly reviewed was 32 (N = 32). The findings suggest that there are benefits in using human–robot interaction to assist with the development of social skills for children with ASD. Specifically, it was found that the majority of studies used humanoid robots, 64% relied on a small number of participants and sessions, while few of the studies included a control group or follow-up sessions. Based on these findings, this paper tried to identify areas that have not been extensively addressed to propose several directions for future improvements for studies in this field, such as control groups with typical developmental children, minimum number of sessions and participants, as well as standardization of criteria for assessing the level of functionality for ASD children. © 2021 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology",,"Anthropomorphic robots; Economic and social effects; Autism spectrum disorders; Children and adolescents; Control groups; Future improvements; Humanoid robot; Literature reviews; Robot interactions; Scientific journals; Social robots",,,,,"FWF-P12945","T. Ungar is grateful to the Hungarian Science Foundations for OTKA T029701 and FKFP 0116/1997. Thanks are also due to J. Gubicza and P. Hanák for their kind assistance in the x-ray diffraction work. M. Zehetbauer thanks the Austrian Science Foundation for financial support under project FWF-P12945.",,,,,,,,,,"Connor, M., Children on the autistic spectrum: guidelines for mainstream practice (1999) Support Learn, 14 (2), pp. 80-886; Kirkovski, M., A review of the role of female gender in autism spectrum disorders (2013) J. Autism Dev. Disord., 43 (11), pp. 2584-2603; Frith, U., Autism: Explaining the Enigma, (2015), p. 249. , 2nd ed., p., Blackwell,, Malden, MA; Dominick, K.C., (2007) Atypical behaviors in children with autism and children with a history of language impairment, 28 (2), pp. 145-162. , Res. Dev. Disabilities; Mundy, P., Defining the social deficits of autism: the contribution of non-verbal communication measures (1986) J. Child Psychol. Psychiatry, 27 (5), pp. 657-669; Gibson, J., Social communication disorder outside autism? A diagnostic classification approach to delineating pragmatic language impairment, high functioning autism and specific language impairment (2013) J. Child Psychol. Psychiatry, 54 (11), pp. 1186-1197; Frith, C., Role of facial expressions in social interactions (2009) Philos. Trans. R. Soc. B: Biol. Sci., 364 (1535), pp. 3453-3458; Rogers, S.J., Ozonoff, S., Annotation: what do we know about sensory dysfunction in autism? A critical review of the empirical evidence (2005) J Child Psychol. Psychiatry, 46 (12), pp. 1255-1268; McConnell, S.R., Interventions to facilitate social interaction for young children with autism: review of available research and recommendations for educational intervention and future research (2002) J. Autism and Dev. Disord., 32 (5), pp. 351-372; Owens, G., LEGO ® therapy and the social use of language programme: an evaluation of two social skills interventions for children with high functioning autism and asperger syndrome (2008) J. Autism Dev. Disord., 38 (10), pp. 1944-1957; Giullian, N., Detailed requirements for robots in autism therapy 2010 IEEE International Conference on Systems, Man and Cybernetics – SMC, , Istanbul, Turkey, 10–13 Oct. 2010; Warren, Z.E., Can robotic interaction improve joint attention skills? (2015) J. Autism Dev. Disord., 45 (11), pp. 3726-3734; Shamsuddin, S., Humanoid robot NAO interacting with autistic children of moderately impaired intelligence to augment communication skills (2012) Proc. Eng., 41, pp. 1533-1538; Shamsuddin, S., Initial response in HRI: a case study on evaluation of child with autism spectrum disorders interacting with a (2012) Hum. Robot NAO. Procedia Eng., 41, pp. 1448-1455; Valadão, C.T., Analysis of the use of a robot to improve social skills in children with autism spectrum disorder (2016) Res. Biomed. Eng., 32 (2), pp. 161-175; Shimaya, J., Advantages of indirect conversation via a desktop humanoid robot: case study on daily life guidance for adolescents with autism spectrum disorders (2016) 2016 25th IEEE International Symposium onRobot and Human Interactive Communication (RO-MAN), , New York, NY, USA, 26-31 Aug; Hinds, P.J., Whose job is it anyway? a study of human-robot interaction in a collaborative task (2004) Hum.-Comput. Interact., 19 (1), pp. 151-181; Tapus, A., Children with autism social engagement in interaction with Nao, an imitative robot: a series of single case experiments (2012) Interact. Stud., 13 (3), pp. 315-347; Wainer, J., The effectiveness of using a robotics class to foster collaboration among groups of children with autism in an exploratory study (2010) Pers. Ubiquitous Comput., 14 (5), pp. 445-455; Robins, B., Dautenhahn, K., Tactile interactions with a humanoid robot: novel play scenario implementations with children with autism (2014) Int. J. Soc. Rob., 6 (3), pp. 397-415; Jordan, K., Feasibility of using a humanoid robot for enhancing attention and social skills in adolescents with autism spectrum disorder (2013) Int. J. Rehabil. Res., 36 (3), pp. 221-227; Costescu, C.A., Reversal learning task in children with autism spectrum disorder: a robot-based approach (2015) J. Autism Dev. Disord., 45 (11), pp. 3715-3725; Boccanfuso, L., A low-cost socially assistive robot and robot-assisted intervention for children with autism spectrum disorder: field trials and lessons learned (2017) Auton. R., 41 (3), pp. 637-655; Kim, E.S., Social robots as embedded reinforcers of social behavior in children with autism (2013) J. Autism Dev. Disord, 43 (5), pp. 1038-1049; Van Straten, C.L., Effects of robots’ intonation and bodily appearance on robot-mediated communicative treatment outcomes for children with autism spectrum disorder (2017) Pers. Ubiquitous Comp.; Mengoni, S.E., Feasibility study of a randomised controlled trial to investigate the effectiveness of using a humanoid robot to improve the social skills of children with autism spectrum disorder (Kaspar RCT): a study protocol (2017) BMJ Open, 7 (6); Huskens, B., Improving collaborative play between children with autism spectrum disorders and their siblings: the effectiveness of a robot-mediated intervention based on Lego(R) therapy (2015) J. Autism Dev. Disord., 45 (11), pp. 3746-3755; Kaboski, J.R., Brief report: a pilot summer robotics camp to reduce social anxiety and improve social/vocational skills in adolescents with ASD (2015) J. Autism Dev. Disord., 45 (12), pp. 3862-3869; Warren, Z., Brief report: development of a robotic intervention platform for young children with ASD (2015) J. Autism Dev. Disord., 45 (12), pp. 3870-3876; Srinivasan, S.M., A comparison of the effects of rhythm and robotic interventions on repetitive behaviors and affective states of children with autism spectrum disorder (ASD) (2015) Res. Autism Spectr. Disord., 18, pp. 51-63; Bekele, E., Pilot clinical application of an adaptive robotic system for young children with autism (2014) Autism, 18 (5), pp. 598-608; Wainer, J., A pilot study with a novel setup for collaborative play of the humanoid robot KASPAR with children with autism (2014) Int. J. Soc. Rob., 6 (1), pp. 45-65; Pop, C.A., Enhancing play skills, engagement and social skills in a play task in ASD children by using robot-based interventions. A pilot study (2014) Interact. Stud., 15 (2), pp. 292-320; Simut, R.E., Children with autism spectrum disorders make a fruit salad with probo, the social robot: an interaction study (2016) J. Autism Dev. Disord., 46 (1), pp. 113-126; Giannopulu, I., Minimalistic toy robot to analyze a scenery of speaker-listener condition in autism (2016) Cogn. Process., 17 (2), pp. 195-203; Anzalone, S.M., Quantifying patterns of joint attention during human-robot interactions: an application for autism spectrum disorder assessment (2018) Pattern Recog. Lett.; Saadatzi, M.N., Small-group technology-assisted instruction: virtual teacher and robot peer for individuals with autism spectrum disorder (2018) J. Autism Dev. Disord., 48, pp. 3816-3830; Taheri, A., Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates (2018) Int. J. Soc. Rob., 10 (1), pp. 93-113; Pour, A.G., Human–robot facial expression reciprocal interaction platform: case studies on children with autism (2018) Int. J. Soc. Rob., 10 (2), pp. 179-198; So, W.-C., Robot-based intervention may reduce delay in the production of intransitive gestures in Chinese-speaking preschoolers with autism spectrum disorder (2018) Mol. Autism, 9 (1). , 34; Di Nuovo, A., Deep learning systems for estimating visual attention in robot-assisted therapy of children with autism and intellectual disability (2018) Robotics, 7 (2), p. 25; Desideri, L., Using a humanoid robot as a complement to interventions for children with autism spectrum disorder: a pilot study (2018) Adv. Neurodev. Disord., 2 (3), pp. 273-285; Feng, Y., Jia, Q., Wei, W., A control architecture of robot-assisted intervention for children with autism spectrum disorders (2018) J. Rob., 2018; David, D.O., Developing joint attention for children with autism in robot-enhanced therapy (2018) Int. J. Soc. Rob., 10 (5), pp. 595-605; Yun, S.-S., Social skills training for children with autism spectrum disorder using a robotic behavioral intervention system (2017) Autism Res., 10 (7), pp. 1306-1323; Huskens, B., Promoting question-asking in school-aged children with autism spectrum disorders: effectiveness of a robot intervention compared to a human-trainer intervention (2013) Dev. Neurorehab., 16 (5), pp. 345-356; Barakova, E.I., Long-term LEGO therapy with humanoid robot for children with ASD (2015) Expert Syst., 32 (6), pp. 698-709; Boccanfuso, L., O 'Kane, J.M., CHARLIE: an adaptive robot design with hand and face tracking for use in autism therapy (2011) Int. J. Soc. Rob., 3 (4), pp. 337-347","Bamidis, P.D.; Lab of Medical Physics, Greece; email: bamidis@med.auth.gr",,,"John Wiley and Sons Inc",,,,,20533713,,,,"English","Healthc. Technol. Lett.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105923742
"Lachner J., Allmendinger F., Hobert E., Hogan N., Stramigioli S.","57212339974;54790643800;57223371231;35572189800;35572162100;","Energy budgets for coordinate invariant robot control in physical human–robot interaction",2021,"International Journal of Robotics Research","40","8-9",,"968","985",,,"10.1177/02783649211011639","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105721487&doi=10.1177%2f02783649211011639&partnerID=40&md5=d03ff4b3586678987f9181694fd517b5","Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands; KUKA Deutschland GmbH, Zugspitzstraße 140, Augsburg, Germany; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States; International Laboratory of Biomechatronics and Energy-efficient Robotics, ITMO University, St. Petersburg, Russian Federation","Lachner, J., Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands, KUKA Deutschland GmbH, Zugspitzstraße 140, Augsburg, Germany; Allmendinger, F., KUKA Deutschland GmbH, Zugspitzstraße 140, Augsburg, Germany; Hobert, E., Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands; Hogan, N., Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States; Stramigioli, S., Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands, International Laboratory of Biomechatronics and Energy-efficient Robotics, ITMO University, St. Petersburg, Russian Federation","In this work we consider the current certification process of applications with physical human–robot interaction (pHRI). Two major hazards are collisions and clamping scenarios. The implementation of safety measures in pHRI applications typically depends strongly on coordinates, e.g., to monitor the robot velocity or to predict external forces. We show that the current certification process does not, in general, guarantee a safe robot behavior. In particular, in unstructured environments it is not possible to predict all risks in advance. We therefore propose to control the energy of the robot, which is a coordinate invariant entity. For an impedance controlled robot, the total energy consists of potential energy and kinetic energy. The energy flow from task description to physical interaction follows a strict causality. We assign a safe energy budget for the robot. With this energy budget, the presented controller auto-tunes its parameters to limit the exchanged kinetic energy during a collision and the potential energy during clamping scenarios. In contact, the robot behaves compliantly and therefore eliminates clamping danger. After contact, the robot automatically continues to follow the desired trajectory. With this approach the number of safety-related parameters to be determined can be reduced to one energy value, which has the potential to significantly speed up the commissioning of pHRI applications. The proposed technique is validated by experiments. © The Author(s) 2021.","coordinate invariance; energy-aware control; impedance control; Physical human–robot interaction","Agricultural robots; Budget control; Kinetic energy; Kinetics; Molecular physics; Potential energy; Certification process; Desired trajectories; Physical interactions; Robot controls; Robot interactions; Safety measures; Task description; Unstructured environments; Human robot interaction",,,,,"688188; Massachusetts Institute of Technology, MIT; Centers for Mechanical Engineering Research and Education, Massachusetts Institute of Technology","This work was partially funded by the MURAB project (grant agreement number 688188). N. Hogan was supported in part by the Centers for Mechanical Engineering Research and Education at MIT and SUSTech.","We would like to thank KUKA Systems GmbH for providing us the measurement equipment. A special thanks goes to Florian Groschup and Nicolas Brueckmann. This work was partially funded by the MURAB project (grant agreement number 688188). N. Hogan was supported in part by the Centers for Mechanical Engineering Research and Education at MIT and SUSTech.",,,,,,,,,"Albu-Schaffer, A., Ott, C., Frese, U., Hirzinger, G., (2003) Cartesian impedance control of redundant robots: Recent results with the DLR-light-weight-arms, pp. 3704-3709. , IEEE International Conference on Robotics and Automation, In; Beetz, M., Bartels, G., Albu-Schaffer, A., (2015) Robotic agents capable of natural and safe physical interaction with human co-workers, pp. 6528-6535. , 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),. 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In; Raiola, G., Cardenas, C.A., Tadele, T.S., de Vries, T., Stramigioli, S., Development of a safety- and energy-aware impedance controller for collaborative robots (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 1237-1244; Rosenstrauch, M.J., Kruger, J., (2017) Safe human–robot collaboration - introduction and experiment using ISO/TS 15066, pp. 740-744. , 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), In; Rozo, L., Calinon, S., Caldwell, D., Jimenez, P., Torras, C., (2013) Learning collaborative impedance-based robot behaviors, 27 (1). , Proceedings of the AAAI Conference on Artificial Intelligence, In; Sadrfaridpour, B., Wang, Y., Collaborative assembly in hybrid manufacturing cells: An integrated framework for human–“robot interaction (2018) IEEE Transactions on Automation Science and Engineering, 15 (3), pp. 1178-1192; Schaal, S., Dynamic movement primitives - A framework for motor control in humans and humanoid robotics (2006) Adaptive Motion of Animals and Machines, pp. 261-280. , Kimura H., Tsuchiya K., Ishiguro A., Witte H., (eds), Tokyo, Springer, In:, (eds; Schindlbeck, C., Haddadin, S., (2015) Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks, pp. 440-447. , 2015 IEEE International Conference on Robotics and Automation (ICRA), In; Shahriari, E., Johannsmeier, L., Jensen, E., Haddadin, S., Power flow regulation, adaptation, and learning for intrinsically robust virtual energy tanks (2020) IEEE Robotics and Automation Letters, 5 (1), pp. 211-218; Shahriari, E., Kramberger, A., Gams, A., Ude, A., Haddadin, S., (2017) Adapting to contacts: Energy tanks and task energy for passivity-based dynamic movement primitives, pp. 136-142. , 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), In; Siciliano, B., Khatib, O., (2008) Springer Handbook of Robotics, , Berlin, Springer Science+Business Media, (eds; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics: Modelling, Planning and Control, , London, Springer; Stramigioli, S., (2001) Modeling and IPC Control of Interactive Mechanical Systems - A Coordinate-Free Approach (Lecture Notes in Control and Information Sciences, 266. , London, Springer; Stramigioli, S., (2015) Energy-Aware Robotics (Mathematical Control Theory I, 461. , Cham, Springer, (ed; Tadele, T.S., de Vries, T.J.A., Stramigioli, S., (2014) Combining energy and power based safety metrics in controller design for domestic robots, pp. 1209-1214. , 2014 IEEE International Conference on Robotics and Automation (ICRA), In; Takegaki, M., Arimoto, S., A new feedback method for dynamic control of manipulators (1981) Journal of Dynamic Systems, Measurement, and Control, 103 (2), pp. 119-125; Wassink, M., Stramigioli, S., (2007) Towards a novel safety norm for domestic robotics, pp. 3354-3359. , IEEE/RSJ International Conference on Intelligent Robots and Systems, In","Lachner, J.; Faculty of Electrical Engineering, Netherlands; email: j.lachner@utwente.nl",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85105721487
"Shi D., Zhang W., Zhang W., Ju L., Ding X.","56978601000;24832573100;55805950200;57222658160;7401929247;","Human-centred adaptive control of lower limb rehabilitation robot based on human–robot interaction dynamic model",2021,"Mechanism and Machine Theory","162",,"104340","","",,3,"10.1016/j.mechmachtheory.2021.104340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103613947&doi=10.1016%2fj.mechmachtheory.2021.104340&partnerID=40&md5=c017cab6d1719d051bc26487116fcac4","School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China","Shi, D., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Zhang, W., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China; Zhang, W., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Ju, L., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Ding, X., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China","A rehabilitation robot can effectively alleviate the shortcomings and deficiencies of traditional treatments. The early stages of rehabilitation training use passive control based on a predefined trajectory. The goal of passive control is to control the motion of the robot, rather than the real motion of the human. However, the human and robot are connected by bands; thus, errors exist between them, and these errors affect the outcome of the rehabilitation training. This study proposes human-centred adaptive control of a lower limb rehabilitation robot based on a dynamic model of human–robot interactions. An equivalent spring model in three-dimensional space is proposed for expressing the interaction torque between the human and robot, reflecting the torque exerted by the robot on the human. A dynamic model for the human–robot system is established based on the human–robot interaction model. To overcome the uncertainty regarding the parameters of the human limbs and robots and that of the stiffness coefficient in the interaction force model, an adaptive controller is designed, and the stability of the system is analysed. The simulation results indicate the effectiveness of the controller. © 2021","Adaptive stepping control; Dynamical modelling; Human-robot interaction; Rehabilitation robot","Adaptive control systems; Controllers; Dynamic models; Neuromuscular rehabilitation; System stability; Adaptive Control; Adaptive stepping control; Dynamical model; Dynamics modelling; Humans-robot interactions; Interaction dynamics; Lower limbs rehabilitation robots; Passive control; Rehabilitation robot; Rehabilitation training; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 91748201, 91848104; National Key Research and Development Program of China, NKRDPC: 2016YFE0105000","This work was supported by the National Key R&D Program of China (Grant number 2016YFE0105000) and the National Natural Science Foundation of China (Grant number 91848104 and 91748201 ). The authors thank Jiao Chen, Zhitao Ling, Xianzhong Li, and Liduan Wang for their contributions to this study.",,,,,,,,,,"Brief report on stroke prevention and treatment in China, 2019 (in Chinese) (2020) Chin. J. Cerebrovasc. Dis., 17, pp. 272-281; Shi, D., Zhang, W., Zhang, W., Ding, X., A review on lower limb rehabilitation exoskeleton robots (2019) Chin. J. Mech. Eng., 32, pp. 74-84; Banala, S.K., Kim, S.H., Agrawal, S.K., Scholz, J.P., Robot assisted gait training with active leg exoskeleton (ALEX) (2009) IEEE Trans. Neural Syst. Rehabil. Eng., 17, pp. 2-8; van Kammen, K., Boonstra, A.M., van der Woude, L.H., Reinders-Messelink, H.A., den Otter, R., The combined effects of guidance force, bodyweight support and gait speed on muscle activity during able-bodied walking in the Lokomat (2016) Clin. 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Robot., 12; Zhang, W., Zhang, W., Shi, D., Ding, X., Design of hip joint assistant asymmetric parallel mechanism and optimization of singularity-free workspace (2018) Mech. Mach. Theory, 122, pp. 389-403; Lenzi, T., Carrozza, M.C., Agrawal, S.K., Powered hip exoskeletons can reduce the user's hip and ankle muscle activations during walking (2013) IEEE Trans. Neural Syst. Rehabil. Eng., 21, pp. 938-948; Nascimento, B.G.D., Vimieiro, C.B.S., Nagem, D.A.P., Pinotti, M.J.A.O., Hip orthosis powered by pneumatic artificial muscle: voluntary activation in absence of myoelectrical signal (2008) Artif. Organs, 32, pp. 317-322; Giovacchini, F., Vannetti, F., Fantozzi, M., Cempini, M., Vitiello, N., A light-weight active orthosis for hip movement assistance (2014) Robot. Auton. Syst., 73, pp. 123-134; Kong, K., Bae, J., Tomizuka, M., Torque mode control of a cable-driven actuating system by sensor fusion (2013) J. Dyn. Syst. Meas. 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"Wen K., Nguyen T.S., Harton D., Laliberte T., Gosselin C.","56067011700;57221307383;57197862171;6602468451;57210720538;","A Backdrivable Kinematically Redundant (6+3)-Degree-of-Freedom Hybrid Parallel Robot for Intuitive Sensorless Physical Human-Robot Interaction",2021,"IEEE Transactions on Robotics","37","4","9306904","1222","1238",,2,"10.1109/TRO.2020.3043723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098774058&doi=10.1109%2fTRO.2020.3043723&partnerID=40&md5=9d447e730a0eb8d40e5c4237a7e43509","Department of Mechanical Engineering, Université Laval, Québec, QC, Canada","Wen, K., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Nguyen, T.S., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Harton, D., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Laliberte, T., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Gosselin, C., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada","A novel backdrivable 3-[R(RR-RRR)SR] kinematically redundant (6+3)-degree-of-freedom (DOF) spatial hybrid parallel robot with revolute actuators is proposed for low-impedance physical human-robot interaction. The kinematic model is developed based on the constraint conditions of the robot. It is shown that the type II (parallel) singularities can be completely avoided, thereby yielding a very large translational and orientational workspace. A workspace analysis is presented in order to demonstrate the capabilities of the robot. Mechanisms are then introduced to use the redundant DOF of the robot to operate a gripper with the robot actuators, which are mounted on or close to the base, thus reducing the inertia of the moving parts. The architecture of the robot makes it possible to use direct drive motors, thereby making the robot easily backdrivable and allowing the use of a very simple and effective controller. A prototype of the robot is then designed and built and the large workspace of the robot as well as the effortless physical human-robot interaction are demonstrated. The controller of the robot is then described, including a position control mode and a control mode for physical interaction, which does not require the use of a force/torque sensor or joint torque sensors. Because of its backdrivability and low moving inertia, the robot is particularly well-suited for physical human-robot interaction, as demonstrated in the accompanying videos. © 2004-2012 IEEE.","Hybrid robot; kinematic redundancy; orientational workspace; parallel robot; physical human-robot interaction; remotely operated gripper; singularities","Actuators; Agricultural robots; Controllers; Degrees of freedom (mechanics); Kinematics; Position control; Robot applications; Constraint conditions; Degree of freedom (dof); Direct drive motor; Force/torque sensor; Joint torque sensors; Physical interactions; Position control modes; Workspace analysis; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs; Fonds Québécois de la Recherche sur la Nature et les Technologies, FQRNT; Fonds de recherche du Québec – Nature et technologies, FRQNT","Mr. Harton received postgraduate scholarships from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds québécois de la recherche sur la nature et les technologies (FRQNT).","Manuscript received July 7, 2020; revised October 15, 2020; accepted November 6, 2020. Date of publication December 24, 2020; date of current version August 5, 2021. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by the Canada Research Chair program, and in part by the Fonds Québécois de la Recherche sur la Nature et les Technologies (FRQNT). This article has supplementary downloadable material available at https://doi.org/10.1109/TFUZZ.2019.2933787, provided by the authors. This paper was recommended for publication by Associate Editor Q. Li and Editor E. Yoshida upon evaluation of the reviewers’ comments. (Corresponding author: Clément Gosselin.) The authors are with the Department of Mechanical Engineering, Univer-sité Laval, Québec, QC G1V 0A6, Canada (e-mail: kefei.wen.1@ulaval.ca; tan-sy.nguyen.1@ulaval.ca; david.harton.1@ulaval.ca; thierry@gmc.ulaval.ca; gosselin@gmc.ulaval.ca).",,,,,,,,,"Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput.-Integr. Manuf., 40, pp. 1-13; Gealy, D.V., Quasi-direct drive for low-cost compliant robotic manipulation (2019) Proc. IEEE Int. Conf.Robot.Autom., pp. 437-443. , May; Pratt, G.A., Williamson, M.M., Dillworth, P., Pratt, J., Wright, A., Stiffness isn't everything (1997) Experimental Robotics IV. Berlin, Germany: Springer, pp. 253-262; Albu-Schäffer, A., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Ind. Robot: An Int. J., 34 (5), pp. 376-385; Eppinger, S.D., Three dynamic problems in robot force control (1992) IEEE Trans. Robot. 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Conf., pp. 199-215; Isaksson, M., Gosselin, C., Marlow, K., An introduction to utilising the redundancy of a kinematically redundant parallel manipulator to operate a gripper (2016) Mechanism Mach. Theory, 101, pp. 50-59; Fauteux, P., Lauria, M., Heintz, B., Michaud, F., Dual-differential rheological actuator for high-performance physical robotic interaction (2010) IEEE Trans. Robot., 26 (4), pp. 607-618. , Aug; Anderson, R.J., Spong, M.W., Hybrid impedance control of robotic manipulators (1988) IEEE J. Robot. Autom., 4 (5), pp. 549-556. , Oct","Gosselin, C.; Department of Mechanical Engineering, Canada; email: gosselin@gmc.ulaval.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85098774058
"Leichtmann B., Nitsch V.","57191978459;25929508000;","Is the Social Desirability Effect in Human–Robot Interaction overestimated? A Conceptual Replication Study Indicates Less Robust Effects",2021,"International Journal of Social Robotics","13","5",,"1013","1031",,,"10.1007/s12369-020-00688-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090435803&doi=10.1007%2fs12369-020-00688-z&partnerID=40&md5=aee9ef253f9335aa8578299b7ca172b7","Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany","Leichtmann, B., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany; Nitsch, V., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany","The “Computers are social actors” (CASA) assumption (Nass and Moon in J Soc Issues 56:81–103, 2000. https://doi.org/10.1111/0022-4537.00153) states that humans apply social norms and expectations to technical devices. One such norm is to distort one’s own response in a socially desirable direction during interviews. However, findings for such an effect are mixed in the literature. Therefore, a new study on the effect of social desirability bias in human–robot evaluation was conducted, aiming for a conceptual replication of previous findings. In a between-subject laboratory experiment, N= 107 participants had to evaluate the robot and the interaction quality after a short conversation in three different groups: In one group, the evaluation was conducted using (1) the same robot of the former interaction, (2) a different robot, (3) a tablet computer. According to the CASA assumption, it was expected, that evaluations on likability and quality of interaction, are higher in the condition with the same robot conducting the evaluation, compared to a different robot or a tablet computer because robots are treated as social actors and hence humans distort ratings in a socially desirable direction. Based on previous findings, we expected robots to evoke higher anthropomorphism and feelings of social presence compared to the tablet computer as potential explanation. However, the data did not support the hypotheses. Low sample size, low statistical power, lack of measurement validation and other problems that could lead to an overestimation of effect sizes—in this study and the literature in general—are discussed in light of the replicability crisis. © 2020, The Author(s).","Anthropomorphism; Media equation; Replicability crisis; Social desirability bias; Social presence; Social robotics","Agricultural robots; Quality control; Social robots; Computers are social actors; Interaction quality; Laboratory experiments; Quality of interaction; Replication study; Robot interactions; Social desirability; Statistical power; Economic and social effects",,,,,,"This research was funded by the Bundeswehr University Munich. 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J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85090435803
"Gao Q., Liu J., Ju Z.","57214809236;7410106559;25655056800;","Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction",2021,"Expert Systems","38","5","e12490","","",,7,"10.1111/exsy.12490","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078064988&doi=10.1111%2fexsy.12490&partnerID=40&md5=68a33aadaf893294e532de07da9c540d","State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; University of Chinese Academy of Sciences, Beijing, China; School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Gao, Q., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, University of Chinese Academy of Sciences, Beijing, China; Liu, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; Ju, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Hand gesture recognition plays an important role in human–robot interaction. The accuracy and reliability of hand gesture recognition are the keys to gesture-based human–robot interaction tasks. To solve this problem, a method based on multimodal data fusion and multiscale parallel convolutional neural network (CNN) is proposed in this paper to improve the accuracy and reliability of hand gesture recognition. First of all, data fusion is conducted on the sEMG signal, the RGB image, and the depth image of hand gestures. Then, the fused images are generated to two different scale images by downsampling, which are respectively input into two subnetworks of the parallel CNN to obtain two hand gesture recognition results. After that, hand gesture recognition results of the parallel CNN are combined to obtain the final hand gesture recognition result. Finally, experiments are carried out on a self-made database containing 10 common hand gestures, which verify the effectiveness and superiority of the proposed method for hand gesture recognition. In addition, the proposed method is applied to a seven-degree-of-freedom bionic manipulator to achieve robotic manipulation with hand gestures. © 2020 John Wiley & Sons, Ltd.","hand gesture recognition; multimodal data fusion; parallel CNN; sEMG signal","Convolution; Convolutional neural networks; Data fusion; Degrees of freedom (mechanics); Manipulators; Palmprint recognition; Social robots; Degree of freedom; Fused images; Hand-gesture recognition; Multimodal data fusion; parallel CNN; Robot interactions; Robotic manipulation; Semg signals; Gesture recognition",,,,,"Seventh Framework Programme, FP7; National Natural Science Foundation of China, NSFC: 51575412, 51775541; Chinese Academy of Sciences, CAS: JCTD‐2018‐11; Seventh Framework Programme, FP7: 611391; National Key Research and Development Program of China, NKRDPC: 2018YFB1304600","National Key R&D Program of China, Grant 2018YFB1304600; Natural Science Foundation of China, Grants 51775541 and 51575412; CAS Interdisciplinary Innovation Team, Grant JCTD‐2018‐11; the EU Seventh Framework Programme (FP7)‐ICT, Grant 611391.","National Key R&D Program of China, Grant 2018YFB1304600; Natural Science Foundation of China, Grants 51775541 and 51575412; CAS Interdisciplinary Innovation Team, Grant JCTD-2018-11; the EU Seventh Framework Programme (FP7)-ICT, Grant 611391.",,,,,,,,,"Benalcázar, M.E., Jaramillo, A.G., Zea, A., Páez, A., Andaluz, V.H., (2017) Hand gesture recognition using machine learning and the myo armband, pp. 1040-1044. , 2017 25th European Signal Processing Conference (EUSIPCO), IEEE; Boyali, A., Hashimoto, N., Matsumoto, O., (2015) Hand posture and gesture recognition using MYO armband and spectral collaborative representation based classification, pp. 200-201. , 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), IEEE; Chen, C., Jafari, R., Kehtarnavaz, N., (2015) Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor, pp. 168-172. , 2015 IEEE International conference on image processing (ICIP), IEEE; 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Smith, A.V.W., Sutherland, A.I., Lemoine, A., Mcgrath, S., (2000) Hand gesture recognition system and method, , US 128,003; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826","Liu, J.; State Key Laboratory of Robotics, China; email: liujinguo@sia.cn",,,"John Wiley and Sons Inc",,,,,02664720,,EXSYE,,"English","Expert Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85078064988
"Wagner A.R.","7401456914;","Robot-Guided Evacuation as a Paradigm for Human-Robot Interaction Research",2021,"Frontiers in Robotics and AI","8",,"701938","","",,,"10.3389/frobt.2021.701938","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111589215&doi=10.3389%2ffrobt.2021.701938&partnerID=40&md5=5ee1bc1dc0d424bc8c04673280088cd4","Robot Ethics and Aerial Vehicles Laboratory, Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA, United States","Wagner, A.R., Robot Ethics and Aerial Vehicles Laboratory, Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA, United States","This paper conceptualizes the problem of emergency evacuation as a paradigm for investigating human-robot interaction. We argue that emergency evacuation offers unique and important perspectives on human-robot interaction while also demanding close attention to the ethical ramifications of the technologies developed. We present a series of approaches for developing emergency evacuation robots and detail several essential design considerations. This paper concludes with a discussion of the ethical implications of emergency evacuation robots and a roadmap for their development, implementation, and evaluation. © Copyright © 2021 Wagner.","emergency robotics; evacuation; human-robot interaction; human-robot trust; robot ethics",,,,,,"National Science Foundation, NSF: CNS-1830390, IIS-2045146","This material is based upon work partially supported by the National Science Foundation under Grant No. CNS-1830390 and No. IIS-2045146.",,,,,,,,,,"Averill, J.D., Peacock, R.D., Kuligowski, E.D., Analysis of the Evacuation of the World Trade Center Towers on September 11, 2001 (2013) Fire Technol, 49, pp. 37-63; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Sabanovic, S., (2020) Human-Robot Interaction: An Introduction, , Cambridge, United Kingdom, Cambridge University Press; Boukas, E., Kostavelis, I., Gasteratos, A., Sirakoulis, G., Robot Guided Crowd Evacuation (2015) IEEE Trans. Autonmation Sci. 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Syst, 21 (4), pp. 18-21; Nayyar, M., Wagner, A.R., Effective Robot Evacuation Strategies in Emergencies (2019) Proceedings of the 28th International Conference on Robot Human Interactive Communication (RO-MAN), pp. 1-6. , IEEE; Nayyar, M., Zoloty, Z., McFarland, C., Wagner, A.R., Exploring the Effect of Explanations during Robot-Guided Emergency Evacuation (2020) Proceedings of the 2020 International Conference on Social Robotics (ICSR), pp. 13-22. , Springer; Nomura, T., Kanda, T., Kidokoro, H., Suehiro, Y., Yamada, S., Why Do Children Abuse Robots? (2016) Is, 17 (3), pp. 347-369; Park, H.W., Gelsomini, M., Lee, J.J., Breazeal, C., Telling Stories to Robots: The Effect of Backchanneling on a Child's Storytelling (2017) Proceedings of the 2017 International Conference on Human-Robot Interaction (HRI), pp. 100-108. , IEEE; Proulx, G., Playing for Free—Understanding Human Behavior in Burning Buildings (2003) Ashrae J, 45 (7), pp. 33-35; Robinette, P., Howard, A., Emergency Evacuation Robot Design (2011) ANS EPRRSD-13th Robotics Remote Syst. Hazard. Environments 11th Emerg. Preparedness Response; Robinette, P., Howard, A.M., Trust in Emergency Evacuation Robots (2012) Proceedings of the 2012 International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1-6. , IEEE; Robinette, P., Howard, A.M., Wagner, A.R., Effect of Robot Performance on Human-Robot Trust in Time-Critical Situations (2017) IEEE Trans. Human-mach. Syst, 47 (4), pp. 425-436; Robinette, P., Howard, A.M., Wagner, A.R., Timing Is Key for Robot Trust Repair (2015) Proceedings of the 2015 International Conference on Social Robotics (ICSR), pp. 574-583. , IEEE; Robinette, P., Vela, P.A., Howard, A.M., Information Propagation Applied to Robot-Assisted Evacuation (2012) Proceedings of the 2012 International Conference on Robotics and Automation (ICRA), pp. 856-861. , IEEE; Robinette, P., Wagner, A.R., Howard, A.M., Assessment of Robot Guidance Modalities Conveying Instructions to Humans in Emergency Situations (2014) Proceedings of the 23rd International Conference on Robot Human Interactive Communication (RO-MAN), pp. 1043-1049. , IEEE; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of Robots in Emergency Evacuation Scenarios (2016) Proceedings of the 2016 International Conference on Human-Robot Interaction (HRI), pp. 101-108. , a, IEEE; Robinette, P., Wagner, A.R., Howard, A.M., Assessment of Robot to Human Instruction Conveyance Modalities across Virtual, Remote and Physical Robot Presence (2016) Proceedings of the 25th International Conference on Robot Human Interaction Communication (RO-MAN), pp. 1044-1050. , b, IEEE; Robinette, P., Wagner, A.R., Howard, A.M., Investigating Human-Robot Trust in Emergency Scenarios: Methodological Lessons Learned (2016) Robust Intelligence and Trust in Autonomous Systems, pp. 143-166. , c; Sakour, I., Hu, H., Robot-assisted Crowd Evacuation under Emergency Situations: A Survey (2017) Robotics, 6 (2), p. 8; Song, X., Shibasaki, R., Yuan, N., Xie, X., Li, T., Adachi, R., Deepmob: Learning Deep Knowledge of Human Emergency Behavior and Mobility from Big and Heterogeneous Data (2017) ACM Trans. Inf. Syst. (Tois), 35 (4), p. 41; Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R., Prediction of Human Emergency Behaviorand Their Mobility Following Large-Scale Disaster (2014) Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 5-14. , New York, NY, ACM; Tang, B., Jiang, C., He, H., Guo, Y., Human Mobility Modeling for Robot-Assisted Evacuation in Complex Indoor Environments (2016) IEEE Trans. Human-mach. Syst, 46 (5), pp. 694-707; Verdiere, N., Lanza, V., Charrier, R., Provitolo, D., Dubos-Paillard, E., Bertelle, C., Mathematical Modeling of Human Behaviors during Catastrophic Events (2014) International Conference on Complex Systems and Applications, pp. 67-74. , Le Havre, France; Wagner, A.R., Robinette, P., Howard, A., Modeling the Human-Robot Trust Phenomenon (2018) ACM Trans. Interact. Intell. Syst, 8 (4), pp. 1-24; Winerman, L., Fighting Fire with Psychology (2004) Monitor on Psychology, 35 (8); Zachiotis, G.A., Andrikopoulos, G., Gornez, R., Nakamura, K., Nikolakopoulos, G., A Survey on the Application Trends of home Service Robotics (2018) Proceedings of 2018 International Conference on Robotics and Biominmetics (ROBIO), pp. 1999-2006. , IEEE; Zhang, S., Guo, Y., Distributed Multi-Robot Evacuation Incorporating Human Behavior (2015) Asian J. Control, 17 (1), pp. 34-44","Wagner, A.R.; Robot Ethics and Aerial Vehicles Laboratory, United States; email: alan.r.wagner@psu.edu",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85111589215
"Yousaf S.N., Esmatloo P., Ghonasgi K., Deshpande A.D.","57212314873;57195415034;57190752341;7202168327;","A method for the analysis of physical human-robot interaction",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"1249","1254",,,"10.1109/AIM46487.2021.9517358","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114963409&doi=10.1109%2fAIM46487.2021.9517358&partnerID=40&md5=d61d739292b0b0bcdf8272da23b1b935","The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States","Yousaf, S.N., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States; Esmatloo, P., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States; Ghonasgi, K., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States; Deshpande, A.D., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States","Physical human-robot interaction (pHRI) interfaces are responsible for ensuring safe, comfortable, and effective force transfer between wearable devices and their users. However, analysis is often oversimplified by treating the human-robot attachment as a rigid connection and using gross load measurements. As a result, information about the distribution of forces across the human-robot contact surface is lost. In this paper, we present an analysis method to predict distributed loading across a pHRI interface based on a model with discretized elastic elements that account for compliance from human soft tissue and the robot attachment. Stiffness properties of a proxy upper arm are measured with an indenter and used in the pHRI interface model. The analysis is performed assuming a rigid arm model, consistent with the underlying assumption in literature, and repeated using the proposed compliant arm model with measured elastic properties. The distributed loading predicted by the pHRI interface model is validated with measurements from a sensorized upper arm cuff on the Harmony exoskeleton. Our results reveal that a model incorporating compliance at the human-robot attachment is necessary to improve prediction of distributed interface loads. This motivates the need for human-centered analysis which can enable finer control of interaction forces and help design more ergonomic attachment interfaces. © 2021 IEEE.",,"Elasticity; Exoskeleton (Robotics); Intelligent mechatronics; Man machine systems; Distributed interfaces; Distributed loading; Elastic properties; Interaction forces; Load measurements; Physical human-robot interactions; Physical humanrobot interaction (phri); Stiffness properties; Human robot interaction",,,,,"National Science Foundation, NSF: 1941260, 2019704, DGE-1610403","This work was funded, in part, by the National Science Foundation (NSF) grant numbers 2019704 and 1941260, as well as the NSF Graduate Research Fellowship Program (GRFP) grant number DGE-1610403.",,,,,,,,,,"Viteckova, S., Kutilek, P., Jirina, M., Wearable lower limb robotics: A review (2013) Biocybernetics and Biomedical Engineering, 33 (2), pp. 96-105; MacIejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S., A survey on robotic devices for upper limb rehabilitation (2014) Journal of Neuroengineering and Rehabilitation, 11 (1), p. 3; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art (2008) IEEE Transactions on Robotics, 24 (1), pp. 144-158; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Rocon, E., Ruiz, A., Raya, R., Schiele, A., Pons, J.L., Belda-Lois, J., Poveda, R., Moreno, J., Human-robot physical interaction (2008) Wearable Robots: Biomechatronic Exoskeletons, pp. 127-163; Biddiss, E., Chau, T., Upper-limb prosthetics: Critical factors in device abandonment (2007) American Journal of Physical Medicine & Rehabilitation, 86 (12), pp. 977-987; Pezent, E., Fani, S., Clark, J., Bianchi, M., O'Malley, M.K., Spatially separating haptic guidance from task dynamics through wearable devices (2019) IEEE Transactions on Haptics, 12 (4), pp. 581-593; Kung, P.-C., Lin, C.-C.K., Ju, M.-S., Neuro-rehabilitation robotassisted assessments of synergy patterns of forearm, elbow and shoulder joints in chronic stroke patients (2010) Clinical Biomechanics, 25 (7), pp. 647-654; Schiele, A., Ergonomics of exoskeletons: Objective performance metrics (2009) World Haptics 2009-Third Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems., pp. 103-108; Jarrassé, N., Morel, G., Connecting a human limb to an exoskeleton (2011) IEEE Transactions on Robotics, 28 (3), pp. 697-709; Schiele, A., Helm Der Van, F.C., Kinematic design to improve ergonomics in human machine interaction (2006) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14 (4), pp. 456-469; Accoto, D., Sergi, F., Tagliamonte, N.L., Carpino, G., Sudano, A., Guglielmelli, E., Robomorphism: A nonanthropomorphic wearable robot (2014) IEEE Robotics & Automation Magazine, 21 (4), pp. 45-55; Sengeh, D.M., Herr, H., A variable-impedance prosthetic socket for a transtibial amputee designed from magnetic resonance imaging data (2013) JPO: Journal of Prosthetics and Orthotics, 25 (3), pp. 129-137; Varghese, R.J., Mukherjee, G., King, R., Keller, S., Deshpande, A.D., Designing variable stiffness profiles to optimize the physical human robot interface of hand exoskeletons (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 1101-1108; Donati, M., Vitiello, N., De Rossi, S.M.M., Lenzi, T., Crea, S., Persichetti, A., Giovacchini, F., Munih, M., A flexible sensor technology for the distributed measurement of interaction pressure (2013) Sensors, 13 (1), pp. 1021-1045; Tamez-Duque, J., Cobian-Ugalde, R., Kilicarslan, A., Venkatakrishnan, A., Soto, R., Contreras-Vidal, J.L., Real-time strap pressure sensor system for powered exoskeletons (2015) Sensors, 15 (2), pp. 4550-4563; Rathore, A., Wilcox, M., Ramirez, D.Z.M., Loureiro, R., Carlson, T., Quantifying the human-robot interaction forces between a lower limb exoskeleton and healthy users (2016) 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 586-589; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) The International Journal of Robotics Research, 36 (4), pp. 414-435; De Oliveira, A.C., Rose, C.G., Warburton, K., Ogden, E.M., Whitford, B., Lee, R.K., Deshpande, A.D., Exploring the capabilities of harmony for upper-limb stroke therapy (2019) 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 637-643; Ghonasgi, K., Yousaf, S.N., Esmatloo, P., Deshpande, A.D., A modular design for distributed measurement of human-robot interaction forces in wearable devices (2021) Sensors, 21 (4), p. 1445; Petron, A., Duval, J.-F., Herr, H., Multi-indenter device forin vivobiomechanical tissue measurement (2016) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (5), pp. 426-435; Shih, R., (2014) Introduction to Finite Element Analysis Using Solidworks Simulation 2014, , SDC publications; Ambrose, A.B., Hammond, F.L., Soft pneumatic system for interface pressure regulation and automated hands-free donning in robotic prostheses (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4848-4854; Yousaf, S.N., Joshi, V.S., Britt, J.E., Rose, C.G., O'Malley, M.K., Design and characterization of a passive instrumented hand ASME Letters in Dynamic Systems and Control, 1 (1); Jones, D.H., Kilgour, R.D., Comtois, A.S., Test-retest reliability of pressure pain threshold measurements of the upper limb and torso in young healthy women (2007) The Journal of Pain, 8 (8), pp. 650-656","Deshpande, A.D.; The University of Texas, United States; email: ashish@austin.utexas.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114963409
"Vincze D., Gacsi M., Kovacs S., Korondi P., Miklosi A., Niitsuma M.","26221966300;6603300873;22034981100;55809786600;7003466993;9332897100;","Towards the automatic observation and evaluation of ethologically inspired Human-Robot Interaction",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"586","591",,,"10.1109/AIM46487.2021.9517561","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114963347&doi=10.1109%2fAIM46487.2021.9517561&partnerID=40&md5=a2677b58f11aa331fdb44f340e09ca28","Chuo University, Department of Precision Mechanics, Tokyo, Japan; Eötvös Loránd Research Network, MTA-ELTE Comparative Ethology Res. Grp., Budapest, Hungary; University of Miskolc, Department of Information Sciences, Miskolc, Hungary; Budapest Univ. of Technology and Economics, Department of MOGI, Budapest, Hungary; Eötvös Loránd Research Network, Department of Ethology, Budapest, Hungary","Vincze, D., Chuo University, Department of Precision Mechanics, Tokyo, Japan; Gacsi, M., Eötvös Loránd Research Network, MTA-ELTE Comparative Ethology Res. Grp., Budapest, Hungary; Kovacs, S., University of Miskolc, Department of Information Sciences, Miskolc, Hungary; Korondi, P., Budapest Univ. of Technology and Economics, Department of MOGI, Budapest, Hungary; Miklosi, A., Eötvös Loránd Research Network, Department of Ethology, Budapest, Hungary; Niitsuma, M., Chuo University, Department of Precision Mechanics, Tokyo, Japan","Human-Robot Interaction (HRI) evaluation is rapidly gaining more interest as the field of social robotics advances, but determining the effectiveness of the various HRI models can be a challenging task. Recently, ethology has been successfully incorporated in HRI models, and has been suggested to be used in HRI evaluation methods also. In ethological experiments, trained observers manually identify and record the exhibited behaviours of the participating subjects, a procedure called as coding. However, precise observation and coding of the behaviour of subjects is a resource demanding process and prone to human mistakes. Therefore, the goal of the paper is twofold. First, to introduce automatical coding as a possible method to provide accurate, unbiased, objective evaluation of HRI experiments. Second, to abolish the need for manual human work in the coding process. Accordingly, we propose a possible method which is able to automatically code simple behaviour elements in HRI experiments. The task of the proposed system is to automatically evaluate the behaviour of a mobile robot by distinguishing the cardinal behaviour elements without any information on the inner states of the model controlling the mobile robot, based solely on data gathered from the environment of the interaction. Various test scenarios were evaluated, while the mobile robot agent was exhibiting pre-programmed behaviours, where the proposed system could properly recognize and code the behaviour elements shown by the mobile robot. © 2021 IEEE.","Behaviour measurement; Ethology; HRI evaluation; Human-Robot Interaction; Observation","Biology; Intelligent mechatronics; Man machine systems; Mobile robots; Coding process; Evaluation methods; Human robot Interaction (HRI); Objective evaluation; Social robotics; Test scenario; Social robots",,,,,,"Dávid Vincze is an International Research Fellow of the Japan Society for the Promotion of Science (Postdoctoral Fellowships for Research in Japan (Standard)). This work was supported by the MTA-ELTE Comparative Ethology Research Group, grant number 01 031.",,,,,,,,,,"Blumstein, D.T., Daniel, J.C., (2007) Quantifying Behavior the JWatcher Way, , Sinauer Associates, Inc. Sunderland, MA; Deni, R., Szijarto, K., Eisler, A., Fantauzzo, C., BASIC programs for observational research using the TRS-80 Model 100 portable and Model 4 computers (1983) Behavior Research Methods & Instrumentation, 15, p. 616; Durrett, H.J., Jr., Inexpensive microcomputer systems for Research and Instruction: A dream or reality? (1978) Behavior Research Methods & Instrumentation, 10, pp. 345-351; Friard, O., Gamba, M., BORIS: A free, versatile open-source eventlogging software for video/audio coding and live observations (2016) Methods in Ecology and Evolution, 7 (11), pp. 1325-1330; Gacsi, M., Topál, J., Miklósi, Á., Dóka, A., Csányi, V., Attachment behavior of adult dogs (Canis familiaris) living at rescue centers: Forming new bonds (2001) Journal of Comparative Psychology, 115 (4), pp. 423-431; Grandgeorge, M., Evaluating human-robot interaction with ethology (2020) Human-Robot Interaction. Springer Series on Bio and Neurosystems, 12; Griffin, W.A., Pyobs: Open-source software for computer-assisted behavioral observation coding (2018) Journal of Open Research Software, 6 (1), p. 5; Ichikawa, N., Niitsuma, M., Nonverbal human-robot communication for ambient assisted living applications based on ethologically inspired social behavior model (2018) Proc. IECON 2018-44th Annual Conf. Of the IEEE Industrial Electronics Society, pp. 6045-6050. , Washington, DC; Jost, C., Le Pevedic, B., Belpaeme, T., Grandgeorge, M., Evaluating human-robot interaction with ethology (2016) The 25th IEEE RO-MAN, , New York, United States; Kerepesi, A., Kubinyi, E., Jonsson, G.K., Magnusson, M.S., Miklosi, A., Behavioural comparison of human-animal (dog) and human-robot (AIBO) interactions (2006) Behav. Proc., 73 (1), pp. 92-99; Kovács, B., Szayer, G., Tajti, F., Burdelis, M., Korondi, P., A novel potential field method for path planning of mobile robots by adapting animal motion attributes (2016) Robotics and Autonomous Systems, 82, pp. 24-34; Kovacs, Sz., Vincze, D., Gacsi, M., Miklósi, Á., Korondi, P., Ethologically inspired robot behavior implementation (2011) Proc. 4th Int. Conf. On Human System Interaction (HSI 2011), pp. 64-69. , Keio University, Yokohama, Japan; Kovacs, Sz., Vincze, D., Gacsi, M., Miklósi, Á., Korondi, P., Interpolation based fuzzy automaton for Human-Robot (2009) IFAC Proceedings Volumes, 42 (16), pp. 317-322; Kovacs, Sz., Vincze, D., Gacsi, M., Miklósi, Á., Korondi, P., Interpolation based fuzzy automaton for human-robot interaction (2009) Preprints of the 9th Intl. Symp. On Robot Control (SYROCO'09), the Intl. Federation of Automatic Control (IFAC), pp. 451-456. , Nagaragawa Convention Center, Gifu, Japan, September 9-12; Lorenz, K.Z., (1981) The Foundations of Ethology, , Springer-Verlag, Vienna; (2021) Solutions for Your Research, , https://www.mangold-international.com/, product brochure; Martin, N., Oliver, C., Hall, S., ObsWin: Observational data collection and analysis for Windows (1999) CTI Psychology Software News, 9, pp. 14-16; Miklósi, Á., Korondi, P., Matellán, V., Gacsi, M., Ethorobotics: A new approach to human-robot relationship (2017) Frontiers in Psychology, 8, p. 958; Murphy, R.R., Schreckenghost, D., Survey of metrics for humanrobot interaction (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 197-198. , Tokyo, Japan; Noldus, L.P.J.J., The Observer: A software system for collection and analysis of observational data (1991) Behavior Research Methods, Instruments, & Computers, 23, pp. 415-429; Noldus, L.P.J.J., Trienes, R.J.H., Hendriksen, A.H.M., Jansen, H., Jansen, R.G., The Observer Video-Pro: New software for the collection, management, and presentation of time-structured data from videotapes and digital media files (2000) Behavior Research Methods, Instruments, & Computers, 32, pp. 197-206; (2021) Noldus Information Technology: Official Company Website, , https://www.noldus.com/; Péter, A., (2011) An Introduction to Solomon Coder, , https://solomon.andraspeter.com/SolomonIntro.pdf, presentation, 11-12 October, Milano, Italy; Piller, I., Vincze, D., Kovács, Sz., Declarative language for behaviour description (2015) Emergent Trends in Robotics and Intelligent Systems: Advances in Intelligent Systems and Computing, 316, pp. 103-112. , Springer, Cham; Piller, I., Kovács, Sz., Fuzzy behavior description language: A declarative language for interpolative behavior modeling (2019) Acta Polytechnica Hungarica, 16 (9), pp. 47-72; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human-robot social interaction outside the lab (2006) The Advanced Motion Control, 9th IEEEcongress, , Turkey; Tapp, J., Wehby, J., Ellis, D., A multiple option observation system for experimental studies: MOOSES (1995) Behavior Research Methods, Instruments, & Computers, 27, pp. 25-31; Vincze, D., Gacsi, M., Kovacs, S., Niitsuma, M., Korondi, P., Miklósi, Á., Towards the automatic observation and coding of simple behaviours in ethological experiments (2021) Proc. Of IEEE/SICE Intl. Symp. Of System Integration (SII), pp. 841-842; Vincze, D., Kovács, Sz., Niitsuma, M., Hashimoto, H., Korondi, P., Gacsi, M., Miklósi, Á., Ethologically inspired human-robot interaction interfaces (2012) Proc. Of the 2012 Joint Intl. Conf. Of Human-Centered Computer Environments, pp. 51-57",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114963347
"Huo Y., Li X., Zhang X., Sun D.","57215558671;57225158100;57259736300;56220414100;","Intention-driven variable impedance control for physical human-robot interaction",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"1220","1225",,,"10.1109/AIM46487.2021.9517438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114961221&doi=10.1109%2fAIM46487.2021.9517438&partnerID=40&md5=f7e4fab7d7b7c69fdc8b4197fd56bb3d","City University of Hong Kong, Department of Biomedical Engineering, Hong Kong; Tsinghua University, Department of Automation, China; Tsinghua Shenzhen International Graduate School, China","Huo, Y., City University of Hong Kong, Department of Biomedical Engineering, Hong Kong; Li, X., Tsinghua University, Department of Automation, China; Zhang, X., Tsinghua Shenzhen International Graduate School, China; Sun, D., City University of Hong Kong, Department of Biomedical Engineering, Hong Kong","The technology of physical human-robot interaction (pHRI) aims to combine the advantages of both humans and robots, and it is now playing a significant role in many industrial and medical applications. However, the open issues of safety and efficiency have not been systematically addressed for pHRI. In this paper, a new intention-driven variable impedance controller is proposed for compliantly-driven robots, where the robot is controlled to track the estimated human motion intention under a variable desired impedance model. The human motion intention is represented with the desired angle of the human limb and approximated with adaptive neural networks (NNs). Then, the impedance parameters are adjusted online according to the offset between the desired angle and the current joint configuration of the robot. Such a formulation allows the robot to predict human behavior for better collaboration and hence improve the safety and the efficiency of pHRI. The stability of the closed-loop system is rigorously proved with Lyapunov methods, and experiments on a lower-limb exoskeleton are conducted to evaluate the performance of the proposed controller. © 2021 IEEE.",,"Behavioral research; Closed loop systems; Compliance control; Efficiency; Exoskeleton (Robotics); Industrial robots; Intelligent mechatronics; Lyapunov methods; Man machine systems; Medical applications; Medical robotics; Adaptive neural networks; Human motion intention; Impedance parameters; Joint configuration; Physical human-robot interactions; Physical humanrobot interaction (phri); Safety and efficiencies; Variable impedance control; Social robots",,,,,"National Natural Science Foundation of China, NNSFC: 61803321","Y. Huo and D. Sun are with the Department of Biomedical Engineering, City University of Hong Kong, Hong Kong. X. Li is with the Department of Automation, Tsinghua University, China. X. Zhang is with the Tsinghua Shenzhen International Graduate School, China. This work was supported by the National Natural Science Foundation of China under Grant no. 61803321. Corresponding author: Xiang Li (xiangli@tsinghua.edu.cn).",,,,,,,,,,"Li, X., Chi, G., Vidas, S., Cheah, C.C., Human-guided robotic co-manipulation: Two illustrative scenarios (2016) IEEE Transactions on Control Systems Technology, 24 (5), pp. 1751-1763; Li, X., Pan, Y., Chen, G., Yu, H., Multi-modal control scheme for rehabilitation robotic exoskeletons (2017) The International Journal of Robotics Research, 36 (5-7), pp. 759-777; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and Stage-of-the Art (2008) IEEE Trans. Robotics, 24 (1), pp. 144-158; Kazerooni, H., Racine, J.L., Huang, L., Steger, R., On the control of the Berkeley Lower Extremity Exoskeleton (BLEEX) (2005) IEEE Int. Conf. Robotics Automat., pp. 4353-4360; Krebs, H.I., Volpe, B., Williams, D., Celestino, J., Charles, S., Lynch, D., Hogan, N., Robot-aided neurorehabilitation: A robot for wrist rehabilition (2007) IEEE Trans. Neural Syst. Rehab. Eng., 15 (3), pp. 327-335; Chowdhury, A., Raza, H., Keena, Y.K., Dutta, A., Prasad, G., An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation (2019) Journal of Neuroscience Methods, 312, pp. 1-11; Chae, Y., Jeong, J., Jo, S., Toward brain-actuated humanoid robots: Asynchronous direct control using an EEG-based BCI (2012) IEEE Transactions on Robotics, 28 (5), pp. 1131-1144; Zhang, D., Yao, L., Chen, K., Wang, S., Chang, X., Liu, Y., Making sense of spatio-temporal preserving representations for EEG-based human intention recognition (2020) IEEE Transactions on Cybernetics, 50 (7), pp. 3033-3044. , July; Kawamoto, H., Sankai, Y., Power assist method based on phase sequence and muscle force condition for HAL (2005) Advanced Robotics, 19 (7), pp. 717-734; Ho, M.A.T., Yamada, Y., Umetani, Y., An adaptive visual attentive tracker for human communicational behaviors using HMM-based TD learning with new state distinction capability (2005) IEEE Transactions on Robotics, 21 (3), pp. 497-504; Hiatt, L.M., Narber, C., Bekele, E., Khemlani, S.S., Trafton, J.G., Human modeling for human-robot collaboration (2017) The International Journal of Robotics Research, 36 (5-7), pp. 580-596; Rahman, M., Ikeura, R., Mizutani, K., Investigating the impedance characteristic of human arm for development of robots to co-operate with human operators (1999) IEEE International Conference on Systems, Man, and Cybernetics, pp. 676-681; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2014) IEEE/ASME Transactions on Mechatronics, 19 (3), pp. 1007-1014; Li, Z., Huang, B., Ajoudani, A., Yang, C., Su, C.-Y., Bicchi, A., Asymmetric bimanual control of dual-arm exoskeletons for humancooperative manipulations (2018) IEEE Transactions on Robotics, 34 (1), pp. 264-271; Van Damme, M., Vanderborght, B., Verrelst, B., Van Ham, R., Daerden, F., Lefeber, D., Proxy-based sliding mode control of a planar pneumatic manipulator (2009) The International Journal of Robotics Research, 28 (2), pp. 266-284; Yang, C., Ganesh, G., Haddadin, S., Parusel, S., Albu-Schaeffer, A., Burdet, E., Human-like adaptation of force and impedance in stable and unstable interactions (2011) IEEE Transactions on Robotics, 27 (5), pp. 918-930; Li, Y., Tee, K.P., Chan, W.L., Yan, R., Chua, Y., Limbu, D.K., Continuous role adaptation for human-robot shared control (2015) IEEE Trans. Robotics, 31 (3), pp. 672-681; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Trans. Robotics, 31 (2), pp. 233-245; Dietrich, A., Ott, C., Hierarchical impedance-based tracking control of kinematically redundant robots (2019) IEEE Transactions on Robotics; Ferraguti, F., Landi, C.T., Sabattini, L., Bonfe, M., Fantuzzi, C., Secchi, C., A variable admittance control strategy for stable physical human-robot interaction (2019) The International Journal of Robotics Research, 38 (6), pp. 747-765; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) IEEE/RSJ Int. Conf. Intelligent Robots Syst., pp. 399-406; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Transactions on Robotics, 33 (1), pp. 169-182; Arimoto, S., (1996) Control Theory of Non-Linear Mechanical Systems., , Oxford University Press; Cheah, C.C., Li, X., (2015) Task-Space Sensory Feedback Control of Robot Manipulators, , Springer; Li, X., Cheah, C.C., Adaptive neural network control of robot based on a unified objective bound (2013) IEEE Transactions on Control Systems Technology, 22 (3), pp. 1032-1043; Cheah, C.C., Wang, D., Learning impedance control for robotic manipulators (1998) IEEE Trans. Robot. Automat., 14 (3), pp. 452-465; Li, X., Liu, Y.-H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7; Li, X., Liu, Y.-H., Yu, H., Adaptive impedance control for compliantly actuated robots with a unified safety measure (2018) The World Congress on Intelligent Control and Automation, pp. 444-449","Li, X.; Tsinghua University, China; email: xiangli@tsinghua.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114961221
"Jaramillo J., Lin A., Sung E., Richter I.J.H., Petersen K.","57226723168;57226708329;57226718623;57226728565;57188685048;","Mobile, inflatable interface to support human robot interaction studies",2021,"2021 18th International Conference on Ubiquitous Robots, UR 2021",,,"9494648","320","325",,,"10.1109/UR52253.2021.9494648","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112429195&doi=10.1109%2fUR52253.2021.9494648&partnerID=40&md5=aae4d7bda682e465fea5b760aafc7dbd","Cornell Engineering, Ithaca, NY  14853, United States","Jaramillo, J., Cornell Engineering, Ithaca, NY  14853, United States; Lin, A., Cornell Engineering, Ithaca, NY  14853, United States; Sung, E., Cornell Engineering, Ithaca, NY  14853, United States; Richter, I.J.H., Cornell Engineering, Ithaca, NY  14853, United States; Petersen, K., Cornell Engineering, Ithaca, NY  14853, United States","Robots capable of engaging with individuals in large crowds can be of use in diverse scenarios such as festivals, conventions, and during building evacuation. Such situations pose a unique combination of hardware constraints involving safety for human contact, durability to withstand physical inter-action with novice users, agility to keep up with moving crowds in non-trivial terrains, versatility to adapt to the situation and audience, and low cost to permit mass deployment. Here, we present a new mobile robot platform composed of a small rover base and a soft human-scale inflatable interface, capable of visual, tactile, and audible human interaction. The inflatable interface allows the robot to maneuver discretely or in confined spaces when deflated, yet grow to encourage interaction; it combines an internal projector, a camera, speakers, and a microphone to emit and receive user information. The rover base is designed to keep up with humans at jogging speeds over relatively uneven terrain. Low weight further permits easy handling and transport. The entire robot costs less than 1.2K USD, and can serve as a general purpose, open source test platform for future human-robot interaction research. © 2021 IEEE.",,"Agricultural robots; Man machine systems; Building evacuation; Confined space; Hardware constraints; Human interactions; Mobile robot platforms; Test platforms; Uneven terrain; User information; Human robot interaction",,,,,"National Science Foundation, NSF: 1830471","Funding for this research was provided by NSF #1830471, GETTYLABS, and a Packard Fellowship for Science and Engineering. 1 Cornell Engineering, Ithaca, NY 14853, USA. jdj78, yl656, hs699, ijr25, kirstin @cornell.edu",,,,,,,,,,"Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics. Springer, pp. 1935-1972; Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P., Krug, R., Interactive, collaborative robots: Challenges and opportunities (2018) IJCAI, pp. 18-25; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A., Brave new world: Service robots in the frontline (2018) Journal of Service Management; Schulte, J., Rosenberg, C., Thrun, S., Spontaneous, short-term interaction with mobile robots (1999) Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), 1, pp. 658-663; Shibata, T., Irie, R., Artificial emotional creature for human-robot interaction-A new direction for intelligent systems (1997) Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. IEEE, p. 47; Polygerinos, P., Correll, N., Morin, S.A., Mosadegh, B., Onal, C.D., Petersen, K., Cianchetti, M., Shepherd, R.F., Soft robotics: Review of fluid-driven intrinsically soft devices; Manufacturing, sensing, control, and applications in human-robot interaction Advanced Engineering Materials, pp. 1700016-n/a. , http://dx.doi.org/10.1002/adem.201700016; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction (2004) RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759). IEEE, pp. 591-594; Rossini, N., Esposito, A., Modeling human behaviour in emergency: A research agenda for the creation of a rescue robot (2011) WIRN, pp. 235-246; Garzotto, F., Gelsomini, M., Kinoe, Y., Puffy: A mobile inflatable interactive companion for children with neurodevelopmental disorder (2017) IFIP Conference on Human-Computer Interaction. Springer, pp. 467-492; Borkowski, S., Sabry, S., Crowley, J.L., Projector-camera pair: An universal io device for human machine interaction (2004) Polish National Robotics Conference KKR VIII; Sareen, H., Umapathi, U., Shin, P., Kakehi, Y., Ou, J., Ishii, H., Maes, P., Printflatables: Printing human-scale, functional and dynamic inflatable objects (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, pp. 3669-3680; Penney, C.G., Modality effects in short-term verbal memory (1975) Psychological Bulletin, 82 (1), p. 68; Bradski, G., Kaehler, A., Learning OpenCV: Computer vision with the OpenCV library (2008) O'Reilly Media, Inc.; Suzuki, S., Abe, K., Topological structural analysis of digitized binary images by border following (1985) Computer Vision, Graphics, and Image Processing, 30 (1), pp. 32-46; Hu, Y., Bejarano, S.M., Hoffman, G., Shadowsense: Detecting human touch in a social robot using shadow image classification (2020) Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (4), pp. 1-24",,,,"Institute of Electrical and Electronics Engineers Inc.","18th International Conference on Ubiquitous Robots, UR 2021","12 July 2021 through 14 July 2021",,170755,,9781665438995,,,"English","Int. Conf. Ubiquitous Robot., UR",Conference Paper,"Final","",Scopus,2-s2.0-85112429195
"Gasteiger N., Hellou M., Ahn H.S.","57204357716;57216352354;56218250500;","Optimizing human-robot interaction through personalization: An evidence-informed guide to designing social service robots",2021,"2021 18th International Conference on Ubiquitous Robots, UR 2021",,,"9494695","53","56",,,"10.1109/UR52253.2021.9494695","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112404524&doi=10.1109%2fUR52253.2021.9494695&partnerID=40&md5=5b9da5eddc8308f585f84c8700bb3018","The University of Auckland, CARES, Department of Psychological Medicine, The Department of Electrical, Computer and Software Engineering, Auckland, New Zealand; School of Health Sciences, The University of Manchester, United Kingdom; Sorbonne Université, Facultés des Sciences et Ingénieries, Département d'Informatique, Paris, France; The University of Auckland, CARES, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand","Gasteiger, N., The University of Auckland, CARES, Department of Psychological Medicine, The Department of Electrical, Computer and Software Engineering, Auckland, New Zealand, School of Health Sciences, The University of Manchester, United Kingdom; Hellou, M., Sorbonne Université, Facultés des Sciences et Ingénieries, Département d'Informatique, Paris, France, The University of Auckland, CARES, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand; Ahn, H.S., The University of Auckland, CARES, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand","The application of social service robots is increasing in our daily lives, including in healthcare, education and public spaces (e.g., hospitality or museums). With this near-ubiquity of robots comes the requirement to interact with a diverse group of individuals; each with their own needs, preferences and expectations. This paper identifies and synthesizes previous literature on personalization and culturalization within the field of human-robot interaction. Three over-arching considerations are discussed: (1) intended purpose and actions (i.e. service and behavior), (2) interactive functions (i.e. communication, language and proxemics) and (3) physical appearance of robots. Lastly, recommendations are made for other roboticists, for designing individually responsive social service robots. © 2021 IEEE.","Design; Human-robot interaction; Literature review; Service robots; Social robots","Agricultural robots; Man machine systems; Mobile robots; Social robots; Daily lives; Interactive functions; Personalizations; Public space; Social service; Educational robots",,,,,"Ministry of Science, ICT and Future Planning, MSIP: 2020-0-00842; Institute for Information and Communications Technology Promotion, IITP","ACKNOWLEDGMENT This work was supported by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-00842, Development of Cloud Robot Intelligence for Continual Adaptation to User Reactions in Real Service Environments).",,,,,,,,,,"Clabaugh, C., Mahajan, K., Jain, S., Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders (2019) Frontiers in Robotics and AI, 6 (10); Coşar, S., Fernandez-Carmona, M., Agrigoroaie, R., ENRICHME: Perception and interaction of an assistive robot for the elderly at home (2020) International Journal of Social Robotics; Portugal, D., Santos, L., Alvito, P., SocialRobot: An interactive mobile robot for elderly home care (2015) IEEE/SICE International Symposium on System Integration; Bazzano, F., Lamberti, F., Human-robot interfaces for interactive receptionist systems and wayfinding applications (2018) Robotics, 7 (3); Shi, C., Satake, S., Kanda, T., How would store managers employ social robots? (2016) IEEE, pp. 519-520; Giuliani, M., Petrick, R., Foster, M., Comparing task-based and socially intelligent behaviour in a robot bartender (2013) 15th International Conference on Multimodal Interaction (ICMI 2013); Keizer, S., Foster, M., Wang, Z., Machine learning for social multiparty human-robot interaction (2014) ACM Transactions on Interactive Intelligent Systems, 4 (3); Henkemans, O., Bierman, B., Janssen, J., Using a robot to personalise health education for children with diabetes type 1: A pilot study (2013) Patient Education and Counseling, 92, pp. 174-181; Lee, M., Forlizzi, J., Kiesler, S., Personalization in HRI: A longitudinal field experiment (2012) HRI'12, , Boston: ACM; Di Nuovo, A., Broz, F., Wang, N., The multi-modal interface of Robot-Era multi-robot services tailored for the elderly (2018) Intelligent Service Robotics Volume, 11, pp. 109-126; Hofstede, G., Hofstede, G., Minkov, M., (2010) Cultures and Organizations: Software of the Mind, , New York: McGraw-Hill; O'neill-Brown, P., (1997) Setting the Stage for the Culturally Adaptive Agent, , AAAI Technical Report FS-97-02; Lee, H., Šabanović, S., Culturally variable preferences for robot design and use in South Korea, Turkey, and the United States (2014) HRI'14, , Germany: ACM; Bartneck, C., Suzuki, T., Kanda, T., The influence of people's culture and prior experiences with Aibo on their attitude towards robots (2007) AI & Soc, 21, pp. 217-230; Destephe, M., Brandao, M., Kishi, T., Walking in the uncanny valley: Importance of the attractiveness on the acceptance of a robot as a working partner (2015) Front. Psychol, 6 (204), pp. 1-11; Bartneck, C., Nomura, T., Kanda, T., A cross-cultural study on attitudes towards robots (2005) HCI International, , Las Vegas; Haring, K., Mougenot, C., Ono, F., Cultural differences in perception and attitude towards robots (2014) International Journal of Affective Engineering, 13 (3), pp. 149-157; Mavridis, N., Katsaiti, M., Naef, S., Opinions and attitudes toward humanoid robots in the Middle East (2012) AI & Soc, 27, pp. 517-534; Nomura, T., Syrdal, D., Dautenhahn, K., Differences on social acceptance of humanoid robots between Japan and the UK (2015) 4th International Symposium on New Frontiers in Human-Robot Interaction; Takayama, L., Ju, W., Nass, C., Beyond dirty, dangerous and dull: What everyday people think robots should do (2008) HRI'08, , Amsterdam: ACM; Zlotowski, J., Khalil, A., Abdallah, S., One robot doesn't fit all: Aligning social robot appearance and job suitability from a Middle Eastern perspective (2019) AI & Soc, 35, pp. 485-500; Nomura, T., Kanda, T., Suzuki, T., Implications on humanoid robots in pedagogical applications from cross-cultural analysis between Japan, korea, and the USA (2007) The 16th IEEE International Symposium on Robot and Human Interactive Communication: IEEE; Li, H., Milani, S., Krishnamoorthy, V., Perceptions of domestic robots' normative behavior across cultures (2019) The 2019 AAAI/ACM Conference; Haring, K., Silvera-Tawil, D., Matsumoto, Y., Perception of an android robot in Japan and Australia: A cross-cultural comparison (2014) ICSR'14, pp. 166-174. , Beetz M, Ed Switzerland: Springer; Butler, J., Agah, A., Psychological effects of behavior patterns of a mobile personal robot (2001) Autonomous Robots, 10, pp. 185-202; Rau, P., Li, Y., Li, D., Effects of communication style and culture on ability to accept recommendations from robots (2009) Computers in Human Behavior, 25, pp. 587-595; Salem, M., Ziadee, M., Sakr, M., Marhaba, how may i help you? Effects of politeness and culture on robot acceptance and anthropomorphization (2014) HRI'14, , Germany: ACM; Sanoubari, E., Young, J., Explicit, neutral, or implicit: A cross-cultural exploration of communication-style preferences in human robot interaction (2018) HRI '18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 237-238; Wang, L., Rau, P., Evers, V., When in Rome: The role of culture & context in adherence to robot recommendations (2010) 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , Japan: IEEE; Trovato, G., Zecca, M., Sessa, S., Cross-cultural study on humanrobot greeting interaction: Acceptance and discomfort by Egyptians and Japanese (2013) Paladyn, Journal of Behavioral Robotics, 42 (2), pp. 83-93; Trovato, G., Ham, J., Hashimoto, K., Investigating the effect of relative cultural distance on the acceptance of robots (2015) ICSR'15, pp. 664-673. , In: Tapus A, ed: Springer; Hüttenrauch, H., Eklundh, K., Green, A., Investigating spatial relationships in human-robot interactions (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems; Walters, M., Dautenhahn, K., Koay, K., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005) IEEE-RAS International Conference on Humanoid Robots, , Japan: IEEE; Eresha, G., Haring, M., Endrass, B., Investigating the influence of culture on proxemic behaviors for humanoid robots (2013) IEEE RO-MAN: The 22nd IEEE International Symposium on Robot and Human Interactive Communication, , Korea; Joosse, M., Poppe, R., Lohse, M., Cultural differences in how an engagement-seeking robot should approach a group of people (2014) CABS'14, , Japan; Neerincx, A., Kaptein, R., Oleari, E., Child's culture-related experiences with a social robot at diabetes camps (2016) IEEE, pp. 485-486; Syrdal, D., Koay, K., Walters, M., A personalized robot companion?-The role of individual differences on spatial preferences in HRI scenarios (2007) 16th IEEE International Conference on Robot & Human Interactive Communication, pp. 1143-1148. , Korea; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) Proceedings of the Intelligent Robotic Systems: IROS, pp. 5495-5502. , St Louis; Walters, M., Dautenhahn, K., Boekhorst, R., The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment (2005) IEEE, , Japan; Syrdal, D., Dautenhahn, K., Woods, S., Doing the right thing wrong-Personality and tolerance to uncomfortable robot approaches (2006) The 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN06), , Hatfield, UK; Cortellessa, G., Svedberg, G., Scopelliti, M., A cross-cultural evaluation of domestic assistive robots (2008) Association for the Advancement of Artificial Intelligence; Bartneck, C., Who like androids more: Japanese or US Americans? (2008) 17th IEEE International Symposium on Robot and Human Interactive Communication, , Germany: IEEE; Agah, A., The influence of robot appearance and interactive ability in HRI: A cross-cultural study (2016) ICSR'16, , Springer; Hegel, F., Lohse, M., Wrede, B., Effects of visual appearance on the attribution of applications in social robotics (2009) 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 64-71. , Japan: IEEE; Rizvanoǧlu, K., Öztürk, Ö., Adiyaman, Ö., The impact of human likeness on the older adults' perceptions and preferences of humanoid robot appearance (2014) DUXU'14: Springer, pp. 164-172. , In: Marcus A, ed; Walters, M., Syrdal, D., Koay, K., Human approach distances to a mechanical-looking robot with different robot voice styles (2008) The 17th IEEE International Symposium on Robot and Human Interactive Communication, , Germany; Ho, Y., Sato-Shimokawara, E., Yamaguchi, T., Interaction robot system considering culture differences (2013) IEEE Workshop on Advanced Robotics and Its Social Impacts, , Japan; Makenova, R., Karsybayeva, R., Sandygulova, A., Exploring cross-cultural differences in persuasive robotics (2018) Companion of the 2018 ACM/IEEE International Conference; Lee, H., Kang, H., Kwak, S., How people perceive human-and product-like robots: Cross-cultural analysis between Japan and korea (2015) HAI '15: Proceedings of the 3rd International Conference on Human-Agent Interaction, pp. 315-318","Ahn, H.S.; The University of Auckland, New Zealand; email: hs.ahn@auckland.ac.nz",,,"Institute of Electrical and Electronics Engineers Inc.","18th International Conference on Ubiquitous Robots, UR 2021","12 July 2021 through 14 July 2021",,170755,,9781665438995,,,"English","Int. Conf. Ubiquitous Robot., UR",Conference Paper,"Final","",Scopus,2-s2.0-85112404524
"Westhoven M., Tegtmeier P.","56964308200;57200210841;","Influence of Personality Traits on Helping Behaviour in Human-Robot Interaction",2021,"Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO","2021-July",,,"78","84",,,"10.1109/ARSO51874.2021.9542832","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116469872&doi=10.1109%2fARSO51874.2021.9542832&partnerID=40&md5=db70d6bc34d87a3c0083e36af771bd09","German Federal Institute for Occupational Safety and Health BAuA, Dortmund, Germany","Westhoven, M., German Federal Institute for Occupational Safety and Health BAuA, Dortmund, Germany; Tegtmeier, P., German Federal Institute for Occupational Safety and Health BAuA, Dortmund, Germany","Trait models of personality can be used to predict prosocial behaviour. In the research field of human-robot interaction, there are many hints that interaction patterns learned from interacting with other humans also guide human behaviour regarding robots. This leads to the question, whether behaviour towards robots can be predicted by trait models of personality as well. To answer this question for the case of helping behaviour towards robots, an experiment was designed in which 50 participants answered questionnaires on personality (HEXACO-PI-R-100) and on negative attitudes towards robots (NARS), and were observed while being repeatedly asked for help by a robot. The personality dimensions of Conscientiousness and Openness influenced the observed behaviour significantly. These results show that personality also affects human-robot interaction. Nonetheless, this present work is only a first step towards a more thorough understanding of the influencing factors of successful human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Man machine systems; Surveys; Helping behaviours; Human behaviors; Humans-robot interactions; Interaction pattern; Personality dimensions; Personality traits; Research fields; Human robot interaction",,,,,,,,,,,,,,,,"Rosenthal, S., Veloso, M., Mobile robot planning to seek help with spatially-situated tasks (2012) Twenty-Sixth Aaai Conference on Artificial Intelligence; Cha, E., Matarić, M., Fong, T., Nonverbal signaling for nonhumanoid robots during human-robot collaboration (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction. Ieee Press, pp. 601-602; Hall, E.T., A system for the notation of proxemic behavior 1 (1963) American Anthropologist, 65 (5), pp. 1003-1026; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may i serve you?: A robot companion approaching a seated person in a helping context (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Humanrobot Interaction, pp. 172-179; Kato, Y., Kanda, T., Ishiguro, H., May i help you?: Design of human-like polite approaching behavior (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. Acm, pp. 35-42; Batson, C.D., Van Lange, P.A., Ahmad, N., Lishner, D.A., Altruism and helping behavior (2003) The Sage Handbook of Social Psychology, 1, pp. 279-295; Srinivasan, V., Takayama, L., Help me please: Robot politeness strategies for soliciting help from humans (2016) Proceedings of the 2016 Chi Conference on Human Factors in Computing Systems, pp. 4945-4955; Cameron, D., Loh, E., Collins, E., Aitken, J., Law, J., Robot-stated limitations but not intentions promote user assistance (2016) 5th International Symposium on New Frontiers in Human-Robot Interaction, , Sheffield; Kaniarasu, P., Steinfeld, A.M., Effects of blame on trust in human robot interaction (2014) The 23rd Ieee International Symposium on Robot and Human Interactive Communication. Ieee, pp. 850-855; Robert, L., Personality in the human robot interaction literature: A review and brief critique (2018) Personality in the Human Robot Interaction Literature: A Review and Brief Critique, Proceedings of the 24th Americas Conference on Information Systems, pp. 16-18. , Robert, LP (2018), Aug; Woods, S., Dautenhahn, K., Kaouri, C., Boekhorst, R., Koay, K.L., Is this robot like me? Links between human and robot personality traits (2005) 5th IEEE-RAS International Conference on Humanoid Robots, 2005. Ieee, pp. 375-380; Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Kaouri, C., Woods, S., Nehaniv, C., Werry, I., The influence of subjects' personality traits on personal spatial zones in a humanrobot interaction experiment (2005) Roman 2005. Ieee International Workshop on Robot and Human Interactive Communication, 2005. Ieee, pp. 347-352; Tapus, A., Tapus, C., Matarić, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics, 1 (2), p. 169; Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., Paiva, A., The influence of empathy in human-robot relations (2013) International Journal of Human-computer Studies, 71 (3), pp. 250-260; Correia, F., Mascarenhas, S.F., Gomes, S., Arriaga, P., Leite, I., Prada, R., Melo, F.S., Paiva, A., Exploring prosociality in human-robot teams (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Ieee, pp. 143-151; Fischer, P., Jander, K., Krueger, J., (2018) Sozialpsychologie für Bachelor, , Springer; Hartshorne, H., May, M.A., (1928) Studies in the Nature of Character: Studies in Deceit, , I. Macmillan; Davis, M.H., (1980) A Multidimensional Approach to Individual Differences in Empathy; Penner, L.A., Escarraz, J., Ellis, B.B., Sociopathy and helping: Looking out for number one (1983) Academic Psychology Bulletin; Penner, L.A., Fritzsche, B.A., Craiger, J.P., Freifeld, T.R., Measuring the prosocial personality (1995) Advances in Personality Assessment, 10, pp. 147-163; Penner, L., Fritzsche, B., The measurement of individual differences in prosocial tendencies (1993) Sixth International Conference on Personality and Social Behavior, , Boca Raton, FL; Piliavin, J.A., (1981) Emergency Intervention, , Academic Pr; Gerrig, R.J., Zimbardo, P., (2014) Psychologie (20. Aufl.), , Hallbergmoos: Pearson; Habashi, M.M., Graziano, W.G., Hoover, A.E., Searching for the prosocial personality: A big five approach to linking personality and prosocial behavior (2016) Personality and Social Psychology Bulletin, 42 (9), pp. 1177-1192; Ashton, M.C., Lee, K., Empirical, theoretical, and practical advantages of the hexaco model of personality structure (2007) Personality and Social Psychology Review, 11 (2), pp. 150-166; Ashton, M.C., Lee, K., De Vries, R.E., The hexaco honestyhumility, agreeableness, and emotionality factors: A review of research and theory (2014) Personality and Social Psychology Review, 18 (2), pp. 139-152; Hilbig, B.E., Glöckner, A., Zettler, I., Personality and prosocial behavior: Linking basic traits and social value orientations (2014) Journal of Personality and Social Psychology, 107 (3), p. 529; Thalmayer, A.G., Saucier, G., Eigenhuis, A., Comparative validity of brief to medium-length big five and big six personality questionnaires (2011) Psychological Assessment, 23 (4), p. 995; Budde, V., (2018) Roboter und Prosoziales Verhalten: Untersuchung der Effekte Einer Bitte Um Hilfe Auf Die Wahrnehmung und Bewertung Einer Mensch-roboter Interaktion, , Master's thesis, Universität Duisburg-Essen, in German; Lee, K., Ashton, M.C., Psychometric properties of the hexaco-100 (2018) Assessment, 25 (5), pp. 543-556; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interaction Studies, 7 (3), pp. 437-454; Morgan, T.M., Case, L.D., Conservative sample size determination for repeated measures analysis of covariance (2013) Annals of Biometrics & Biostatistics, 1 (1); Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kato, K., Cultural differences in attitudes towards robots (2005) Aisb; Xu, R., Measuring explained variation in linear mixed effects models (2003) Statistics in Medicine, 22 (22), pp. 3527-3541; Hüttenrauch, H., Severinson-Eklundh, K., To help or not to help a service robot (2006) Interaction Studies, 7 (3), pp. 455-477; Eng, J., Sample size estimation: A glimpse beyond simple formulas (2004) Radiology, 230 (3), pp. 606-612; Westhoven, M., Grinten Der, T.Van, Mueller, S., Perceptions of a help-requesting robot-effects of eye-expressions, colored lights and politeness of speech (2019) Proceedings of Mensch und Computer 2019, pp. 43-54","Westhoven, M.; German Federal Institute for Occupational Safety and Health BAuAGermany; email: westhoven.martin@baua.bund.de",,,"IEEE Computer Society","2021 IEEE International Conference on Advanced Robotics and Its Social Impacts, ARSO 2021","8 July 2021 through 10 July 2021",,172042,21627568,9781665449533,,,"English","Proc. IEEE Workshop Adv. Rob. Soc. Impacts, ARSO",Conference Paper,"Final","",Scopus,2-s2.0-85116469872
"Eyam A.T., Mohammed W.M., Martinez Lastra J.L.","57225048869;57164551100;55665160500;","Emotion-driven analysis and control of human-robot interactions in collaborative applications",2021,"Sensors","21","14","4626","","",,,"10.3390/s21144626","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109025481&doi=10.3390%2fs21144626&partnerID=40&md5=32533a6ae5f139c5613bbf797ba8417c","FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, 3320, Finland","Eyam, A.T., FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, 3320, Finland; Mohammed, W.M., FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, 3320, Finland; Martinez Lastra, J.L., FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, 3320, Finland","The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Adaptive systems; Cobot; Collaborative robots; Electroencephalography (EEG); Emotion recognition; Emotion-driven; Emotions; Hu-man-machine interface; Human-in-the-loop; Human-robot collaboration; Human-robot interaction; Industrial applications; Trust","Electroencephalography; Electrophysiology; Industrial robots; Manufacture; Robotics; Analysis and controls; Automation levels; Collaborative application; Collaborative robots; Computational capability; Manufacturing domains; Physical interactions; Trustworthy environments; Social robots; automation; electroencephalography; emotion; human; industry; robotics; Automation; Electroencephalography; Emotions; Humans; Industry; Robotics",,,,,,"The APC was funded by Faculty of Engineering and Natural Sciences at Tampere University.",,,,,,,,,,"Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M., Industry 4.0 (2014) Bus. Inf. Syst. 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(accessed on 28 April 2021)","Mohammed, W.M.; FAST-Lab, Finland; email: wael.mohammed@tuni.fi",,,"MDPI AG",,,,,14248220,,,"34300366","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85109025481
"Naeem M.A., Sarwar A., Humayun A., Waseem M.","57213518794;57346254800;57346739200;57346254900;","The Development of Robot Control via Virtual Reality for Safe Human-Robot Interaction",2021,"Pakistan Journal of Engineering and Applied Sciences","29",,,"72","80",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119440330&partnerID=40&md5=a0aa16544d907f6aa781aa2d8e36cc10","Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan","Naeem, M.A., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan; Sarwar, A., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan; Humayun, A., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan; Waseem, M., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan","In Human-Robot Interaction (HRI) domain the prime focus is laid on the safety of the involved humans. High inertias, velocities and extensive work envelop, particularly of most of the currently used industrial robots, are reasons for the hazards associated with the interaction of humans with robots. Several strategies are being investigated for ensuring safety which includes lightweight robots (LWR), force-limiting control, start-stop monitoring, speed limiting mode, etc. These approaches often require a range of hardware peripherals to be incorporated within and around the robotic system. Alternatively, the use of Virtual Reality (VR) systems to interact with otherwise dangerous robots allows for contactless interaction. In this paper we present a Virtual Reality based arrangement that allows the interaction with the robot from distance, in a virtually created environment, to ensure human safety. Keeping in view the frequent requirements of changes in robot’s movement plan, the proposed scheme demonstrates the capability of programming these plans in the virtual environment as well. We have shown the results of controlling a real robot in VR and the root mean square error (RMSE) between the two joint angles of the real and the virtual robot is 2.61 degrees and 3.08 degrees. This leads to remote/tele-control of the robot which is one of the future demands considering the social impact of COVID-19. © 2021. Pakistan Journal of Engineering and Applied Sciences. All Rights Reserved.","Human-Robot Interaction; Robot Programming; Virtual Reality",,,,,,,,,,,,,,,,,"Jerčić, P., Hagelbäck, J., Lindley, C., An affective serious game for collaboration between humans and robots (2019) Entertainment Computing, 32, p. 100319. , [1]; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , [2]; Oyekan, J. O., Hutabarat, W., Tiwari, A., Grech, R., Aung, M. H., Mariani, M. P., Dupuis, C., The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans (2019) Robotics and Computer-Integrated Manufacturing, 55, pp. 41-54. , [3]; El Beheiry, M., Doutreligne, S., Caporal, C., Ostertag, C., Dahan, M., Masson, J. B., Virtual reality: beyond visualization (2019) Journal of molecular biology, 431 (7), pp. 1315-1321. , [4]; Ohtani, H., Tamura, Y., Kageyama, A., Ishiguro, S., Scientific visualization of plasma simulation results and device data in virtual-reality space (2011) IEEE Transactions on plasma Science, 39 (11), pp. 2472-2473. , [5]; Li, J., Liu, Q., Su, H., Virtual reality method of portal slewing crane based on WPF (2013) Advances in Mechanical Engineering, 5, p. 320757. , [6]; Xu, X., Wang, L., Li, Z., Yao, S., Fang, X., Modeling of submarine initial pipe-laying process and its real-time semi-physical virtual reality system (2018) Advances in Mechanical Engineering, 10 (1), p. 1687814017747734. , [7]; Usher, W., Klacansky, P., Federer, F., Bremer, P. T., Knoll, A., Yarch, J., Pascucci, V., A virtual reality visualization tool for neuron tracing (2017) IEEE transactions on visualization and computer graphics, 24 (1), pp. 994-1003. , [8]; Das, M. T., Dülger, L. C., Mathematical modelling, simulation and experimental verification of a scara robot (2005) Simulation Modelling Practice and Theory, 13 (3), pp. 257-271. , [9]; Wolfartsberger, J., Analyzing the potential of Virtual Reality for engineering design review (2019) Automation in Construction, 104, pp. 27-37. , [10]","Naeem, M.A.; Department of Mechatronics & Control Engineering, Pakistan; email: mahsan.naeem@uet.edu.pk",,,"University of Engineering and Technology, Lahore",,,,,19951302,,,,"English","Pak. J. Eng. Appl. Sci.",Article,"Final","",Scopus,2-s2.0-85119440330
"Jang M., Ahn H.S., Choi J.-S., Kirstein F., Yoichi L.","55682654610;56218250500;55994218300;56085062900;57225155326;","Editorial: Special issue on social human–robot interaction for human-care robots",2021,"Intelligent Service Robotics","14","3",,"327","328",,,"10.1007/s11370-021-00376-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109317402&doi=10.1007%2fs11370-021-00376-5&partnerID=40&md5=abf6d3da357a03fe519b96a579dbf6ed","Electronics and Telecommunications Research Institute, Daejeon, South Korea; University of Auckland, Auckland, New Zealand; Korea Institute of Science and Technology, Seoul, South Korea; Blue Ocean Robotics, Odense, Denmark; Nagoya University, Nagoya, Japan","Jang, M., Electronics and Telecommunications Research Institute, Daejeon, South Korea; Ahn, H.S., University of Auckland, Auckland, New Zealand; Choi, J.-S., Korea Institute of Science and Technology, Seoul, South Korea; Kirstein, F., Blue Ocean Robotics, Odense, Denmark; Yoichi, L., Nagoya University, Nagoya, Japan",[No abstract available],,,,,,,,,,,,,,,,,,"Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: a review (2009) Gerontechnology, 8 (2), pp. 94-103; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) Int J Soc Robot, 7 (4), pp. 465-478; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Sci Robot, 3 (21), p. eaat5954; Mubin, O., Ahmad, M.I., Kaur, S., Shi, W., Khan, A., Social robots in public spaces: A meta-review (2018) International Conference on Social Robotics, pp. 213-220. , Springer, Cham; Fischer, K., Why collaborative robots must be social (and even emotional) actors (2019) Techné: Res Philos Technol, 23 (3), pp. 270-289; Görür, O.C., Rosman, B., Sivrikaya, F., Albayrak, S., Social cobots: Anticipatory decision-making for collaborative robots incorporating unexpected human behaviors (2018) Proceedings of the 2018 ACM/IEEE International Conference on human–robot Interaction, pp. 398-406","Jang, M.; Electronics and Telecommunications Research InstituteSouth Korea; email: minsu@etri.re.kr",,,"Springer Science and Business Media Deutschland GmbH",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Editorial,"Final","All Open Access, Bronze",Scopus,2-s2.0-85109317402
"Vianello L., Mouret J.-B., Dalin E., Aubry A., Ivaldi S.","57224317245;23019477600;57224323899;23033362000;26643860200;","Human Posture Prediction during Physical Human-Robot Interaction",2021,"IEEE Robotics and Automation Letters","6","3","9447230","6046","6053",,1,"10.1109/LRA.2021.3086666","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107369930&doi=10.1109%2fLRA.2021.3086666&partnerID=40&md5=88fe4912ac44ca8be9fa518ebfa9a6fe","University of Lorraine, Cnrs Inria, France","Vianello, L., University of Lorraine, Cnrs Inria, France; Mouret, J.-B., University of Lorraine, Cnrs Inria, France; Dalin, E., University of Lorraine, Cnrs Inria, France; Aubry, A., University of Lorraine, Cnrs Inria, France; Ivaldi, S., University of Lorraine, Cnrs Inria, France","When a human is interacting physically with a robot to accomplish a task, his/her posture is inevitably influenced by the robot movement. Since the human is not controllable, an active robot imposing a collaborative trajectory should predict the most likely human posture. This prediction should consider individual differences and preferences of movement execution, and it is necessary to evaluate the impact of the robot's action from an ergonomics standpoint. Here, we propose a method to predict, in probabilistic terms, the human postures of an individual for a given robot trajectory executed in a collaborative scenario. We formalize the problem as the prediction of the human joints velocity given the current posture and robot end-effector velocity. The key idea of our approach is to learn the distribution of the null space of the Jacobian and the weights of the weighted pseudo-inverse from demonstrated human movements: both carry information about human postural preferences, to leverage redundancy and ensure that the predicted posture will be coherent with the end-effector position. We validate our approach in a simulated toy problem and on two real human-robot interaction experiments where a human is physically interacting with a Franka robot. © 2016 IEEE.","ergonomics; pHRI; posture prediction","Agricultural robots; End effectors; Ergonomics; Forecasting; Inverse problems; Man machine systems; Human movements; Human postures; Individual Differences; Physical human-robot interactions; Robot end effector; Robot movements; Robot trajectory; Weighted pseudo inverse; Human robot interaction",,,,,"ANR-15-IDEX04-LUE; Horizon 2020: 731540; European Regional Development Fund, ERDF","Manuscript received December 21, 2020; accepted April 24, 2021. Date of publication June 4, 2021; date of current version June 30, 2021. This letter was recommended for publication by Associate Editor K. Ramirez-Amaro and Editor T. Asfour upon evaluation of the reviewers’ comments. This work was partly supported by the European Union Horizon 2020 Research Program under Grant 731540 (project AnDy), the CHIST-ERA project HEAP, the European FEDER in the context of the CPER Sciarat, and the Lorraine University of Excellence (LUE) project C-SHIFT under Grant ANR-15-IDEX04-LUE. (Corresponding author: Serena Ivaldi.) Lorenzo Vianello is with the University of Lorraine, CNRS, Inria, LORIA F-54000, France, and also with the Université de Lorraine, CNRS, CRAN F-54000, France (e-mail: lorenzo.vianello@univ-lorraine.fr).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auton. Robots, 42 (5), pp. 957-975; Malaisé, A., Maurice, P., Colas, F., Ivaldi, S., Activity recognition for ergonomics assessment of industrial tasks with automatic feature selection (2019) IEEE Robot. Automat. Lett, 4 (2), pp. 1132-1139. , Apr; Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., Hirche, S., The role of roles: Physical cooperation between humans and robots (2012) Int. J. Robot. Res, 31 (13), pp. 1656-1674; Peternel, L., Fang, C., Tsagarakis, N., Ajoudani, A., A selective muscle fatigue management approach to ergonomic human-robot comanipulation (2019) Robot. Comput.-Integr. Manuf, 58, pp. 69-79; Shafti, A., Ataka, A., Lazpita, B.U., Shiva, A., Wurdemann, H.A., Althoefer, K., Real-time robot-assisted ergonomics (2019) Proc. IEEE Int. Conf. Robot. Automat, pp. 1975-1981; Vos, T., Barber, R.M., Bell, B., Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-analysis for the global burden of disease study2013 (2015) Lancet, 386 (9995), pp. 743-800; Rasmussen, C.E., Gaussian processes in machine learning (2003) Summer Sch. Mach. Learn., pp. 63-71. , Springer; Maurice, P., Human movement and ergonomics: An industryoriented dataset for collaborative robotics (2019) Int. J. Robot. Res, 38 (14), pp. 1529-1537; Yazdani, A., Novin, R.S., Posture estimation and optimization in ergonomically intelligent teleoperation systems (2021) Proc. Companion 2021 ACM/IEEE Int. Conf. Human-Robot Interact, pp. 604-606; Rahal, R., Caring about the human operator: Haptic shared control for enhanced user comfort in robotic telemanipulation (2020) IEEE Trans. Haptics, 13 (1), pp. 197-203. , Jan.-Mar; Dermy, O., Paraschos, A., Ewerton, M., Charpillet, F., Peters, J., Ivaldi, S., Prediction of intention during interaction with iCub with probabilistic movement primitives (2017) Front. Robot. Ai, 4, pp. 1-45; Paraschos, A., Daniel, C., Peters, J., Neumann, G., Using probabilistic movement primitives in robotics (2018) Auton. Robots, 42 (3), pp. 529-551; Paraschos, A., Lioutikov, R., Peters, J., Neumann, G., Probabilistic prioritization ofmovement primitives (2017) IEEE Robot. Automat. Lett, 2 (4), pp. 2294-2301. , Oct; Zhang, J., Liu, H., Chang, Q., Wang, L., Gao, R.X., Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly (2020) Cirp Ann, 69 (1), pp. 9-12; Kratzer, P., Toussaint, M., Mainprice, J., Prediction of human full-body movements with motion optimization and recurrent neural networks (2020) Proc. IEEE Int. Conf. Robot. Automat, pp. 1792-1798; Cheng, Y., Zhao, W., Liu, C., Tomizuka, M., Human motion prediction using semi-adaptable neural networks (2019) Proc. Amer. Control Conf, pp. 4884-4890; Tevatia, G., Schaal, S., Inverse kinematics for humanoid robots (2000) Proc. Int. Conf. Robot. Automat, pp. 294-299; Spaa Der Van, L.F., Bates, T., Gienger, M., Kober, J., Predicting and optimizing ergonomics in physical human-robot cooperation tasks (2020) Proc. IEEE Int. Conf. Robot. Automat, pp. 1799-1805; Maurice, P., Padois, V., Measson, Y., Bidaud, P., Digital human modeling for collaborative robotics (2019) Dhm Posturography, pp. 771-779. , Elsevier; Bassani, T., Stucovitz, E., Qian, Z., Briguglio, M., Galbusera, F., Validation of the anybody full body musculoskeletal model in computing lumbar spine loads at l4l5 level (2017) J. Biomech, 58, pp. 89-96; Maurice, P., (2015) Virtual Ergonomics for the Design of Collaborative Robots, pp. 58-70. , Ph.D. dissertation, Université Pierre et Marie Curie; Busch, B., Maeda, G., Mollard, Y., Demangeat, M., Lopes, M., Postural optimization for an ergonomic human-robot interaction (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 2778-2785; Burdick, J.W., On the inverse kinematics of redundant manipulators: Characterization of the self-motion manifolds (1989) Adv. Robot, pp. 25-34. , Springer; Dietrich, A., Ott, C., Albu-Schäffer, A., An overview of null space projections for redundant, torque-controlled robots (2015) Int. J. Robot. Res, 34 (11), pp. 1385-1400; Siciliano, B., Kinematic control of redundant robot manipulators: A tutorial (1990) J. Intell. Robotic Syst, 3 (3), pp. 201-212; Huang, M.Z., Varma, H., Optimal rate allocation in kinematicallyredundant manipulators-the dual projection method (1991) Proc. IEEE Int. Conf. Robot. Automat, pp. 702-707; Chatzilygeroudis, K., Mouret, J.-B., Using parameterized black-box priors to scale up model-based policy search for robotics (2018) Proc. Int. Conf. Robot. Automat, pp. 5121-5128; Hansen, N., Benchmarking a bi-population CMA-ES on the BBOB-2009 function testbed (2009) Proc. 11th Annu. Conf. Companion Genet. Evol. Comput. Conf. : Late Breaking Letters, pp. 2389-2396; Chatzilygeroudis, K., Rama, R., Kaushik, R., Goepp, D., Vassiliades, V., Mouret, J.-B., Black-box data-efficient policy search for robotics (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 51-58; Carpentier, J., Valenza, F., Mansard, N., (2015) Pinocchio: Fast Forward and Inverse Dynamics for Poly-Articulated Systems, , https://stack-of-tasks.github.io/pinocchio","Vianello, L.; University of Lorraine, France; email: lorenzo.vianello@univ-lorraine.fr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85107369930
"Oleinikov A., Kusdavletov S., Shintemirov A., Rubagotti M.","57195555281;57224194792;19639432700;24725558700;","Safety-Aware Nonlinear Model Predictive Control for Physical Human-Robot Interaction",2021,"IEEE Robotics and Automation Letters","6","3","9440695","5665","5672",,,"10.1109/LRA.2021.3083581","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107182589&doi=10.1109%2fLRA.2021.3083581&partnerID=40&md5=a6036ebf91421b351dbbe0debcfeea9e","Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan","Oleinikov, A., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Kusdavletov, S., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Shintemirov, A., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Rubagotti, M., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan","This letter proposes a nonlinear model predictive control (NMPC) approach for real-time planning of point-to-point motions of serial robot manipulators that share their workspace with a human. The NMPC law solves a nonlinear program online, based on a kinematic model, and guarantees safety by constraining the robot speed within the time-varying bounds determined by the speed-and-separation-monitoring (SSM) principle. Closed-loop stability is proven in detail, and the performance (in terms of productivity) of the proposed method is tested against standard SSM schemes via experiments on a Kinova Gen3 robot. © 2016 IEEE.","human-aware motion planning; nonlinear model predictive control; Optimization and optimal control; physical human-robot interaction; speed and separation monitoring","Agricultural robots; Industrial manipulators; Kinematics; Manipulators; Model predictive control; Nonlinear programming; Nonlinear systems; Predictive control systems; Robot applications; Robot programming; Closed loop stability; Kinematic model; Nonlinear model predictive control; Nonlinear programs; Physical human-robot interactions; Point-to-point motion; Real-time planning; Serial robots; Human robot interaction",,,,,"AP08052091; Nazarbayev University, NU: 091019CRP2118","Manuscript received December 17, 2020; accepted May 14, 2021. Date of publication May 25, 2021; date of current version June 3, 2021. This letter was recommended for publication by Associate Editor K. N. Kaipa and Editor L. Pallottino upon evaluation of the reviewers’ comments. This work was supported by Nazarbayev University Collaborative Research Project no. 091019CRP2118 “Stochastic and Learning-Based Predictive Control Methods for Physical Human-Robot Interaction” and Kazakhstan Ministry of Education and Science young researchers grant project no. AP08052091. (Corresponding author: Matteo Rubagotti.) The authors are with the Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan 010000, Kazakhstan (e-mail: artemiy.oleinikov@nu.edu.kz; sanzhar.kusdavletov@nu. edu.kz; ashintemirov@nu.edu.kz; rubagotti@ieee.org).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auton. Robot., 42 (5), pp. 957-975; Haddadin, S., Croft, E., Physical human-robot interaction (2016) Springer Handbook of Robotics. Cham: Springer, pp. 1835-1874; ISO-TS 15066: Robots and robotic devices-collaborative robots (2016) Int. Org. Standardization, Geneva, Switzerland; Marvel, J.A., Performance metrics of speed and separation monitoring in shared workspaces (2013) IEEE Trans. Automat. Sci. Eng., 10 (2), pp. 405-414. , Apr; Marvel, J.A., Norcross, R., Implementing speed and separation monitoring in collaborative robot workcells (2017) Robot. Comput.-Int. Manuf., 44, pp. 144-155; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Byner, C., Matthias, B., Ding, H., Dynamic speed and separation monitoring for collaborative robot applications-concepts and performance (2019) Robot. Comput.-Int. Manuf., 58, pp. 239-252; Houska, B., Ferreau, H.J., Diehl, M., ACADO toolkit-an open-source framework for automatic control and dynamic optimization (2011) Optim. Control. Appl. Methods, 32 (3), pp. 298-312; Grüne, L., Pannek, J., (2017) Nonlinear Model Predictive Control, , Springer, Cham, Switzerland; Faulwasser, T., Weber, T., Zometa, P., Findeisen, R., Implementation of nonlinear model predictive path-following control for an industrial robot (2017) IEEE Trans. Control Syst. Technol., 25 (4), pp. 1505-1511. , Jul; Ardakani, M.M.G., Olofsson, B., Robertsson, A., Johansson, R., Model predictive control for real-time point-to-point trajectory generation (2019) IEEE Trans. Automat. Sci. Eng., 16 (2), pp. 972-983. , Apr; Rubagotti, M., Taunyazov, T., Omarali, B., Shintemirov, A., Semiautonomous robot teleoperation with obstacle avoidance via model predictive control (2019) IEEE Robot. Automat. Lett., 4 (3), pp. 2746-2753. , Jul; Hogan, F.R., Rodriguez, A., Reactive planar non-prehensile manipulation with hybrid model predictive control (2020) Int. J. Robot. Res, 39 (7), pp. 755-773; Dai, L., Yu, Y., Zhai, D.-H., Huang, T., Xia, Y., Robust model predictive tracking control for robot manipulators with disturbances (2021) IEEE Trans. Ind. Electron., 68 (5), pp. 4288-4297. , May; Faroni, M., Beschi, M., Bianco, C.G.L., Visioli, A., Predictive joint trajectory scaling for manipulators with kinodynamic constraints (2020) Control Eng. Pract., 95; Casalino, A., Rocco, P., Prandini, M., Hybrid control ofmanipulators in human-robot coexistence scenarios (2018) Proc. Amer. Control. Conf., pp. 1172-1177; Faroni, M., Beschi, M., Pedrocchi, N., An MPC framework for online motion planning in human-robot collaborative tasks (2019) Proc. IEEE Int. Conf. Emerg. Technol. Factory Automat., pp. 1555-1558; Ding, H., Wijaya, K., Reißig, G., Stursberg, O., Optimizing motion of robotic manipulators in interaction with human operators (2011) Proc. Int. Conf. Intell. Robot. Appl., pp. 520-531; Sadrfaridpour, B., Wang, Y., Collaborative assembly in hybrid manufacturing cells: An integrated framework for human-robot interaction (2018) IEEE Trans. Automat. Sci. Eng., 15 (3), pp. 1178-1192. , Jul; Maurice, P., Human movement and ergonomics: An industryoriented dataset for collaborative robotics (2019) Int J. Robot. Res., 38 (14), pp. 1529-1537; Mayne, D.Q., Michalska, H., Receding horizon control of nonlinear systems (1990) IEEE Trans. Automat.Control, 35 (7), pp. 814-824; Del Prete, A., Joint position and velocity bounds in discrete-time acceleration/ torque control of robot manipulators (2018) IEEE Robot. Automat. Lett., 3 (1), pp. 281-288. , Jan; Ferreau, H.J., Kirches, C., Potschka, A., Bock, H.G., Diehl, M., QpOASES: A parametric active-set algorithm for quadratic programming (2014) Math. Program. Comput., 6 (4), pp. 327-363","Rubagotti, M.; Department of Robotics and Mechatronics, Kazakhstan; email: rubagotti@ieee.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85107182589
"Chuah S.H.-W., Yu J.","57192803371;57220033296;","The future of service: The power of emotion in human-robot interaction",2021,"Journal of Retailing and Consumer Services","61",,"102551","","",,9,"10.1016/j.jretconser.2021.102551","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103705475&doi=10.1016%2fj.jretconser.2021.102551&partnerID=40&md5=672de991eef721892e4c2fbfed4bd8b3","Institute of Innovation and Circular Economy & Department of Business Administration, Asia University, No. 500, Liufeng Rd., Wufeng Dist., Taichung City, 413, Taiwan; Department of Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg, A-5412, Austria","Chuah, S.H.-W., Institute of Innovation and Circular Economy & Department of Business Administration, Asia University, No. 500, Liufeng Rd., Wufeng Dist., Taichung City, 413, Taiwan; Yu, J., Department of Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg, A-5412, Austria","Astoundingly, recent technological advancements have enabled robots to display emotions. Yet, while emotional expression is valued in the field of service, understanding emotions in human-robot interaction remains underexplored. Since emotions are contagious/transmittable, this study utilised Instagram data to uncover how emotional robots influence potential consumers’ affective feelings. By employing machine learning algorithms and sentiment analysis, the findings suggest that the expressions of surprise and happiness are key to creating positive impacts on potential consumers. The cross-disciplinary nature of this study lays the groundwork for next-level social, design, and creative experiences in artificial intelligence research regarding consumer service and experience contexts. © 2021 The Authors","Consumer experience; Emotional contagion; Facial recognition; Human-robot interaction; Machine learning; Sentiment; Service robot","algorithm; artificial intelligence; automation; computer simulation; machine learning; robotics; service quality; social media",,,,,"Ministry of Science and Technology, Taiwan, MOST: 1 08-2218-E−468-003","This study was supported by Ministry of Science and Technology, Taiwan (Grant number: MOST 1 08-2218-E−468-003 ).",,,,,,,,,,"Appel, M., Izydorczyk, D., Weber, S., Mara, M., Lischetzke, T., The uncanny of mind in a machine: humanoid robots as tools, agents, and experiencers (2020) Comput. Hum. 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"Guo Y., Shi C., Yang X.J.","57201850598;55791297900;23975212000;","Reverse Psychology in Trust-Aware Human-Robot Interaction",2021,"IEEE Robotics and Automation Letters","6","3","9382074","4851","4858",,1,"10.1109/LRA.2021.3067626","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103276225&doi=10.1109%2fLRA.2021.3067626&partnerID=40&md5=bdf627e6421394a00d6e58ca10df2177","Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States","Guo, Y., Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States; Shi, C., Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States; Yang, X.J., Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States","To facilitate effective human-robot interaction (HRI), trust-aware HRI has been proposed, wherein the robotic agent explicitly considers the human's trust during its planning and decision making. The success of trust-aware HRI depends on the specification of a trust dynamics model and a trust-behavior model. In this study, we proposed one novel trust-behavior model, namely the reverse psychology model, and compared it against the commonly used disuse model. We examined how the two models affect the robot's optimal policy and the human-robot team performance. Results indicate that the robot will deliberately 'manipulate' the human's trust under the reverse psychology model. To correct this 'manipulative' behavior, we proposed a trust-seeking reward function that facilitates trust establishment without significantly sacrificing the team performance. © 2016 IEEE.","human-robot interaction; Trust; trust dynamics","Agricultural robots; Decision making; Man machine systems; Robot programming; Human robot Interaction (HRI); Human-robot-team; Optimal policies; Psychology Model; Reward function; Team performance; Trust dynamics; Trust establishment; Social robots",,,,,"Army Research Laboratory, ARL: W911NF-20-2-0087","Manuscript received October 15, 2020; accepted February 21, 2021. Date of publication March 19, 2021; date of current version April 14, 2021. This letter was recommended for publication by Associate Editor A. LaViers and Editor G. Venture upon evaluation of the reviewers’ comments. This work was supported by ARL Cooperative Agreement Number W911NF-20-2-0087. (Corresponding author: X. Jessie Yang.) The authors are with the Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48105, U.S. (e-mail: yaohuig@umich.edu; shicong@umich.edu; xijyang@umich.edu). Digital Object Identifier 10.1109/LRA.2021.3067626",,,,,,,,,,"Sheridan, T.B., Human-robot interaction: Status and challenges (2016) Hum. Factors, 58 (4), pp. 525-532; Azevedo-Sa, H., Jayaraman, S.K., Yang, X.J., Robert, L.P., Tilbury, D.M., Context-adaptive management of drivers' trust in automated vehicles (2020) IEEE Robot. Automat. Lett., 5 (4), pp. 6908-6915. , Oct; Yang, X.J., Unhelkar, V.V., Li, K., Shah, J.A., Evaluating effects of user experience and system transparency on trust in automation (2017) Proc. 12thACM/IEEE Int. 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Psychol., 61 (5), pp. 630-633. , Oct; Faulkner, P., Simpson, T., (2017) The Philosophy of Trust, , London, U.K.: Oxford University Press; Lewis, M., Sycara, K., Walker, P., (2018) The Role of Trust in Human-Robot Interaction, pp. 135-159. , Cham: Springer International Publishing","Yang, X.J.; Department of Industrial and Operations Engineering, United States; email: xijyang@umich.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85103276225
"Dills P., Dawson-Elli A., Gruben K., Adamczyk P., Zinn M.","57211140140;57225245673;6701562142;57197613393;7004128002;","An Investigation of a Balanced Hybrid Active-Passive Actuator for Physical Human-Robot Interaction",2021,"IEEE Robotics and Automation Letters","6","3","9372770","5849","5856",,,"10.1109/LRA.2021.3064497","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102637000&doi=10.1109%2fLRA.2021.3064497&partnerID=40&md5=dbfe7e880c8fc1721999257ef626f9b2","Mechanical Engineering Department, University of Wisconsin Madison, Madison, WI  53726, United States; Deparment of Kinsesiology, University of Wisconsin Madison, Madison, WI  53726, United States","Dills, P., Mechanical Engineering Department, University of Wisconsin Madison, Madison, WI  53726, United States; Dawson-Elli, A., Mechanical Engineering Department, University of Wisconsin Madison, Madison, WI  53726, United States; Gruben, K., Deparment of Kinsesiology, University of Wisconsin Madison, Madison, WI  53726, United States; Adamczyk, P., Mechanical Engineering Department, University of Wisconsin Madison, Madison, WI  53726, United States; Zinn, M., Mechanical Engineering Department, University of Wisconsin Madison, Madison, WI  53726, United States","Cooperative robots or 'cobots' promise to allow humans and robots to work together more closely while maintaining safety. However, to date the capabilities of cobots are greatly diminished compared to industrial robots in terms of the force and power they are able to safely produce. This is in part due to the actuation choices of cobots. Low impedance robotic actuators aim to solve this problem by attempting to provide an actuator with a combination of low output impedance and a large bandwidth of force control. In short the ideal actuator has a large dynamic range. Existing actuators success and performance has been limited. We propose a high force and high power balanced hybrid active-passive actuator which aims to increase the actuation capability of low impedance actuators and to safely enable high performance larger force and workspace robots. Our balanced hybrid actuator does so, by combining and controlling a series elastic actuator, a small DC motor, and a particle brake in parallel. The actuator provides low and high frequency power producing active torques, along with power absorbing passive torques. Control challenges and advantages of hybrid actuators are discussed and overcome through the use of trajectory optimization, and the safety of the new actuator is evaluated. © 2016 IEEE.","actuation; Human-robot interaction","Actuators; Agricultural robots; DC motors; Industrial robots; Actuation capability; Control challenges; Cooperative robots; Low and high frequencies; Low output impedance; Physical human-robot interactions; Series elastic actuators; Trajectory optimization; Human robot interaction",,,,,"National Science Foundation, NSF: NSF 1830516","Manuscript received October 15, 2020; accepted February 16, 2021. Date of publication March 8, 2021; date of current version June 16, 2021. This letter was recommended for publication by Associate Editor D. Losey and Editor G. Venture upon evaluation of the reviewers’ comments. This work was supported by the National Science Foundation under Grant NSF 1830516. (Corresponding author: Patrick Dills.) Patrick Dills, Alexander Dawson-Elli, Peter Adamczyk, and Michael Zinn are with the Mechanical Engineering Department, University of Wisconsin Madison, Madison, WI 53726 USA (e-mail: pdills @wisc.edu; dawsonelli @wisc.edu; peter.adamczyk@wisc.edu; mzinn @wisc.edu).",,,,,,,,,,"Vallery, H., Veneman, J., Van Asseldonk, E., Ekkelenkamp, R., Buss, M., Kooij Der, H.Van, Compliant actuation of rehabilitation robots (2008) Ieee Robot. Automat. Mag., 15 (3), pp. 60-69. , Sep; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Hum. Robot Interaction Cooperative Robots, 1, pp. 399-406. , Pittsburgh, PA, USA; Zinn, M., Roth, B., Khatib, O., Salisbury, J.K., A new actuation approach for human friendly robot design (2004) Int. J. Robot. 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Ieee World Haptics Conf., pp. 283-288. , Tokyo, Japan, Jul; Klamka, J., (2019) Controllability Minimum Energy Control, 162. , Cham: Springer International Publishing; Robinson, D.W., (2000) Design and Analysis of Series Elasticity in Closed-loop Actuator Force Control, , Ph.D. Dissertation, Dept. of Mechanical Eng., Massachusetts Institute of Technology, Cambridge, MA, USA; Haddadin, S., Albu-Schaffer, A., Hirzinger, G., The role of the robot mass and velocity in physical human-robot interaction -Part I: Nonconstrained blunt impacts (2008) Proc. Ieee Int. Conf. Robot. Automat., pp. 1331-1338. , Pasadena, CA, USA, May; Robla-Gomez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Torre-Ferrero, C., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) Ieee Access, 5, pp. 26754-26773; Shin, D., (2011) A New Actuation Approach for Bio-inspired Human-freindly Robots, , Ph.D. Dissertation, Dept. Mech. Eng., Stanford Univ., Stanford, CA, USA; Kelly, M., An introduction to trajectory optimization: Howto do your own direct collocation (2017) Siam Rev., 59 (4), pp. 849-904. , Jan; Dills, P., Colonnese, N., Agarwal, M., Zinn, P., A hybrid active-passive actuation and control approach for kinesthetic handheld haptics (2020) Proc. Ieee Haptics Symp. (HAPTICS), pp. 690-697. , Crystal City, VA, USA, Mar","Dills, P.; Mechanical Engineering Department, United States; email: pdills@wisc.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85102637000
"Innes J.M., W. Morrison B.","57193917288;35867834000;","Experimental Studies of Human–Robot Interaction: Threats to Valid Interpretation from Methodological Constraints Associated with Experimental Manipulations",2021,"International Journal of Social Robotics","13","4",,"765","773",,2,"10.1007/s12369-020-00671-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087668679&doi=10.1007%2fs12369-020-00671-8&partnerID=40&md5=9c1bd72caf5ee15b13e2fddebc081834","Australian College of Applied Psychology, 16 Coglin Street, Adelaide, SA  5000, Australia; University of South Australia, Adelaide, Australia; Charles Sturt University, Bathurst, Australia; Macquarie University, Sydney, Australia","Innes, J.M., Australian College of Applied Psychology, 16 Coglin Street, Adelaide, SA  5000, Australia, University of South Australia, Adelaide, Australia; W. Morrison, B., Charles Sturt University, Bathurst, Australia, Macquarie University, Sydney, Australia","The rapid development of artificial intelligence brings with it the increasing likelihood of ubiquitous interaction between humans and robots. A significant contribution to studying human–robot interactions (HRI) comes from experimental studies, whereby humans and robots interact in controlled conditions and researchers observe and measure the reactions of humans (and robots). The use of experiments to understand human interactions has long been a central source of information in the field of experimental social psychology. These studies have yielded numerous major insights into the causes and outcomes of interaction. The methodology of experiments, however, including the demands made upon human participants to behave in predictable ways and the impact of experimenters’ expectancies upon results, has been a focus of much critical analysis. We examined a sample of 100 high impact HRI studies for evidence of potentially contaminating experimental artefacts and/or authors’ awareness of such factors. In our conclusions we highlight several methodological issues that appeared frequently in our sample, which may impede generalisations from laboratory experiments to real-world settings. Ultimately, we suggest that researchers may need to reformulate the methodologies used to study the unique features of HRI, and offer a number of recommendations for researchers designing HRI experiments. © 2020, Springer Nature B.V.","Demand characteristics; Experiment artefacts; Experimenter bias; Human–robot interaction","Agricultural robots; Intelligent robots; Controlled conditions; Critical analysis; Human interactions; Laboratory experiments; Real world setting; Robot interactions; Social psychology; Unique features; Social robots",,,,,,,,,,,,,,,,"Barber, T.X., Silver, M.J., Fact, fiction and the experimenter-bias effect (1968) Psychol Bull, 70, pp. 1-29; Bargh, J.A., Chen, M., Burrows, L., Automaticity of social behaviour: direct effect of trait construct and stereotype activation in action (1996) J Pers Soc Psychol, 71, pp. 230-244; Baumeister, R.F., Vohs, K.D., Funder, D.C., Psychology as the science of self-reports and finger movements: What happened to actual behavior? 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85087668679
"Onnasch L., Roesler E.","35111634600;57217215081;","A Taxonomy to Structure and Analyze Human–Robot Interaction",2021,"International Journal of Social Robotics","13","4",,"833","849",,6,"10.1007/s12369-020-00666-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087380676&doi=10.1007%2fs12369-020-00666-5&partnerID=40&md5=6a11d6a7f8cf4039bb6480612266a6c6","Humboldt-Universität zu Berlin, Rudower Chaussee 18, Berlin, 12489, Germany; Technische Universität Berlin, Marchstr. 12, Berlin, 10587, Germany","Onnasch, L., Humboldt-Universität zu Berlin, Rudower Chaussee 18, Berlin, 12489, Germany; Roesler, E., Technische Universität Berlin, Marchstr. 12, Berlin, 10587, Germany","Robotic systems are one of the core technologies that will shape our future. Robots already change our private and professional life by working together with humans in various domains. Evoked by this increasing trend, great variability exists in terms of robots and interaction scenarios. This has boosted research regarding shaping factors of human–robot interaction (HRI). Nevertheless, this variety hinders the comparability and the generalizability of insights. What is needed for efficient research is a structured approach that allows the analysis of superordinate attributes, making previous HRI research comparable, revealing research gaps and thus guiding future research activities. Based on the review of previous HRI frameworks we developed a new HRI taxonomy that (1) takes into account the human, the robot, the interaction and the context of the HRI, (2) is applicable to various HRI scenarios and (3) provides predefined categories to enable structured comparisons of different HRI scenarios. A graphical representation of the taxonomy, including all possible classifications, eases the application to specific HRI scenarios. To demonstrate the use and value of this taxonomy, it is applied to different studies in HRI in order to identify possible reasons for contrasting results. The exemplified applications of the taxonomy underline its value as a basis for reviews and meta-analyses. Moreover, the taxonomy offers a framework for future HRI research as it offers guidance for systematic variations of distinctive variables in HRI. © 2020, The Author(s).","Collaboration; Collaborative robots; HRI framework; Human–robot interaction; Robot morphology; Taxonomy","Agricultural robots; Taxonomies; Core technology; Graphical representations; Professional life; Research activities; Robot interactions; Robotic systems; Structured approach; Systematic variation; Human robot interaction",,,,,"Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, BAuA: F2369","Open Access funding provided by Projekt DEAL. This article is based on an earlier published short essay in baua:Fokus, an online publication platform of the Federal Institute for Occupational Safety and Health (Germany) []. The HRI taxonomy was developed as part of the project “Mensch-Roboter-Teams (F2369)”, funded by the Federal Institute for Occupational Safety and Health (BAuA), and together with HFC Human-Factors-Consult GmbH.",,,,,,,,,,"Rosheim, M., (2006) Leonardo’s lost robots, , Springer, Berlin; Čapek, K., (1920) Rossum’s universal robots, p. 1. , Prague, London; Miller, R.K., Robots in industry (1989) Industrial robot handbook. VNR Competitive Manufacturing Series, pp. 1-9. , Springer, Boston; Hinds, P.J., Roberts, T.L., Jones, H., Whose job is it anyway? 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Baua, , https://doi.org/10.21934/baua:fokus20160630, Berlin","Onnasch, L.; Humboldt-Universität zu Berlin, Rudower Chaussee 18, Germany; email: linda.onnasch@hu-berlin.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85087380676
"Sandoval E.B., Brandstatter J., Yalcin U., Bartneck C.","37057915200;57217310473;57217309619;6602256988;","Robot Likeability and Reciprocity in Human Robot Interaction: Using Ultimatum Game to determinate Reciprocal Likeable Robot Strategies",2021,"International Journal of Social Robotics","13","4",,"851","862",,1,"10.1007/s12369-020-00658-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087083511&doi=10.1007%2fs12369-020-00658-5&partnerID=40&md5=75d4954c8f599bc854d11b1141c01f8a","University of New South Wales, Arts and Design, Sydney, Australia; HITLab NZ, University of Canterbury, Christchurch, New Zealand","Sandoval, E.B., University of New South Wales, Arts and Design, Sydney, Australia, HITLab NZ, University of Canterbury, Christchurch, New Zealand; Brandstatter, J., HITLab NZ, University of Canterbury, Christchurch, New Zealand; Yalcin, U., HITLab NZ, University of Canterbury, Christchurch, New Zealand; Bartneck, C., HITLab NZ, University of Canterbury, Christchurch, New Zealand","Among of the factors that affect likeability, reciprocal response towards the other party is one of the multiple variables involved in social interaction. However, in HRI, likeability is constrained to robot behavior, since mass-produced robots will have identical physical embodiment. A reciprocal robot response is desirable in order to design robots as likeable agents for humans. In this paper, we discuss how perceived likeability in robots is a crucial multi-factorial phenomenon that has a strong influence on interactions based on reciprocal robot decisions. Our general research question is: What type of reciprocal robot behavior is perceived as likeable for humans when the robot’s decisions affect them? We designed a between/within 2 × 2 × 2 experiment in which the participant plays our novel Alternated Repeated Ultimatum Game (ARUG) for 20 rounds. The robot used in the experiment is an NAO robot using four different reciprocal strategies. Our results suggest that participants tend to reciprocate more towards the robot who starts the game and using the pure reciprocal strategy compared with other combined strategies (Tit for Tat, Inverse Tit for Tat and Reciprocal Offer and Inverse Reciprocal Offer). These results confirm that the Norm of the Reciprocity applies in HRI when participants play ARUG with social robots. However, the human reciprocal response also depends on the profits gained in the game and who starts the interaction. Similarly, the likeability score is affected by robot strategies such as reciprocal (Robot A) and generous (Robot C). and there are some discrepancies in the likeability score between the reciprocal robot and the generous robot behavior. © 2020, Springer Nature B.V.","Alternated Repeated Ultimatum Game; Cooperation; Game Theory; Human–robot interaction; Reciprocity","Agricultural robots; Social robots; General research questions; Likeability; Robot behavior; Social interactions; Tit for tats; Ultimatum game; Machine design",,,,,"John Templeton Foundation, JTF: 36617; University of Canterbury, UC; Consejo Nacional de Ciencia y Tecnología, CONACYT","The authors would like to acknowledge the support from NZi3 and NEC NZ Corp., in particular to David Humm and Glen Cameron. We also want to thank the support of the UC International Doctoral Scholarship, CONACYT Scholarship and John Templeton Foundation (Award ID 36617). Additionally, we wish to thank the other members in the HIT Lab NZ for their useful advice and good ideas. Thanks to Philippa Beckman and Julian Beiboer for their extensive proofreading. This experiment was approved by the Human Ethics Committee of the University of Canterbury under the reference 2013/23/LR-PS","The authors would like to acknowledge the support from NZi3 and NEC NZ Corp., in particular to David Humm and Glen Cameron. We also want to thank the support of the UC International Doctoral Scholarship, CONACYT Scholarship and John Templeton Foundation (Award ID 36617). Additionally, we wish to thank the other members in the HIT Lab NZ for their useful advice and good ideas. Thanks to Philippa Beckman and Julian Beiboer for their extensive proofreading. This experiment was approved by the Human Ethics Committee of the University of Canterbury under the reference 2013/23/LR-PS",,,,,,,,,"Kahn, P.H., Ishiguro, H., Friedman, B., Kanda, T., What is a human?—Toward psychological benchmarks in the field of human–robot interaction. 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Evaluation through the Ultimatum Game (2012) IEEE RO-MAN, 2012, pp. 809-814; Short, E., Hart, J., Vu, M., Scassellati, B., No fair. An interaction with a cheating robot (2010) 2010 5Th ACM/IEEE International Conference on human–robot Interaction (HRI), pp. 219-226; Litoiu, A., Ullman, D., Kim, J., Scassellati, B., Evidence that robots trigger a cheating detector in humans (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on human–robot Interaction, New York, NY, USA, pp. 165-172; Gouldner, A.W., The norm of reciprocity: a preliminary statement (1960) Am Sociol Rev, 25 (2), pp. 161-178","Sandoval, E.B.; University of New South Wales, Australia; email: e.sandoval@unsw.edu.au",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85087083511
"Webb H., Dumitru M., van Maris A., Winkle K., Jirotka M., Winfield A.","57189389446;57226003940;57193757921;57201504964;8850301900;7006158694;","Role-Play as Responsible Robotics: The Virtual Witness Testimony Role-Play Interview for Investigating Hazardous Human-Robot Interactions",2021,"Frontiers in Robotics and AI","8",,"644336","","",,,"10.3389/frobt.2021.644336","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110129864&doi=10.3389%2ffrobt.2021.644336&partnerID=40&md5=f4b1c642dda294d35f75ea8e46ca3b2d","Department of Computer Science, University of Oxford, Oxford, United Kingdom; Bristol Robotics Lab, University of West of England, Bristol, United Kingdom; Royal Institute of Technology, Stockholm, Sweden","Webb, H., Department of Computer Science, University of Oxford, Oxford, United Kingdom; Dumitru, M., Department of Computer Science, University of Oxford, Oxford, United Kingdom; van Maris, A., Bristol Robotics Lab, University of West of England, Bristol, United Kingdom; Winkle, K., Royal Institute of Technology, Stockholm, Sweden; Jirotka, M., Department of Computer Science, University of Oxford, Oxford, United Kingdom; Winfield, A., Bristol Robotics Lab, University of West of England, Bristol, United Kingdom","The development of responsible robotics requires paying attention to responsibility within the research process in addition to responsibility as the outcome of research. This paper describes the preparation and application of a novel method to explore hazardous human-robot interactions. The Virtual Witness Testimony role-play interview is an approach that enables participants to engage with scenarios in which a human being comes to physical harm whilst a robot is present and may have had a malfunction. Participants decide what actions they would take in the scenario and are encouraged to provide their observations and speculations on what happened. Data collection takes place online, a format that provides convenience as well as a safe space for participants to role play a hazardous encounter with minimal risk of suffering discomfort or distress. We provide a detailed account of how our initial set of Virtual Witness Testimony role-play interviews were conducted and describe the ways in which it proved to be an efficient approach that generated useful findings, and upheld our project commitments to Responsible Research and Innovation. We argue that the Virtual Witness Testimony role-play interview is a flexible and fruitful method that can be adapted to benefit research in human robot interaction and advance responsibility in robotics. © Copyright © 2021 Webb, Dumitru, van Maris, Winkle, Jirotka and Winfield.","human-robot interaction; interview; methods; responsible research and innovation; role-play",,,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/S005099/1","RoboTIPS: Developing Responsible Robots for the Digital Economy is an EPSRC Established Career Fellowship awarded to Professor Marina Jirotka. Award reference EP/S005099/1.",,,,,,,,,,"Benford, S., Greenhalgh, C., Anderson, B., Jacobs, R., Golembewski, M., Jirotka, M., The Ethical Implications of HCI's Turn to the Cultural (2015) ACM Trans. Comput.-Hum. 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Pol, 42 (9), pp. 1568-1580; Stokoe, E., The Conversation Analytic Role-Play Method (CARM): A Method for Training Communication Skills as an Alternative to Simulated Role-Play (2014) Res. Lang. Soc. Interaction, 47 (3), pp. 255-265; Tonkin, M., Vitale, J., Herse, S., Williams, M.-A., Judge, W., Wang, X., Design Methodology for the UX of HRI (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415. , New York, Association for Computing Machinery; Vallès-Peris, N., Angulo, C., Domènech, M., Children's Imaginaries of Human-Robot Interaction in Healthcare (2018) Ijerph, 15 (5), p. 970; Villamor, G.B., Badmos, B.K., Grazing Game: A Learning Tool for Adaptive Management in Response to Climate Variability in Semiarid Areas of Ghana (2016) Ecol. Soc, 21 (1). , https://www.jstor.org/stable/26270341; Webb, H., Jirotka, M., F.T. Winfield, A., Winkle, K., Human-robot Relationships and the Development of Responsible Social Robots (2019) ACM Proc. Halfway Future Symp, pp. 1-7; Weiss, A., Huber, A., (2016) User Experience of a Smart Factory Robot: Assembly Line Workers Demand Adaptive Robots, , 1606.03846; Weiss, A., Huber, A., Minichberger, J., Ikeda, M., First Application of Robot Teaching in an Existing Industry 4.0 Environment: Does It Really Work? (2016) Societies, 6 (3), p. 20; Wurhofer, D., Meneweger, T., Fuchsberger, V., Tscheligi, M., Deploying Robots in a Production Environment: A Study on Temporal Transitions of Workers’ Experiences (2015) Human-Computer Interaction – INTERACT 2015, pp. 203-220. , Julio A., (ed), Springer International Publishing, Editors; Winfield, A.F.T., Jirotka, M., The Case for an Ethical Black Box (2017) Lecture Notes in Computer Science, 10454. , Gao Y., Fallah S., Jin Y., Lekakou C., (eds), Cham, Springer, Editors; Winfield, A.F.T., Winkle, K., Webb, H., Lyngs, U., Jirotka, M., MacraeC Jirotka, M., Robot Accident Investigation: a Case Study in Responsible Robotics (2021) Software Engineering for Robotics, , Cham, Springer, :, Editors","Jirotka, M.; Department of Computer Science, United Kingdom; email: marina.jirotka@cs.ox.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110129864
"Alves-Oliveira P., Lupetti M.L., Luria M., Löffler D., Gamboa M., Albaugh L., Kamino W., K. Ostrowski A., Puljiz D., Reynolds-Cuéllar P., Scheunemann M., Suguitan M., Lockton D.","56515305600;56685107500;56895782800;56096474900;57222464648;57190166429;57220182215;57217659921;55809478000;57197787644;56549821200;57201559535;34875194700;","Collection of Metaphors for Human-Robot Interaction",2021,"DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere",,,,"1366","1379",,,"10.1145/3461778.3462060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110173056&doi=10.1145%2f3461778.3462060&partnerID=40&md5=03240c08b21f31984263a9ece05fd2b0","University of Washington, United States; Delft University of Technology, Netherlands; Carnegie Mellon University, United States; University of Siegen, Germany; Chalmers University of Technology, Sweden; Indiana University, United States; Mit Media Lab Mit, United States; Karlsruhe Institute of Technology, Germany; University of Hertfordshire, United Kingdom; Cornell University, United States; Eindhoven University of Technology, Netherlands","Alves-Oliveira, P., University of Washington, United States; Lupetti, M.L., Delft University of Technology, Netherlands; Luria, M., Carnegie Mellon University, United States; Löffler, D., University of Siegen, Germany; Gamboa, M., Chalmers University of Technology, Sweden; Albaugh, L., Carnegie Mellon University, United States; Kamino, W., Indiana University, United States; K. Ostrowski, A., Mit Media Lab Mit, United States; Puljiz, D., Karlsruhe Institute of Technology, Germany; Reynolds-Cuéllar, P., Mit Media Lab Mit, United States; Scheunemann, M., University of Hertfordshire, United Kingdom; Suguitan, M., Cornell University, United States; Lockton, D., Eindhoven University of Technology, Netherlands","The word ""robot""frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect - they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term ""robots,""and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots' physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future. © 2021 ACM.","design research; fixation; human-robot interaction; metaphors; robot assumptions","Man machine systems; Existing problems; Real-world; Reframing; Surface current; Human robot interaction",,,,,,,,,,,,,,,,"Ahmad, M.I., Mubin, O., Orlando, J., Adaptive social robot for sustaining social engagement during long-term children-robot interaction (2017) International Journal of Human-Computer Interaction, 33 (12), pp. 943-962; Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Guide to build yolo, a creativity-stimulating robot for children (2019) HardwareX, 6, p. e00074; Arthur, K., (1964) The Act of Creation, 751. , London: Hutchinson; Auger, J., Living with robots: A speculative design approach (2014) Journal of Human-Robot Interaction, 3 (1), pp. 20-42; Baraka, K., Alves-Oliveira, P., Ribeiro, T., An extended framework for characterizing social robots (2020) Human-Robot Interaction, pp. 21-64. , Springer, Cham; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanovic, S., (2020) Humanrobot Interaction: An Introduction, , Cambridge University Press; Boer, L., Bewley, H., Reconfiguring the appearance and expression of social robots by acknowledging their otherness (2018) Conference on Designing Interactive Systems, pp. 667-677. , June; Bradshaw, J.M., Hoffman, R.R., Woods, D.D., Johnson, M., The seven deadly myths of"" autonomous systems (2013) Ieee Intelligent Systems, 28 (3), pp. 54-61; Casakin, H., Metaphors in the design studio: Implications for education (2004) Ds 33: Proceedings of E&pde 2004, the 7th International Conference on Engineering and Product Design Education, pp. 265-273. , Delft, the Netherlands, 02.-03.09. 2004; Casakin, H.P., Assessing the use of metaphors in the design process (2006) Environment and Planning B: Planning and Design, 33 (2), pp. 253-268; Casakin, H.P., Metaphors in design problem solving: Implications for creativity (2007) International Journal of Design, 1 (2); Cheon, E., Su, N.M., Futuristic autobiographies: Weaving participant narratives to elicit values around robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 388-397. , February; Cila, N., Hekkert, P., Visch, V., Source selection in product metaphor generation: The effects of salience and relatedness (2014) International Journal of Design, 8 (1); Coyne, R., (1995) Designing Information Technology in the Postmodern Age: From Method to Metaphor, , Mit Press; Coulton, P., Lindley, J.G., More-than human centred design: Considering other things (2019) The Design Journal, 22 (4), pp. 463-481; Crilly, N., Fixation and creativity in concept development: The attitudes and practices of expert designers (2015) Design Studies, 38, pp. 54-91; Dorst, K., The core of 'design thinking' and its application (2011) Design Studies, 32 (6), pp. 521-532; Dunne, A., (2008) Hertzian Tales: Electronic Products, Aesthetic Experience, and Critical Design, , MIT press; Fernaeus, Y., Ljungblad, S., Jacobsson, M., Taylor, A., Where third wave hci meets hri: Report from a workshop on user-centred design of robots (2009) 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 293-294. , March), IEEE; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-308. , March), IEEE; Dunne, A., Raby, F., (2007) Technological Dreams Series: No. 1. 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The usus evaluation framework for human-robot interaction (2009) AISB2009: Proceedings of the Symposium on New Frontiers in Human-robot Interaction, 4 (1), pp. 11-26; (2021) Why Robotics Companies Fail, , https://www.freshconsulting.com/wp-content/uploads/2020/06/Why-Robotics-Fail-Fresh-Consulting.pdf, Web. 10 February; Youmans, R.J., The effects of physical prototyping and group work on the reduction of design fixation (2011) Design Studies, 32 (2), pp. 115-138",,,"ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","Association for Computing Machinery, Inc","2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere, DIS 2021","28 June 2021 through 2 July 2021",,169894,,9781450384766,,,"English","DIS - Proc. ACM Des. Interact. Syst. Conf.: Nowhere Everywhere",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85110173056
"Cila N., Zaga C., Lupetti M.L.","55078412200;56429717300;56685107500;","Learning from robotic artefacts: A quest for strong concepts in Human-Robot Interaction",2021,"DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere",,,,"1356","1365",,,"10.1145/3461778.3462095","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110114214&doi=10.1145%2f3461778.3462095&partnerID=40&md5=9a1181c58904de666c21c253e89d2d00","Department of Human-Centered Deign, Delft University of Technology, Netherlands; Human-Centred Design Group, University of Twente, Netherlands","Cila, N., Department of Human-Centered Deign, Delft University of Technology, Netherlands; Zaga, C., Human-Centred Design Group, University of Twente, Netherlands; Lupetti, M.L., Department of Human-Centered Deign, Delft University of Technology, Netherlands","This paper is a methodological replication of Barendregt et al. [11], who urged Child-Computer Interaction field to embrace Intermediate Level Knowledge as a meaningful and valid way of generating knowledge. We extend this epistemological gap to the Human-Robot Interaction (HRI). Currently, artefact-centered papers - papers that present the development of an artefact - seem to be one of the primary ways that the HRI field generates knowledge. In this paper, we made an analysis of all papers presented at the HRI Conference from 2006 to 2020. Our results indicate that the 41,2 % of the papers were artefact-centered; and the impact of them, measured in the number of citations, was significantly lower than other kinds of papers. We used 23 artefact-centered papers to formulate two strong concepts and investigate how the foundational design epistemology about intermediate-level knowledge and RtD can contribute to other design-related disciplines to produce useful and valuable knowledge. © 2021 Owner/Author.","Artefact-centered paper; Epistemology; Human-Robot Interaction; Intermediate-level knowledge; Strong concepts","Educational robots; Man machine systems; Robotics; Child-computer interactions; Human robot Interaction (HRI); Intermediate level; Human robot interaction",,,,,,,,,,,,,,,,"Orn Adalgeirsson, S., Breazeal, C., Mebot: A robotic platform for socially embodied telepresence (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Ieee, pp. 15-22. , https://doi.org/10.1109/HRI.2010.5453272; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63. , https://doi.org/10.5898/JHRI.6.1.Admoni, 2017; Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Yolo, a robot for creativity: A co-design study with children (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 423-429. , https://doi.org/10.1145/3078072.3084304; Anderson-Bashan, L., Megidish, B., Erel, H., Wald, I., Hoffman, G., Zuckerman, O., Grishko, A., The greeting machine: An abstract robotic object for opening encounters (2018) 2018 27th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN). Ieee, pp. 595-602. , https://doi.org/DOI:10.1109/ROMAN.2018.8525516; Andrist, S., Gleicher, M., Mutlu, B., Looking coordinated: Bidirectional gaze mechanisms for collaborative interaction with virtual characters (2017) Proceedings of the 2017 Chi Conference on Human Factors in Computing Systems, pp. 2571-2582. , https://doi.org/10.1145/3025453.3026033; Andrist, S., Zhi Tan, X., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Ieee, pp. 25-32; Araujo, T., Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions (2018) Computers in Human Behavior, 85, pp. 183-189. , https://doi.org/10.1016/j.chb.2018.03.051, 2018; Auger, J., Living with robots: A speculative design approach (2014) Journal of Human-Robot Interaction, 3 (1), pp. 20-42. , 2014; Barbier, L., Fointiat, V., To be or not be human-like in virtual world (2020) Frontiers in Computer Science, 2 (15), p. 00015. , https://doi.org/10.3389/fcomp.2020, 2020; Bardzell, J., Bardzell, S., Dalsgaard, P., Gross, S., Halskov, K., Documenting the research through design process (2016) Proceedings of the 2016 Acm Conference on Designing Interactive Systems, pp. 96-107. , https://doi.org/10.1145/2901790.2901859; Barendregt, W., Torgersson, O., Eriksson, E., Börjesson, P., Intermediate-level knowledge in child-computer interaction: A call for action (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 7-16; Bartneck, C., Reviewers' scores do not predict impact: Bibliometric analysis of the proceedings of the human-robot interaction conference (2017) Scientometrics, 110 (1), pp. 179-194. , https://doi.org/10.1145/2317956.2317967, 2017; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanovic, S., (2020) Human-robot Interaction: An Introduction, , Cambridge University Press; Amund Basballe, D., Halskov, K., Dynamics of research through design (2012) Proceedings of the Designing Interactive Systems Conference, pp. 58-67; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of hri to methodology and reporting recommendations (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 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ACM Des. Interact. Syst. Conf.: Nowhere Everywhere",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85110114214
"Abdullah M., Ahmad M., Han D.","26650323000;57210116830;56152561800;","Hierarchical Attention Approach in Multimodal Emotion Recognition for Human Robot Interaction",2021,"2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021",,,"9501446","","",,,"10.1109/ITC-CSCC52171.2021.9501446","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114002304&doi=10.1109%2fITC-CSCC52171.2021.9501446&partnerID=40&md5=9948b15625d02b02febfbf0a488b8ecb","Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea","Abdullah, M., Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea; Ahmad, M., Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea; Han, D., Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea","The ability to perceive human emotions is one of the key elements that may promise a natural, genuine and more reliable human robot interaction. Though emotional perception in human robot interaction has been challenged by many difficulties, the lack of contextual understanding is can be attributed as the biggest hurdle in this regard. Most of the literature refers to the datasets developed in controlled environment to validate the performance of their systems which happens to be really good. Still those systems are very far from achieving that kind of performance in real-life scenarios. In this paper a multimodal emotion recognition strategy is presented, that uses voice features in addition to the facial expressions to determine the emotional state of the user. A hierarchical attention layer is used to for feature fusion purpose. The final system is end-to-end trainable, multimodal approach makes it more resilient to the changes in environment. The achieved 76.3% accuracy for eNterface05 video dataset, which is higher than the any single modality approach in the comparison. © 2021 IEEE.","Acoustic Emotion Recognition; Affective Computing; Emotion Recognition; Facial Expressions Recognition; HCI; HRI; Human Computer Interaction; Human Robot Interaction; Internet of Things; Multimodal","Behavioral research; Computer circuits; Man machine systems; Speech recognition; Contextual understanding; Controlled environment; Emotional state; Facial Expressions; Feature fusion; Multi-modal approach; Multimodal emotion recognition; Video dataset; Social robots",,,,,,,,,,,,,,,,"Weiser, M., The computer for the 21st century (1991) Scientific American, 265 (3), pp. 66-75; Salovey, P., Mayer, J.D., Emotional intelligence (1990) Imaginat. Cogn. 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Tools Appl., 49 (2), pp. 277-297. , Aug; Sahoo, S., Routray, A., Emotion recognition from audio-visual data using rule based decision level fusion (2016) IEEE Students' Technology Symposium; Zhang, S.H., Wang, X.I., Zhang, G.A., Zhao, X.I., Multimodal emotion recognition integrating affective speech with facial expression (2014) WSEAS Transactions on Signal Processing., pp. 526-537; Bejani, M., Gharavian, D., Charkari, N.M., Audiovisual emotion recognition using anova feature selection method and multi-classifier neural networks (2014) Neural Comput. Appl., 24 (2), pp. 399-412. , Feb; Zhalehpour, S., Onder, O., Akhtar, Z., Erdem, C.E., BAUM-1: A spontaneous audio-visual face database of affective and mental states (2017) IEEE Trans. Affect. Comput., 8, pp. 300-313",,,,"Institute of Electrical and Electronics Engineers Inc.","36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021","27 June 2021 through 30 June 2021",,171257,,9781665435536,,,"English","Int. Tech. Conf. Circuits/Syst., Comput. Commun., ITC-CSCC",Conference Paper,"Final","",Scopus,2-s2.0-85114002304
"Feingold-Polak R., Levy-Tzedek S.","57201547249;42061792400;","Personalized Human Robot Interaction in the Unique Context of Rehabilitation",2021,"UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization",,,,"126","127",,,"10.1145/3450614.3463415","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109219139&doi=10.1145%2f3450614.3463415&partnerID=40&md5=ba34aa5937b7a6b03e3705f29c145b69","Ben-Gurion University of the Negev, Israel","Feingold-Polak, R., Ben-Gurion University of the Negev, Israel; Levy-Tzedek, S., Ben-Gurion University of the Negev, Israel","Socially assistive robots (SARs) can help meet the growing need for rehabilitation assistance; We argue that personalization of human-robot interactions in the context of rehabilitation is multi-layered, and needs to be frequently updated, as opposed to a single setting that might suffice in other contexts. In rehabilitation, personalization is not only important in order to establish engagement, but it is an essential component for the recovery of motor and cognitive abilities over a long-term interaction, and is an essential part of establishing trust between the patient and the SAR. © 2021 ACM.","Care Robots; Human-Robot Interaction; Personalization; Rehabilitation; Socially Assistive Robots","Man machine systems; Social robots; Cognitive ability; Long-term interaction; Multi-layered; Personalizations; Socially assistive robots; Patient rehabilitation",,,,,,,,,,,,,,,,"Kellmeyer, P., Social robots in rehabilitation: A question of trust (2018) Science Robotics, 3, p. eaat1587; Mataric, M.J., Socially assistive robotics: Human augmentation versus automation (2017) Science Robotics, 2 (4), p. eaam5410; Clabaugh, C., Mataric, M., Robots for the people, by the people: Personalizing human-machine interaction (2018) Science Robotics, 3 (21), p. eaat7451; Bamm, E.L., Exploring client-centered care experiences in in-patient rehabilitation settings (2015) Global Qualitative Nursing Research, 2, pp. 1-11; Winstein, C., Varghese, R., Been there, done that, so what's next for arm and hand rehabilitation in stroke (2018) NeuroRehabilitation, 43 (1), pp. 3-18; Langer, A., Trust in socially assistive robots: Considerations for use in rehabilitation (2019) Neuroscience Biobehavioral Reviews, 104, pp. 231-239; Kashi, S., A machine-learning model for automatic detection of movement compensations in stroke patients (2020) IEEE Transactions on Emerging Topics in Computing; Feingold Polak, R., Novel gamified system for post-stroke upper-limb rehabilitation using a social robot: Focus groups of expert clinicians 2019 International Conference on Virtual Rehabilitation (ICVR). 2019, IEEE, pp. 1-7; Feingold Polak, R., Levy-Tzedek, S., Social robot for rehabilitation: Expert clinicians and post-stroke patients' evaluation following a long-term intervention (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction., pp. 151-160; Feingold Polak, R., Levy-Tzedek, S., A robot goes to rehab: A novel gamified system for stroke rehabilitation using a socially assistive robot: Methodology feasibility testing (2020) PREPRINT, Avilable at Research Square, p. 29",,,"ACM SIGCHI;ACM SIGWEB","Association for Computing Machinery, Inc","29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021","21 June 2020 through 25 June 2020",,169764,,9781450383677,,,"English","UMAP - Adjun. Publ. ACM Conf. User Model., Adapt. Pers.",Conference Paper,"Final","",Scopus,2-s2.0-85109219139
"Rossi A., Rossi S.","57189499587;55619320887;","Engaged by a Bartender Robot: Recommendation and Personalisation in Human-Robot Interaction",2021,"UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization",,,,"115","119",,1,"10.1145/3450614.3463423","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109216352&doi=10.1145%2f3450614.3463423&partnerID=40&md5=117ae88046c7dbd5fcf39dc76c24a503","Interdepartmental Center for Advances in Robotic Surgery, University of Naples Federico Ii, Naples, Italy; Department of Electrical Engineering and Information Technologies, University of Naples Federico Ii, Naples, Italy","Rossi, A., Interdepartmental Center for Advances in Robotic Surgery, University of Naples Federico Ii, Naples, Italy; Rossi, S., Department of Electrical Engineering and Information Technologies, University of Naples Federico Ii, Naples, Italy","BRILLO (Bartending Robot for Interactive Long-Lasting Operations) project aims to deploy an autonomous robotic bartender that can naturally interact with customers in a real-world service point. In such a scenario, this work presents a multi-modal personalised recommendation method for increasing users' engagement with a bartending robot. The personalised recommendation is adopted at two levels: one for the recommendation of the service (i.e. drinks), and another for selecting the human-robot interaction (HRI) modalities. The envisioned multi-modal personalised recommendation method enables the BRILLO robot to intelligently adapt its dialogues, pose and gestures, according to the user's moods, attention behaviours, personal traits, and situational context (drink orders, group dynamics, etc.). © 2021 ACM.","human-robot interaction; personalisation; recommendation system; Service robotics","Beverages; Man machine systems; Autonomous robotics; Group dynamics; Human robot Interaction (HRI); Long lasting; Personal traits; Personalisation; Personalised recommendations; Situational context; Human robot interaction",,,,,,,,,,,,,,,,"Berezina, K., Ciftci, O., Cobanoglu, C., (2019) Robots, Artificial Intelligence, and Service Automation in Restaurants, pp. 185-219. , https://doi.org/10.1108/978-1-78756-687-320191010; Castellano, G., Pereira, A., Leite, I., Paiva, A., McOwan, P.W., Detecting user engagement with a robot companion using task and social interaction-based features (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces (ICMI-MLMI '09), pp. 119-126. , https://doi.org/10.1145/1647314.1647336, Association for Computing Machinery, New York, NY, USA; Choi, J., Lee, B., Zhang, B., (2016) Human Body Orientation Estimation Using Convolutional Neural Network, , [cs. RO]; Corina, P., Irina, E., Interaction between consumers and emerging forms of artificial intelligence: A discriminant analysis studia universitatis vasile goldis (2020) Arad-Economics Series, 30 (2), pp. 1-12. , https://doi.org/10.2478/sues-2020-0008, June 2020; Gaschler, A., Jentzsch, S., Giuliani, M., Huth, K., De Ruiter, J., Knoll, A., Social behavior recognition using body posture and head pose for human-robot interaction (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2128-2133. , https://doi.org/10.1109/IROS.2012.6385460; Hidayanti, I., Edy Herman, L., Farida, N., Engaging customers through social media to improve industrial product development: The role of customer co-creation value (2018) Journal of Relationship Marketing, 17 (1), pp. 17-28. , https://doi.org/10.1080/15332667.2018.1440137, 2018; Kuhn, S., Mostert, P., Relationship intention and relationship quality as predictors of clothing retail customers' loyalty (2018) The International Review of Retail, Distribution and Consumer Research, 28 (2), pp. 206-230. , https://doi.org/10.1080/09593969.2017.1380067, 2018; Lui, J.H., Samani, H., Tien, K., An affective mood booster robot based on emotional processing unit (2017) 2017 International Automatic Control Conference (CACS), pp. 1-6. , https://doi.org/10.1109/CACS.2017.8284239; Masuda, T., Misaki, D., Development of Japanese green tea serving robot t-bartender (2005) IEEE International Conference Mechatronics and Automation, 2005, 2, pp. 1069-1074. , https://doi.org/10.1109/ICMA.2005.1626700; Moro, C., Lin, S., Nejat, G., Mihailidis, A., Social robots and seniors: A comparative study on the influence of dynamic social features on human-robot interaction (2019) International Journal of Social Robotics, 11, pp. 5-24. , 2019; Krupasindhu Panigrahi, S., Azlinna Azizan, N., Ali Khan, M., Investigating the empirical relationship between service quality, trust, satisfaction, and intention of customers purchasing life insurance products (2018) Indian Journal of Marketing, 48 (1). , 2018; Blagojevic Popovic, O., Nikic, V., Bulatovic, I., Delibasic, M., Modeling perceived quality, customer satisfaction and probability of guest returning to the destination (2018) Montenegrin Journal of Economics, 14 (1), pp. 69-78. , https://ideas.repec.org/a/mje/mjejnl/v14y2018i1p69-78.html, 2018; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., Holthaus, P., Evaluating people's perceptions of trust in a robot in a repeated interactions study (2020) Social Robotics, pp. 453-465. , Alan R. Wagner, David Feil-Seifer, Kerstin S. Haring, Silvia Rossi, Thomas Williams, Hongsheng He, and Shuzhi Sam Ge (Eds.). Springer International Publishing, Cham; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., How social robots influence people's trust in critical situations (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1020-1025. , https://doi.org/10.1109/RO-MAN47096.2020.9223471; Rossi, A., Staffa, M., Origlia, A., Di Maro, M., Rossi, S., BRILLO: A robotic architecture for personalised long-lasting interactions in a bartending domain (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (Boulder, CO, USA) (HRI '21 Companion), pp. 426-429. , https://doi.org/10.1145/3434074.3447206, Association for Computing Machinery, New York, NY, USA; Rossi, S., Dell'Aquila, E., Russo, D., Maggi, G., Increasing engagement with chameleon robots in bartending services (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 464-469. , https://doi.org/10.1109/RO-MAN47096.2020.9223488; Rossi, S., Ferland, F., Tapus, A., User profiling and behavioral adaptation for hri: A survey (2017) Pattern Recognition Letters, 99, pp. 3-12. , https://doi.org/10.1016/j.patrec.2017.06.002UserProfilingandBehaviorAdaptationforHuman-RobotInteraction, 2017; Staffa, M., Rossi, S., Recommender interfaces: The more human-like, the more humans like (2016) Social Robotics, pp. 200-210. , Arvin Agah, John-John Cabibihan, Ayanna M. Howard, Miguel A. Salichs, and Hongsheng He (Eds.). Springer International Publishing, Cham; Szabóová, M., Sarnovský, M., Maslej Kresáková, V., Machová, K., Emotion analysis in human-robot interaction (2020) Electronics, 9 (11). , https://doi.org/10.3390/electronics9111761, 2020; Tome, D., Russell, C., Agapito, L., Lifting from the deep: Convolutional 3d pose estimation from a single image (2017) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",,,"ACM SIGCHI;ACM SIGWEB","Association for Computing Machinery, Inc","29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021","21 June 2020 through 25 June 2020",,169764,,9781450383677,,,"English","UMAP - Adjun. Publ. ACM Conf. User Model., Adapt. Pers.",Conference Paper,"Final","",Scopus,2-s2.0-85109216352
"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"687031","","",,,"10.3389/frobt.2021.687031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109005855&doi=10.3389%2ffrobt.2021.687031&partnerID=40&md5=ed2fcc4bfe304ec6b8e93e880b833815","InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium","Shi, L., InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Copot, C., InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Vanlanduit, S., InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium","Safety is an important issue in human–robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision. Common repulsive actions include distance methods, potential field methods, and safety field methods. Approaches based on machine learning are less explored regarding the selection of the repulsive action. Few research works focus on the uncertainty of the data-based approaches and consider the efficiency of the executing task during collision avoidance. In this study, we describe a system that can avoid collision with human hands while the robot is executing an image-based visual servoing (IBVS) task. We use Monte Carlo dropout (MC dropout) to transform a deep neural network (DNN) to a Bayesian DNN, and learn the repulsive position for hand avoidance. The Bayesian DNN allows IBVS to converge faster than the opposite repulsive pose. Furthermore, it allows the robot to avoid undesired poses that the DNN cannot avoid. The experimental results show that Bayesian DNN has adequate accuracy and can generalize well on unseen data. The predictive interval coverage probability (PICP) of the predictions along x, y, and z directions are 0.84, 0.94, and 0.95, respectively. In the space which is unseen in the training data, the Bayesian DNN is also more robust than a DNN. We further implement the system on a UR10 robot, and test the robustness of the Bayesian DNN and the IBVS convergence speed. Results show that the Bayesian DNN can avoid the poses out of the reach range of the robot and it lets the IBVS task converge faster than the opposite repulsive pose.1 © Copyright © 2021 Shi, Copot and Vanlanduit.","Bayesian neural network; deep learning; human–robot interaction; image-based visual servoing; safety",,,,,,,,,,,,,,,,,"Anvaripour, M., Saif, M., Collision Detection for Human-Robot Interaction in an Industrial Setting Using Force Myography and a Deep Learning Approach (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2149-2154. , IEEE; Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., Corke, P., Training Deep Neural Networks for Visual Servoing (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-8. , IEEE; Bertoni, L., Kreiss, S., Alahi, A., Monoloco: Monocular 3d Pedestrian Localization and Uncertainty Estimation (2019) Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6861-6871. , In; Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B., Simple Online and Realtime Tracking (2016) 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464-3468. , IEEE; Bjelonic, M., (2018) YOLO ROS: Real-Time Object Detection for ROS; Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D., (2015) Weight Uncertainty in Neural Networks, , arXiv preprint; Chaumette, F., Hutchinson, S., Corke, P., Visual Servoing (2016) Handbook of Robotics, pp. 841-866. , Springer, chap. Visual servoing. 2nd edition edn; Chaumette, F., Hutchinson, S., Visual Servo Control. I. Basic Approaches (2006) IEEE Robot. Automat. 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Mag, 12, pp. 40-52; Michelmore, R., Wicker, M., Laurenti, L., Cardelli, L., Gal, Y., Kwiatkowska, M., Uncertainty Quantification with Statistical Guarantees in End-To-End Autonomous Driving Control (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 7344-7350. , IEEE; Mukhoti, J., Gal, Y., (2018) Evaluating Bayesian Deep Learning Methods for Semantic Segmentation, , arXiv preprint; Nair, V., Hinton, G.E., Rectified Linear Units Improve Restricted Boltzmann Machines (2010) ICML; Parigi Polverini, M., Zanchettin, A.M., Rocco, P., A Computationally Efficient Safety Assessment for Collaborative Robotics Applications (2017) Robotics and Computer-Integrated Manufacturing, 46, pp. 25-37; Pawlowski, N., Brock, A., Lee, M.C., Rajchl, M., Glocker, B., (2017) Implicit Weight Uncertainty in Neural Networks, , arXiv preprint; Pearce, T., Brintrup, A., Zaki, M., Neely, A., High-quality Prediction Intervals for Deep Learning: A Distribution-free, Ensembled Approach (2018) International Conference on Machine Learning, pp. 4075-4084. , In; Poggi, M., Aleotti, F., Tosi, F., Mattoccia, S., On the Uncertainty of Self-Supervised Monocular Depth Estimation (2020) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3227-3237. , In; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You Only Look once: Unified, Real-Time Object Detection (2016) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788. , In; Sadeghian, H., Villani, L., Kamranian, Z., Karami, A., Visual Servoing with Safe Interaction Using Image Moments (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5479-5485. , IEEE; Safeea, M., Neto, P., Minimum Distance Calculation Using Laser Scanner and Imus for Safe Human-Robot Interaction (2019) Robotics and Computer-Integrated Manufacturing, 58, pp. 33-42; Sharkawy, A.-N., Koustoumpardis, P.N., Aspragathos, N., Human-robot Collisions Detection for Safe Human-Robot Interaction Using One Multi-Input-Output Neural Network (2020) Soft Comput, 24, pp. 6687-6719; Shi, L., Copot, C., Vanlanduit, S., A Deep Regression Model for Safety Control in Visual Servoing Applications (2020) 2020 Fourth IEEE International Conference on Robotic Computing, pp. 360-366. , Taichung, Taiwan, IRC; Shi, L., Copot, C., Vanlanduit, S., Application of Visual Servoing and Eye Tracking Glass in Human Robot Interaction: A Case Study (2019) 2019 23rd International Conference on System Theory, Control and Computing, pp. 515-520. , ICSTCCIEEE; Tahri, O., Chaumette, F., Point-based and Region-Based Image Moments for Visual Servoing of Planar Objects (2005) IEEE Trans. Robot, 21, pp. 1116-1127; Wang, Y., Ye, X., Yang, Y., Zhang, W., Collision-free Trajectory Planning in Human-Robot Interaction through Hand Movement Prediction from Vision (2017) 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 305-310. , IEEE","Shi, L.; InViLab, Belgium; email: lei.shi@uantwerpen.be",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85109005855
"Younis H.A., Mohamed A.S.A., Ab Wahab M.N., Jamaludin R., Salisu S.","57210408507;57190968285;57223397087;54401335900;57204799122;","A New Speech Recognition Model in a Human-Robot Interaction Scenario Using NAO Robot: Proposal and Preliminary Model",2021,"International Conference on Communication and Information Technology, ICICT 2021",,,,"215","220",,,"10.1109/ICICT52195.2021.9568457","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118471605&doi=10.1109%2fICICT52195.2021.9568457&partnerID=40&md5=3be5399e0e51b7d5a2dd6c1393f1d233","Universiti Sains Malaysia, School of Computer Sciences, USM, Penang, 11800, Malaysia; College of Education for Women, University of Basrah, Basrah, Iraq; Universiti Sains Malaysia, School of Computer Sciences, Minden, Penang, 11800, Malaysia; Universiti Sains Malaysia, Centre For Instructional Technology Multimedia, Minden, Penang, 11800, Malaysia; Information Technology, Federal University Dutse, Jigawa, Kano, Nigeria","Younis, H.A., Universiti Sains Malaysia, School of Computer Sciences, USM, Penang, 11800, Malaysia, College of Education for Women, University of Basrah, Basrah, Iraq; Mohamed, A.S.A., Universiti Sains Malaysia, School of Computer Sciences, Minden, Penang, 11800, Malaysia; Ab Wahab, M.N., Universiti Sains Malaysia, School of Computer Sciences, USM, Penang, 11800, Malaysia; Jamaludin, R., Universiti Sains Malaysia, Centre For Instructional Technology Multimedia, Minden, Penang, 11800, Malaysia; Salisu, S., Universiti Sains Malaysia, School of Computer Sciences, USM, Penang, 11800, Malaysia, Information Technology, Federal University Dutse, Jigawa, Kano, Nigeria","There are several terms for speech recognition. Auto speech recognition (ASR), speech-to-text, and computer speech recognition are all terms used to describe speech recognition. A single user's voice it is necessary to distinguish between speech recognition and voice recognition. The first is to translate speech into text, such as, the audible voice and concept (human speech), and the second is to define only sound, such as, animal sound, car, etc. There is no algorithm that is specifically designed for this field; instead, techniques such as N-grams and neural networks are used to explain and treat this type. Natural Language Processing (NLP), Hidden Markov Model (HMM), and Speaker Diarization (SD). The last type would be addressed in my work. Natural language processing is a computational technique that can be used and applied to various levels of linguistic analysis (dare, deep analysis) to represent natural language in a useful or more representation. It is still possible to improve current recognition and identification systems in order to achieve greater accuracy. A new approach has been proposed that distinguishes speech in four stages: speech recognition, tokenization, extracting features of speech from texts, and part speech: The three patterns of Name Entity Recognition (NER), followed by the possibility of implementing the proposed model It achieved more accurate and applied results in an educational environment by using a NAO-robot. © 2021 IEEE.","Model; NAO-robot; Natural Language Processing (NLP); Speech Recognition","Character recognition; Hidden Markov models; Human robot interaction; Linguistics; Speech; Speech recognition; Auto speech recognition; Computer speech recognition; Human speech; Humans-robot interactions; N-grams; NAO-robot; Natural language processing; Preliminary model; Recognition models; Single users; Natural language processing systems",,,,,"Universiti Sains Malaysia","This project is supported by the RUI grant (1001 / PMEDIA / 8016096), Universiti Sains Malaysia. Author acknowledges the contributions from Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia and the School of Computer Sciences at Universiti Sains Malaysia.",,,,,,,,,,"Abdulkareem, A., Somefun, T.E., Chinedum, O.K., Agbetuyi, F., Design and implementation of speech recognition system integrated with internet of things (2021) International Journal of Electrical and Computer Engineering (IJECE), 11 (2), pp. 1796-1803. , April; Bartneck, C., The end of the beginning: A reflection on the first five years of the hri conference (2011) Scientometrics, 86 (2), pp. 487-504; Khilari, P., A review on speech to text conversion methods (2015) Int. J. Adv. Res. Comput. Eng. Technol., 4 (7), pp. 3067-3072. , http://ijarcet.org/wp-content/uploads/IJARCET-VOL-4-ISSUE-7-3067-3072.pdf, P B.V. [Online]; A review on automatic speech recognition architecture and approaches (2016) Int. J. Signal Process. Image Process. Pattern Recognit., 9 (4), pp. 393-404. , K. S and C. E; Mukherjee, P., Santra, S., Bhowmick, S., Paul, A., Chatterjee, P., Deyasi, A., Development of gui for text-To-speech recognition using natural language processing (2018) 2018 2nd Int. Conf. Electron. Mater. Eng. Nano-Technology, IEMENTech 2018, pp. 1-4; Razak, Z., Sumali, S.R., Yamani, M., Idris, I., Ahmedy, I., To-Text engine for jawi character (2010) Cssr, pp. 565-568; Samad, S.A., Hussain, A., Fah, L.K., Pitch detection of speech signals using the cross-correiation technique (2000) Tencon 2000.Proceedings., pp. 283-286; Syiam, M.M., Klash, H.M., Mahmoud, I., Haggag, S.S., Hardware implementation of neural network on FPGA for accidents diagnosis of the multi-purpose research reactor of Egypt (2003) Proceedings of the 15th International Conference on Microelectronics, pp. 326-329; Blunt, P., Haskins, B., A model for incorporating an automatic speech recognition system in a noisy educational environment (2019) Proc.-2019 Int. Multidiscip. Inf. Technol. Eng. Conf. 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Human-Robot Interact., pp. 399-406",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Communication and Information Technology, ICICT 2021","5 June 2021 through 6 June 2021",,173106,,9781665439145,,,"English","Int. Conf. Commun. Inf. Technol., ICICT",Conference Paper,"Final","",Scopus,2-s2.0-85118471605
"Wang Y., Yang Y., Zhao B., Qi X., Hu Y., Li B., Sun L., Zhang L., Meng M.Q.-H.","57205460526;57206629149;55874456100;48161630700;56201676100;55628528575;57204292698;57210475746;57221185664;","Variable admittance control based on trajectory prediction of human hand motion for physical human-robot interaction",2021,"Applied Sciences (Switzerland)","11","12","5651","","",,,"10.3390/app11125651","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108882387&doi=10.3390%2fapp11125651&partnerID=40&md5=ebf0be1c9d1e39ace82e97d6096518eb","Harbin Institute of Technology, Shenzhen, 518055, China; Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; College of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China; Department of Orthopaedics, Chinese PLA General Hospital, Beijing, 100853, China; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","Wang, Y., Harbin Institute of Technology, Shenzhen, 518055, China, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Yang, Y., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Zhao, B., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Qi, X., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Hu, Y., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Li, B., Harbin Institute of Technology, Shenzhen, 518055, China; Sun, L., College of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China; Zhang, L., Department of Orthopaedics, Chinese PLA General Hospital, Beijing, 100853, China; Meng, M.Q.-H., Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","In order to achieve effective physical human–robot interaction, human dynamic characteristics needs to be considered in admittance control. This paper proposes a variable admittance control method for physical human–robot interaction based on trajectory prediction of human hand motion. By predicting the moving direction of the robot end tool under human guidance, the admittance control parameters are adjusted to reduce the interaction force. The end tool trajectory of the robot under human guidance is used for offline training of long and short-term memory neural network to generate trajectory predictors. Then the trajectory predictors are used in variable admittance control to predict the trajectory and movement direction of the robot end tool in real time. The variable admittance controller adjusts the damping matrix to reduce the damping value in the moving di-rection. Experiment results show that, using the constant admittance method as a benchmark, the interaction force of the proposed method is reduced by 23%, the trajectory error is reduced by 51%, and the operating jerk is reduced by at least 21%, which proves that the proposed method improves the accuracy and compliance of the operation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Compliance control; Human motion prediction; Long short-term memory network; Medical robots and systems; Physical human–robot interaction; Variable admittance control",,,,,,"JCYJ20180507182215361, JCYJ20190807170407391; National Natural Science Foundation of China, NSFC: U1713218; Guangdong Provincial Key Laboratory of Robotics and Intelligent Systems; Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001, 2020B0909020002; CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences","Funding: This research was funded by Key-Area Research and Development Program of Guangdong Province (No. 2019B090915001, No.2020B0909020002), in part by the National Natural Science Foundation of China (No. U1713218), in part by Shenzhen Fundamental Research Funds (No. JCYJ20180507182215361, JCYJ20190807170407391). This work is also supported by CAS Key Laboratory of Human–Machine Intelligence-Synergy Systems and Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology.",,,,,,,,,,"Su, H., Yang, C., Ferrigno, G., de Momi, E., Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery (2019) IEEE Robot. Autom. Lett, 4, pp. 1447-1453. , [CrossRef]; Losey, P.D., McDonald, G.C., Battaglia, E., O’Malley, K.M., A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction (2018) Appl. Mech. Rev, 70, p. 010804. , [CrossRef]; Dai, K., Liu, Y., Okui, M., Yamada, Y., Nakamura, T., Variable viscoelasticity handshake manipulator for physical human-robot interaction using artificial muscle and MR brake (2019) Smart Mater. Struct, 28, p. 064002. , [CrossRef]; Ohta, P., Valle, L., King, J., Design of a lightweight soft robotic arm using pneumatic artificial muscles and inflatable sleeves (2018) Soft Robot, 5, pp. 204-215. , [CrossRef] [PubMed]; Rahman, M.M., Ikeura, R., Mizutani, K., Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans (2002) JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf, 45, pp. 510-518. , [CrossRef]; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 1, pp. 644-650. , Washington, DC, USA, 11–15 May; Erden, M.S., Tomiyama, T., Identifying welding skills for training and assistance with robot (2009) Sci. Technol. Weld. 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Eng, 8, pp. 59-68; Sharkawy, A.N., Koustournpardis, P.N., Aspragathos, N., Variable admittance control for human-robot collaboration based on online neural network training Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1334-1339. , Madrid, Spain, 1–5 October 2018; Dimeas, F., Aspragathos, N., Reinforcement learning of variable admittance control for human-robot co-manipulation (2015) Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1011-1016. , Hamburg, Germany, 1 October; Maithani, H., Ramon, J.A.C., Lequievre, L., Exoscarne: Assistive strategies for an industrial meat cutting system based on physical human-robot interaction (2021) Appl. Sci, 11, p. 3907. , [CrossRef]; Medina, J.R., Endo, S., Hirche, S., Impedance-based gaussian processes for predicting human behavior during physical interaction Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3055-3061. , Stockholm, Sweden, 16–21 May 2016; Lee, K.H., Baek, S.G., Lee, H.J., Enhanced transparency for physical human-robot interaction using human arm impedance compensation (2018) IEEE/ASME Trans. Mechatron, 23, pp. 2662-2670. , [CrossRef]; He, W., Xue, C., Yu, X., Li, Z., Yang, C., Admittance-based controller design for physical human–robot interaction in the constrained task space (2020) IEEE Trans. Autom. Sci. Eng, 17, pp. 1937-1949. , [CrossRef]","Yang, Y.; Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, China; email: yy.yang1@siat.ac.cn
Li, B.; Harbin Institute of TechnologyChina; email: libing.sgs@hit.edu.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85108882387
"Toan N.-V., Khoi P.-B., Yi S.-Y.","57216717021;57201260426;7201404755;","A mlp-hedge-algebras admittance controller for physical human–robot interaction",2021,"Applied Sciences (Switzerland)","11","12","5459","","",,,"10.3390/app11125459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108624560&doi=10.3390%2fapp11125459&partnerID=40&md5=df3557aa99da74f8e4b83b962fa2ee88","Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, 01811, South Korea; School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Viet Nam","Toan, N.-V., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, 01811, South Korea; Khoi, P.-B., School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Viet Nam; Yi, S.-Y., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, 01811, South Korea","Recently, the identification of inertia and damping matrices (IIDM) and safety issues, as well as natural cooperation, are interestingly considered to enhance the quality of the physical human–robot interaction (pHRI). To cover all of these issues, advanced admittance controllers, such as those based on fuzzy logic or hedge algebras, have been formulated and successfully applied in several industrial problems. However, the inference mechanism of those kinds of controllers causes the discreteness of the super surface describing the input–output relationship in the Cartesian coordinates. As a consequence, the quality of the safe-natural cooperation between humans and robots is negatively affected. This paper presents an alternative admittance controller for pHRI by using a combination of hedge algebras and multilayer perceptron neural network (MLP), whose purpose is to create a more accurate inference mechanism for the admittance controller. To our best knowledge, this is the first time that such a neural network is considered for the inference mechanism of hedge algebras and also the first time that such an admittance controller is used for pHRI. The proposed admittance controller is verified on a teaching task using a 6-DOF manipulator. Experimental results have shown that the proposed method provides better cooperation compared with previous methods. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Fuzzy control; Hedge algebras; MLP neural network; Natural linguistic semantics; Physical human–robot interaction",,,,,,"Seoul National University of Science and Technology","Funding: This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).",,,,,,,,,,"Navarro, B., Cherubini, A., Fonte, A., Passama, R., Poisson, G., Fraisse, P., An ISO10218-compliant adaptive damping controller for safe Physical Human-Robot Interaction Proceedings of the 2016 IEEE International Conference on Robotics and Automation(ICRA), pp. 3043-3048. , Stockholm, Sweden, 16–21 May 2016; Jindai, M., Watanabe, T., A Handshake Robot System Based on a Shake-Motion Leading Model Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3330-3335. , Nice, France, 22–26 September 2008; Kovács, S., Vincze, D., Gacsi, M., Miklósi, A., Korondi, P., Fuzzy Automaton based Human-Robot Interaction Proceedings of the SAMI 2010 8th IEEE International Symposium on Applied Machine Intelligence and Informatics, pp. 165-169. , Herlany, Slovakia, 28–30 January 2010; Kovács, S., Vincze, D., Gácsi, M., Korondi, P., Interpolation based Fuzzy Automaton for Human-Robot Interaction (2009) Proceedings of the 9th IFAC Symposium on Robot Control, 42, pp. 317-322. , Gifu, Japan, 9–12 September; Dimeas, F., Moulianitis, V.C., Papakonstantinou, C., Aspragathos, N., Manipulator Performance Constraints in Cartesian Admittance Control for Human-Robot Cooperation Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3049-3054. , Stockholm, Sweden, 16–21 May 2016; Papageorgiou, D., Doulgeri, Z., A Kinematic Controller for Human-Robot Handshaking using Internal Motion Adaption Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5622-5627. , Seattle, WA, USA, 26–30 May 2015; Zhong, H., Li, X., Gao, L., Li, C., Toward Safe Human-Robot Interaction: A Fast-Response Admittance Control Method for Series Elastic Actuator (2021) IEEE Trans. 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Appl, 4, pp. 2-9. , [CrossRef]; Daniela, A., Fuzzy Rule Base Complexity Reduction: A Survey Proceedings of the 15th International Conference on Control Systems and Computer Science, pp. 33-37. , Politehnica University of Bucharest, Bucharest, Romania, 25–27 May 2005; Hsuan-Shih, L., An optimal algorithm for computing the max-min transitive closure of a fuzzy similarity matrix (2001) Fuzzy Sets Syst, 123, pp. 129-136; Feilong, L., An Efficient Centroid Type Reduction Strategy for General Type-2 Fuzzy Logic System (2008) Inf. Sci, 178, pp. 2224-2236; Toraichi, K., Katagishi, K., Sekita, I., Mori, R., Computational complexity of spline interpolation (1987) Int. J. Syst. Sci, 18, pp. 945-954. , [CrossRef]; Acharya, T., Tsai, P.S., Computational Foundations of Image Interpolation Algorithms (2007) ACM Ubiquity, 8, pp. 1-17. , [CrossRef]; Serpen, G., Gao, Z., Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network (2014) Procedia Comput. 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Yi, S.-Y.; Department of Electrical and Information Engineering, South Korea; email: suylee@seoultech.ac.kr",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85108624560
"Scalera L., Seriani S., Gallina P., Lentini M., Gasparetto A.","57191853169;56351739600;57193734123;57222726983;7005379319;","Human–robot interaction through eye tracking for artistic drawing",2021,"Robotics","10","2","54","","",,5,"10.3390/robotics10020054","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103834425&doi=10.3390%2frobotics10020054&partnerID=40&md5=c7b34c127e1c26c9cace330d8fe4e1d4","Polytechnic Department of Engineering and Architecture, University of Udine, Udine, 33100, Italy; Department of Engineering and Architecture, University of Trieste, Trieste, 34127, Italy","Scalera, L., Polytechnic Department of Engineering and Architecture, University of Udine, Udine, 33100, Italy; Seriani, S., Department of Engineering and Architecture, University of Trieste, Trieste, 34127, Italy; Gallina, P., Department of Engineering and Architecture, University of Trieste, Trieste, 34127, Italy; Lentini, M., Department of Engineering and Architecture, University of Trieste, Trieste, 34127, Italy; Gasparetto, A., Polytechnic Department of Engineering and Architecture, University of Udine, Udine, 33100, Italy","In this paper, authors present a novel architecture for controlling an industrial robot via an eye tracking interface for artistic purposes. Humans and robots interact thanks to an acquisition system based on an eye tracker device that allows the user to control the motion of a robotic manipulator with his gaze. The feasibility of the robotic system is evaluated with experimental tests in which the robot is teleoperated to draw artistic images. The tool can be used by artists to investigate novel forms of art and by amputees or people with movement disorders or muscular paralysis, as an assistive technology for artistic drawing and painting, since, in these cases, eye motion is usually preserved. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Artistic painting; Collaborative robotics; Eye tracking; Human–robot interaction; Robotics",,,,,,,,,,,,,,,,,"Majaranta, P., Bulling, A., Eye tracking and eye-based human–computer interaction (2014) Advances in Physiological Computing, pp. 39-65. , Springer: Berlin, Germany; Pasqualotto, E., Matuz, T., Federici, S., Ruf, C.A., Bartl, M., Olivetti Belardinelli, M., Birbaumer, N., Halder, S., Usability and workload of access technology for people with severe motor impairment: A comparison of brain-computer interfacing and eye tracking (2015) Neurorehabilit. Neural Repair, 29, pp. 950-957. , [CrossRef] [PubMed]; Orquin, J.L., Holmqvist, K., Threats to the validity of eye-movement research in psychology (2018) Behav. Res. Methods, 50, pp. 1645-1656. , [CrossRef] [PubMed]; dos Santos, R.D.O.J., de Oliveira, J.H.C., Rocha, J.B., Giraldi, J.D.M.E., Eye tracking in neuromarketing: A research agenda for marketing studies (2015) Int. J. Psychol. 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Humanit. Rev, 4, pp. 141-158; Tresset, P., Leymarie, F.F., Portrait drawing by Paul the robot (2013) Comput. Graph, 37, pp. 348-363. , [CrossRef]; Gülzow, J.M., Paetzold, P., Deussen, O., Recent Developments Regarding Painting Robots for Research in Automatic Painting, Artificial Creativity, and Machine Learning (2020) Appl. Sci, 10, p. 3396. , [CrossRef]; Igno-Rosario, O., Hernandez-Aguilar, C., Cruz-Orea, A., Dominguez-Pacheco, A., Interactive system for painting artworks by regions using a robot (2019) Robot. Auton. Syst, 121, p. 103263. , [CrossRef]; Karimov, A.I., Kopets, E.E., Rybin, V.G., Leonov, S.V., Voroshilova, A.I., Butusov, D.N., Advanced tone rendition technique for a painting robot (2019) Robot. Auton. Syst, 115, pp. 17-27. , [CrossRef]; Scalera, L., Seriani, S., Gasparetto, A., Gallina, P., Watercolour robotic painting: A novel automatic system for artistic rendering (2019) J. Intell. Robot. 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Neurorobot, 12, p. 81. , [CrossRef] [PubMed]; Scalera, L., Seriani, S., Gasparetto, A., Gallina, P., A Novel Robotic System for Painting with Eyes (2021) Mech. Mach. Sci, 91, pp. 191-199. , [CrossRef]; Yarbus, A.L., Eye movements during perception of complex objects (1967) Eye Movements and Vision, pp. 171-211. , Springer: Berlin, Germany; Carpenter, R.H., (1988) Movements of the Eyes, , 2nd ed.; Pion Limited: London, UK; Land, M., Tatler, B., (2009) Looking and Acting: Vision and Eye Movements in Natural Behaviour, , Oxford University Press: Oxford, UK; Rayner, K., The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search (2009) Q. J. Exp. Psychol, 62, pp. 1457-1506. , [CrossRef] [PubMed]; Young, L.R., Sheena, D., Survey of eye movement recording methods (1975) Behav. Res. Methods Instrum, 7, pp. 397-429. , [CrossRef]; Gibaldi, A., Vanegas, M., Bex, P.J., Maiello, G., Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research (2017) Behav. Res. Methods, 49, pp. 923-946. , [CrossRef] [PubMed]; Biagiotti, L., Melchiorri, C., (2008) Trajectory Planning for Automatic Machines and Robots, , Springer: Berlin, Germany; (2020), https://www.thispersondoesnotexist.com/, This Person Does Not Exist. (accessed on 26 January 2021)","Gasparetto, A.; Polytechnic Department of Engineering and Architecture, Italy; email: alessandro.gasparetto@uniud.it",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103834425
"Tognon M., Alami R., Siciliano B.","56742951400;7003803131;7006263661;","Physical Human-Robot Interaction with a Tethered Aerial Vehicle: Application to a Force-Based Human Guiding Problem",2021,"IEEE Transactions on Robotics","37","3","9353246","723","734",,1,"10.1109/TRO.2020.3038700","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101466642&doi=10.1109%2fTRO.2020.3038700&partnerID=40&md5=fed7d46c6ddf39d84b49ce728987a0d2","Autonomous Systems Lab, Department of Mechanical and Process Engineering, ETH Zürich, Zürich, Switzerland; LAAS-CNRS, Universit DeToulouse, CNRS and Artificial and Natural Intelligence Toulouse Institute, Toulouse, France; Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy","Tognon, M., Autonomous Systems Lab, Department of Mechanical and Process Engineering, ETH Zürich, Zürich, Switzerland; Alami, R., LAAS-CNRS, Universit DeToulouse, CNRS and Artificial and Natural Intelligence Toulouse Institute, Toulouse, France; Siciliano, B., Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy","Today, physical human-robot interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, aerial physical interaction is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittance-based approach with a controller, inspired by the literature on flexible manipulators, that computes the desired interaction forces that properly guide the human. The stability of the system is formally proved with a Lyapunov-based argument. The system is also shown to be passive, and thus robust to nonidealities like model and tracking errors, additional human forces, time-varying inputs, and other external disturbances. We also design a maneuver regulation policy to simplify the path following problem. The global method has been experimentally validated on a group of four subjects, showing a reliable and safe pHRI. © 2004-2012 IEEE.","Aerial systems; force control; mechanics and control; motion control; physical human-robot interaction (pHRI)","Agricultural robots; Antennas; Man machine systems; System stability; Vehicles; External disturbances; Indirect communication; Interaction forces; Path-following problems; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical interactions; Time varying input; Social robots",,,,,,,,,,,,,,,,"Ruggiero, F., Lippiello, V., Ollero, A., Aerial manipulation: Aliterature review (2018) IEEE Robot. Autom. Lett., 3 (3), pp. 1957-1964. , Jul; Bartelds, T., Capra, A., Hamaza, S., Stramigioli, S., Fumagalli, M., Compliant aerial manipulators: Toward a new generation of aerial robotic workers (2016) IEEE Robot. Autom,. 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Autom., pp. 5203-5208. , Singapore, May; Lee, T., Leoky, M., McClamroch, N.H., Geometric tracking control of a quadrotor UAV on SE(3) (2010) Proc. 49th IEEE Conf. Decis. Control, pp. 5420-5425. , Atlanta, GA, USA, Dec; Tognon, M., Dash, S.S., Franchi, A., Observer-based control of position and tension for an aerial robot tethered to amoving platform (2016) IEEE Robot. Autom. Lett., 1 (2), pp. 732-737. , Jul; Khambhaita, H., Alami, R., Viewing robot navigation in human environment as a cooperative activity (2020) Robotics Research, pp. 285-300. , Cham, Switzerland: Springer; Fiore, M., Khambhaita, H., Milliez, G., Alami, R., An adaptive and proactive human-aware robot guide (2015) Proc. 7th Int. Conf. Social Robot., pp. 194-203. , Paris, France: Springer, Oct","Tognon, M.; Autonomous Systems Lab, Switzerland; email: mtognon@ethz.ch",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85101466642
"de Wit J., Krahmer E., Vogt P.","57201503099;6602935272;7202392224;","Introducing the NEMO-Lowlands iconic gesture dataset, collected through a gameful human–robot interaction",2021,"Behavior Research Methods","53","3",,"1353","1370",,2,"10.3758/s13428-020-01487-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092743058&doi=10.3758%2fs13428-020-01487-0&partnerID=40&md5=95d1edb71ca84855a683795acde4cea0","Department of Communication and Cognition, Tilburg Center for Cognition and Communication, Tilburg University, PO Box 90153, Tilburg, 5000LE, Netherlands; Department of Cognitive Science and Artificial Intelligence, Tilburg University, PO Box 90153, Tilburg, 5000LE, Netherlands","de Wit, J., Department of Communication and Cognition, Tilburg Center for Cognition and Communication, Tilburg University, PO Box 90153, Tilburg, 5000LE, Netherlands; Krahmer, E., Department of Communication and Cognition, Tilburg Center for Cognition and Communication, Tilburg University, PO Box 90153, Tilburg, 5000LE, Netherlands; Vogt, P., Department of Cognitive Science and Artificial Intelligence, Tilburg University, PO Box 90153, Tilburg, 5000LE, Netherlands","This paper describes a novel dataset of iconic gestures, together with a publicly available robot-based elicitation method to record these gestures, which consists of playing a game of charades with a humanoid robot. The game was deployed at a science museum (NEMO) and a large popular music festival (Lowlands) in the Netherlands. This resulted in recordings of 428 participants, both adults and children, performing 3715 silent iconic gestures for 35 different objects in a naturalistic setting. Our dataset adds to existing collections of iconic gesture recordings in two important ways. First, participants were free to choose how they represented the broad concepts using gestures, and they were asked to perform a second attempt if the robot did not recognize their gesture the first time. This provides insight into potential repair strategies that might be used. Second, by making the interactive game available we enable other researchers to collect additional recordings, for different concepts, and in diverse cultures or contexts. This can be done in a consistent manner because a robot is used as a confederate in the elicitation procedure, which ensures that every data collection session plays out in the same way. The current dataset can be used for research into human gesturing behavior, and as input for the gesture recognition and production capabilities of robots and virtual agents. © 2020, The Author(s).","Gesture dataset; Human–robot interaction; Semi-structured elicitation","adult; child; gesture; human; Netherlands; robotics; Adult; Child; Gestures; Humans; Netherlands; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 688014; University of Warwick: HRI2019","This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No. 688014.","The authors would like to thank Rianne van den Berghe, Mirjam de Haas, Marije Merckens, Reinjet Oostdijk, Chani Savelberg, Sabine Verdult, Bram Willemsen, and Pieter Wolfert for their help with running the experiment. The authors are also very grateful to the NEMO science museum, and to the Lowlands Science event at the Lowlands music festival for graciously hosting us and for drawing visitors? attention towards our experiment. A poster with the design of this experiment was presented at the Gesture & Technology Workshop 2018 at Warwick University. Furthermore, a short paper was published and presented as a poster at the Annual Conference on Human?Robot Interaction (HRI2019) (de Wit et al., 2019), and this poster was also presented at the GESPIN 2019 conference. We thank the attendees to these events for their valuable feedback. New in this paper are a comprehensive description of the design and development of the system, and the distribution of the accompanying source code and the gesture dataset itself. 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Res. Methods",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85092743058
"Inamura T., Mizuchi Y.","7102940240;36809967300;","SIGVerse: A Cloud-Based VR Platform for Research on Multimodal Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"549360","","",,2,"10.3389/frobt.2021.549360","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107991005&doi=10.3389%2ffrobt.2021.549360&partnerID=40&md5=a6a06d71cf4ac5704022570c0bd401c3","National Institute of Informatics, Tokyo, Japan; Department of Informatics, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan","Inamura, T., National Institute of Informatics, Tokyo, Japan, Department of Informatics, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan; Mizuchi, Y., National Institute of Informatics, Tokyo, Japan","Research on Human-Robot Interaction (HRI) requires the substantial consideration of an experimental design, as well as a significant amount of time to practice the subject experiment. Recent technology in virtual reality (VR) can potentially address these time and effort challenges. The significant advantages of VR systems for HRI are: 1) cost reduction, as experimental facilities are not required in a real environment; 2) provision of the same environmental and embodied interaction conditions to test subjects; 3) visualization of arbitrary information and situations that cannot occur in reality, such as playback of past experiences, and 4) ease of access to an immersive and natural interface for robot/avatar teleoperations. Although VR tools with their features have been applied and developed in previous HRI research, all-encompassing tools or frameworks remain unavailable. In particular, the benefits of integration with cloud computing have not been comprehensively considered. Hence, the purpose of this study is to propose a research platform that can comprehensively provide the elements required for HRI research by integrating VR and cloud technologies. To realize a flexible and reusable system, we developed a real-time bridging mechanism between the robot operating system (ROS) and Unity. To confirm the feasibility of the system in a practical HRI scenario, we applied the proposed system to three case studies, including a robot competition named RoboCup@Home. via these case studies, we validated the system’s usefulness and its potential for the development and evaluation of social intelligence via multimodal HRI. © Copyright © 2021 Inamura and Mizuchi.","dataset of interaction; robot competition; social and embodied intelligence; software platform; virtual reality",,,,,,"Japan Society for the Promotion of Science, KAKEN: 19K20379; New Energy and Industrial Technology Development Organization, NEDO; Core Research for Evolutional Science and Technology, CREST","This work was supported by the New Energy and Industrial Technology Development Organization (NEDO) and JST CREST (Core Research for Evolutional Science and Technology) JSPS (KAKENHI) under Grant Number 19K20379.",,,,,,,,,,"Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T., Power to the People: The Role of Humans in Interactive Machine Learning (2014) AIMag, 35, pp. 105-120; Bates, T., Ramirez-Amaro, K., Inamura, T., Cheng, G., On-line Simultaneous Learning and Recognition of Everyday Activities from Virtual Reality Performances (2017) 2017IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3510-3515; Bazzano, F., Gentilini, F., Lamberti, F., Sanna, A., Paravati, G., Gatteschi, V., Immersive Virtual Reality-Based Simulation to Support the Design of Natural Human-Robot Interfaces for Service Robotic Applications (2016) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 33-51. , Lecce, Italy, Springer; Beattie, C., Leibo, J.Z., Teplyashin, D., Ward, T., Wainwright, M., Lefrancq, A., (2016) DeepMind Lab. arXiv, pp. 1-11; Bilberg, A., Malik, A.A., Digital Twin Driven Human-Robot Collaborative Assembly (2019) CIRP Ann, 68, pp. 499-502; Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., (2016) OpenAI Gym; Clarke, S., Schillhuber, G., Zaeh, M.F., Ulbrich, H., Prediction-based Methods for Teleoperation across Delayed Networks (2007) Multimedia Syst, 13, pp. 253-261; Codd-Downey, R., Forooshani, P.M., Speers, A., Wang, H., Jenkin, M., From ROS to unity: Leveraging Robot and Virtual Environment Middleware for Immersive Teleoperation (2014) 2014IEEE International Conference on Information and Automation (ICIA), pp. 932-936. , Hailar, China, IEEE; Coolican, H., (2017) Research Methods and Statistics in Psychology, , Psychology Press; Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., Batra, D., Embodied Question Answering (2018) 2018IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1-10. , Salt Lake City, IEEE; Diankov, R., Kuffner, J., (2008) OpenRAVE: A Planning Architecture for Autonomous Robotics, , Pittsburgh, PA, Tec, Robotics Institute; Duchowski, A.T., House, D.H., Gestring, J., Congdon, R., Świrski, L., Dodgson, N.A., Comparing Estimated Gaze Depth in Virtual and Physical Environments (2014) Eye Tracking Research and Applications Symposium (ETRA), pp. 103-110. , NewYork, ACM; El Saddik, A., Digital Twins: The Convergence of Multimedia Technologies (2018) IEEE Multimedia, 25, pp. 87-92; Escalera, S., Gonzàlez, J., Baró, X., Reyes, M., Lopes, O., Guyon, I., Multi-modal Gesture Recognition challenge 2013: Dataset and Results (2013) Proceedings Of the ACM International Conference On Multimodal Interaction (ICMI), pp. 445-452. , Sydney Australia, ACM; Fang, B., Wei, X., Sun, F., Huang, H., Yu, Y., Liu, H., Skill Learning for Human-Robot Interaction Using Wearable Device (2019) Tinshhua Sci. 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Intell, 34, pp. 2441-2453; Phillips, L., Ries, B., Kaedi, M., Interrante, V., Avatar Self-Embodiment Enhances Distance Perception Accuracy in Non-photorealistic Immersive Virtual Environments (2010) 2010 IEEE Virtual Reality Conference (VR), pp. 115-1148. , Waltham, IEEE; Plappert, M., Mandery, C., Asfour, T., The KIT Motion-Language Dataset (2016) Big Data, 4, pp. 236-252; Pruks, V., Lee, K.-H., Ryu, J.-H., Shared Teleoperation for Nuclear Plant Robotics Using Interactive Virtual Guidance Generation and Shared Autonomy Approaches (2018) 2018 15th International Conference on Ubiquitous Robots, pp. 91-95. , Honolulu, Hawaii, IEEE; Puig, X., Ra, K., Boben, M., Li, J., Wang, T., Fidler, S., VirtualHome: Simulating Household Activities via Programs (2018) The IEEE Conference on Computer Vision and Pattern Recognition, pp. 8494-8502. , Salt Lake City, IEEE; Ramirez-Amaro, K., Inamura, T., Dean-Leon, E., Beetz, M., Cheng, G., Bootstrapping Humanoid Robot Skills by Extracting Semantic Representations of Human-like Activities from Virtual Reality (2014) 2014IEEE-RAS International Conference on Humanoid Robots, pp. 438-443. , Madrid, Spain, IEEE; Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., Pinkal, M., Grounding Action Descriptions in Videos (2013) Tacl, 1, pp. 25-36; Riek, L., Wizard of Oz Studies in HRI: A Systematic Review and New Reporting Guidelines (2012) Jhri, pp. 119-136; Robinette, P., Novitzky, M., Duncan, B., Jeon, M., Wagner, A., Park, C.H., Dangerous Hri: Testing Real-World Robots Has Real-World Consequences (2019) Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction, pp. 687-688. , Daegu, Korea, ACM/IEEE; Rohmer, E., Singh, S.P.N., Freese, M., V-REP: A Versatile and Scalable Robot Simulation Framework (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1321-1326. , Tokyo, Japan, IEEE/RSJ; Savva, M., Chang, A.X., Dosovitskiy, A., Funkhouser, T., Koltun, V., (2017) MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments; Scholtz, J.C., Antonishek, B., Young, J.D., Evaluation of Human-Robot Interaction in the NIST Reference Search and Rescue Test Arenas (2004) Performance Metrics for Intelligent Systems 2004 Workshop Proceedings, , Gaithersburg, MD, in; Sharma, S., Otunba, S., Collaborative Virtual Environment to Study Aircraft Evacuation for Training and Education (2012) International Conference On Collaboration Technologies And Systems (CTS), pp. 569-574. , Denver, ACM/IEEE; Sidenmark, L., Gellersen, H., Eye, Head and Torso Coordination during Gaze Shifts in Virtual Reality (2019) ACM Trans. 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Automat, 20, pp. 602-612; Zhang, T., McCarthy, Z., Jow, O., Lee, D., Chen, X., Goldberg, K., Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2018) IEEE International Conference On Robotics And Automation (ICRA), pp. 5628-5635. , Brisbane, Australia, IEEE","Inamura, T.; National Institute of InformaticsJapan; email: inamura@nii.ac.jp",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85107991005
"Ding Z., Song C., Xu J., Dou Y.","57226590844;35085626400;55714104900;57216907884;","Human-Robot Interaction System Design for Manipulator Control Using Reinforcement Learning",2021,"Proceedings - 2021 36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021",,,"9486647","660","665",,,"10.1109/YAC53711.2021.9486647","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112049639&doi=10.1109%2fYAC53711.2021.9486647&partnerID=40&md5=a446dc9bc4e9687a0625cccde469a88c","Beijing Institute of Technology, School of Automation, Beijing, China","Ding, Z., Beijing Institute of Technology, School of Automation, Beijing, China; Song, C., Beijing Institute of Technology, School of Automation, Beijing, China; Xu, J., Beijing Institute of Technology, School of Automation, Beijing, China; Dou, Y., Beijing Institute of Technology, School of Automation, Beijing, China","In this article, a novel human-robot interaction (HRI) system is presented and applied in the robotic arm coordinated operation control task. The presented HRI system includes two parts, the impedance model controller and the robotic arm controller, which allows the operator to manipulate the robotic arm to accomplish the given task with minimal human effort. First, the model-based reinforcement learning (RL) method is applied in the impedance model for operator adaptation. The impedance model controller can transform human input into the specific signal for the manipulator. Second, a novel adaptive manipulator controller is designed. In contrast to existing controllers, a velocity-free filter is implemented in our controller, which is developed to replace the manipulator actuator's speed signal. The effectiveness of the presented HRI system is verified by the simulation based on real manipulator parameters. © 2021 IEEE.","Adaptive impedance control; Human-robot interaction; Reinforcement learning","Controllers; Industrial manipulators; Learning systems; Machine design; Man machine systems; Manipulators; Reinforcement learning; Robotic arms; Robotics; Arm controllers; Coordinated operation; Existing controllers; Human robot Interaction (HRI); Impedance modeling; Manipulator control; Manipulator parameters; Model-based reinforcement learning; Social robots",,,,,,,,,,,,,,,,"Cai, S., Ma, Z., Skibniewski, M.J., Bao, S., Construc-Tion automation and robotics for high-rise buildings over the past decades: A comprehensive review (2019) Advanced Engineering Informatics, 42, p. 100989; Han, D., Huang, R., Liu, X., Yang, Y., Combined spacecraft stabilization control after multiple impacts during the capture of a tumbling target by a space robot (2020) Acta Astronáutica, 176, pp. 24-32; Goodrich, M.A., Schultz, A.C., (2008) Human-Robot Inter-Action: A Survey, , Now Publishers Inc; Liu, Z., Liu, J., He, W., Dynamic modeling and vibration control of a flexible aerial refueling hose (2016) Aerospace Science and Technology, 55, pp. 92-102; Yang, Y., Vamvoudakis, K.G., Modares, H., Yin, Y., Wunsch, D., Hamiltonian-driven hybrid adaptive dynamic programming (2020) IEEE Transactions on Systems, Man and Cybernetics: Systems; Liu, Z., Zhao, Z., Ahn, C.K., Boundary constrained control of flexible string systems subject to disturbances (2020) IEEE Transactions on Circuits and Systems II: Express Briefs, 67 (1), pp. 112-116; Yang, Y., Gao, W., Modares, H., Xu, C., Robust actor-critic learning for continuous-Time nonlinear systems with unmodeled dynamics (2021) IEEE Transactions on Fuzzy Systems; Lee, H., Kim, J., A survey on robot teaching: Categorization and brief review (2013) Applied Mechanics and Materials, 330, pp. 648-656; Li, Z., Zhao, T., Chen, F., Hu, Y., Su, C.Y., Fukuda, T., Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator (2018) IEEE/ ASME Trans. Mechatronics, 23 (1), pp. 121-131; Liu, Z., He, X., Zhao, Z., Ahn, C.K., Li, H.X., Vibration control for spatial aerial refueling hoses with bounded actuators (2021) IEEE Transactions on Industrial Electronics, 68 (5), pp. 4209-4217; Yang, Y., Liu, Z., Li, Q., Wunsch, D.C., Output con-strained adaptive controller design for nonlinear satura-Tion systems (2021) IEEE/ CAA Journal of Automatica Sinica, 8 (2), pp. 441-454; Yang, Y., Vamvoudakis, K.G., Modares, H., Yin, Y., Wunsch, D.C., Safe intermittent reinforcement learning with static and dynamic event generators (2020) IEEE Trans-Actions on Neural Networks and Learning Systems, 31 (12), pp. 5441-5455; Yang, Y., Xu, C., Adaptive fuzzy leader-follower syn-chronization of constrained heterogeneous multiagen-T systems (2020) IEEE Transactions on Fuzzy Systems; Hogan, N., Impedance control: An approach to manip-ulation. I-Theory. II-Implementation. Ill-Applications (1985) ASME Trans. J. Dyn. Syst. Meas. Control, 107, pp. 1-24; Yao, B., Zhou, Z., Wang, L., Xu, W., Liu, Q., Liu, A., Sensorless and adaptive admittance control of indus-Trial robot in physical human robot interaction (2018) Robot. Comput.-Integr. Manuf, 51, pp. 158-168; Modares, H., Ranatunga, I., Alqaudi, B., Lewis, F.L., Popa, D.O., Intelligent human-robot interaction systems using reinforcement learning and neural networks (2017) Trends in Control and Decision-Making for HumanRobot Collaboration Systems, , Wang Y, Zhang F. eds; Schindlbeck, C., Haddadin, S., Unified passivity-based cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks (2015) Proc IEEE Int. Conf. Robot. Autom. (ICRA, pp. 440-447; Toedtheide, A., Shahriari, E., Haddadin, S., Tank based unified torque/impedance control for a pneumat-ically actuated antagonistic robot joint (2017) Proc IEEE Int. Conf. Robot. Autom. (ICRA, pp. 1255-1262; Mu, C., Wang, D., He, H., Data-driven finite-horizon approximate optimal control for discrete-Time nonlinear systems using iterative HDP approach (2018) IEEE Transac-Tions on Cybernetics, 48 (10), pp. 2948-2961; Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C., Phys-ical human-robot interaction of a robotic exoskeleton by admittance control (2018) IEEE Transactions on Industrial Electronics, 65 (12), pp. 9614-9624; Lewis, F.L., Dawson, D.M., Abdallah, C.T., (2003) Robot Manipulator Control: Theory and Practice, , CRC Press; Zhang, F., Dawson, D.M., De Queiroz, M.S., Dixon, W.E., Global adaptive output feedback tracking control of robot manipulators (2000) IEEE Transactions on Automatic Control, 45 (6), pp. 1203-1208; Kokotovic, P., The joy of feedback nonlinear and adap-Tive (1992) IEEE Control Systems Magazine, 12, pp. 7-17",,,,"Institute of Electrical and Electronics Engineers Inc.","36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021","28 May 2021 through 30 May 2021",,170605,,9781665437127,,,"English","Proc. - Youth Acad. Annu. Conf. Chin. Assoc. Autom., YAC",Conference Paper,"Final","",Scopus,2-s2.0-85112049639
"Martin L., Gonzalez-Romo M., Sahnoun M., Bettayeb B., He N., Gao J.","57226709186;57226707881;37662006000;23982271700;55853476800;7404476178;","Effect of Human-Robot Interaction on the Fleet Size of AIV Transporters in FMS",2021,"2021 1st International Conference On Cyber Management and Engineering, CyMaEn 2021",,,"9497273","","",,1,"10.1109/CyMaEn50288.2021.9497273","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112400976&doi=10.1109%2fCyMaEn50288.2021.9497273&partnerID=40&md5=f081fb8ee85720aec69b63e415096710","University of Greenwich, Faculty of Engineering and Science, Chatham, United Kingdom; University of Exeter, College of Engineering Mathematics and Physical Sciences, Exeter, United Kingdom; LINEACT/CESI, Research Department, Saint-Etienne-du-Rouvray, France","Martin, L., University of Greenwich, Faculty of Engineering and Science, Chatham, United Kingdom; Gonzalez-Romo, M., University of Exeter, College of Engineering Mathematics and Physical Sciences, Exeter, United Kingdom; Sahnoun, M., LINEACT/CESI, Research Department, Saint-Etienne-du-Rouvray, France; Bettayeb, B., LINEACT/CESI, Research Department, Saint-Etienne-du-Rouvray, France; He, N., University of Exeter, College of Engineering Mathematics and Physical Sciences, Exeter, United Kingdom; Gao, J., University of Greenwich, Faculty of Engineering and Science, Chatham, United Kingdom","The execution of material handling tasks using autonomous guided vehicles (AGVs) has proven a real success during the last decade. Nevertheless, the installation of AGVs is costly as it needs to modify the workshop's configuration by defining dedicated movement zones. Recently, more flexible and collaborative mobile robots known as autonomous intelligent robots (AIV) can be used in manufacturing systems. This new generation of intelligent mobile robots does not need specific zones and can interact with unexpected or mobile obstacles such as human operators. This paper focuses on AIV fleet size definition in a variable and unexpected environment with humans while keeping AIV assigned transportation tasks on time. A simulation that model the complexity of the AIV travel time estimation under the mentioned circumstances and the improvement brought by IoT, Big Data and sensors by using them as the real-time data source is developed. © 2021 IEEE.","Fleet management optimization; Human operator behavior; Industry 4.0; Industry 5.0; Scheduling; Simulation","Automatic guided vehicles; Fleet operations; Intelligent robots; Manufacture; Materials handling; Mobile robots; Navigation; Travel time; Autonomous guided vehicles; Fleet sizes; Human operator; Intelligent mobile robot; Material handling; Mobile obstacles; Real-time data; Travel time estimation; Human robot interaction",,,,,"European Regional Development Fund, ERDF","— This research was made possible thanks to AC2.6 million financial support from the European Regional Development Fund provided by the Interreg V France Channel England Programme in context of CoRoT Project",,,,,,,,,,"Demir, K.A., Döven, G., Sezen, B., Industry 5.0 and human-robot co-working (2019) Procedia Computer Science, 158, pp. 688-695. , Elsevier B.V., jan; De Ryck, M., Versteyhe, M., Debrouwere, F., Automated guided vehicle systems, state-of-the-art control algorithms and techniques (2020) Journal of Manufacturing Systems, 54, pp. 152-173; Antakly, D., Loiseau, J.J., Abbou, R., A temporised conflictfree routing policy for agvs (2017) IFAC-PapersOnLine, 50 (1), pp. 11169-11174; Le-Anh, T., De Koster, M.B., A review of design and control of automated guided vehicle systems (2006) European Journal of Operational Research, 171, pp. 1-23. , may; Fazlollahtabar, H., Saidi-Mehrabad, M., Fazlollahtabar, H., Saidi-Mehrabad, M., Methodologies to optimize automated guided vehicle scheduling and routing problems: A review study (2015) J Intell Robot Syst, 77, pp. 525-545; Vivaldini, K., Rocha, L.F., Martarelli, N.J., Becker, M., Moreira, A.P., Integrated tasks assignment and routing for the estimation of the optimal number of agvs (2016) The International Journal of Advanced Manufacturing Technology, 82, pp. 719-736. , jan; Bilge, U., Ulusoy, G., A time window approach to simultaneous scheduling of machines and material handling system in an fms (1995) Operations Research, 43 (6), pp. 1058-1070; Hasan, H., Zainal Abidin, M.S., Azimi, S., Muhamad Said, M.F., Automated guided vehicle routing: Static, dynamic and free range (2019) International Journal of Engineering and Advanced Technology, 8, pp. 1-7. , sep; Valmiki, P., Simha Reddy, A., Panchakarla, G., Kumar, K., Purohit, R., Suhane, A., A study on simulation methods for agv fleet size estimation in a flexible manufacturing system (2018) Materials Today: Proceedings, 5, pp. 3994-3999. , Elsevier Ltd, jan; Arifin, R., Egbelu, P.J., Determination of vehicle requirements in automated guided vehicle systems: A statistical approach (2000) Production Planning and Control, 11, pp. 258-270. , jan; Lyu, X., Song, Y., He, C., Lei, Q., Guo, W., Approach to integrated scheduling problems considering optimal number of automated guided vehicles and conflict-free routing in flexible manufacturing systems (2019) Ieee Access, 7, pp. 74909-74924; Fragapane, G., De Koster, R., Sgarbossa, F., Strandhagen, J.O., Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda (2021) European Journal of Operational Research, , jan; Sahnoun, M., Xu, Y., Abdelaziz, F.B., Baudry, D., Optimization of transportation collaborative robots fleet size in flexible manufacturing systems (2019) 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), pp. 1-5; Zhang, D.Z., Anosike, A.I., Lim, M.K., Akanle, O.M., An agent-based approach for e-manufacturing and supply chain integration (2006) Computers and Industrial Engineering, 51, pp. 343-360. , oct; Jennings, N.R., Wooldridge, M., Applications of intelligent agents (1998) Agent Technology, pp. 3-28. , Berlin, Heidelberg: Springer Berlin Heidelberg; Jennings, N.R., On agent-based software engineering (2000) Artificial Intelligence, 117 (2), pp. 277-296",,,,"Institute of Electrical and Electronics Engineers Inc.","1st International Conference On Cyber Management and Engineering, CyMaEn 2021","26 May 2021 through 28 May 2021",,170765,,9781665432009,,,"English","Int. Conf. Cyber Manag. Eng., CyMaEn",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85112400976
"Chen K.","57219527386;","Emotion Regulation with Markov Decision Process for Human-robot Interaction",2021,"Proceedings - 2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021",,,"9475923","80","85",,,"10.1109/CogSIMA51574.2021.9475923","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112867106&doi=10.1109%2fCogSIMA51574.2021.9475923&partnerID=40&md5=7057025f26a2f0aedc22a49ac1dd25bc","University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States","Chen, K., University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States","Emotion Regulation is an essential aspect since it helps people avoid harmful emotions. Shaping emotions and satisfying people are some of the key reasons to value a social robot in human-robot interaction. Thus, in this paper, a multi-weighted Markov Decision Process Emotion Regulation (MDPER) robot is created to maximize the transfer from a negative emotional arousal to positive one while minimizing the robot service spendings in cost and steps. By using an emotion regulation method external stimuli as a robot action set, the MDPER robot generates a series of its actions to help people regulate their emotions. Personality and emotion/intention degrees are weighted by the analytic hierarchy process under a specialized Markov Decision Process framework. The simulation and the experiment are implemented to prove that the MDPER system successfully achieves the goal. © 2021 IEEE.","Emotion Regulation; Human-Robot Interaction; Markov Decision Process","Behavioral research; Man machine systems; Markov processes; Emotion regulations; External stimulus; Markov Decision Processes; Robot actions; Robot service; Weighted Markov decision process; Social robots",,,,,,,,,,,,,,,,"Gross, J.J., Emotion regulation: Current status and future prospects (2015) Psychological Inquiry, 26 (1), pp. 1-26; Gross, J.J., (2013) Handbook of Emotion Regulation, , Guilford publications; Gross, J.J., John, O.P., Individual differences in two emotion regulation processes: Implications for affect, relationships, and wellbeing (2003) Journal of Personality and Social Psychology, 85 (2), p. 348; Evers, C., Stok, F.M., De Ridder, D.T., Feeding your feelings: Emotion regulation strategies and emotional eating (2010) Personality and Social Psychology Bulletin, 36 (6), pp. 792-804; Vinola, C., Vimaladevi, K., A survey on human emotion recognition approaches, databases and applications (2015) Elcvia Electronic Letters on Computer Vision and Image Analysis, 14 (2), pp. 24-44; Iengo, S., Origlia, A., Staffa, M., Finzi, A., Attentional and emotional regulation in human-robot interaction (2012) 2012 Ieee RO-MAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication, pp. 1135-1140; Jung, M.F., Affective grounding in human-robot interaction (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 263-273; Desideri, L., Ottaviani, C., Malavasi, M., Di Marzio, R., Bonifacci, P., Emotional processes in human-robot interaction during brief cognitive testing (2019) Computers in Human Behavior, 90, pp. 331-342; Hao, M., Cao, W., Liu, Z., Wu, M., Yuan, Y., Emotion regulation based on multi-objective weighted reinforcement learning for human robot interaction (2019) 2019 12th Asian Control Conference (ASCC), pp. 1402-1406; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3 (1), pp. 53-67; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) Journal of Social Issues, 56 (1), pp. 81-103; Forlizzi, J., DiSalvo, C., Service robots in the domestic environment: A study of the roomba vacuum in the home (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-robot Interaction, pp. 258-265; Knox, W.B., Stone, P., Reinforcement learning from simultaneous human and MDP reward (2012) Aamas, pp. 475-482; Coan, J.A., Allen, J.J., (2007) Handbook of Emotion Elicitation and Assessment, , Oxford university press; Campbell-Sills, L., Barlow, D.H., Brown, T.A., Hofmann, S.G., Effects of suppression and acceptance on emotional responses of individuals with anxiety and mood disorders (2006) Behaviour Research and Therapy, 44 (9), pp. 1251-1263; Blanchette, I., Richards, A., The influence of affect on higher level cognition: A review of research on interpretation, judgement, decision making and reasoning (2010) Cognition & Emotion, 24 (4), pp. 561-595; Saaty, T.L., (1990) Multi Criteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation; Mikhailov, L., Tsvetinov, P., Evaluation of services using a fuzzy analytic hierarchy process (2004) Applied Soft Computing, 5 (1), pp. 23-33; Saaty, T.L., What is the analytic hierarchy process? (1988) Mathematical Models for Decision Support, pp. 109-121. , Springer; Zahedi, F., The analytic hierarchy processa survey of the method and its applications (1986) Interfaces, 16 (4), pp. 96-108; Limonero, J.T., Fernández-Castro, J., Soler-Oritja, J., Moleiro, M.A.L., Emotional intelligence and recovering from induced negative emotional state (2015) Frontiers in Psychology, 6, p. 816; Kurdi, B., Lozano, S., Banaji, M.R., Introducing the open affective standardized image set (oasis) (2017) Behavior Research Methods, 49 (2), pp. 457-470; White, E.L., Rickard, N.S., Emotion response and regulation to happy and sad music stimuli: Partial synchronization of subjective and physiological responses (2016) Musicae Scientiae, 20 (1), pp. 11-25; Eerola, T., Vuoskoski, J.K., A comparison of the discrete and dimensional models of emotion in music (2011) Psychology of Music, 39 (1), pp. 18-49; Kotov, R., Gamez, W., Schmidt, F., Watson, D., Linking big personality traits to anxiety, depressive, and substance use disorders: A meta analysis (2010) Psychological Bulletin, 136 (5), p. 768","Chen, K.; University of Texas at Austin, United States; email: kunyechen@utexas.edu","McGeorge N.Ruvinsky A.Teichmann M.Motus L.Freiman M.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021","14 May 2021 through 22 May 2021",,170822,,9781728176987,,,"English","Proc. - IEEE Int. Conf. Cogn. Comput. Asp. Situat. Manag., CogSIMA",Conference Paper,"Final","",Scopus,2-s2.0-85112867106
"Schellen E., Bossi F., Wykowska A.","57206779571;56963595100;15924284200;","Robot Gaze Behavior Affects Honesty in Human-Robot Interaction",2021,"Frontiers in Artificial Intelligence","4",,"663190","","",,1,"10.3389/frai.2021.663190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113578371&doi=10.3389%2ffrai.2021.663190&partnerID=40&md5=9b73f3755ebfad3f367bfd7f1483a6d6","Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy; MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy","Schellen, E., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy; Bossi, F., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy, MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy; Wykowska, A., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy","As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-“face” with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty toward this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction. © Copyright © 2021 Schellen, Bossi and Wykowska.","deception; eye contact; gaze; honesty; human-robot interaction",,,,,,"Horizon 2020 Framework Programme, H2020: ERC-2016-StG-715058; European Research Council, ERC; Horizon 2020","Funding. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant awarded to AW, titled InStance: Intentional Stance for Social Attunement. G.A. No: ERC-2016-StG-715058).","This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant awarded to AW, titled InStance: Intentional Stance for Social Attunement. G.A. No: ERC-2016-StG-715058).",,,,,,,,,"Barrett, L.F., Kensinger, E.A., Context is routinely encoded during emotion perception (2010) Psychol. Sci, 21, pp. 595-599. , 20424107; Bennett, C.C., Šabanović, S., Fraune, M.R., Shaw, K., Context congruency and robotic facial expressions: do effects on human perceptions vary across culture?, (2014) Robot and Human Interactive Communication, 2014 RO-MAN: The 23rd IEEE International Symposium, pp. 465-470. , Edinburgh; Bradley, M.T., Janisse, M.P., Accuracy demonstrations, threat, and the detection of deception: cardiovascular, electrodermal, and pupillary measures (1981) Psychophysiology, 18, pp. 307-315. , 7291448; Burnham, T.C., Engineering altruism: a theoretical and experimental investigation of anonymity and gift giving (2003) J. Econ. Behav. Organ, 50, pp. 133-144; DePaulo, B.M., Lindsay, J.J., Malone, B.E., Muhlenbruck, L., Charlton, K., Cooper, H., Cues to deception (2003) Psychol. 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Rev, 35, pp. 35-46. , 25462112; Raffard, S., Bortolon, C., Khoramshahi, M., Salesse, R.N., Burca, M., Marin, L., Humanoid robots versus humans: how is emotional valence of facial expressions recognized by individuals with schizophrenia? An exploratory study (2016) Schizophr. Res, 176, pp. 506-513. , 27293136; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot?: effects of error, task type and personality on human-robot cooperation and trust, (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 141-148. , Portland, OR; Schilbach, L., Wohlschlaeger, A.M., Kraemer, N.C., Newen, A., Shah, N.J., Fink, G.R., Being with virtual others: neural correlates of social interaction (2006) Neuropsychologia, 44, pp. 718-730. , 16171833; Schniter, E., Shields, T.W., Sznycer, D., Trust in humans and robots: economically similar but emotionally different (2020) J. Econ. 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Robot, 7, pp. 767-781. , a; Wykowska, A., Kajopoulos, J., Ramirez-Amaro, K., Cheng, G., Autistic traits inversely correlate with implicit sensitivity to human-like behavior (2015) Interaction Stud, 16, pp. 219-248. , b; Wykowska, A., Wiese, E., Prosser, A., Müller, H.J., Beliefs about the minds of others influence how we process sensory information (2014) PLoS ONE, 9, p. e45391. , 24714419","Bossi, F.; Social Cognition in Human-Robot Interaction, Italy; email: francesco.bossi@imtlucca.it",,,"Frontiers Media S.A.",,,,,26248212,,,,"English","Frontier. Artif. Intell.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113578371
"Farhan S.K.A., Khan M.N.R., Swaron M.R., Shukhon R.N.S., Islam M.M., Razzak M.A.","57230305900;57193666613;57230306000;57230097600;57225862405;7003567682;","Improvement of Verbal and Non-Verbal Communication Skills of Children with Autism Spectrum Disorder using Human Robot Interaction",2021,"2021 IEEE World AI IoT Congress, AIIoT 2021",,,"9454193","356","359",,,"10.1109/AIIoT52608.2021.9454193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113393375&doi=10.1109%2fAIIoT52608.2021.9454193&partnerID=40&md5=3516996e5659b30b771687b49ad38694","Independent University, Dept. of Electrical and Electronic Engineering, Dhaka, Bangladesh; Society for the Welfare of Autistic Children, Dhaka, Bangladesh","Farhan, S.K.A., Independent University, Dept. of Electrical and Electronic Engineering, Dhaka, Bangladesh; Khan, M.N.R., Independent University, Dept. of Electrical and Electronic Engineering, Dhaka, Bangladesh; Swaron, M.R., Independent University, Dept. of Electrical and Electronic Engineering, Dhaka, Bangladesh; Shukhon, R.N.S., Independent University, Dept. of Electrical and Electronic Engineering, Dhaka, Bangladesh; Islam, M.M., Society for the Welfare of Autistic Children, Dhaka, Bangladesh; Razzak, M.A., Independent University, Dept. of Electrical and Electronic Engineering, Dhaka, Bangladesh","This paper shows the primary feedback of children with Autism Spectrum Disorder (ASD) interacting with Humanoid Robot NAO. Four autistic children from Society for the Welfare of Autistic Children (SWAC) is being picked based on their neurological and physical limitations which has been identified by specialist teachers, ASD specialist researchers, and faculty members. Their Intelligence Quotient depends on question-answer based Intelligence Test has been first carried out and they have undergone the autism diagnoses based on Autism Diagnostic Observation sessions by autism specialist teachers from Society for the Welfare of Autistic Children (SWAC). The four of Children with ASD will then involve in the Robot-based Interaction Program, which started from session 1 to session 4. The interaction between Children with ASD and Humanoid Robot NAO is being recorded with Android Phone Video Camera and one mini camera included on the middle of Humanoid Robot NAO for initial feedback analysis based on verbal and non-verbal communications. The interaction session between the children and robot has been developed by using the software named choreographe of Humanoid Robot NAO. © 2021 IEEE.","ASD; Choreographe; Human-Robot Interaction; Humanoid Robot NAO; Neurological limitations","Anthropomorphic robots; Artificial intelligence; Biofeedback; Diseases; Internet of things; Video cameras; Autistic children; Children with autisms; Humanoid robot NAO; Initial feedback; Intelligence quotients; Intelligence tests; Non-verbal communications; Physical limitations; Social robots",,,,,,,,,,,,,,,,"Khan, M.N.R., Bin Shahin, F., Alam, M., Ehsanul Haque Mashuk, A.K., Adif Farhan, S.K., Tanzirul Iqbal, M.M., Chronological way to represent humanoid robot (2018) 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (Icrieece), pp. 1401-1405. , Bhubaneswar, India; Strabala, K.W., Lee, M.K., Dragan, A.D., Forlizzi, J.L., Srinivasa, S., Cakmak, M., Micelli, V., Towards seamless human-robot handovers' (2013) J. Human-Robot Interact., 2 (1), pp. 112-132; Nakashima, K., Kurazume, R., Service robot system with an informationally structured environment (2015) Robotics and Autonomous Systems; VukobratoviC, M., Zero moment point-thirty five years of its life (2014) Int J Human Robot; Nakashima, K., Kurazume, R., Service robot system with an informationally structured environment (2015) Ras, 74, pp. 148-165. , December; Vukobratovi, M.C., Zero moment point-thirty-five years of its life (2014) Int J Human Robot, 1, p. 157; Magid, E., Zakiev, A., Tsoy, T., Lavrenov, R., Rizvanov, A., Automating pandemic mitigation (2021) Advanced Robotics; Nasfikur, Md., Khan, R., Sonet, H.H., Yasmin, F., Yesmin, S., Sarker, F., Mamun, K.A., Bolte chai'-an android application for verbally challenged children (2017) Advances in Electrical Engineering (ICAEE) 2017 4th International Conference on, pp. 541-545; David, E., María-José, T.-L., Velasco-Quintana Paloma, J., Rosa-María, R.-J., Valle, A.-M., A case study of a robot-Assisted speech therapy for children with language disorders (2021) Sustainability, 13 (5), p. 2771; Mehigan, T.J., Harnessing accelerometer technology for inclusive mobile learning (2009) 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI09), , Bonn, Germany, September; Alexander, J.C., Maddocks, J.H., On the kinematics of wheeled mobile robots (1989) Int. J. Robotics Res., 8 (5), pp. 15-27; Mehigan, T.J., Harnessing accelerometer technology for inclusive mobile learning (2009) 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI09), , Bonn, Germany, September; Efstratiou, R., Teaching daily life skills in autism spectrum disorder (asd) interventions using the social robot pepper (2021) Robotics in Education. RiE 2020. Advances in Intelligent Systems and Computing, 1316. , In: Lepuschitz W., Merdan M., Koppensteiner G., Balogh R., Obdrẑálek D. (eds), Springer, Cham; Cao, H.-L., Robot-Assisted joint attention: A comparative study between children with autism spectrum disorder and typically developing children in interaction with nao Ieee Access, 8, pp. 223325-223334; Khan, M.N.R., Pias, M.N.H., Habib, K., Hossain, M., Sarker, F., Mamun, K.A., Bolte chai: An augmentative and alternative communication device for enhancing communication for nonverbal children (2016) 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), pp. 1-4. , Dhaka, Bangladesh; Ferrante, G., Vitale, G., Licari, A., Montalbano, L., Pilato, G., Infantino, I., Augello, A., Stefania la gruttasocial robots and therapeutic adherence: A new challenge in pediatric asthma? (2020) Paediatric Respiratory Reviews; Khan, Md.N.R., Shahin, F.B., Sunny, F.I., Khan, M.R., Ehsanul, A.K., Mashuk, H., Al Mamun, K.A., An innovative and augmentative android application for enhancing mediated communication of verbally disabled people (2019) Computing Communication and Networking Technologies (ICCCNT) 2019 10th International Conference on, pp. 1-5; Farzana, W., Sarker, F., Hossain, Q.D., Chau, T., Mamun, K.A., (2020) Hci International 2020-Late Breaking Posters, 1294, p. 51",,"Paul R.","IEEE Region 1;IEEE USA;Institute of Engineering and Management (IEM);SMART;University of Engineering and Management (UEM)","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE World AI IoT Congress, AIIoT 2021","10 May 2021 through 13 May 2021",,170932,,9781665435680,,,"English","IEEE World AI IoT Congr., AIIoT",Conference Paper,"Final","",Scopus,2-s2.0-85113393375
"Gomez-Monroy C.G.","57223406245;","Correlation of Qualitative User Experience Feedback and Interface-Generated Quantitative Data during Human-Robot Interaction",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,,"10.1145/3411763.3443430","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105783709&doi=10.1145%2f3411763.3443430&partnerID=40&md5=b06529fcb1394a16f2aff213f76a5f1f","Posgraduate Engineering School, Universidad Nacional Autónoma de México, Mexico","Gomez-Monroy, C.G., Posgraduate Engineering School, Universidad Nacional Autónoma de México, Mexico","My research is on Human-Robot Interaction (HRI) from a User Centered Design (UCD) perspective and values the user's qualitative assessment of their interaction under the concept of User Experience (UX). Traditionally, UX is retrieved in a written questionnaire as a satisfaction survey, nevertheless, with the hike in electronic devices equipped with a diversity of sensors, actuators and processing capabilities (turning everyday appliances into robots) comes an intention to migrate from qualitative to quantitative assessment of the UX; for example, visual recognition of emotional facial expressions. In this regard, what I present as a novelty in the research area is the study of interaction data generated through the robot's interface during its intended use (everyday use), while providing an anonymous experience (no cameras, no mics) to the user. For the experimental setup, I designed and built a robotic Desk-Lamp with 5 degrees of freedom (height, brightness, projection angle, sensitivity of the interface and ambient lighting) as test bench, equipped with: an interface instrumented to sense the force delivered by the user while ""pushing buttons""; 18 variable data-logging system at 20 SPS (Sample Per Second); high precision and high repeatability electronics and mechanisms; real-time sensing of the user and low-latency operation due to parallel processing. The test bench is designed to be used in multiple experiments with users, varying the robot behavior and interaction procedures to compare the interaction data generated in the interface with a written UX questionnaire. © 2021 Owner/Author.","Design of experiments (DoE); User centered design (UCD); User experience (UX)","Biofeedback; Degrees of freedom (mechanics); Electronic assessment; Feedback; Machine design; Man machine systems; Surveys; User centered design; User experience; Experience feedback; Human robot Interaction (HRI); Parallel processing; Processing capability; Qualitative assessments; Quantitative assessments; User Centered Design(UCD); User experiences (ux); Human robot interaction",,,,,"Consejo Nacional de Ciencia y Tecnología, CONACYT; Universidad Nacional Autónoma de México, UNAM: DGAPA-PAPIIT IT-103320, IT-101718","This research is possible thanks to the fully-funded fellowship program for Ph.D. students by Consejo Nacional de Ciencia y Tecnología (CONACYT), Mexico.","This research is funded by Universidad Nacional Autónoma de México through the projects: DGAPA-PAPIIT IT-103320 and IT-101718.",,,,,,,,,"Ghosh, D., Olewnik, A., Lewis, K., Kim, J., Lakshmanan, A., CYBER-empathic design-a data driven frame-work for product design (2017) Journal of Mechanical Design, 139. , https://doi.org/10.1115/1.4036780, 05 2017; (2010) Ergonomics of Human-system Interaction: Human-centered Design for Interactive Systems, , ISO 9241:210(E) Standard. International Organization for Standardization, Geneva, SW; Laugwitz, B., Held, T., Schrepp, M., Construction and evaluation of a user experience questionnaire (2008) HCI and Usability for Education and Work, pp. 63-76. , Andreas Holzinger (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg; Shen, W., Al Khemeiri, A., Almehrzi, A., Al Enezi, W., Rahwan, I., Crandall, J.W., ReguLating highly automated robot ecologies: Insights from three user studies (2017) Proceedings of the 5th International Conference on Human Agent Interaction (Bielefeld, Germany) (HAI '17), pp. 111-120. , https://doi.org/10.1145/3125739.3125758, Association for Computing Machinery, New York, NY, USA; Vitale, J., Tonkin, M., Herse, S., Ojha, S., Clark, J., Williams, M., Wang, X., Judge, W., Be more transparent and users will like you: A robot privacy and user experience design experiment (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction(Chicago, IL, USA) (HRI '18), pp. 379-387. , https://doi.org/10.1145/3171221.3171269, Association for Computing Machinery, New York, NY, USA; Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., Park, T., Methodological issues in scenario-based evaluation of human-robot interaction (2015) International Journal of Social Robotics, 7 (2), pp. 279-291. , https://doi.org/10.1007/s12369-014-0248-9, 01 Apr 2015","Gomez-Monroy, C.G.; Posgraduate Engineering School, Mexico; email: gilbertogomo@gmail.com",,"ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","Association for Computing Machinery","2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021","8 May 2021 through 13 May 2021",,168787,,9781450380959,,,"English","Conf Hum Fact Comput Syst Proc",Conference Paper,"Final","",Scopus,2-s2.0-85105783709
"Tian L., Carreno-Medrano P., Allen A., Sumartojo S., Mintrom M., Coronado Zuniga E., Venture G., Croft E., Kulic D.","56151869000;56336138700;57219633402;55548214000;6602830007;57188864138;15623574000;7003817722;10244652900;","Redesigning Human-Robot Interaction in Response to Robot Failures: A Participatory Design Methodology",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,2,"10.1145/3411763.3443440","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105772870&doi=10.1145%2f3411763.3443440&partnerID=40&md5=321ef5a42afb04c1078b182908d0caa3","Department of Electrical and Computer Systems Engineering, Monash University, Australia; Monash Art Design and Architecture, Monash University, Australia; Faculty of Art Monash University, Australia; Gvlab Tuat, Japan","Tian, L., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Carreno-Medrano, P., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Allen, A., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Sumartojo, S., Monash Art Design and Architecture, Monash University, Australia; Mintrom, M., Faculty of Art Monash University, Australia; Coronado Zuniga, E., Gvlab Tuat, Japan; Venture, G., Gvlab Tuat, Japan; Croft, E., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Kulic, D., Department of Electrical and Computer Systems Engineering, Monash University, Australia","People are increasingly encountering robots in public spaces. To increase the robustness of such in-the-wild robotic applications and to achieve their designed outcomes, existing research focuses on improving the technical reliability of robots and identifying effective strategies to prevent or recover from technical failures. However, in human-robot interaction (HRI), a user's perception of a robot failure may not necessarily relate to technical issues. We focus on understanding users' perception of robot behaviours and interactions within the context of a public space. In our exploratory study using a novel participatory design methodology, participants designed robot behaviours for applications in public spaces, and tested their design both in a simulator and on the physical robot. We investigate how participants' perception and expectations change during this iterative participatory prototyping process, especially when the robot exhibits erratic or unexpected behaviours. Our work provides insights on users' perception of robot failures, and how users' design of robot behaviours shifts as they observe the robot within the spatial and social context. © 2021 Owner/Author.","Human-Robot Interaction; Participatory Prototyping; Social Robotics","Human engineering; Iterative methods; Machine design; Man machine systems; Exploratory studies; Human robot Interaction (HRI); Participatory design; Prototyping process; Robotic applications; Technical failures; User's perceptions; Users' perception; Human robot interaction",,,,,,"We would like to thank all our research participants. This project is supported by the Monash University Data Futures Institute Interdisciplinary Research Seed Grant. Aimee Allen is supported by the Australian Government Research Training Program PhD Scholarship.",,,,,,,,,,"Azenkot, S., Feng, C., Cakmak, M., Enabling building service robots to guide blind people a participatory design approach (2016) HRI'16, IEEE, pp. 3-10; Bartneck, C., Kuli, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Ben-Youssef, A., Varni, G., Essid, S., Clavel, C., On-the-fly detection of user engagement decrease in spontaneous human-robot interaction using recurrent and deep neural networks (2019) International Journal of Social Robotics, 11 (5), pp. 815-828. , 2019; Björling, E.A., Rose, E., Davidson, A., Ren, R., Wong, D., Can we keep him forever? 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"Esterwood C., Essenmacher K., Yang H.","57216331965;57223996509;57224005501;","A meta-analysis of human personality and robot acceptance in human-robot interaction",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,4,"10.1145/3411764.3445542","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106713994&doi=10.1145%2f3411764.3445542&partnerID=40&md5=59b4fa1826b5a75089aa1b8a0d6adfab","School of Information, University of Michigan, Ann Arbor, MI, United States","Esterwood, C., School of Information, University of Michigan, Ann Arbor, MI, United States; Essenmacher, K., School of Information, University of Michigan, Ann Arbor, MI, United States; Yang, H., School of Information, University of Michigan, Ann Arbor, MI, United States","Human personality has been identifed as a predictor of robot acceptance in the human-robot interaction (HRI) literature. Despite this, the HRI literature has provided mixed support for this assertion. To better understand the relationship between human personality and robot acceptance, this paper conducts a meta-analysis of 26 studies. Results found a positive relationship between human personality and robot acceptance. However, this relationship varied greatly by the specifc personality trait along with the study sample's age, gender diversity, task, and global region. This meta-analysis also identifed gaps in the literature. Namely, additional studies are needed that investigate both the big fve personality traits and other personality traits, examine a more diverse age range, and utilize samples from previously unexamined regions of the globe. © 2021 ACM.","Adaptability; Anxiety; Enjoyment; Hri; Human-robot interaction; Individual diferences; Meta-analysis; Personality; Robot acceptance; Sociability; Social presence; Trust","Human engineering; Man machine systems; Gender diversity; Global regions; Human robot Interaction (HRI); Meta analysis; Mixed supports; Personality traits; Robot acceptances; Study samples; Social robots",,,,,,,,,,,,,,,,"(1937) Personality: A Psychological Interpretation., , Gordon Willard Allport., 1937; Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 325-332. , IEEE; Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Proceedings of the 33rd Annual Acm Conference on Human Factors in Computing Systems., pp. 3603-3612. , ACM; Ashton, M.C., Lee, K., Vries De, R.E., The hexaco honesty-humility, agreeableness, and emotionality factors: A review of research and theory (2014) Pers. 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"Porfrio D., Stegner L., Cakmak M., Sauppe A., Albarghouthi A., Mutlu B.","57223994731;57219542965;22940323800;56084504000;36336887800;15060220700;","Figaro: A tabletop authoring environment for human-robot interaction",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,1,"10.1145/3411764.3446864","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106680868&doi=10.1145%2f3411764.3446864&partnerID=40&md5=f842718ed10940228da567a669f2a192","University of Washington, Seattle, WA, United States","Porfrio, D., University of Washington, Seattle, WA, United States; Stegner, L., University of Washington, Seattle, WA, United States; Cakmak, M., University of Washington, Seattle, WA, United States; Sauppe, A., University of Washington, Seattle, WA, United States; Albarghouthi, A., University of Washington, Seattle, WA, United States; Mutlu, B., University of Washington, Seattle, WA, United States","Human-robot interaction designers and developers navigate a complex design space, which creates a need for tools that support intuitive design processes and harness the programming capacity of state-of-the-art authoring environments.We introduce Figaro, an expressive tabletop authoring environment for mobile robots, inspired by shadow puppetry, that provides designers with a natural, situatedrepresentation of human-robot interactions while exploiting the intuitiveness of tabletop and tangible programming interfaces. On the tabletop, Figaro projects a representation of an environment. Users demonstrate sequences of behaviors, or scenes, of an interaction by manipulating instrumented fgurines that represent the robot and the human. During a scene, Figaro records the movement. © 2021 ACM.","Authoring environments; Human-robot interaction; Program synthesis; Shadow puppetry; Tabletop interfaces","Application programming interfaces (API); End effectors; Human engineering; Industrial manipulators; Interface states; Machine design; Man machine systems; Robot programming; Authoring environments; Complex designs; Design process; Shadow puppetries; State of the art; Tangible programming; Human robot interaction",,,,,"National Science Foundation, NSF: 1651129, 1925043","This work was supported by the National Science Foundation (NSF) awards 1651129 and 1925043 and an NSF Graduate Research Fellowship. 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"Cheng Y., Yi P., Liu R., Dong J., Zhou D., Zhang Q.","57224515634;23993138800;56706689400;55278050700;13105639000;56383216300;","Human-robot Interaction Method Combining Human Pose Estimation and Motion Intention Recognition",2021,"Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021",,,"9437772","958","963",,,"10.1109/CSCWD49262.2021.9437772","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107743916&doi=10.1109%2fCSCWD49262.2021.9437772&partnerID=40&md5=b15cd0462058761a0922d24b25ca5b84","Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Dalian University and Dalian University of Technology, Dalian, China","Cheng, Y., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Yi, P., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Liu, R., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Dong, J., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Zhou, D., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Zhang, Q., Dalian University and Dalian University of Technology, Dalian, China","Although human pose estimation technology based on RGB images is becoming more and more mature, most of the current mainstream methods rely on depth camera to obtain human joints information. These interaction frameworks are affected by the infrared detection distance so that they cannot well adapt to the interaction scene of different distance. Therefore, the purpose of this paper is to build a modular interactive framework based on RGB images, which aims to alleviate the problem of high dependence on depth camera and low adaptability to distance in the current human-robot interaction (HRI) framework based on human body by using advanced human pose estimation technology. To enhance the adaptability of the HRI framework to different distances, we adopt optical cameras instead of depth cameras as acquisition equipment. Firstly, the human joints information is extracted by a human pose estimation network. Then, a joints sequence filter is designed in the intermediate stage to reduce the influence of unreasonable skeletons on the interaction results. Finally, a human intention recognition model is built to recognize the human intention from reasonable joints information, and drive the robot to respond according to the predicted intention. The experimental results show that our interactive framework is more robust in the distance than the framework based on depth camera and is able to achieve effective interaction under different distances, illuminations, costumes, customers, and scenes. © 2021 IEEE.","human pose estimation; human-robot interaction; intention recognition","Cameras; Interactive computer systems; Man machine systems; Motion estimation; Acquisition equipments; Effective interactions; Human pose estimations; Human robot Interaction (HRI); Infrared detection; Interaction framework; Intermediate stage; Motion intention recognition; Social robots",,,,,"National Natural Science Foundation of China, NSFC; Dalian Science and Technology Innovation Fund: 2020JJ25CY001; Special Project of Central Government for Local Science and Technology Development of Hubei Province: 2021JH6/10500140","This work was supported in part by the Key Program of NSFC (Grant No.U1908214), Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province and Dalian University, and in part by the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CY001).",,,,,,,,,,"Mandal, F.B., Nonverbal communication in humans (2014) Journal of Human Behaviour in the Social Environment, 24 (4), pp. 417-421; Wang, L., Gao, R.X., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) Cirp Annals-Manufacturing Technology, 68 (2), pp. 701-726; Fankhauser, P., Michael, B., Diego, R., Ralf, K., Marco, H., Roland, Y.S., Kinect v2 for mobile robot navigation: Evaluation and modelling (2015) 2015 International Conference on Advanced Robotics (ICAR). Istanbul, pp. 388-394; De Carli, D., Hohert, E., Parker, C.A., Zoghbi, S., Leonard, S., Croft, E., Bicchi, A measuring intent in human-robot cooperative manipulation (2009) 2009 Ieee International Workshop on Haptic Audio Visual Environments and Games, pp. 159-163. , Lecco; Zhu, C., Sun, W., Sheng, W., Wearable sensors based human intention recognition in smart assisted living systems (2008) 2008 International Conference on Information and Automation. Changsha, pp. 954-959; Wang, Z., Mülling, K., Deisenroth, M.P., Ben Amor, H., Vogt, D., Scholkopf, B., Probabilistic movement modeling for intention inference in human-robot interaction (2013) The International Journal of Robotics Research, 32 (7), pp. 841-858; Elfring, J., Molengraft De, R.Van., Steinbuch, M., Learning intentions for improved human motion prediction (2014) Robotics and Autonomous Systems, 62 (4), pp. 591-602; Petković, T., Marković, I., Petrović, I., Human intention recognition in flexible robotized warehouses based on markov decision processes (2017) Iberian Robotics Conference, pp. 629-640. , Seville; Liu, T., Wang, J., Meng, M.Q.H., Human robot cooperation based on human intention inference (2017) Ieee International Conference on Robotics & Biomimetics, , Zhuhai; Yu, X., He, W., Li, Y., Xue, C., Li, J., Zou, J., Yang, C., Bayesian estimation of human impedance and motion intention for human-robot collaboration (2019) Ieee Transactions on Cybernetics; Sigalas, M., Baltzakis, H., Trahanias, P., Gesture recognition based on arm tracking for human-robot interaction (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5424-5429. , Piscataway; Li, X., Human-robot interaction based on gesture and movement recognition (2020) Signal Processing: Image Communication, 81, p. 115686; Luo, X., Amighetti, A., Zhang, D., A human-robot interaction for a mecanum wheeled mobile robot with real-time 3d two-hand gesture recognition (2019) Journal of Physics: Conference Series, 1267, p. 012056; Koppula, H.S., Saxena, A., Anticipating human activities using object affordances for reactive robotic response (2015) Ieee Transactions on Pattern Analysis and Machine Intelligence, 38 (1), pp. 14-29; Canal, G., Escalera, S., Angulo, C., A real-time human-robot interaction system based on gestures for assistive scenarios (2017) Computer Vision and Image Understanding, 149, pp. 65-77; Li, K., Wu, J., Zhao, X., Tan, M., Real-time human-robot interaction for a service robot based on 3d human activity recognition and human-mimicking decision mechanism (2018) 2018 Ieee 8th Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 498-503. , Tianjin; Mazhar, O., Navarro, B., Ramdani, S., Passama, R., Cherubini, A., A real-time human-robot interaction framework with robust background invariant hand gesture detection (2019) Robotics and Computer-Integrated Manufacturing, 60, pp. 34-48; (1988) GB10000-88 Human Dimensions of Chinese Adults[S], , Chinese Academy of Standardization and Information Classified Coding, Beijing: Standard Press of China; Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Long-term recurrent convolutional networks for visual recognition and description (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition. Boston, pp. 2625-2634; Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y., Towards 3d human pose estimation in the wild: A weakly-supervised approach (2017) Proceedings of the Ieee International Conference on Computer Vision, pp. 398-407. , Honolulu","Yi, P.; Key Laboratory of Advanced Design and Intelligent Computing, China; email: istrrr@163.com
Zhang, Q.; Dalian University and Dalian University of TechnologyChina; email: zhangq@dlut.edu.cn","Shen W.Barthes J.-P.Luo J.Shi Y.Zhang J.",,"Institute of Electrical and Electronics Engineers Inc.","24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021","5 May 2021 through 7 May 2021",,169216,,9781728165974,,,"English","Proc. IEEE Int. Conf. Comput. Support. Coop. Work Des., CSCWD",Conference Paper,"Final","",Scopus,2-s2.0-85107743916
"Lv H., Yi P., Liu R., Hou Y., Zhou D., Zhang Q., Wei X.","57224510996;23993138800;56706689400;25924942100;13105639000;56383216300;7402117019;","Asymmetric Anomaly Detection for Human-Robot Interaction",2021,"Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021",,,"9437868","372","377",,,"10.1109/CSCWD49262.2021.9437868","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107716284&doi=10.1109%2fCSCWD49262.2021.9437868&partnerID=40&md5=8ac024ce2305c7da1bf8aaa96cbf229f","National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; School of Information Science and Technology, Taishan University, Tai'an, China","Lv, H., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Yi, P., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Liu, R., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Hou, Y., School of Information Science and Technology, Taishan University, Tai'an, China; Zhou, D., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Zhang, Q., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Wei, X., School of Information Science and Technology, Taishan University, Tai'an, China","Security in human-robot interaction is the focus of research in this field. Rapid detection of abnormal events that may cause danger in the interaction process can effectively reduce the probability of occurrence of danger. In general anomaly detection methods, 2D or 3D convolutional autoencoders are widely used for anomaly detection. Among them, 2D convolutional autoencoders are with good real-time performance and lower detection accuracy, while 3D convolutional autoencoders are with higher detection accuracy and insufficient real-time performance. In order to ensure realtime performance and obtain higher accuracy, an end-to-end asymmetric convolutional autoencoder network (ACANet) using both 2D and 3D convolutions is designed. Specifically, 3D convolution is used to build the encoder to learn comprehensive information in continuous input frames, and 2D convolution is used to build the decoder to model the information fast, a dimensional alignment module is constructed to connect the encoder and the decoder while avoiding a large number of calculations in the latent space of the 3D features output by the encoder, and the skip connections module is used to obtain accurate predictions. Anomaly detection can then be completed by evaluating the differences between results predicted by the ACANet and real frames. The experimental results show that our method achieves competitive accuracy on mainstream datasets and at the same time obtains the fastest speed. Compared with mainstream methods, this method is more suitable for anomaly detection tasks in human-robot interaction. © 2021 IEEE.","anomaly detection; asymmetric autoencoder; human-robot interaction","Convolution; Convolutional neural networks; Decoding; Human robot interaction; Interactive computer systems; Learning systems; Man machine systems; Signal encoding; Accurate prediction; Anomaly detection methods; Comprehensive information; Detection accuracy; Focus of researches; Interaction process; Probability of occurrence; Real time performance; Anomaly detection",,,,,"National Natural Science Foundation of China, NSFC; Dalian Science and Technology Innovation Fund: 2020JJ25CYOO 1; Special Project of Central Government for Local Science and Technology Development of Hubei Province: 2021JH6/10500140","This work was supported in part by the Key Program of NSFC (Grant No.Ul908214), Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province and Dalian University, and in part by the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CYOO 1 ).",,,,,,,,,,"Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) 2013 Ieee International Conference on Robotics and Automation; Wang, L., (2019) Symbiotic Human-robot Collaborative Assembly, 68 (2), pp. 701-726; Zong, B., Deep autoencoding gaussian mixture model for unsupervised anomaly detection (2018) International Conference on Ions; Sabokrou, M., Adversarially learned one-class classifier for novelty detection (2018) Computer Vision and Pattern Recognition; Chandola, V., Baneijee, A., Kumar, V., Anomaly detection: A survey (2009) Acm Comput. Surv, 41 (3). , Article 15; Hasan, M., Learning temporal regularity in video sequences (2016) Computer Vision and Pattern Recognition; Chen, B., Video imagination from a single image with transformation generation (2017) Computer Vision and Pattern Recognition; Mathieu, M., Couprie, C., LeCun, Y., (2015) Deep Multi-scale Video Prediction beyond Mean Square Error; Lu, C., Shi, J., Jia, J., Abnormal event detection at 150 fps in matlab (2013) International Conference on Computer Vision; Park, H., Noh, J., Ham, B., Learning memory-guided normality for anomaly detection (2020) Proceedings of the IEEE/ Cvf Conference on Computer Vision and Pattern Recognition; Zhao, Y., Spatio-temporal autoencoder for video anomaly detection (2017) Acm Multimedia; Fryman, J., Matthias, B., Safety of industrial robots: From conventional to collaborative applications (2012) Robotik 2012; 7th German Conference on Robotics; Ribeiro, M., Lazzaretti, A.E., Lopes, H.S., A study of deep convolutional auto-encoders for anomaly detection in videos (2018) Pattern Recognition Letters, 105, pp. 13-22; Gong, D., Memorizing normality to detect anomaly: Memoryaugmented deep autoencoder for unsupervised anomaly detection (2019) International Conference on Computer Vision; Ouali, Y., Hudelot, C., Tami, M., (2020) Semi-Supervised Semantic Segmentation with Cross-Consistency Training; Wang, L., Dual super-resolution learning for semantic segmentation (2020) 2020 IEEE/ Cvf Conference on Computer Vision and Pattern Recognition (CVPR); Liu, W., Future frame prediction for anomaly detection-a new baseline (2018) Computer Vision and Pattern Recognition; Ravanbakhsh, M., Abnormal event detection in videos using generative adversarial nets (2017) International Conference on Image Processing; Chang, Y., Clustering driven deep autoencoder for video anomaly detection (2020) European Conference on Computer Vision (ECCV); Luo, W., Liu, W., Gao, S., A revisit of sparse coding based anomaly detection in stacked rnn framework (2017) International Conference on Computer Vision; Yan, A., Pa3d: Pose-action 3d machine for video recognition (2019) Computer Vision and Pattern Recognition; Diba, A., Temporal 3d convnets: New architecture and transfer learning for video classification (2017) Computer Vision and Pattern Recognition; Sultani, W., Chen, C., Shah, M., Real-world anomaly detection in surveillance videos (2018) Computer Vision and Pattern Recognition","Zhou, D.; National and Local Joint Engineering Laboratory of Computer Aided Design, China; email: zhouds@dlu.edu.cn","Shen W.Barthes J.-P.Luo J.Shi Y.Zhang J.",,"Institute of Electrical and Electronics Engineers Inc.","24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021","5 May 2021 through 7 May 2021",,169216,,9781728165974,,,"English","Proc. IEEE Int. Conf. Comput. Support. Coop. Work Des., CSCWD",Conference Paper,"Final","",Scopus,2-s2.0-85107716284
"Mocanu I., Tudor A., Cramariuc O.","22985958700;57226602299;6507860414;","Gesture-Based Human-Robot Interaction",2021,"Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021",,,"9480992","151","156",,,"10.1109/CSCS52396.2021.00032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112041227&doi=10.1109%2fCSCS52396.2021.00032&partnerID=40&md5=0122bd8c081914aa29bf1d6ab535d72a","University Politehnica of Bucharest, Computer Science Department, Bucharest, Romania; IT Center for Science and Technology, Bucharest, Romania","Mocanu, I., University Politehnica of Bucharest, Computer Science Department, Bucharest, Romania; Tudor, A., University Politehnica of Bucharest, Computer Science Department, Bucharest, Romania; Cramariuc, O., IT Center for Science and Technology, Bucharest, Romania","The paper proposes a method for human robot interaction through gestures. Even if there are different methods that perform gesture recognition, many of them are tested in ideal conditions. This paper proposes a convolutional neural network that is used for gesture recognition applied for human-robot interaction in different environmental conditions: such as: blurring, low or high illumination, low quality image or losing part of frames. We started from a pre-trained ResNeXt 101 model and we increased the accuracy by adding a preprocessing stage for improving the quality of image, applying data augmentation, using different optimisers and learning rate scheduler. The proposed model obtained an improvement of 0.9% compared to the baseline model. Online evaluation of gesture recognition was performed using the Turtlebot robot. © 2021 IEEE.","Gesture recognition; Humat-robot interaction; Turtlebot robot","Computer control systems; Convolutional neural networks; Gesture recognition; Image enhancement; Man machine systems; Baseline models; Data augmentation; Environmental conditions; Learning rates; Low qualities; On-line evaluation; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: AAL-2017-059-INCARE; European Commission, EC; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, UEFISCDI","This work was supported by a grant of the CCDI–UEFISCDI and of the AAL Programme with co-funding from the European Union’s Horizon 2020 research and innovation programme project “INCARE – Integrated Solution for Innovative Elderly Care”, project number AAL-2017-059-INCARE.","This work was supported by a grant of the CCDI-UEFISCDI and of the AAL Programme with co-funding from the European Union's Horizon 2020 research and innovation programme project INCARE - Integrated Solution for Innovative Elderly Care, project number AAL-2017-059-INCARE",,,,,,,,,"(2021) World Population Ageing 2015, , United Nations. 2017, accessed on March; Soomro, K., Zamir, A., Shah, M., (2012) UCF101: A Dataset of 101 Human Actions Classes from Videos in the Wild, , abs/1212.0402; Lionel, P., Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video (2018) International Journal of Computer Vision, 126 (2-4), pp. 430-439; Limin, W., Temporal segment networks: Towards good practices for deep action recognition (2016) European Conference on Computer Vision. Springer, Cham; Shuiwang, J., 3D convolutional neural networks for human action recognition (2012) IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (1), pp. 221-231; Okan, K., Kose, N., Rigoll, G., Motion fused frames: Data level fusion strategy for hand gesture recognition (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; Okan, K., Real-time hand gesture detection and classification using convolutional neural networks (2019) 2019 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019). IEEE; Materzynska, J., Berger, G., Bax, I., Memisevic, R., The jester dataset: A large-scale video dataset of human gestures (2019) 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 2874-2882. , Seoul, Korea (South); Nitish, S., Dropout: A simple way to prevent neural networks from overfitting (2014) The Journal of Machine Learning Research, 15 (1), pp. 1929-1958; (2021) Various Optimization Algorithms for Training Neural Network, , https://towardsdatascience.com/optimizers-for-training-neural-network-59450d71caf6, accessed on March; Sergey, I., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; Vinod, N., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) ICML; (2021) The 20BN-jester Dataset V1, , https://20bn.com/datasets/jester, accessed on March; Saining, X., Aggregated residual transformations for deep neural networks (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; (2021) Chalearn LAP RGB-D Isolated Gesture, , http://chalearnlap.cvc.uab.es/challenge/25/track/23/description/, accessed on March; Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J., Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks (2016) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4207-4215. , Las Vegas, NV, USA",,,"IEEE Romania Section;IEEE Romanian Chapter of Computer Society;Romanian Academy - Science and Information Technology Section;Romanian Association for Artificial Intelligence (ARIA);Romanian Society of Control Engineering and Technical Informatics (SRAIT);The Faculty of Automatic Control and Computers (A and C) of University Politehnica of Bucharest","Institute of Electrical and Electronics Engineers Inc.","23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021","26 May 2021 through 28 May 2021",,170600,,9781665439398,,,"English","Proc. - Int. Conf. Control Syst. Comput. Sci. Technol., CSCS",Conference Paper,"Final","",Scopus,2-s2.0-85112041227
"Tian L., Oviatt S.","56151869000;7003592124;","A taxonomy of social errors in human-robot interaction",2021,"ACM Transactions on Human-Robot Interaction","10","2","13","","",,6,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106723180&partnerID=40&md5=e52b402e4c33ab39db7f35d977027f38","Faculty of Engineering, Monash University, 14 Alliance Lane, Clayton, VIC  3800, Australia","Tian, L., Faculty of Engineering, Monash University, 14 Alliance Lane, Clayton, VIC  3800, Australia; Oviatt, S., Faculty of Engineering, Monash University, 14 Alliance Lane, Clayton, VIC  3800, Australia","Robotic applications have entered various aspects of our lives, such as health care and educational services. In such Human-robot Interaction (HRI), trust and mutual adaption are established and maintained through a positive social relationship between a user and a robot. This social relationship relies on the perceived competence of a robot on the social-emotional dimension. However, because of technical limitations and user heterogeneity, current HRI is far from error-free, especially when a system leaves controlled lab environments and is applied to in-the-wild conditions. Errors in HRI may either degrade a user's perception of a robot's capability in achieving a task (defined as performance errors in this work) or degrade a user's perception of a robot's socio-affective competence (defined as social errors in this work). The impact of these errors and effective strategies to handle such an impact remains an open question. We focus on social errors in HRI in this work. In particular, we identify the major attributes of perceived socio-affective competence by reviewing human social interaction studies and HRI error studies. This motivates us to propose a taxonomy of social errors in HRI. We then discuss the impact of social errors situated in three representative HRI scenarios. This article provides foundations for a systematic analysis of the social-emotional dimension of HRI. The proposed taxonomy of social errors encourages the development of user-centered HRI systems, designed to offer positive and adaptive interaction experiences and improved interaction outcomes. © 2021 Copyright held by the owner/author(s).","Affective computing; human-robot interaction; social norms; social robotics; socio-affective competence","Agricultural robots; Behavioral research; Educational robots; Errors; Man machine systems; Social aspects; Taxonomies; Educational services; Emotional dimensions; Human robot Interaction (HRI); Human social interactions; Perceived competence; Robotic applications; Social relationships; Technical limitations; Social robots",,,,,,,,,,,,,,,,"Adam, C., Gaudou, B., BDI agents in social simulations: A survey (2016) Knowl. Eng. 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"Maithani H., Ramon J.A.C., Lequievre L., Mezouar Y., Alric M.","57209266536;35769026000;34167996000;6602736108;57212599321;","Exoscarne: Assistive strategies for an industrial meat cutting system based on physical human-robot interaction",2021,"Applied Sciences (Switzerland)","11","9","3907","","",,1,"10.3390/app11093907","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106658381&doi=10.3390%2fapp11093907&partnerID=40&md5=19609ee91d6152daef17dc8cd3c5b375","Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain; ADIV, ZAC des Gravanches, 10 Rue Jacqueline Auriol, Clermont-Ferrand, 63100, France","Maithani, H., Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Ramon, J.A.C., Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain; Lequievre, L., Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Mezouar, Y., Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Alric, M., ADIV, ZAC des Gravanches, 10 Rue Jacqueline Auriol, Clermont-Ferrand, 63100, France","Musculoskeletal disorders of the wrist are common in the meat industry. A proof of concept of a physical human-robot interaction (pHRI)-based assistive strategy for an industrial meat cutting system is demonstrated which can be transferred to an exoskeleton later. We discuss how a robot can assist a human in pHRI, specifically in the context of an industrial project i.e for the meat cutting industry. We developed an impedance control-based system that enables a KUKA LWR robot to provide assistive forces to a professional butcher while simultaneously allowing motion of the knife (tool) in all degrees of freedom. We developed two assistive strategies—a force amplification strategy and an intent prediction strategy—and integrated them into an impedance controller. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Assistive robotics; Collaborative robots; Physical human-robot interaction",,,,,,"Horizon 2020 Framework Programme, H2020: 869855","Funding: This research received funding from the French government research program Investisse-ments d‘Avenir through the UMTs ACTIA Mécarnéo-AgRobErgo and the project Exoscarne (Call P3A-ICF2A-2I2A, FranceAgriMer) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 869855 (SoftManBot project).",,,,,,,,,,"(2020) Statistiques Accidents du Travail et Maladies Professionnelles, , Carsat Bretagne: Rennes, France; (2019) L’Assurance Maladie-Risques Professionels, Éléments Statistiques et Financiers; Caisse nationale de l’Assurance Maladie des travailleurs salariés, , Rapport Annuel Paris, France, 2020; (2019) État de Santé des Salariés de la Filière Viande du Régime Agricole en Bretagne, , Institut de Veille Sanitaire: Saint-Maurice, France; De Medeiros Esper, I., From, P.J., Mason, A., Robotisation and intelligent systems in abattoirs (2021) Trends Food Sci. 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Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85106658381
"Hanoch Y., Arvizzigno F., Hernandez Garciá D., Denham S., Belpaeme T., Gummerum M.","6602359888;57223807516;56520258500;7006765598;13103663500;24174326300;","The Robot Made Me Do It: Human-Robot Interaction and Risk-Taking Behavior",2021,"Cyberpsychology, Behavior, and Social Networking","24","5",,"337","342",,,"10.1089/cyber.2020.0148","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106155283&doi=10.1089%2fcyber.2020.0148&partnerID=40&md5=dd7dd232c07e41fcc7297897d9801364","Southampton Business School, Faculty of Social Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom; Department of Education Science, Psychology Communication Science, University of Bari Aldo Moro, Bari, Italy; Department of Computer Science, Manchester University, Manchester, United Kingdom; Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom; IDLab - Imec, Ghent University, Ghent, Belgium; Department of Psychology, University of Warwick, Coventry, United Kingdom","Hanoch, Y., Southampton Business School, Faculty of Social Sciences, University of Southampton, Southampton, SO17 1BJ, United Kingdom; Arvizzigno, F., Department of Education Science, Psychology Communication Science, University of Bari Aldo Moro, Bari, Italy; Hernandez Garciá, D., Department of Computer Science, Manchester University, Manchester, United Kingdom; Denham, S., Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom; Belpaeme, T., IDLab - Imec, Ghent University, Ghent, Belgium; Gummerum, M., Department of Psychology, University of Warwick, Coventry, United Kingdom","Empirical evidence has shown that peer pressure can impact human risk-taking behavior. With robots becoming ever more present in a range of human settings, it is crucial to examine whether robots can have a similar impact. Using the balloon analogue risk task (BART), participants' risk-taking behavior was measured when alone, in the presence of a silent robot, or in the presence of a robot that actively encouraged risk-taking behavior. In the BART, shown to be a proxy for real risk-taking behavior, participants must weigh risk against potential payout. Our results reveal that participants who were encouraged by the robot did take more risks, while the mere presence of the robot in the robot control condition did not entice participants to show more risk-taking behavior. Our results point to both possible benefits and perils that robots might pose to human decision-making. Although increasing risk-taking behavior in some cases has obvious advantages, it could also have detrimental consequences that are only now starting to emerge. © Yaniv Hanoch et al. 2021; Published by Mary Ann Liebert, Inc. 2021.","human-robot interaction; peer pressure; risk taking; robot","adult; decision making; high risk behavior; human; male; peer pressure; robotics; social behavior; young adult; Adult; Decision Making; Humans; Male; Peer Influence; Risk-Taking; Robotics; Social Behavior; Young Adult",,,,,,,,,,,,,,,,"Asch, SE., Effects of group pressure upon the modification and distortion of judgments (1951) Groups, Leadership, and Men, pp. 177-190. , Guetzkow H, ed. 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(accessed Aug. 10, 2020); Steinberg, L, Monahan, KC., Age differences in resistance to peer influence Developmental Psychology2007, 43, pp. 1531-1543; Shepherd, JL, Lane, DJ, Tapscott, RL, Susceptible to social influence: risky ''driving'' in response to peer pressure (2011) Journal of Applied Social Psychology, 41, pp. 773-797; Gheorghiu, G, Delhomme, P, Felonneau, ML., Peer pressure and risk taking in young drivers' speeding behavior (2015) Transportation Research part F: Traffic Psychology and Behaviour, 35, pp. 101-111; Pradhan, AK, Li, K, Bingham, CR, Peer passenger influences on male adolescent drivers' visual scanning behavior during simulated driving (2014) Journal of Adolescent Health, 54, pp. S42-S49; Simons-Morton, BG, Lerner, N, Singer, J., The observed effects of teenage passengers on the risky driving behavior of teenage drivers (2005) Accident Analysis and Prevention, 37, pp. 973-982; Toxopeus, R, Ramkhalawansingh, R, Trick, RLM., The influence of passenger-driver interaction on young drivers (2011) Proceedings of the Sixth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp. 66-72. , https://pdfs.semanticscholar.org/d98d/41abff8c5a294475c0705800f19bcd7287a7.pdf, (accessed Aug. 10, 2020); Reynolds, EK, MacPherson, L, Scwartz, S, Analogue study of peer influence on risk-taking behavior in older adolescents (2014) Preventive Science, 15, pp. 842-849; Lejuez, CW, Read, JP, Kahler, CW, Evaluation of a behavioral measure of risk taking: the balloon analogue risk task (BART) (2002) Journal of Experimental Psychology: Applied, 8, pp. 75-84; Hopko, DR, Lejuez, CW, Daughters, SB, Construct validity of the balloon analogue risk task (BART): relationship with MDMA use by inner-city drug users in residential treatment (2005) Journal of Psychopathology and Behavioral Assessment, 28, pp. 95-101; Lejuez, CW, Aklin, MW, Jones, HA, The balloon analogue risk task (BART) differentiate smokers and nonsmokers (2003) Experimental and Clinical Psychopharmacology, 11, pp. 26-33; Lejuez, CW, Simmons, BL, Aklin, WM, Risk-taking propensity and risky sexual behavior of individuals in residential substance use treatment (2004) Addictive Behaviour, 29, pp. 1643-1647; Bartneck, C, Kulic, D, Croft, E, Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robot, 1, pp. 71-81; Dohmen, T, Falk, A, Huffman, A, Individual risk attitudes: measurement, determinants, and behavioral consequences (2011) Journal of the European Economics Association, 9, pp. 522-550; Chou, EY, Nordgren, LF., Safety in numbers: why the mere physical presence of others affects risk-taking behaviors (2017) Journal of Behavioral Decision Making, 30, pp. 671-682; Cottrell, NB, Wack, DL, Sekerak, GJ, Social facilitation of dominant responses by the presence of an audience and the mere presence of others (1968) Journal of Personality and Social Psychology, 9, pp. 245-250; Guerin, B, Innes, JM., Explanations of social facilitation: a review (1984) Current Psychological Research and Review, 3, pp. 32-52; Gray, HM, Gray, K, Wegner, DM., Dimensions of mind perception (2007) Science, 315, p. 619; Terada, K, Shamoto, T, Mei, H, Reactive movements of non-humanoid robots cause intention attribution in humans (2007) Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3715-3720. , https://ieeexplore.ieee.org/abstract/document/4399429, (accessed Aug. 10, 2020); Biele, G, Rieskamp, J, Gonzalez, R., Computational models for the combination of advice and individual learning (2009) Cognitive Science, 33, pp. 206-242; Rendell, L., Fogarty, L, Hoppitt, WJ, Cognitive culture: theoretical and empirical insights into social learning strategies (2011) Trends in Cognitive Science, 15, pp. 68-76","Hanoch, Y.; Southampton Business School, United Kingdom; email: y.hanoch@soton.ac.uk",,,"Mary Ann Liebert Inc.",,,,,21522715,,,"33211533","English","Cyberpsychol. Behav. Soc. Networking",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85106155283
"Glogowski P., Böhmer A., Hypki A., Kuhlenkötter B.","57195317864;57223199871;6506910986;7801348331;","Robot Speed Adaption in Multiple Trajectory Planning and Integration in a Simulation Tool for Human-Robot Interaction",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","102","1","25","","",,,"10.1007/s10846-020-01309-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105223926&doi=10.1007%2fs10846-020-01309-7&partnerID=40&md5=d55697aa2a42d4b3c0dda87fdd331fd3","Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany","Glogowski, P., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany; Böhmer, A., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany; Hypki, A., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany; Kuhlenkötter, B., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany","Speed and separation monitoring (SSM) is one of the four permissible collaborative operations in human-robot interaction (HRI). At all times, it must be ensured that the speed-dependent separation distance is maintained. To guarantee this, the robot speed or the robot path can be adapted. In this paper, the robot speed adaption for multiple trajectories is implemented in an HRI simulation tool and tested in an application example. Thereby, numerous complex process situations, such as a temporary robot stop or obstacles in the collaborative workspace, can be simulated. The simulation tool enables a comprehensive simulation, analysis and optimisation of human and robot motions within the HRI, already in the planning phase. © 2021, The Author(s).","Human-robot interaction; Multiple trajectory planning; Robot speed adaption; Speed and separation monitoring","Agricultural robots; End effectors; Man machine systems; Robot programming; Speed; Application examples; Collaborative operations; Collaborative workspace; Complex Processes; Human robot Interaction (HRI); Planning phase; Separation distances; Trajectory Planning; Human robot interaction",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 02P15A060","Open Access funding enabled and organized by Projekt DEAL. The research and development project “KoMPI” was funded by the German Federal Ministry of Education and Research (BMBF) within the Framework Concept “Research for Tomorrow’s Production” (fund number 02P15A060) and managed by the Project Management Agency Forschungszentrum Karlsruhe, Production and Manufacturing Technologies Division (PTKA-PFT).",,,,,,,,,,"(2016), ISO/TS 15066: Robots and Robotic Devices – Collaborative Robots; Glogowski, P., Lemmerz, K., Hypki, A., Kuhlenkötter, B., ROS-based Robot Simulation in Human-Robot Collaboration (2018) Developing Support Technologies: Integrating Multiple Perspectives to Create Assistance that People Really Want, pp. 237-246. , Karafillidis, A., Weidner, R; Glogowski, P., Lemmerz, K., Schulte, L., Barthelmey, A., Hypki, A., Kuhlenkötter, B., Deuse, J., Task-based Simulation Tool for Human-Robot Collaboration within Assembly Systems (2017) Tagungsband Des 2. Kongresses Montage Handhabung Industrieroboter, pp. 155-163. , Schöppstuhl, T, Franke, J., Tracht, K., Springer, Berlin; Lemmerz, K., Glogowski, P., Hypki, A., Kuhlenkötter, B., Functional Integration of a Robotics Software Framework into a Human Simulation System (2018) 50Th International Symposium on Robotics (ISR), , Munich; Lacevic, B., Rocco, P., Kinetostatic Danger Field – a Novel Safety Assessment for Human-Robot Interaction (2010) IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2169-2174; Lacevic, B., Rocco, P., Safety-Oriented Control of Robotic Manipulators – a Kinematic Approach (2011) IFAC Proc Vol, 44 (1), pp. 11508-11513; Polverini, M.P., Zanchettin, A.M., Rocco, P., Real-time Collision Avoidance in Human-Robot Interaction based on Kinetostatic Safety Field (2014) IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4136-4141; Marvel, J.A., Performance Metrics of Speed and Separation Monitoring in Shared Workspaces (2013) IEEE Trans. Autom. Sci. Eng., 10 (2), pp. 405-414; Marvel, J.A., Norcross, R., Implementing Speed and Separation Monitoring in Collaborative Robot Workcells (2017) Robot. Comput. Integr. Manuf., 44, pp. 144-155; Kim, E., Kirschner, R., Yamada, Y., Okamoto, S., Estimating Probability of Human Hand Intrusion for Speed and Separation Monitoring using Interference Theory (2020) Robot. Comput. Integr. Manuf., 61; Savur, C., Kumar, S., Arora, S., Hazbar, T., Sahin, F., (2019) Hrc-Sos: Human Robot Collaboration Experimentation Platform as System of Systems. Arxiv, 1905, p. 01026; Byner, C., Matthias, B., Ding, H., Dynamic Speed and Separation Monitoring for Collaborative Robot Applications – Concepts and Performance (2019) Robot. Comput. Integr. Manuf., 58, pp. 239-252; Lasota, P.A., Rossano, G.F., Shah, J.A., Toward Safe Close-Proximity Human-Robot Interaction with Standard Industrial Robots (2014) IEEE International Conference on Automation Science and Engineering (CASE), pp. 339-344; Kumar, S., Arora, S., Sahin, F., Speed and Separation Monitoring using On-Robot Time-of-Flight Laser-ranging Sensor Arrays (2019) IEEE International Conference on Automation Science and Engineering (CASE), pp. 1684-1691; Dröder, K., Bobka, P., Germann, T., Gabriel, F., Dietrich, F., A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration (2018) Procedia CIRP, 76, pp. 187-192; Glogowski, P., Lemmerz, K., Hypki, A., Kuhlenkötter, B., Menschzentrierte Simulation mit adaptiver kollisionsfreier Roboterbahnplanung in der Mensch-Roboter-Kollaboration (2018) Band Zur Dritten Transdisziplinären Konferenz “Technische Unterstützungssysteme, Die Die Menschen Wirklich wollen”, pp. 47-57. , Weidner, R., Karafillidis, A; Schmidt, B., Wang, L., Contact-less and Programming-less Human-Robot Collaboration (2013) Procedia CIRP, 7, pp. 545-550; Liu, Z., Wang, X., Cai, Y., Xu, W., Liu, Q., Zhou, Z., Pham, D.T., Dynamic Risk Assessment and Active Response Strategy for Industrial Human-Robot Collaboration (2020) Comput. Ind. Eng., 141; Vicentini, F., Giussani, M., Tosatti, L.M., Trajectory-dependent Safe Distances in Human-Robot Interaction (2014) IEEE Emerging Technology and Factory Automation (ETFA), pp. 1-4; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in Human-Robot Collaborative Manufacturing Environments: Metrics and Control (2015) IEEE Trans. Autom. Sci. Eng., 13 (2), pp. 882-893; Rosenstrauch, M.J., Pannen, T.J., Krüger, J., Human Robot Collaboration – Using Kinect v2 for ISO/TS 15066 Speed and Separation Monitoring (2018) Procedia CIRP, 76, pp. 183-186; Glogowski, P., Lemmerz, K., Hypki, A., Kuhlenkötter, B., Extended Calculation of the Dynamic Separation Distance for Robot Speed Adaption in the Human-Robot Interaction (2019) International Conference on Advanced Robotics (ICAR), Belo Horizonte, Brazil; Deuflhard, P., Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms (2011) Springer Science & Business Media, Vol, p. 35; Gourdon, X., Sebah, P., (2001) Newton’s Method and High Order Iterations; Bauer, S., (2015) Prozesssprachenbasiertes System Zur Ansteuerung Digitaler Menschmodelle Als Teilkomponente Einer Software Zur Planung Und Visualisierung Menschlicher Arbeit in Der Digitalen Fabrik, , Ph.D. Thesis; Fritzsche, L., Jendrusch, R., Leidholdt, W., Bauer, S., Jäckel, T., Pirger, A., Introducing ema (Editor for Manual Work Activities) – A New Tool for Enhancing Accuracy and Efficiency of Human Simulations in Digital Production Planning (2011) International Conference on Digital Human Modeling, pp. 272-281. , Springer; Sucan, I.A., (2012) Task and Motion Planning for Mobile Manipulators, , Ph.D. Thesis; Sucan, I.A., Moll, M., Kavraki, L.E., The Open Motion Planning Library (2012) IEEE Robot. Autom. Mag., 19 (4), pp. 72-82; Kuffner, J.J., Lavalle, S.M., RRT-Connect: An Efficient Approach to Single-Query Path Planning (2000) International Conference on Robotics and Automation, Vol. 2, Pp. 995–1001. IEEE","Glogowski, P.; Faculty of Mechanical Engineering, Universitätsstr. 150, Germany; email: glogowski@lps.ruhr-uni-bochum.de",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105223926
"Fox J., Gambino A.","7404282916;57190125239;","Relationship Development with Humanoid Social Robots: Applying Interpersonal Theories to Human-Robot Interaction",2021,"Cyberpsychology, Behavior, and Social Networking","24","5",,"294","299",,5,"10.1089/cyber.2020.0181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104996747&doi=10.1089%2fcyber.2020.0181&partnerID=40&md5=2c9b2e4f1fe28cbe4b393ec956dad64d","School of Communication, The Ohio State University, 3016 Derby Hall, 154 North Oval Mall, Columbus, Ohio  43210-1339, United States; Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, Pennsylvania, United States","Fox, J., School of Communication, The Ohio State University, 3016 Derby Hall, 154 North Oval Mall, Columbus, Ohio  43210-1339, United States; Gambino, A., Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, Pennsylvania, United States","Humanoid social robots (HSRs) are human-made technologies that can take physical or digital form, resemble people in form or behavior to some degree, and are designed to interact with people. A common assumption is that social robots can and should mimic humans, such that human-robot interaction (HRI) closely resembles human-human (i.e., interpersonal) interaction. Research is often framed from the assumption that rules and theories that apply to interpersonal interaction should apply to HRI (e.g., the computers are social actors framework). Here, we challenge these assumptions and consider more deeply the relevance and applicability of our knowledge about personal relationships to relationships with social robots. First, we describe the typical characteristics of HSRs available to consumers currently, elaborating characteristics relevant to understanding social interactions with robots such as form anthropomorphism and behavioral anthropomorphism. We also consider common social affordances of modern HSRs (persistence, personalization, responsiveness, contingency, and conversational control) and how these align with human capacities and expectations. Next, we present predominant interpersonal theories whose primary claims are foundational to our understanding of human relationship development (social exchange theories, including resource theory, interdependence theory, equity theory, and social penetration theory). We consider whether interpersonal theories are viable frameworks for studying HRI and human-robot relationships given their theoretical assumptions and claims. We conclude by providing suggestions for researchers and designers, including alternatives to equating human-robot relationships to human-human relationships. © Jesse Fox and Andrew Gambino 2021; Published by Mary Ann Liebert, Inc. 2021.","anthropomorphism; computers are social actors; human-robot interaction; relationship development; social affordances; social robots","human; human relation; interpersonal communication; man machine interaction; procedures; robotics; social interaction; Communication; Humans; Interpersonal Relations; Man-Machine Systems; Robotics; Social Interaction",,,,,,,,,,,,,,,,"Bickmore, TW, Picard, RW., Establishing and maintaining long-term human-computer relationships (2005) ACM Transactions on Computer-Human Interaction, 12, pp. 293-327; De Melo, CM, Gratch, J., Beyond believability: quantifying the differences between real and virtual humans (2015) 15th International Conference on Intelligent Virtual Agents, pp. 109-118. , Delft, Netherlands: Springer; Krämer, NC, Von Der Pütten, AM, Eimler, SC., Humanagent and human-robot interaction theory: similarities to and differences from human-human interaction (2012) Human-computer interaction: the agency perspective, pp. 215-240. , Zacarias M, De Oliveira JV, eds. Berlin, Germany: Springer; Nass, C, Brave, S., (2005) Wired for speech: how voice activates and advances the human-computer relationship, , Cambridge, MA: MIT Press; Breazeal, C., (2002) Designing sociable robots, , Cambridge, MA: MIT Press; Hegel, F, Muhl, C, Wrede, B, Understanding social robots (2009) 2nd International Conference on Advances in Computer-Human Interactions, pp. 169-184. , Cancun, Mexico: IEEE; Zhao, S., Humanoid social robots as a medium of communication (2006) New Media and Society, 8, pp. 401-419; Liu, B, Sundar, SS., Should machines express sympathy and empathy? Experiments with a health advice chatbot (2018) Cyberpsychology, Behavior, and Social Networking, 21, pp. 625-636; Cassell, J, Sullivan, J, Churchill, E, (2000) Embodied conversational agents, , Cambridge, MA: MIT Press; Di Dio, C, Manzi, F, Peretti, G, Shall I trust you? From child human-robot interaction to trusting relationships (2020) Frontiers in Psychology, 11, p. 469; Kahn, PH, Gary, HE, Shen, S., Children's social relationships with current and near-future robots (2013) Child Development Perspectives, 7, pp. 32-37; Nass, C, Moon, Y., Machines and mindlessness: social responses to computers (2000) Journal of Social Issues, 56, pp. 81-103; Eyssel, F, Hegel, F., (S)he's got the look: gender stereotyping of robots (2012) Journal of Applied Social Psychology, 42, pp. 2213-2230; Edwards, A, Edwards, C, Westerman, D, Initial expectations, interactions, and beyond with social robots (2019) Computers in Human Behavior, 90, pp. 308-314; Gambino, A, Fox, J, Ratan, RA., Building a stronger CASA: extending the computers are social actors paradigm (2020) Human-Machine Communication, 1, pp. 71-86; Reeves, B, Nass, C., (1996) The media equation: how people treat computers, television, and new media like real people and places, , New York, NY: Cambridge; Beer, JM, Fisk, AD, Rogers, WA., Toward a framework for levels of robot autonomy in human-robot interaction (2014) Journal of Human-Robot Interaction, 3, pp. 74-99; Bigman, YE, Waytz, A, Alterovitz, R, Holding robots responsible: the elements of machine morality (2019) Trends in Cognitive Sciences, 23, pp. 365-368; Fox, J, McEwan, B., Distinguishing technologies for social interaction: the perceived social affordances of communication channels scale (2017) Communication Monographs, 84, pp. 298-318; Rodríguez-Hidalgo, C., Me and my robot smiled at one another: the process of socially enacted communicative affordance in human-machine communication (2020) Human Machine Communication, 1, pp. 55-69; Epley, N, Waytz, A, Cacioppo, JT., On seeing human: a threefactor theory of anthropomorphism (2007) Psychological Review, 114, pp. 864-886; Leite, I, Martinho, C, Paiva, A., Social robots for long-term interaction: a survey (2013) International Journal of Social Robotics, 5, pp. 291-308; Ruijten, PA, Haans, A, Ham, J, Perceived humanlikeness of social robots: testing the Rasch model as a method for measuring anthropomorphism (2019) International Journal of Social Robotics, 11, pp. 477-494; Van Straten, CL, Peter, J, Kühne, R., Child-robot relationship formation: a narrative review of empirical research (2020) International Journal of Social Robotics, 12, pp. 325-344; Nowak, KL, Fox, J., Avatars and computer-mediated communication: a review of the definitions, uses, and effects of digital representations (2018) Review of Communication Research, 6, pp. 30-53; Strohkorb, S, Huang, CM, Ramachandran, A, Establishing sustained, supportive human-robot relationships: building blocks and open challenges (2016) AAAI Spring Symposium Series, pp. 179-182. , Stanford, CA: AAAI; Aylett, R, Kriegel, M, Wallace, I, Do I remember you? Memory and identity in multiple embodi-ments (2013) 2013 22nd IEEE International Symposium on Robot and Human Interactive Communication, pp. 143-148. , Seoul, South Korea: IEEE; Altman, I, Taylor, DA., (1973) Social penetration: the development of interpersonal relationships, , New York, NY: Holt, Rinehart & Winston; Dautenhahn, K., Robots we like to live with?! A developmental perspective on a personalized, life-long robot companion (2004) 2004 13th IEEE International Workshop on Robot and Human Interactive Communication, pp. 17-22. , Kurakshiki, Japan: IEEE; Hinde, RA., (1979) Towards understanding relationships, , London, UK: Academic Press; Roloff, ME., (1981) Interpersonal communication: the social exchange approach, , Thousand Oaks, CA: Sage; Foa, UG, Foa, EB., (1974) Societal structures of the mind, , Oxford, England: Charles C. Thomas; Thibaut, JW, Kelley, HH., (1959) The social psychology of groups, , New York, NY: Wiley; Walster, E, Walster, GW, Berscheid, E., (1978) Equity, , Boston, MA: Allyn & Bacon; Kelley, HH, Thibaut, JW., (1978) Interpersonal relations: a theory of interdependence, , New York, NY: Wiley; Dautenhahn, K., Methodology & themes of human-robot interaction: a growing research field (2007) International Journal of Advanced Robotic Systems, 4, pp. 103-108; Fong, T, Nourbakhsh, I, Dautenhahn, K., A survey of socially interactive robots: concepts, design and applications (2002) Robotics and Autonomous Systems, 42, pp. 142-166; De Graaf, MMA., An ethical review on human-robot relationships (2016) International Journal of Social Robotics, 8, pp. 589-598; Scheutz, M., The inherent dangers of unidirectional emotional bonds between humans and social robots (2012) Robot ethics: the ethical and social implications of robotics, pp. 205-222. , Lin P, Abney J, Bekey JA, eds. Cambridge, MA: MIT Press; Kang, SH, Gratch, J., Virtual humans elicit socially anxious interactants' verbal self-disclosure (2010) Computer Animation and Virtual Worlds, 21, pp. 473-482; Lucas, GM, Rizzo, A, Gratch, J, Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers (2017) Frontiers Robotics AI, 4, p. 51. , article; Pickard, MD, Roster, CA, Chen, Y., Revealing sensitive information in personal interviews: is self-disclosure easier with humans or avatars and under what conditions? (2016) Computers in Human Behavior, 65, pp. 23-30; De Graaf, MMA, Allouch, SB., The influence of prior expectations of a robot's lifelikeness on users' intentions to treat a zoomorphic robot as a companion (2017) International Journal of Social Robotics, 9, pp. 17-32; Krämer, NC, Eimler, S, Von Der Pütten, A, Theory of companions: what can theoretical models contribute to applications and understanding of human-robot interaction? (2011) Applied Artificial Intelligence, 25, pp. 474-502; Riva, G, Banos, RM, Botella, C, Positive technology: using interactive technologies to promote positive functioning (2012) Cyberpsychology, Behavior, and Social Networking, 15, pp. 69-77","Fox, J.; School of Communication, 3016 Derby Hall, 154 North Oval Mall, United States; email: fox.775@osu.edu",,,"Mary Ann Liebert Inc.",,,,,21522715,,,"33434097","English","Cyberpsychol. Behav. Soc. Networking",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85104996747
"Alarcon G.M., Gibson A.M., Jessup S.A., Capiola A.","35101277300;57209638343;57194107331;55155617300;","Exploring the differential effects of trust violations in human-human and human-robot interactions",2021,"Applied Ergonomics","93",,"103350","","",,1,"10.1016/j.apergo.2020.103350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100020047&doi=10.1016%2fj.apergo.2020.103350&partnerID=40&md5=45f461bd6f560ba3a379a6a03c8107af","Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States; Consortium of Universities, 1020 19th St NW #500, Washington, DC, 20036, United States","Alarcon, G.M., Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States; Gibson, A.M., Consortium of Universities, 1020 19th St NW #500, Washington, DC, 20036, United States; Jessup, S.A., Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States; Capiola, A., Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States","There is sparse research directly investigating the effects of trust manipulations in human-human and human-robot interactions. Moreover, studies on human-human versus human-robot trust have leveraged unusual or low vulnerability contexts to investigate such effects and have focused mostly on robot performance. In the present research, we seek to remedy these limitations and compare trust in human-human versus human-robot collaborations in an augmented and adapted version of the Trust Game. We used a mixed factorial design to examine the effects of trust and trust violations on human-human and human-robot interactions over time with an emphasis on anthropomorphic robots in a social context. We found consistent and significant effects of partner behavior. Specifically, partner distrust behaviors led to participants’ lower levels of trustworthiness perceptions, trust intentions, and trust behaviors over time compared to partner trust behaviors. We found no significant effect of partnering with a human versus an anthropomorphic robot over time across the three dependent variables, supporting the computers as social actors (CASA; Nass and Moon, 2000) paradigm. This study demonstrated that there may be instances where the effects of trust violations from an anthropomorphized robot partner are not meaningfully different from those of a human partner in a social context. © 2021","Automation bias; Human-robot interaction; Trust violations","Anthropomorphic robots; Economic and social effects; Machine design; Man machine systems; Dependent variables; Differential effect; Human-robot collaboration; Mixed factorial designs; Robot Partners; Robot performance; Social actors; Social context; Social robots; Article; behavior; behavior assessment; human; human relation; human robot interaction; linear regression analysis; partner distrust behavior; partner trust behavior; perception; probability; social environment; trust; human relation; robotics; trust; Humans; Intention; Interpersonal Relations; Robotics; Social Environment; Trust",,,,,"Air Force Office of Scientific Research, AFOSR: 18RHCR002","Distribuition A. Approved for public release: 88ABW Cleared 10/25/19 88ABW-2019-5214. This work was supported by Air Force Office of Scientific Research, United States , Grant: 18RHCR002 .",,,,,,,,,,"Alarcon, G.M., Lyons, J.B., Christensen, J.C., Klosterman, S.L., Bowers, M.A., Ryan, T.J., Jessup, S.J., Wynne, K.T., The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads (2018) Behav. Res. Methods, 50 (5), pp. 1906-1920; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Sci. Robot., 3 (21). , Article eaat5954; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: a systematic review into effects and effectiveness (2012) J. Am. Med. Dir. Assoc., 13 (2), pp. 114-120; Berg, J., Dickhaut, J., McCabe, K., Trust, reciprocity, and social history (1995) Game. Econ. 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"Skantze G.","6505943187;","Turn-taking in Conversational Systems and Human-Robot Interaction: A Review",2021,"Computer Speech and Language","67",,"101178","","",,8,"10.1016/j.csl.2020.101178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098741102&doi=10.1016%2fj.csl.2020.101178&partnerID=40&md5=0d720eaafb5bf8ab86077d074141cb43","Department of Speech Music and HearingKTH, Sweden","Skantze, G., Department of Speech Music and HearingKTH, Sweden","The taking of turns is a fundamental aspect of dialogue. Since it is difficult to speak and listen at the same time, the participants need to coordinate who is currently speaking and when the next person can start to speak. Humans are very good at this coordination, and typically achieve fluent turn-taking with very small gaps and little overlap. Conversational systems (including voice assistants and social robots), on the other hand, typically have problems with frequent interruptions and long response delays, which has called for a substantial body of research on how to improve turn-taking in conversational systems. In this review article, we provide an overview of this research and give directions for future research. First, we provide a theoretical background of the linguistic research tradition on turn-taking and some of the fundamental concepts in theories of turn-taking. We also provide an extensive review of multi-modal cues (including verbal cues, prosody, breathing, gaze and gestures) that have been found to facilitate the coordination of turn-taking in human-human interaction, and which can be utilised for turn-taking in conversational systems. After this, we review work that has been done on modelling turn-taking, including end-of-turn detection, handling of user interruptions, generation of turn-taking cues, and multi-party human-robot interaction. Finally, we identify key areas where more research is needed to achieve fluent turn-taking in spoken interaction between man and machine. © 2020 The Author","Dialogue systems; Gaze; Prosody; Social robotics; Turn-taking","Man machine systems; Conversational systems; Fundamental concepts; Human-human interactions; Multi-modal; Research traditions; Response delays; Spoken interaction; Turn-taking; Social robots",,,,,"2013-1403; Vetenskapsrådet, VR","This work is supported by the Swedish research council (VR) project Coordination of Attention and Turn-taking in Situated Interaction (#2013-1403). Thanks to Nigel Ward for initial discussions on this review, and to Erik Ekstedt, Martin Johansson and Raveesh Meena for their contributions to our work on modelling turn-taking at KTH. Thanks also to the reviewers and editor for their very helpful remarks.",,,,,,,,,,"Al Moubayed, S., Edlund, J., Beskow, J., Taming mona lisa: communicating gaze faithfully in 2D and 3D facial projections (2012) ACM Trans. Interact. Intell. Syst., 1 (2), pp. 1-25; (2011), pp. 99-102. , Al Moubayed, S., Skantze, G. Turn-taking control using gaze in multiparty human-Computer dialogue: effects of 2D and 3D displays. Proceedings of the International Conference on Audio-Visual Speech Processing; Anderson, A., Bader, M., Bard, E., Boyle, E., Doherty, G., Garrod, S., Isard, S., Weinert, R., The HCRC map task corpus (1991) Lang Speech, 34 (4), pp. 351-366; Andrist, S., Leite, I., Lehman, J., (2013), Fun and fair: influencing turn-taking in a multi-party game with a virtual agent. 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"Cheng P.-J., Ting H.-Y., Huang H.-P.","57137415500;57196039825;7405609488;","Importing the Human Factor into Safe Human-Robot Interaction Function Using the Bond Graph Method",2021,"Robotica","39","5",,"772","786",,,"10.1017/S0263574720000715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095816834&doi=10.1017%2fS0263574720000715&partnerID=40&md5=5120e627ab5fdea0be53058f7ca8d1a1","Department of Product Development, TECHMAN ROBOT Inc., Taoyuan, 33383, Taiwan; Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan","Cheng, P.-J., Department of Product Development, TECHMAN ROBOT Inc., Taoyuan, 33383, Taiwan; Ting, H.-Y., Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan; Huang, H.-P., Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan","The variable stiffness actuator (VSA) is helpful to realize the post-collision safety strategies for safe human-robot interaction.1 The stiffness of the robot will be reduced to protect the user from injury when the collision between the robot and human occurs. However, The VSA has a mechanism limit constraint that can cause harm to users even if the stiffness is minimized. Accordingly, in this article, a concept combining danger index and robust fault detection and isolation is presented and applied to active-passive variable stiffness elastic actuator (APVSEA). APVSEA can actively change joint stiffness with the change of danger index. Experimental results show that this concept can effectively confirm the fault mode and provide additional protection measures to ensure the safety of users when the joint stiffness has been adjusted to the minimum. Copyright © The Author(s), 2020. Published by Cambridge University Press.","Bond graph method; Fault detection; Human-robot interaction; KEYWORDS: Safe; Variable stiffness actuator","Actuators; Agricultural robots; Fault detection; Stiffness; Bond graph method; Elastic actuators; Joint stiffness; Post-collision; Protection measures; Robust fault detection and isolation; Safe human-robot interaction; Variable stiffness; Human robot interaction",,,,,,,,,,,,,,,,"Tadele, T.S., De Vries, T., Stramigioli, S., The safety of domestic robotics: A survey of various safetyrelated publications (2014) IEEE Robot. Autom. Mag., 21 (3), pp. 134-142; Bicchi, A., Peshkin, M.A., Colgate, J.E., Safety for Physical Human-Robot Interaction (2008) Springer Handbook of Robotics, pp. 1335-1348. , (B. Siciliano and O. 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Robot., 6 (1), pp. 19-31; Yun, S.S., A gaze control of socially interactive robots in multiple-person interaction (2017) Robotica, 35 (11), pp. 2122-2138; Flacco, F., Kroger, T., Luca, A.D., Khatib, O., A Depth Space Approach to Human-Robot Collision Avoidance (2012) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 338-345. , Saint Paul, Minnesota (May); Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C., Simmons, R., Sensor Fusion for Human Safety in Industrial Workcells (2012) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3612-3619. , Vilamoura, Portugal (October); Cheng, P.J., Huang, H.P., Model matching contorl for an active-passive variable stiffness actuator (2015) Int. J. Robot. Auto., 6 (3), pp. 48-64; Cheng, P.J., Ting, H.Y., Huang, H.P., Safe human robot inter action using model matching control (2016) J. Chinese Soc. Mech. Engin. (JCSME), 37 (6), pp. 587-596; Wang, R.J., Huang, H.P., Mechanically stiffness-adjustable actuator using a leaf spring for safe physical human-robot interaction (2012) Mechanika, 18 (1), pp. 77-83; (2013) IFA, BG/BGIA Risk Assessment Recommendations According to Machinery Directive: Design of Workplaces with Collaborative Robots","Huang, H.-P.; Department of Mechanical Engineering, Taiwan; email: hanpang@ntu.edu.tw",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85095816834
"Tziafas G., Kasaei H.","57223769158;6602182120;","Few-Shot Visual Grounding for Natural Human-Robot Interaction",2021,"2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021",,,"9429801","50","55",,1,"10.1109/ICARSC52212.2021.9429801","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107144568&doi=10.1109%2fICARSC52212.2021.9429801&partnerID=40&md5=db487d53ef790f79264044272c691c69","University of Groningen, Department of Artificial Intelligence, Netherlands","Tziafas, G., University of Groningen, Department of Artificial Intelligence, Netherlands; Kasaei, H., University of Groningen, Department of Artificial Intelligence, Netherlands","Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments. In such dynamic environments, the robot needs to understand the intention of the user to accomplish a task successfully. Towards addressing this point, we propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. Unlike most grounding methods that tackle the challenge using pre-Trained object detectors via a two-stepped process, we develop a single stage zero-shot model that is able to provide predictions in unseen data. We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets. Experimental results showed that the proposed model performs well in terms of accuracy and speed, while showcasing robustness to variation in the natural language input. © 2021 IEEE.",,"Agricultural robots; Deep neural networks; Man machine systems; Object detection; Dynamic environments; Grounding methods; Human robot Interaction (HRI); Human-centric; Natural languages; Object detectors; Service robots; Target object; Human robot interaction",,,,,,,,,,,,,,,,"International Conference on Robotics and Automation, Icra 2019, Montreal, QC, Canada, May 20-24, 2019, 2019; Chen, K., Kovvuri, R., Nevatia, R., Query-guided regression network with context policy for phrase grounding (2017) CoRR, abs/1708.01676; Escalante, H.J., Hernandez, C.A., Gonzalez, J.A., Lopez-Lopez, A., Montes, M., Morales, E.F., Enrique Sucar, L., Grubinger, M., (2010) The Segmented and Annotated Iapr TC-12 Benchmark. Comput. Vis. 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Cortes, N D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, Editors, Advances in Neural Information Processing Systems 28, pp. 91-99. , Curran Associates, Inc; Ren, S., He, K., Girshick, R.B., Sun, J., Faster R-CNN: Towards real-Time object detection with region proposal networks (2015) CoRR, abs/1506.01497; Sadhu, A., Chen, K., Nevatia, R., Zero-shot grounding of objects from natural language queries (2019) Proceedings of the Ieee International Conference on Computer Vision, pp. 4694-4703; Schuster, M., Paliwal, K., (1997) Bidirectional Recurrent Neural Networks. Trans. Sig. Proc, 45 (11), pp. 2673-2681. , Nov; Shridhar, M., Hsu, D., Interactive visual grounding of referring expressions for human-robot interaction CoRR, Abs/ 1806 03831, p. 2018; Song, S., Lichtenberg, S.P., Xiao, J., Sun rgb-d: A rgb-d scene understanding benchmark suite. 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Chapter Contrast Limited Adaptive Histogram Equalization, pp. 474-485. , Academic Press Professional, Inc., San Diego, CA, USA",,"Santos V.Lau N.Neto P.Lopes A.C.","IEEE Portugal Section;IEEE Portugal Section RA Chapter;Sociedade Portuguesa de Robotica (SPR)","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021","28 April 2021 through 29 April 2021",,169060,,9781665431989,,,"English","IEEE Int. Conf. Autonomous Robot Syst. Competitions, ICARSC",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85107144568
"Fang B., Fang C., Wen L., Manoonpong P.","45261056300;55085186700;14033602700;9734734000;","Editorial: Integrated Multi-modal and Sensorimotor Coordination for Enhanced Human-Robot Interaction",2021,"Frontiers in Neurorobotics","15",,"673659","","",,,"10.3389/fnbot.2021.673659","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105009086&doi=10.3389%2ffnbot.2021.673659&partnerID=40&md5=8e44251a8375ce01b6768d393bd58ecc","Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China; University of Southern Denmark Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark; School of Mechanical Engineering and Automation, Beihang University, Beijing, China; Embodied Artificial Intelligence and Neurorobotics Lab, University of Southern Denmark Biorobotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark; Bio-Inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand","Fang, B., Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China; Fang, C., University of Southern Denmark Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark; Wen, L., School of Mechanical Engineering and Automation, Beihang University, Beijing, China; Manoonpong, P., Embodied Artificial Intelligence and Neurorobotics Lab, University of Southern Denmark Biorobotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark, Bio-Inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, Thailand",[No abstract available],"bio-inspired models; coadaptation in interaction; human-robot interaction; multimodality; sensorimotor coordination mechanism","biomechanics; cognition; Editorial; electromyography; ergonomics; gait; health hazard; human; information processing; machine learning; motor control; motor coordination; multimodal imaging; neurorehabilitation; quality of life; sensorimotor function; task performance",,,,,"Tsinghua University, THU; National Key Research and Development Program of China, NKRDPC: 2017YFB1302302","Funding. This work was supported by the National Key Research and Development Program of China (Grant no. 2017YFB1302302) and Tsinghua University Department of Computer Science and Technology) - Siemens Ltd., China Joint Research Center for Industrial Intelligence and Internet of Things.","This work was supported by the National Key Research and Development Program of China (Grant no. 2017YFB1302302) and Tsinghua University Department of Computer",,,,,,,,,,"Fang, B.; Department of Computer Science and Technology, China; email: fangbin@tsinghua.edu.cn",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Editorial,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105009086
"Ullrich D., Butz A., Diefenbach S.","16680229200;55150450600;25723185400;","The Development of Overtrust: An Empirical Simulation and Psychological Analysis in the Context of Human–Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"554578","","",,4,"10.3389/frobt.2021.554578","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104576644&doi=10.3389%2ffrobt.2021.554578&partnerID=40&md5=d53e4a7c45d7fb398aa0078b5828ce12","Department of Computer Science, LMU Munich, Munich, Germany; Department of Psychology, LMU Munich, Munich, Germany","Ullrich, D., Department of Computer Science, LMU Munich, Munich, Germany; Butz, A., Department of Computer Science, LMU Munich, Munich, Germany; Diefenbach, S., Department of Psychology, LMU Munich, Munich, Germany","With impressive developments in human–robot interaction it may seem that technology can do anything. Especially in the domain of social robots which suggest to be much more than programmed machines because of their anthropomorphic shape, people may overtrust the robot's actual capabilities and its reliability. This presents a serious problem, especially when personal well-being might be at stake. Hence, insights about the development and influencing factors of overtrust in robots may form an important basis for countermeasures and sensible design decisions. An empirical study [N = 110] explored the development of overtrust using the example of a pet feeding robot. A 2 × 2 experimental design and repeated measurements contrasted the effect of one's own experience, skill demonstration, and reputation through experience reports of others. The experiment was realized in a video environment where the participants had to imagine they were going on a four-week safari trip and leaving their beloved cat at home, making use of a pet feeding robot. Every day, the participants had to make a choice: go to a day safari without calling options (risk and reward) or make a boring car trip to another village to check if the feeding was successful and activate an emergency call if not (safe and no reward). In parallel to cases of overtrust in other domains (e.g., autopilot), the feeding robot performed flawlessly most of the time until in the fourth week; it performed faultily on three consecutive days, resulting in the cat's death if the participants had decided to go for the day safari on these days. As expected, with repeated positive experience about the robot's reliability on feeding the cat, trust levels rapidly increased and the number of control calls decreased. Compared to one's own experience, skill demonstration and reputation were largely neglected or only had a temporary effect. We integrate these findings in a conceptual model of (over)trust over time and connect these to related psychological concepts such as positivism, instant rewards, inappropriate generalization, wishful thinking, dissonance theory, and social concepts from human–human interaction. Limitations of the present study as well as implications for robot design and future research are discussed. © Copyright © 2021 Ullrich, Butz and Diefenbach.","demonstration; human–robot interaction; overtrust; prior experience; psychological perspective; reputation",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV8097","Part of this research has been funded by the German Federal Ministry of Education and Research (BMBF), project GINA (FKZ: 16SV8097).",,,,,,,,,,"Albright, J., Bell, A., Schneider, J., Nyce, C., (2015) Marketplace of change: automobile insurance in the era of autonomous vehicles, , https://assets.kpmg/content/dam/kpmg/pdf/2016/06/id-market-place-of-change-automobile-insurance-in-the-era-of-autonomous-vehicles.pdf, KPMG, : (Accessed April 2, 2021; (2018) Autonomes Fahren: crash eines Tesla Model X [Online], , https://www.autobild.de/artikel/autonomes-fahren-crash-eines-tesla-model-x-10516973.html, Available:, Accessed August 5, 2019; Bagheri, N., Jamieson, G.A., The impact of context-related reliability on automation failure detection and scanning behaviour (2004) Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 212-217. , Melbourne, October 17–20, 2004, IEEE; Barber, B., (1983) The logic and limits of trust, , New Brunswick, NJ, Rutgers University Press; Bartneck, C., Kanda, T., Mubin, O., Al Mahmud, A., Does the design of a robot influence its animacy and perceived intelligence? 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AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85104576644
"Miller L., Kraus J., Babel F., Baumann M.","57218917919;57158564500;57204529534;7202494638;","More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety",2021,"Frontiers in Psychology","12",,"592711","","",,,"10.3389/fpsyg.2021.592711","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104972690&doi=10.3389%2ffpsyg.2021.592711&partnerID=40&md5=e2987ec89a868231bc90edc7e5ec1716","Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany","Miller, L., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany; Kraus, J., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany; Babel, F., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany; Baumann, M., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany","With service robots becoming more ubiquitous in social life, interaction design needs to adapt to novice users and the associated uncertainty in the first encounter with this technology in new emerging environments. Trust in robots is an essential psychological prerequisite to achieve safe and convenient cooperation between users and robots. This research focuses on psychological processes in which user dispositions and states affect trust in robots, which in turn is expected to impact the behavior and reactions in the interaction with robotic systems. In a laboratory experiment, the influence of propensity to trust in automation and negative attitudes toward robots on state anxiety, trust, and comfort distance toward a robot were explored. Participants were approached by a humanoid domestic robot two times and indicated their comfort distance and trust. The results favor the differentiation and interdependence of dispositional, initial, and dynamic learned trust layers. A mediation from the propensity to trust to initial learned trust by state anxiety provides an insight into the psychological processes through which personality traits might affect interindividual outcomes in human-robot interaction (HRI). The findings underline the meaningfulness of user characteristics as predictors for the initial approach to robots and the importance of considering users’ individual learning history regarding technology and robots in particular. © Copyright © 2021 Miller, Kraus, Babel and Baumann.","affect; comfort distance; human-robot interaction; state anxiety; trust in automation; trust in robots; trust layers; user dispositions",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7967","This research has been conducted within the interdisciplinary research project “RobotKoop,” which was funded by the German Ministry of Education and Research (Grant No. 16SV7967).",,,,,,,,,,"Aiello, J.R., Human spatial behavior (1987) Handbook of Environmental Psychology, pp. 389-504. , Stokols D., Altman I., (eds), New York, NY, Wiley; Ajzen, I., Attitude structure and behavior (1989) Attitude Structure and Function, pp. 241-274. , Pratkanis A.R., Breckler S.J., Greenwald A.G., (eds), New Jersey, NJ, Lawrence Erlbaum; Ajzen, I., The theory of planned behavior (1991) Organ. 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Autonomous robots threaten human identity, uniqueness, safety, and resources (2017) Int. J. Hum. Comput. Stud, 100, pp. 48-54","Miller, L.; Department Human Factors, Germany; email: linda.miller@uni-ulm.de
Kraus, J.; Department Human Factors, Germany; email: johannes.kraus@uni-ulm.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85104972690
"Lee J.","55441729400;","Generating robotic speech prosody for human robot interaction: A preliminary study",2021,"Applied Sciences (Switzerland)","11","8","3468","","",,,"10.3390/app11083468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104629965&doi=10.3390%2fapp11083468&partnerID=40&md5=ad24de77551e8c58f0aaf1676daef23e","Department of Robotic Science and Technology, Chubu University, Kasugai, 487-8501, Japan","Lee, J., Department of Robotic Science and Technology, Chubu University, Kasugai, 487-8501, Japan","The use of affective speech in robotic applications has increased in recent years, especially regarding the developments or studies of emotional prosody for a specific group of people. The current work proposes a prosody-based communication system that considers the limited parameters found in speech recognition for the elderly, for example. This work explored what types of voices were more effective for understanding presented information, and if the affects of robot voices reflected on the emotional states of listeners. By using functions of a small humanoid robot, two different experiments conducted to find out comprehension level and the affective reflection respectively. University students participated in both tests. The results showed that affective voices helped the users understand the information, as well as that they felt corresponding negative emotions in conversations with negative voices. © 2021 by the author. Licensee MDPI, Basel, Switzerland.","Elderly care; Emotions; Human robot interaction; Social robot; Speech prosody",,,,,,"737858; European Commission, EC; Chubu University; Ministry of Internal Affairs and Communications, MIC","Funding: The part of this work was supported by the EU and Japan coordinated R&D project on “Culture Aware Robots and Environmental Sensor Systems for Elderly Support” commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 No 737858. This work was also supported in part by Chubu University research grant.",,,,,,,,,,"Ahn, H.S., Lee, M.H., Broadbent, E., MacDonald, B.A., Is Entertainment Services of a Healthcare Service Robot for Older People Useful to Young People? Proceedings of the IEEE International Conference on Robotic Computing (IRC), pp. 330-335. , Taichung, Taiwan, 10–12 April 2017; Joh, E.E., Private Security Robots, Artificial Intelligence, and Deadly Force (2017) UCDL Rev, 51, p. 569; Matarić, M.J., Socially assistive robotics: Human augmentation versus automation (2017) Sci. Robot, 2, p. eaam5410. , [CrossRef]; Moyle, W., Arnautovska, U., Ownsworth, T., Jones, C., Potential of telepresence robots to enhance social connectedness in older adults with dementia: an integrative review of feasibility (2017) Int. Psychogeriatr, 29, pp. 1951-1964. , [CrossRef]; Cudd, P., De Witte, L., Robots for Elderly Care: Their Level of Social Interactions and the Targeted End User (2017) Harnessing Power Technol. Improv. Lives, 242, p. 472; Bedaf, S.M., (2017) The Future is Now: The Potential of Service Robots in Elderly Care, , Ph.D. Thesis, Maastricht University, Maastricht, The Netherlands; Wada, K., Shibata, T., Saito, T., Tanie, K., Psychological and social effects of robot assisted activity to elderly people who stay at a health service facility for the aged (2003) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’03), 3, pp. 3996-4001. , Barcelona, Spain, 18–22 April; Das, R., Tuna, A., Demirel, S., Netas, A.S.K., Yurdakul, M.K., A Survey on the Internet of Things Solutions for the Elderly and Disabled: Applications, Prospects, and Challenges (2017) Int. J. Comput. Netw. Appl. (IJCNA), 4, pp. 84-92. , [CrossRef]; Lewis, L., Metzler, T., Cook, L., Evaluating Human-Robot Interaction Using a Robot Exercise Instructor at a Senior Living Community Proceedings of the International Conference on Intelligent Robotics and Applications (ICIRA 2016), pp. 15-25. , Tokyo, Japan, 22–24 August 2016; Crumpton, J., Bethel, C.L., A survey of using vocal prosody to convey emotion in robot speech (2016) Int. J. Soc. Robot, 8, pp. 271-285. , [CrossRef]; Christensen, H.I., Okamura, A., Mataric, M., Kumar, V., Hager, G., Choset, H., (2016) Next generation robotics, , arXiv arXiv:1606.09205; Hammer, S., Kirchner, K., André, E., Lugrin, B., Touch or Talk: Comparing Social Robots and Tablet PCs for an Elderly Assistant Recommender System Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017), pp. 129-130. , Vienna, Austria, 6–9 March 2017; Ishiguro, N., Care robots in Japanese elderly care (2017) The Routledge Handbook of Social Care Work around the World, p. 256. , Taylor & Francis Group: London, UK; Ishi, C., Arai, J., Hagita, N., Prosodic analysis of attention-drawing speech Proceedings of the 2017 Annual Conference of the International Speech Communication Association, pp. 909-913. , Stockholm, Sweden, 20–24 August 2017; Ray, C., Mondada, F., Siegwart, R., What do people expect from robots? Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008 (IROS 2008), pp. 3816-3821. , Nice, France, 22–26 September 2008; Ito, R., Komatani, K., Kawahara, T., Okuno, H.G., Analysis and Detection of Emotional States in Spoken Dialogue with Robot (2003) Inf. Process. Soc. Jpn. SLP, 2003, pp. 107-112. , (In Japanese); Kimura, H., Tomita, Y., Honda, S., Synthesis of emotional voice by changing the parameters in the characteristics of vocal cords and vocal tract (1996) Jpn. J. Ergon, 32, pp. 319-325. , (In Japanese); Mitchell, R.L., Xu, Y., What is the Value of Embedding Artificial Emotional Prosody in Human–Computer Interactions? Implica-tions for Theory and Design in Psychological Science (2015) Front. Psychol, 6, p. 1750. , [CrossRef] [PubMed]; Recupero, D.R., Spiga, F., Knowledge acquisition from parsing natural language expressions for humanoid robot action commands (2020) Inf. Process. Manag, 57, p. 102094. , [CrossRef]; Pullin, G., Cook, A., The value of visualizing tone of voice (2013) Logop. Phoniatr. Vocol, 38, pp. 105-114. , [CrossRef] [PubMed]; Moriyama, T., Mori, S., Ozawa, S., A Synthesis Method of Emotional Speech Using Subspace Constraints in Prosody (2009) J. Inf. Process. Soc. Jpn, 50, pp. 1181-1191; Vinciarelli, A., Pantic, M., Bourlard, H., Pentland, A., Social signal processing: state-of-the-art and future perspectives of an emerging domain Proceedings of the 16th ACM International Conference on Multimedia, pp. 1061-1070. , Vancouver, BC, Canada, 27–31 October 2008; Clinard, C.G., Cotter, C.M., Neural representation of dynamic frequency is degraded in older adults (2015) Hear. Res, 323, pp. 91-98. , [CrossRef]; Crumpton, J., Bethel, C.L., Validation of vocal prosody modifications to communicate emotion in robot speech Proceedings of the 2015 International Conference on Collaboration Technologies and Systems (CTS), pp. 39-46. , Atlanta, GA, USA, 1–5 June 2015; Tielman, M., Neerincx, M., Meyer, J.J., Looije, R., Adaptive emotional expression in robot-child interaction Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-414. , Bielefeld, Germany, 3–6 March 2014; Tejima, N., Rehabilitation Robots for the Elderly-Trend and Futre (1999) J. JSPE, 65, pp. 507-511. , (In Japanese); Sperber, D., Wilson, D., Précis of relevance: Communication and cognition (1987) Behav. Brain Sci, 10, pp. 697-710. , [CrossRef]; Kitayama, S., Ishii, K., Word and voice: Spontaneous attention to emotional utterances in two languages (2002) Cogn. Emot, 16, pp. 29-59. , [CrossRef]; Suzuki, T., Tamura, N., Features of emotional voices: Focus in differences between expression and recognition (2006) Jpn. J. Psychol, 77, pp. 149-156. , (In Japanese) [CrossRef]; Eyben, F., Wöllmer, M., Schuller, B., Opensmile: the munich versatile and fast open-source audio feature extractor Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459-1462. , Firenze, Italy, 25–29 October 2010; Schuller, B., Steidl, S., Batliner, A., The interspeech 2009 emotion challenge (2009) Proceedings of the 10th Annual Conference of the International Speech Communication Association, , Brighton, UK, 6–10 September; Nhat, T.B., Mera, K., Kurosawa, Y., Takezawa, T., Natural Language Dialogue System considering Emotion: Guessed from Acoustic Features (2014) Proceedings of the Human-Agent Interaction Symposium 2014 (HAI’14), , Tsukuba, Japan, 28–31 October; Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software: An update (2009) ACM SIGKDD Explor. Newsl, 11, pp. 10-18. , [CrossRef]; Platt, J., (1998) Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, , Microsoft: Redmond, WA, USA; Hold, B., Schleidt, M., The importance of human odour in non-verbal communication (1977) Ethology, 43, pp. 225-238. , [CrossRef] [PubMed]; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005.(IROS 2005), pp. 708-713. , Edmonton, AB, Canada, 2–6 August 2005; McDuff, D., Mahmoud, A., Mavadati, M., Amr, M., Turcot, J., Kaliouby, R.E., AFFDEX SDK: A cross-platform real-time multi-face expression recognition toolkit Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 3723-3726. , San Jose, CA, USA, 7–12 May 2016; Magdin, M., Benko, L., Koprda, Š., A case study of facial emotion classification using affdex (2019) Sensors, 19, p. 2140. , [CrossRef] [PubMed]; Lopez-Rincon, A., Emotion recognition using facial expressions in children using the NAO Robot Proceedings of the 2019 International Conference on Electronics, Communications and Computers (CONIELECOMP 2019), pp. 146-153. , Cholula, Mexico, 27 February–1 March 2019; Dupré, D., Krumhuber, E.G., Küster, D., McKeown, G.J., A performance comparison of eight commercially available automatic classifiers for facial affect recognition (2020) PLoS ONE, 15, p. e0231968. , [CrossRef] [PubMed]; Kominek, J., Black, A.W., The CMU Arctic speech databases (2004) Proceedings of the Fifth ISCA Workshop on Speech Synthesis, , Pittsburgh, PA, USA, 14–16 June","Lee, J.; Department of Robotic Science and Technology, Japan; email: jaeryounglee@isc.chubu.ac.jp",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85104629965
"Chen L., Li M., Su W., Wu M., Hirota K., Pedrycz W.","55774663200;57204930290;57196287282;57218664297;7402829702;57210775415;","Adaptive feature selection-based AdaBoost-KNN with direct optimization for dynamic emotion recognition in human-robot interaction",2021,"IEEE Transactions on Emerging Topics in Computational Intelligence","5","2","8701502","205","213",,3,"10.1109/TETCI.2019.2909930","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359476&doi=10.1109%2fTETCI.2019.2909930&partnerID=40&md5=2e2f95b28e06755b7bca67d8a7397471","School of Automation, China University of Geosciences, Wuhan, Hubei, China; Tokyo Institute of Technology, Yokohama, Japan; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada","Chen, L., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Li, M., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Su, W., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada","AdaBoost-KNN using adaptive feature selection with direct optimization is proposed for dynamic emotion recognition in human-robot interaction, where the real-time dynamic emotion is recognized based on facial expression. It can make robots capable of understanding human dynamic emotions, in such a way that human-robot interaction is realized in a smooth manner. Based on the facial key points extracted by Candide-3 model, adaptive feature selection is adopted, namely Plus-L Minus-R Selection is completed. It can determine the features that contribute the most to emotion recognition, thereby forming the basis of emotion classification. Emotion classification is based on AdaBoost-KNN, which builds a series of KNN classifiers. AdaBoost-KNN adjusts the weights of the data in an iterative manner. Moreover, global optimal parameters are approximated with direct optimization until the recognition rate reaches its maximal value. The experimental performance of the proposal is verified by a $k$-fold cross-validation. Results show that the recognition rate of the proposed approach is higher than that of the AdaBoost-KNN, adaptive feature selection-based AdaBoost-KNN, and AdaBoost-KNN with direct optimization. It is also higher than the rate achieved by other traditional recognition methods, such as AdaBoost, KNN, and SVM. In addition, preliminary application experiments are developed in our emotional social robot system, composed of two mobile robots. The experiments demonstrate the dynamic emotion understanding ability of robots in human-robot interaction. © 2017 IEEE.","AdaBoost-KNN; Dynamic emotion recognition; Human-robot interaction; Plus-L Minus-R Selection","Adaptive boosting; Classification (of information); Feature extraction; Iterative methods; Man machine systems; Speech recognition; Support vector machines; Adaptive feature selection; Direct optimization; Emotion classification; Emotion recognition; Emotion understanding; Facial Expressions; Real-time dynamics; Recognition methods; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016; China University of Geosciences, Wuhan, CUG: 201839; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040; Wuhan Science and Technology Project: 2017010201010133","Manuscript received September 19, 2018; revised February 10, 2019; accepted March 25, 2019. Date of publication April 29, 2019; date of current version March 25, 2021. This work was supported in part by the National Natural Science Foundation of China under Grants 61603356 and 61733016, in part by the 111 Project under Grant B17040, in part by the Wuhan Science and Technology Project under Grant 2017010201010133, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (201839). (Corresponding author: Min Wu.) L. Chen, M. Li, W. Su, and M. Wu are with the School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, Hubei 430074, China (e-mail:, chenluefeng@ cug.edu.cn; limin2014@cug.edu.cn; suwanjuan@cug.edu.cn; wumin@cug. edu.cn).",,,,,,,,,,"Anderson, K., McOwan, P.W., A real-time automated system for the recognition of human facial expressions (2006) IEEE Trans. Syst. 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Signal Process., 22 (1), pp. 140-146; The Japanese Female Facial Expression (JAFFE) Database, , http://www.kasrl.org/jaff.html; Demišar, J., Schuurmans, D., Statistical comparisons of classifiers over multiple data sets (2006) J. Mach. Learn. Res., 7 (1), pp. 1-30","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,2471285X,,,,"English","IEEE Trans. Emerging Topics Comp. Intell.",Article,"Final","",Scopus,2-s2.0-85103359476
"Nuzzi C., Pasinetti S., Pagani R., Coffetti G., Sansoni G.","57203633842;55763186300;57211168733;57219625215;7003728854;","HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction",2021,"Data in Brief","35",,"106791","","",,1,"10.1016/j.dib.2021.106791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100596728&doi=10.1016%2fj.dib.2021.106791&partnerID=40&md5=f85d430ae8da0836040b03a775e84465","Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy","Nuzzi, C., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Pasinetti, S., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Pagani, R., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Coffetti, G., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Sansoni, G., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy","The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and their corresponding 150 Depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 Depth frames per actor. Data has been collected using a Kinect v2 camera intrinsically calibrated to spatially align RGB data to Depth data. The temporal alignment has been performed offline using MATLAB, aligning frames with a maximum temporal distance of 66 ms. This dataset has been used in [1] and it is freely available at http://dx.doi.org/10.17632/ndrczc35bt.1. © 2021 The Authors","Classification; Hand-Gesture Recognition; Human-Robot Interaction; Object Detector",,,,,,,,,,,,,,,,,"Nuzzi, C., Pasinetti, S., Pagani, R., Ghidini, S., Beschi, M., Coffetti, G., Sansoni, G., MEGURU: a gesture-based robot program builder for Meta-Collaborative workstations (2021) Rob. Comput. Integr. Manuf., 68; Barczak, A.L.C., Reyes, N.H., Abastillas, M., Piccio, A., Susnjak, T., A new 2D static hand gesture colour image dataset for ASL gestures (2011) Res. Lett. Inform. Math. Sci., 15, pp. 12-20; Hoang, V.T., HGM-4: A new multi-cameras dataset for hand gesture recognition (2020) Data in Brief, 30; Hsiao, Y.-S., Sanchez-Riera, J., Lim, T., Hua, K.-L.C.W.-H., LaRED: A Large RGB-D extensible hand gesture dataset (2014) Proceedings of the 5th ACM Multimedia Systems Conference, Singapore; Pisharady, P.K., Vadakkepat, P., Loh, A.P., Attention based detection and recognition of hand postures against complex backgrounds (2013) Int. J. Comput. Vision, 101 (3), pp. 403-419; Ren, Z., Yuan, J.Z.Z., Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera (2011) Proceedings of the 19th ACM International Conference on Multimedia, Scottsdale, Arizona, USA; Suau, X., Alcoverro, M., López-Méndez, A., Ruiz-Hidalgo, J., Casas, J.R., Real-time fingertip localization conditioned on hand gesture classification (2014) Image Vision Comput., 32 (8), pp. 522-532; Guyon, I., Athitsos, V., Jangyodsuk, P., Escalante, H.J., The ChaLearn gesture dataset (CGD 2011) (2014) Mach. Vis. Appl., 25 (8), pp. 1929-1951; Nuzzi, C., Pasinetti, S., Pagani, R., Franco, D., Sansoni, G., Hand gesture recognition for collaborative workstations: a smart command system prototype (2019) Image Analysis and Processing – ICIAP 2019, Trento; Sell, J., O'Connor, P., The Xbox one system on a chip and kinect sensor (2014) IEEE Micro, 34 (2), pp. 44-53; Quigley, M., Gerkey, B.P., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Ng, A., ROS: an open-source Robot Operating System (2009) ICRA Workshop on Open Source Software; Xiang, L., Echtler, F., Kerl, C., Wiedemeyer, T., Lars, H., Gordon, R., Facioni, F., Alistair, libfreenect2: Release 0.2 (2016); Wiedemeyer, T., (2015), https://github.com/code-iai/iai_kinect2, “IAI Kinect2,” [Online]. Available: [Accessed January 2018]; D'Elia, Y., (2013), https://www.thregr.org/∼wavexx/software/facedetect/, “facedetect,” [Online]. Available: [Accessed November 2020]","Nuzzi, C.; Department of Mechanical and Industrial Engineering (DIMI), Italy; email: c.nuzzi@unibs.it",,,"Elsevier Inc.",,,,,23523409,,,,"English","Data Brief",Data Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100596728
"Xing H., Torabi A., Ding L., Gao H., Deng Z., Mushahwar V.K., Tavakoli M.","57191518077;57203129433;57204307692;57204309644;55536964400;6603751651;8668966400;","An admittance-controlled wheeled mobile manipulator for mobility assistance: Human–robot interaction estimation and redundancy resolution for enhanced force exertion ability",2021,"Mechatronics","74",,"102497","","",,1,"10.1016/j.mechatronics.2021.102497","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100067132&doi=10.1016%2fj.mechatronics.2021.102497&partnerID=40&md5=50aa2044bcb00e01198640345f2b67d5","State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada; Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, Alberta, T6G 2E1, Canada","Xing, H., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China, Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada; Torabi, A., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada; Ding, L., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China; Gao, H., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China; Deng, Z., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China; Mushahwar, V.K., Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, Alberta, T6G 2E1, Canada; Tavakoli, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada","In this paper, a novel robotic assistive system (RAS) for mobility assistance of elderly adults is developed based on admittance control with force exertion ability enhancement (FEAE) of a wheeled mobile manipulator (WMM). The RAS can provide supportive force in the vertical direction and be guided by the user with a limited horizontal plane ability. An admittance controller is adopted to realize compliant behaviour between the end-effector and the user, enabling different admittance performances in different directions without requiring the complex system dynamics. The end-effector force needed by the controller is estimated by employing a nonlinear disturbance observer, avoiding the need for pricey force/torque (F/T) sensor. Meanwhile, with consideration of the limited joint torque output, the FEAE approach is implemented in the null-space of the WMM, which can make the system exert more Cartesian force in a given direction with consideration of Cartesian stiffness requirement using the same joint torque limitation through kinematic reconfiguration. Thus, it improves the capability of the system in realizing the desired admittance requirement. The advantages and effectiveness of the proposed approach are experimentally demonstrated with a 4-wheel omnidirectional mobile manipulator. © 2021 Elsevier Ltd","Admittance control; Force exertion ability enhancement; Human–robot interaction estimation; Robotic assistive system; Wheeled mobile manipulator","End effectors; Flow measurement; Industrial manipulators; Social robots; Admittance control; Cartesian stiffness; Complex system dynamics; Mobility assistance; Nonlinear disturbance observer; Omnidirectional mobile manipulator; Redundancy resolution; Wheeled mobile manipulator; Manipulators",,,,,"Canadian Institutes of Health Research, CIHR; Canada Foundation for Innovation; National Natural Science Foundation of China, NSFC: 51822502, 91948202; China Scholarship Council, CSC: [2019]06120165; Ministry of Innovation and Advanced Education; Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; National Key Research and Development Program of China, NKRDPC: SQ2019YFB130016; Fundamental Research Funds for the Central Universities: B07018, HIT.BRETIV201903; Ontario Ministry of Economic Development, Job Creation and Trade, MDECEC","This work was supported by Canada Foundation for Innovation (CFI) , the Natural Sciences and Engineering Research Council (NSERC) of Canada , the Canadian Institutes of Health Research (CIHR) , the Alberta Advanced Education Ministry, the Alberta Economic Development, Trade and Tourism Ministry’s grant to Centre for Autonomous Systems in Strengthening Future Communities, Canada, the National Natural Science Foundation of China (Grant No. 51822502 , 91948202 ), the National Key Research and Development Program of China (No. SQ2019YFB130016 ), the Fundamental Research Funds for the Central Universities, China (Grant No. HIT.BRETIV201903 ), the “111” Project, China (Grant No. B07018 ), and the China Scholarship Council under Grant [2019]06120165 .",,,,,,,,,,"Martins, M.M., Santos, C.P., Frizera-Neto, A., Ceres, R., Assistive mobility devices focusing on smart walkers: Classification and review (2012) Robot Auton Syst, 60 (4), pp. 548-562; 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"Boucher G., Laliberté T., Gosselin C.","57203313747;6602468451;57210720538;","Mechanical Design of a Low-Impedance 6-Degree-of-Freedom Displacement Sensor for Intuitive Physical Human-Robot Interaction",2021,"Journal of Mechanisms and Robotics","13","2","021002","","",,1,"10.1115/1.4049191","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099929101&doi=10.1115%2f1.4049191&partnerID=40&md5=1e77ce2537af992683594ba4a6c3f9cb","Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada","Boucher, G., Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada; Laliberté, T., Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada; Gosselin, C., Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada","This paper presents the mechanical design of a six-degree-of-freedom low-impedance displacement sensor. The sensor is mounted around a link of a serial robot and used as an interface for physical human-robot interaction. The motivation for the use of a low-impedance sensor is first discussed. The mechanical design of each of the elastic components of the sensor is then presented. The kinematic architecture of the mechanism is introduced, and the inverse and forward kinematic problems are solved. The kinematic sensitivity is then used to characterize the accuracy of the mechanism. Finally, the design of a prototype is presented and experimental results are provided. © 2021 by ASME.","mechanism design; parallel platforms; robot design","Agricultural robots; Degrees of freedom (mechanics); Inverse problems; Kinematics; Machine design; Man machine systems; Displacement sensor; Elastic components; Forward kinematics; Kinematic sensitivities; Mechanical design; Physical human-robot interactions; Serial robots; Six degree-of-freedom; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC","The authors would like to acknowledge the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and of the Canada Research Chair program.",,,,,,,,,,"Tadele, T. S., de Vries, T., Stramigioli, S., 1/2The Safety of Domestic Robotics: A Survey of Various Safety-Related Publications, 1/2 (2014) IEEE Robot. Autom. Mag, 21 (3), p. 134. , 1/2142; 2ger, J., Lien, T. 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Theory, 35 (1), p. 151240; Angeles, J., (2002) Fundamentals of Robotic Mechanical Systems, , Springer, New York; Cardou, P., Bouchard, S., Gosselin, C., 1/2Kinematic-Sensitivity Indices for Dimensionally Nonhomogeneous Jacobian Matrices, 1/2 (2010) IEEE Trans. Robot, 26 (1), p. 166. , 1/2173; Labrecque, P. D., Abdallah, M., Gosselin, C., 1/2Low-Impedance Physical Human 1/2Robot Interaction Using ann Active 1/2Passive Dynamics Decoupling, 1/2 (2016) IEEE Robot. Autom. Lett, 1 (2), p. 938. , Hach 1/2, J. M., and, 1/2945","Gosselin, C.; Laboratoire de Robotique, Canada; email: clement.gosselin@gmc.ulaval.ca",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85099929101
"Malik A.A., Brem A.","57200309159;23984395500;","Digital twins for collaborative robots: A case study in human-robot interaction",2021,"Robotics and Computer-Integrated Manufacturing","68",,"102092","","",,20,"10.1016/j.rcim.2020.102092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096706315&doi=10.1016%2fj.rcim.2020.102092&partnerID=40&md5=d77bab8797c8f30d4ecf070dab60e784","Siemens Gamesa Renewable Energy, Brande, Denmark; Chair of Entrepreneurship in Technology and Digitization, Endowed by the Daimler-Fond in the Stifterverband, University of Stuttgart, Stuttgart, Germany","Malik, A.A., Siemens Gamesa Renewable Energy, Brande, Denmark; Brem, A., Chair of Entrepreneurship in Technology and Digitization, Endowed by the Daimler-Fond in the Stifterverband, University of Stuttgart, Stuttgart, Germany","Human-robot collaboration (HRC) can expand the level of automation in areas that have conventionally been difficult to automate such as assembly. However, the need of adaptability and the dynamics of human presence are keeping the full potential of human-robot collaborative systems difficult to achieve. This paper explores the opportunities of using a digital twin to address the complexity of collaborative production systems through an industrial case and a demonstrator. A digital twin, as a virtual counterpart of a physical human-robot assembly system, is built as a ‘front-runner’ for validation and control throughout its design, build and operation. The forms of digital twins along system's life cycle, its building blocks and the potential advantages are presented and discussed. Recommendations for future research and practice in the use of digital twins in the field of cobotics are given. © 2020 Elsevier Ltd","Cobot; Collaborative robot; Digital twin, Demonstrator; Human-robot collaboration; Simulation","Agricultural robots; Digital twin; Life cycle; Machine design; Assembly systems; Building blockes; Collaborative production; Collaborative robots; Collaborative systems; Human robots; Human-robot collaboration; Level of automations; Social robots",,,,,,,,,,,,,,,,"Rauschnabel, P.A., Brem, A., (2015), Ro Y. Augmented Reality Smart Glasses: Definition, Conceptual Insights, and Managerial Importance; Mourtzis, D., Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research 2019:1–23; Bilberg, A., Malik, A.A., Digital twin driven human-robot collaborative assembly (2019) CIRP Annals - Manufacturing Technology, 68, pp. 499-502; Brem, A., Wolfram, P., Research and development from the bottom up-introduction of terminologies for new product development in emerging markets (2014) Journal of Innovation and Entrepreneurship, 3, p. 9; Romero, D., Wuest, T., Stahre, J., Gorecky, D., Social factory architecture: social networking services and production scenarios through the social internet of things, services and people for the social operator 4.0 (2017) IFIP international conference on advances in production management systems, pp. 265-273; Malik, A.A., Masood, T., Bilberg A. Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace. International Journal of Computer Integrated Manufacturing 2019:1–16; Masood, T., Weston, R., An integrated modelling approach in support of next generation reconfigurable manufacturing systems (2011) International Journal of Computer Aided Engineering and Technology, 3, pp. 372-398; Malik, A.A., Masood, T., Kousar, R., Re-purposing factories with robotics in the face of COVID-19 (2020) Science Robotics, p. 5; Müller, R., Vette, M., Geenen, A., Skill-based Dynamic Task Allocation in Human-Robot-Cooperation with the Example of Welding Application (2017) Procedia Manufacturing, 11, pp. 13-21; Meziane, R., Otis, M.J.-D., Ezzaidi, H., Human-robot collaboration while sharing production activities in dynamic environment: SPADER system (2017) Robotics and Computer-Integrated Manufacturing, 48, pp. 243-253; Malik, A.A., Masood, T., Kousar, R., Reconfiguring and ramping-up ventilator production in the face of COVID-19: can robots help? (2020) Journal of Manufacturing Systems; Gualtieri, L., Rauch, E., Vidoni, R., Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review (2020) Robotics and Computer-Integrated Manufacturing, 67; Müller, R., Vette, M., Scholer, M., Robot Workmate: A Trustworthy Coworker for the Continuous Automotive Assembly Line and its Implementation (2016) Procedia CIRP, 44, pp. 263-268; Salunkhe, O., Stensӧta, O., Åkerman, M., Berglund Å.F., Alveflo P.-A. Assembly 4.0: Wheel Hub Nut Assembly Using a Cobot n.d; Choi, S., Jung, K., Do Noh, S., Virtual reality applications in manufacturing industries: Past research, present findings, and future directions (2015) Concurrent Engineering, 23, pp. 40-63; Cheng, J., Zhang, H., Tao, F., Juang, C.-F., DT-II: Digital twin enhanced Industrial Internet reference framework towards smart manufacturing (2020) Robotics and Computer-Integrated Manufacturing, 62; Stark, R., Fresemann, C., Lindow, K., Development and operation of Digital Twins for technical systems and services (2019) CIRP Annals, 68, pp. 129-132; Schlager, K.J., Systems engineering-key to modern development (1956) IRE Transactions on Engineering Management, pp. 64-66; Snowden, D., Complex acts of knowing: paradox and descriptive self-awareness (2002) Journal of Knowledge Management, 6, pp. 100-111; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary Perspectives on Complex Systems, pp. 85-113. , Springer; Perrow, C., Normal accidents: Living with high risk technologies-Updated edition (2011), Princeton University Press; Bi, Z., Luo, M., Miao, Z., Zhang, B., Zhang, W., Wang, L., Safety assurance mechanisms of collaborative robotic systems in manufacturing (2020) Robotics and Computer-Integrated Manufacturing n.d., 67; Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., Xu, X., Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues (2020) Robotics and Computer-Integrated Manufacturing, 61; Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Shaping the digital twin for design and production engineering (2017) CIRP Annals, 66, pp. 141-144; Kahlen, F.-J., Flumerfelt, S., Alves, A., Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (2016), Springer; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary perspectives on complex systems, pp. 85-113. , Springer; Hehenberger, P., Bradley, D., Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and their Designers (2016), Springer; Masood, T., Egger, J., Augmented reality in support of Industry 4.0—Implementation challenges and success factors (2019) Robotics and Computer-Integrated Manufacturing, 58, pp. 181-195; Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Enabling technologies and tools for digital twin (2019) Journal of Manufacturing Systems; Wilhelm, J., Beinke, T., Freitag, M., Improving Human-Machine Interaction with a Digital Twin (2020) International Conference on Dynamics in Logistics, pp. 527-540; Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M., Reengineering aircraft structural life prediction using a digital twin (2011) International Journal of Aerospace Engineering, p. 2011; Naticchia, B., Messi, L., Pirani, M., Bonci, A., Carbonari, A., Tolve, L., Holonic System for Real-Time Emergency Management in Buildings (2019) ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 36, pp. 453-460; Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., A novel cloud-based framework for the elderly healthcare services using digital twin (2019) IEEE Access, 7, pp. 49088-49101; Verdouw, C., Kruize, J., Digital twins in farm management: illustrations from the FIWARE accelerators SmartAgriFood and Fractals (2017) 7th Asian-Australasian Conference on Precision Agriculture Digital, pp. 1-5; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui F. Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 2017:1–14; Tao, F., Zhang, M., Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing (2017) IEEE Access, 5, pp. 20418-20427; Guo, J., Zhao, N., Sun, L., Zhang, S., Modular based flexible digital twin for factory design (2019) Journal of Ambient Intelligence and Humanized Computing, 10, pp. 1189-1200; Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing (2020) Sensors, 20, p. 97; Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X., Makris, S., Symbiotic human-robot collaborative assembly (2019) CIRP Annals - Manufacturing Technology, 68, pp. 701-726; Grieves, M.W., Virtually Intelligent Product Systems: Digital and Physical Twins (2019) Complex Systems Engineering: Theory and Practice, 256, pp. 175-200. , Flumerfelt S. et al. (eds.); Grieves, M., (2014), Digital twin: manufacturing excellence through virtual factory replication. White Paper; Kovsturiak, J., Gregor, M., Simulation in production system life cycle (1999) Computers in Industry, 38, pp. 159-172; Kibira, D., Kibira, D., Kumaraguru, S., Morris, K., Methods and tools for performance assurance of smart manufacturing systems (2015), US Department of Commerce, National Institute of Standards and Technology; Malik, A.A., Bilberg, A., Framework to implement collaborative robots in manual assembly: a lean automation approach (2017) Proceedings of the 28th DAAAM International Symposium, pp. 1726-9679. , Katalinic B. DAAAM International Published by ISSN; Lechler, T., Fischer, E., Metzner, M., Mayr, A., Franke, J., Virtual Commissioning-Scientific review and exploratory use cases in advanced production systems (2019) 26th CIRP Design Conference, 81, pp. 1125-1130; Lee, C.G., Park, S.C., Survey on the virtual commissioning of manufacturing systems (2014) Journal of Computational Design and Engineering, 1, pp. 213-222; Chavarria-Barrientos, D., Villagomez, L.E., Miranda, J., Molina, A., Batres, R., Gutierrez, I., A Methodology to Support Manufacturing System Design Using Digital Models and Simulations: An Automotive Supplier Case Study (2018) IFAC-PapersOnLine, 51, pp. 1598-1603; Gordon, C.C., Blackwell, C.L., Bradtmiller, B., Parham, J.L., Barrientos, P., Paquette, S.P., (2014), 2012 anthropometric survey of US army personnel: methods and summary statistics; Peruzzini, M., Pellicciari, M., Gadaleta, M., A comparative study on computer-integrated set-ups to design human-centred manufacturing systems (2019) Robotics and Computer-Integrated Manufacturing, 55, pp. 265-278; Chaffin, D.B., Digital human modeling for workspace design (2008) Reviews of Human Factors and Ergonomics, 4, pp. 41-74; Ranz, F., Hummel, V., Sihn, W., Capability-based task allocation in human-robot collaboration (2017) Procedia Manufacturing, 9, pp. 182-189; Samy, S., ElMaraghy, H., A model for measuring products assembly complexity (2010) International Journal of Computer Integrated Manufacturing, 23, pp. 1015-1027; Rossano, G.F., Martinez, C., Hedelind, M., Murphy, S., Fuhlbrigge, T.A., Easy robot path programming concepts: An industrial perspective on path creation (2013) Robotics (ISR), 2013 44th International Symposium on, pp. 1-6; Malik, A.A., Bilberg, A., Complexity-based task allocation in human-robot collaborative assembly (2019) Industrial Robot: The International Journal of Robotics Research and Application, 46, pp. 471-480; Denmark, S., Population in Denmark (2017) Population and Elections","Malik, A.A.; Syddansk UniversitetDenmark; email: ali.malik@siemensgamesa.com",,,"Elsevier Ltd",,,,,07365845,,RCIME,,"English","Rob Comput Integr Manuf",Article,"Final","",Scopus,2-s2.0-85096706315
"Prati E., Peruzzini M., Pellicciari M., Raffaeli R.","57219292991;34971929300;26428392600;10739418500;","How to include User eXperience in the design of Human-Robot Interaction",2021,"Robotics and Computer-Integrated Manufacturing","68",,"102072","","",,9,"10.1016/j.rcim.2020.102072","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092106872&doi=10.1016%2fj.rcim.2020.102072&partnerID=40&md5=962685b5312f5cb31b927082029abfc4","Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy; Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy","Prati, E., Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy; Peruzzini, M., Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy; Pellicciari, M., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy; Raffaeli, R., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy","In recent years Human-Robot Collaboration (HRC) has become a strategic research field, considering the emergent need for common collaborative execution of manufacturing tasks, shared between humans and robots within the modern factories. However, the majority of the research focuses on the technological aspects and enabling technologies, mainly directing to the robotic side, and usually neglecting the human factors. This work deals with including the needs of the humans interacting with robots in the design in human-robot interaction (HRI). In particular, the paper proposes a user experience (UX)-oriented structured method to investigate the human-robot dialogue to map the interaction with robots during the execution of shared tasks, and to finally elicit the requirements for the design of valuable HRI. The research adopted the proposed method to an industrial case focused on assembly operations supported by collaborative robots and AGVs (Automated Guided Vehicles). A multidisciplinary team was created to map the HRI for the specific case with the final aim to define the requirements for the design of the system interfaces. The novelty of the proposed approach is the inclusion of typically interaction design tools focusing in the analysis of the UX into the design of the system components, without merely focusing on the technological issues. Experimental results highlighted the validity of the proposed method to identify the interaction needs and to drive the interface design. © 2020 Elsevier Ltd","Human Factors; Human-Robot Collaboration; Human-Robot Interaction; Industrial Ergonomics; User eXperience","Agricultural robots; Automatic guided vehicles; Industrial research; Machine design; Man machine systems; User experience; User interfaces; Automated guided vehicles; Collaborative execution; Enabling technologies; Human robot Interaction (HRI); Human-robot collaboration; Multi-disciplinary teams; Technological aspects; User experiences (ux); Social robots",,,,,,,,,,,,,,,,"Rüßmann, M., Industry 4.0: The future of productivity and growth in manufacturing industries (2015) Bost. Consult. Gr., 9 (1), pp. 54-89; Goodrich, M.A., Schultz, A.C., Human–robot interaction: a survey (2008) Found. Trends® Human–Computer Interact., 1 (3), pp. 203-275; Dautenhahn, K., (2013), Soegaard R.F. Dam M. Ed. The Encyclopedia of Human-Computer Interaction Aarhus, Denmark; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Sabanovic, S., (2019), Human-Robot Interaction – An Introduction. Cambridge</bib>; Probst, L., Frideres, L., Pedersen, B., Caputi, C., (2015), PwC Luxembourg, “Service Innovation for Smart Industry - Human-robot collaboration,”; Saenz, J., Elkmann, N., Gibaru, O., Neto, P., Survey of methods for design of collaborative robotics applications-why safety is a barrier to more widespread robotics uptake (2018) Proceedings of the 2018 4th International Conference on Mechatronics and Robotics Engineering, pp. 95-101; Tang, G., Webb, P., Thrower, J., The development and evaluation of Robot Light Skin: A novel robot signalling system to improve communication in industrial human–robot collaboration (2019) Robot. Comput. Integr. Manuf., 56, pp. 85-94; Ferraguti, F., Landi, C.T., Secchi, C., Fantuzzi, C., Nolli, M., Pesamosca, M., Walk-through programming for industrial applications (2017) Procedia Manuf, 11, pp. 31-38; Magrini, E., Ferraguti, F., Ronga, A.J., Pini, F., De Luca, A., Leali, F., Human-robot coexistence and interaction in open industrial cells (2020) Robot. Comput. Integr. Manuf., 61; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human–robot interaction (2016) Robot. Comput. Integr. Manuf., 40, pp. 1-13; Bortot, D., (2014) Ergonomic Human-Robot Coexistence in the Branch of Production; Thrun, S., Toward a Framework for Human-Robot Interaction (2004) Human–Computer Interact, 19 (1-2), pp. 9-24; (2016), International Organization for Standardization, “ISO/TS 10566:2016,”; Schmidtler, J., Knott, V., Hölzel, C., Bengler, K., Human Centered Assistance Applications for the working environment of the future (2015) Occup. Ergon., 12 (3), pp. 83-95; Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) International Journal of Humanoid Robotics, 5 (1), pp. 47-66; Wang, L., Symbiotic human-robot collaborative assembly (2019) CIRP Ann, 68 (2), pp. 701-726; (2012), International Organization for Standardization, “UNI EN ISO 10218-1:2012 - Robots and robotic devices — Safety requirements for industrial robots — Part 1: Robots,”; Goodrich, M.A., Schultz, A.C., Human-Robot Interaction: A Survey (2007) Found. Trends® Human–Computer Interact. Human-Computer Interact., 1 (3), pp. 203-275; Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human behavior and hand gesture classification for smart human-robot interaction (2017) Procedia Manuf, 11, pp. 91-98; Neto, P., Simão, M., Mendes, N., Safeea, M., Gesture-based human-robot interaction for human assistance in manufacturing (2019) Int. J. Adv. Manuf. Technol., 101 (1-4), pp. 119-135; Villani, V., Capelli, B., Secchi, C., Fantuzzi, C., Sabattini, L., Humans interacting with multi-robot systems: a natural affect-based approach (2020) Auton. Robots, 44 (3), pp. 601-616; Peruzzini, M., Grandi, F., Pellicciari, M., Exploring the potential of Operator 4.0 interface and monitoring (2020) Comput. Ind. Eng., 139; Villani, V., Sabattini, L., Secchi, C., Fantuzzi, C., A framework for affect-based natural human-robot interaction (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1038-1044; Matsas, E., Vosniakos, G.-C., Batras, D., Prototyping proactive and adaptive techniques for human-robot collaboration in manufacturing using virtual reality (2018) Robot. Comput. Integr. Manuf., 50, pp. 168-180; Oyekan, J.O., The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans (2019) Robot. Comput. Integr. Manuf., 55, pp. 41-54; (2011), International Organization for Standardization, “UNI EN ISO 10218-2:2011 - Robots and robotic devices - Safety requirements for industrial robots - Part 2: Robot systems and integration,”; Hentout, A., Aouache, M., Maoudj, A., Key challenges and open issues of industrial collaborative robotics (2018) 2018 The 27th IEEE International Symposium on Workshop on Human-Robot Interaction: from Service to Industry (HRI-SI2018) at Robot and Human Interactive Communication. Proceedings, , IEEE; Dautenhahn, K., (2007), 362. , Socially intelligent robots: dimensions of human–robot interaction, no. 1480. The Royal Society London; https://www.iea.cc/whats/, International Ergonomics Associations, “What is Ergonomics?” [Online]. Available: [Accessed: 25-Feb-2020]; (2019), International Organization for Standardization, “ISO 9241-210 - Ergonomics of human-system interaction - Part 210: Human-centred design for interactive systems,”; Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in ergonomics of manufacturing: Managing the enterprise of the future, pp. 267-277. , Springer; Alenljung, B., Lindblom, J., Andreasson, R., Ziemke, T., User experience in social human-robot interaction (2019) Rapid Automation: Concepts, Methodologies, Tools, and Applications, pp. 1468-1490. , IGI Global; Steinfeld, A., Common metrics for human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction, pp. 33-40; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Crandall, J.W., Goodrich, M.A., Olsen, D.R., Nielsen, C.W., Validating human-robot interaction schemes in multitasking environments (2005) IEEE Trans. Syst. Man, Cybern. A Syst. Humans, 35 (4), pp. 438-449; Olsen, D.R., Goodrich, M.A., “Metrics for Evaluating Human-Robot Interactions.”; Peruzzini, M., Grandi, F., Pellicciari, M., Benchmarking of tools for user experience analysis in industry 4.0 (2017) Procedia Manuf, 11, pp. 806-813; (1999), International Organization for Standardization, “ISO 13407,”; Still, B., Crane, K., Fundamentals of user-centered design: A practical approach (2017), CRC Press; Yu, M., (2017), “Understanding User Interactions Through Link Analysis in Social Networks,”; Kalbach, J., Mapping experiences: A complete guide to creating value through journeys, blueprints, and diagrams (2016), O'Reilly Media, Inc; Hackos, J.T., Redish, J., (1998) User and task analysis for interface design, 1. , Wiley New York; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2020) Human-Robot Interaction – An Introduction, , Cambridge University Press","Peruzzini, M.; Department of Engineering “Enzo Ferrari”, Italy; email: margherita.peruzzini@unimore.it",,,"Elsevier Ltd",,,,,07365845,,RCIME,,"English","Rob Comput Integr Manuf",Article,"Final","",Scopus,2-s2.0-85092106872
"Law T., Chita-Tegmark M., Scheutz M.","57212389268;56700303500;6603548841;","The Interplay Between Emotional Intelligence, Trust, and Gender in Human–Robot Interaction: A Vignette-Based Study",2021,"International Journal of Social Robotics","13","2",,"297","309",,4,"10.1007/s12369-020-00624-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081897325&doi=10.1007%2fs12369-020-00624-1&partnerID=40&md5=81508aca19351aca36e800b1ee42f944","HRI Lab, Tufts University, Medford, MA  02155, United States","Law, T., HRI Lab, Tufts University, Medford, MA  02155, United States; Chita-Tegmark, M., HRI Lab, Tufts University, Medford, MA  02155, United States; Scheutz, M., HRI Lab, Tufts University, Medford, MA  02155, United States","As robots begin to enter roles in which they work closely with human teammates or peers, it is critical to understand how people trust them based on how they interpret the robot’s behavior. In this paper we investigated the interplay between trust in a robot and people’s perceptions of the robot’s emotional intelligence. We used a vignette-based method to explore the following questions: (1) Do subjects perceive differences in robot EI, and is their trust in the robot influenced by differences in the robot’s reliability and capability? (2) Does a robot’s EI influence how much it is trusted and conversely does a robot’s capability and reliability influence how emotionally intelligent it is perceived to be? (3) Do people trust male and female robots differently when the robots exhibit different levels of EI or different levels of capability and reliability, and do gender stereotypical expectations related to EI transfer to trust?; (4) Does focusing on the robot’s EI increase one’s trust in the robot? (5) Is the interplay between trust, EI and gender the same for different levels of evoked social presence and human-likeness (i.e., when the interaction is presented in different modalities, text or spoken dialogue when the robot’s voice is actually heard)? We found that trust in the robot was influenced by the level of the robot’s EI (p < .001) and that gender stereotypical expectations related to EI were transferred to trust (p = .006), but gender effects on trust disappeared when only capability and reliability (robot’s trustworthiness) were manipulated but not the robot’s EI (p = .103). Surprisingly, we found that people trusted the robot more when the interaction was presented in text format (p = .024), going against our hypothesis that spoken dialogue would evoke more social presence and thus bolster EI perception and instill more trust. We suggest that this effect might be due to people’s expectations of a more expressive and human-like voice. Finally, we also found that people’s trust ratings in the robot were higher when they were made to notice and think about the robot’s EI, by answering EI questionnaires prior to trust questionnaires (p = .022). We discuss the implications of our findings for robot design and HRI research. © 2020, Springer Nature B.V.","Emotional intelligence; Gender; Human–robot interaction; Trust","Agricultural robots; Emotional intelligence; Intelligent robots; Machine design; Reliability; Surveys; Gender; Gender effects; Human likeness; Robot designs; Robot interactions; Social presence; Spoken dialogue; Trust; Social robots",,,,,"National Science Foundation, NSF: IIS-1316809; Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0465; Center for Selective C-H Functionalization, National Science Foundation; Center for Hierarchical Manufacturing, National Science Foundation, CHM, NSF","This project was funded in part by the National Science Foundation Grant IIS-1316809 and the AFOSR Grant FA9550-18-1-0465.",,,,,,,,,,"Ashraf, N., Bohnet, I., Piankov, N., Decomposing trust and trustworthiness (2006) Exp Econ, 9 (3), pp. 193-208; Behrens, S.I., Egsvang, A.K.K., Hansen, M., Møllegård-Schroll, A.M., Gendered robot voices and their influence on trust (2018) Companion of the 2018 ACM/IEEE International Conference on human–robot Interaction. ACM, pp. 63-64; Ben-Ner, A., Halldorsson, F., Trusting and trustworthiness: What are they, how to measure them, and what affects them (2010) J Econ Psychol, 31 (1), pp. 64-79; Bernotat, J., Eyssel, F., Sachse, J., The (fe) male robot: how robot body shape impacts first impressions and trust towards robots (2019) Int J Soc Robot, 11, pp. 1-13; Buchan, N.R., Croson, R.T.A., Solnick, S., Trust and gender: an examination of behavior and beliefs in the investment game (2008) J Econ Behav Organ, 68 (3-4), pp. 466-476; Caruso, D.R., Salovey, P., (2004) The emotionally intelligent manager: how to develop and use the four key emotional skills of leadership, , Wiley, New York; Chita-Tegmark, M., Lohani, M., Scheutz, M., Gender effects in perceptions of robots and humans with varying emotional intelligence (2019) 2019 14Th ACM/IEEE International Conference on human–robot Interaction (HRI). IEEE, pp. 230-238; Clarke, N., Emotional intelligence and its relationship to transformational leadership and key project manager competences (2010) Project Manag J, 41 (2), pp. 5-20; Croson, R., Gneezy, U., Gender differences in preferences (2009) J Econ Lit, 47 (2), pp. 448-474; Crowelly, C.R., Villanoy, M., Scheutzz, M., Schermerhornz, P., Gendered voice and robot entities: Perceptions and reactions of male and female subjects. 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85081897325
"Chita-Tegmark M., Scheutz M.","56700303500;6603548841;","Assistive Robots for the Social Management of Health: A Framework for Robot Design and Human–Robot Interaction Research",2021,"International Journal of Social Robotics","13","2",,"197","217",,5,"10.1007/s12369-020-00634-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081639438&doi=10.1007%2fs12369-020-00634-z&partnerID=40&md5=777e048d777f900ee5352f5b1353c48c","Tufts University, 200 Boston Avenue, Medford, MA  01255, United States","Chita-Tegmark, M., Tufts University, 200 Boston Avenue, Medford, MA  01255, United States; Scheutz, M., Tufts University, 200 Boston Avenue, Medford, MA  01255, United States","There is a close connection between health and the quality of one’s social life. Strong social bonds are essential for health and wellbeing, but often health conditions can detrimentally affect a person’s ability to interact with others. This can become a vicious cycle resulting in further decline in health. For this reason, the social management of health is an important aspect of healthcare. We propose that socially assistive robots (SARs) could help people with health conditions maintain positive social lives by supporting them in social interactions. This paper makes three contributions, as detailed below. We develop a framework of social mediation functions that robots could perform, motivated by the special social needs that people with health conditions have. In this framework we identify five types of functions that SARs could perform: (a) changing how the person is perceived, (b) enhancing the social behavior of the person, (c) modifying the social behavior of others, (d) providing structure for interactions, and (e) changing how the person feels. We thematically organize and review the existing literature on robots supporting human–human interactions, in both clinical and non-clinical settings, and explain how the findings and design ideas from these studies can be applied to the functions identified in the framework. Finally, we point out and discuss challenges in designing SARs for supporting social interactions, and highlight opportunities for future robot design and HRI research on the mediator role of robots. © 2020, Springer Nature B.V.","Health management; Human robot interaction; Socially assistive robots","Agricultural robots; Health; Human robot interaction; Machine design; Assistive robots; Health management; Human interactions; Non-clinical settings; Robot interactions; Social interactions; Social management; Socially assistive robots; Social robots",,,,,"National Science Foundation, NSF: IIS-1316809; Directorate for Computer and Information Science and Engineering, CISE: 1316809; Center for Selective C-H Functionalization, National Science Foundation; Center for Hierarchical Manufacturing, National Science Foundation, CHM, NSF","This project was supported by the National Science Foundation Grant IIS-1316809.",,,,,,,,,,"Cohen, S., Social relationships and health (2004) Am Psychol, 59 (8), p. 676; Umberson, D., Karas Montez, J., Social relationships and health: a flashpoint for health policy (2010) J Health Soc Behav, 51, pp. S54-S66; de Jong-Gierveld, J., van Tilburg, T.G., Dykstra, P.A., (2006) Loneliness and Social Isolation. 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Rob.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85081639438
"Freddi A., Iarlori S., Longhi S., Monteriù A.","36682153900;56027864000;56911180600;16638623100;","Development and experimental validation of algorithms for human–robot interaction in simulated and real scenarios",2021,"Journal of Ambient Intelligence and Humanized Computing","12","4",,"4529","4539",,,"10.1007/s12652-019-01676-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077856023&doi=10.1007%2fs12652-019-01676-6&partnerID=40&md5=8c2b4355a1acc93c60e777360d3e4985","Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy","Freddi, A., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy; Iarlori, S., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy; Longhi, S., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy; Monteriù, A., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy","The development of robots, which can safely and effectively interact with people and assist them in structured environments, is an open research problem whose importance has been growing rapidly in the last years. Indeed working in shared environments with human beings, these robots require new ways to achieve human–robot interaction and cooperation. This work presents an approach for performing human–robot interaction by means of robotic manipulators. The interaction is composed by three main steps, namely the selection, the recognition and the grasping of an object. The object selection is recorded on the base of a gesture execution, realized by the user in front of a RGB-D camera and associated to each particular object. The object recognition is achieved by means of the RGB cameras mounted on the two manipulator arms, which send the workspace information to a specific classifier. With the aim of realizing the grasping step, the object position and orientation are extracted in order to correctly rotate the gripper according to the object on the desk in front of the robot. The final goal is to release the grasped object on the hand of the user standing in front of the desk. This system could support people with limited motor skills who are not able to get an object on their own, playing an important role in structured assistive and smart environments, thus promoting the human–robot interaction in activity of daily living. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.","Human–robot cooperation; Human–robot interaction; Robotic manipulators","Cameras; Classification (of information); Flexible manipulators; Object recognition; Robotics; Activity of daily livings; Experimental validations; Research problems; Robot cooperation; Robot interactions; Robotic manipulators; Smart environment; Structured environment; Social robots",,,,,,,,,,,,,,,,"Achic, F., Montero, J., Penaloza, C., Cuellar, F., Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks (2016) 2016 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO), pp. 249-254. , https://doi.org/10.1109/ARSO.2016.7736290, pp; Bekele, E., Wade, J., Bian, D., Fan, J., Swanson, A., Warren, Z., Sarkar, N., Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with autism spectrum disorders (ASD) (2016) IEEE Virtual Reality (VR), pp. 121-130. , https://doi.org/10.1109/VR.2016.7504695, pp; Canal, G., Escalera, S., Angulo, C., A real-time human-robot interaction system based on gestures for assistive scenarios (2016) Comput Vis Image Underst, 149, pp. 65-77. , (special issue on Assistive Computer Vision and Robotics - Assistive Solutions for Mobility, Communication and HMI; Capecci, M., Ceravolo, M., Ferracuti, F., Iarlori, S., Longhi, S., Romeo, L., Russi, S., Verdini, F., (2016) Accuracy Evaluation of the Kinect V2 Sensor during Dynamic Movements in a Rehabilitation Scenario, 2016, pp. 5409-5412. , https://doi.org/10.1109/EMBC.2016.7591950, vol, pp; Cavallo, F., Aquilano, M., Bonaccorsi, M., Limosani, R., Manzi, A., Carrozza, M.C., Dario, P., On the design, development and experimentation of the astro assistive robot integrated in smart environments (2013) : 2013 IEEE International Conference on Robotics and Automation, pp. 4310-4315. , https://doi.org/10.1109/ICRA.2013.6631187, pp; Chance, G., Camilleri, A., Winstone, B., Caleb-Solly, P., Dogramadzi, S., An assistive robot to support dressing - strategies for planning and error handling (2016) 6Th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 774-780. , https://doi.org/10.1109/BIOROB.2016.7523721, pp; 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Le, H.H., Loomes, M.J., Loureiro, R.C.V., Group interaction through a multi-modal haptic framework (2016) 2016 12Th International Conference on Intelligent Environments (IE), pp. 62-67. , https://doi.org/10.1109/IE.2016.18; Lee, H.R., Tan, H., Šabanović, S., That robot is not for me: Addressing stereotypes of aging in assistive robot design (2016) 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 312-317. , https://doi.org/10.1109/ROMAN.2016.7745148; Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human behavior and hand gesture classification for smart human-robot interaction (2017) Proced Manuf, 11, pp. 91-98. , (27th international conference on flexible automation and intelligent manufacturing, FAIM2017, 27–30 June 2017, Modena, Italy; Mettel, M.R., Alekseew, M., Stocklöw, C., Braun, A., Designing and evaluating safety services using depth cameras (2019) J Ambient Intell Humaniz Comput, 10, pp. 747-759; Mishra, A., Skubic, M., Abbott, C., (2015) Development and Preliminary Validation of an Interactive Remote Physical Therapy System, 2015. , https://doi.org/10.1109/EMBC.2015.7318332; Mobini, A., Behzadipour, S., Foumani, M., Accuracy of kinect’s skeleton tracking for upper body rehabilitation applications (2013) Disabil Rehabi Assist Technol; Morales, Y., Miyashita, T., Hagita, N., Social robotic wheelchair centered on passenger and pedestrian comfort (2017) Robot Auton Syst, 87, pp. 355-362; Poncela, A., Coslado, F., García, B., FernÁndez-Carmona, M., Ariza, J., Peinado, G., Demetrio, C., Sandoval, F., Smart care home system: a platform for eassistance (2018) J Ambient Intell Human Comput; Pons, P., Jaen, J., Interactive spaces for children: gesture elicitation for controlling ground mini-robots (2019) J Ambient Intell Human Comput; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A., (2009) Ros: An Open-Source Robot Operating System, 3; Raheja, J.L., Chandra, M., Chaudhary, A., 3d gesture based real-time object selection and recognition (2018) Pattern Recogn Lett, 115, pp. 14-19. , (multimodal Fusion for Pattern Recognition; Romer, G.R.B.E., Stuyt, H.J.A., Peters, A., Cost-savings and economic benefits due to the assistive robotic manipulator (Arm) (2005) 9Th International Conference on Rehabilitation Robotics, 2005, pp. 201-204. , https://doi.org/10.1109/ICORR.2005.1501084, ICORR 2005; Sarcevic, P., Kincses, Z., Pletl, S., Online human movement classification using wrist-worn wireless sensors (2019) J Ambient Intell Humaniz Comput, 10, pp. 89-106; Sciutti, A., Sandini, G., Interacting with robots to investigate the bases of social interaction (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society; Tellaeche, A., Kildal, J., Maurtua, I., A flexible system for gesture based human-robot interaction (2018) Proced CIRP, 72, pp. 57-62. , (51st CIRP Conference on Manufacturing Systems; Tsui, K., Yanco, H., Kontak, D., Beliveau, L., Development and evaluation of a flexible interface for a wheelchair mounted robotic arm (2008) 3Rd ACM/IEEE International Conference on human–robot Interaction (HRI), pp. 105-112; Wen, Z., Liu, D., Liu, X., Zhong, L., Lv, Y., Jia, Y., Deep learning based smart radar vision system for object recognition (2019) J Ambient Intell Humaniz Comput, 10 (3), pp. 829-839","Iarlori, S.; Department of Information Engineering, Via Brecce Bianche, Italy; email: sabrina.iarlori@gmail.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,18685137,,,,"English","J. Ambient Intell. Humanized Comput.",Article,"Final","",Scopus,2-s2.0-85077856023
"Weidemann A., Rußwinkel N.","57190274631;36961405400;","The Role of Frustration in Human–Robot Interaction – What Is Needed for a Successful Collaboration?",2021,"Frontiers in Psychology","12",,"640186","","",,2,"10.3389/fpsyg.2021.640186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103631143&doi=10.3389%2ffpsyg.2021.640186&partnerID=40&md5=e0962bbfb5ef7b121107160f9f8367d5","Cognitive Modeling in Dynamic Human-Machine Systems, Faculty of Mechanical Engineering and Transport Systems, Department of Psychology and Ergonomics, Technical University of Berlin, Berlin, Germany; Junior Research Group MTI-engAge, Control Systems Group, Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany","Weidemann, A., Cognitive Modeling in Dynamic Human-Machine Systems, Faculty of Mechanical Engineering and Transport Systems, Department of Psychology and Ergonomics, Technical University of Berlin, Berlin, Germany, Junior Research Group MTI-engAge, Control Systems Group, Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany; Rußwinkel, N., Cognitive Modeling in Dynamic Human-Machine Systems, Faculty of Mechanical Engineering and Transport Systems, Department of Psychology and Ergonomics, Technical University of Berlin, Berlin, Germany","To realize a successful and collaborative interaction between human and robots remains a big challenge. Emotional reactions of the user provide crucial information for a successful interaction. These reactions carry key factors to prevent errors and fatal bidirectional misunderstanding. In cases where human–machine interaction does not proceed as expected, negative emotions, like frustration, can arise. Therefore, it is important to identify frustration in a human–machine interaction and to investigate its impact on other influencing factors such as dominance, sense of control and task performance. This paper presents a study that investigates a close cooperative work situation between human and robot, and explore the influence frustration has on the interaction. The task for the participants was to hand over colored balls to two different robot systems (an anthropomorphic robot and a robotic arm). The robot systems had to throw the balls into appropriate baskets. The coordination between human and robot was controlled by various gestures and words by means of trial and error. Participants were divided into two groups, a frustration- (FRUST) and a no frustration- (NOFRUST) group. Frustration was induced by the behavior of the robotic systems which made errors during the ball handover. Subjective and objective methods were used. The sample size of participants was N = 30 and the study was conducted in a between-subject design. Results show clear differences in perceived frustration in the two condition groups and different behavioral interactions were shown by the participants. Furthermore, frustration has a negative influence on interaction factors such as dominance and sense of control. The study provides important information concerning the influence of frustration on human–robot interaction (HRI) for the requirements of a successful, natural, and social HRI. The results (qualitative and quantitative) are discussed in favor of how a successful und effortless interaction between human and robot can be realized and what relevant factors, like appearance of the robot and influence of frustration on sense of control, have to be regarded. © Copyright © 2021 Weidemann and Rußwinkel.","collaboration; frustration; human–robot interaction (HRI); influence; recommendations",,,,,,"Bundesministerium für Bildung und Forschung, BMBF","The research presented in this manuscript was primarily supported by the German Ministry of Education and Research.",,,,,,,,,,"Abd, M.A., Gonzalez, I., Nojoumian, M., Engeberg, E.D., Trust, satisfaction and frustration measurements during human-robot interaction (2017) Proceedings of the 30th Florida Conference on Recent Advances in RoboticsMay 11-12, 2107, , Boca Raton, FL, Florida Atlantic University; Amsel, A., (1992) Frustration Theory: An Analysis of Dispositional Learning and Memory, , Cambridge, Cambridge University Press; Bartneck, C., Croft, E., Kulic, D., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. 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Weidemann, A.; Junior Research Group MTI-engAge, Germany; email: a.weidemann@tu-berlin.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103631143
"Gemeinboeck P.","10043705000;","The Aesthetics of Encounter: A Relational-Performative Design Approach to Human-Robot Interaction",2021,"Frontiers in Robotics and AI","7",,"577900","","",,3,"10.3389/frobt.2020.577900","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103832305&doi=10.3389%2ffrobt.2020.577900&partnerID=40&md5=c7e9d1481e65171f2e5458350049cf2d","Department of Media Theory, University of Applied Arts Vienna, Vienna, Austria; Centre for Transformative Media Technologies, School of Arts, Social Sciences and Humanities, Swinburne University of Technology, Melbourne, VIC, Australia","Gemeinboeck, P., Department of Media Theory, University of Applied Arts Vienna, Vienna, Austria, Centre for Transformative Media Technologies, School of Arts, Social Sciences and Humanities, Swinburne University of Technology, Melbourne, VIC, Australia","This article lays out the framework for relational-performative aesthetics in human-robot interaction, comprising a theoretical lens and design approach for critical practice-based inquiries into embodied meaning-making in human-robot interaction. I explore the centrality of aesthetics as a practice of embodied meaning-making by drawing on my arts-led, performance-based approach to human-robot encounters, as well as other artistic practices. Understanding social agency and meaning as being enacted through the situated dynamics of the interaction, I bring into focus a process of bodying-thinging; entangling and transforming subjects and objects in the encounter and rendering elastic boundaries in-between. Rather than serving to make the strange look more familiar, aesthetics here is about rendering the differences between humans and robots more relational. My notion of a relational-performative design approach—designing with bodying-thinging—proposes that we engage with human-robot encounters from the earliest stages of the robot design. This is where we begin to manifest boundaries that shape meaning-making and the potential for emergence, transformation, and connections arising from intra-bodily resonances (bodying-thinging). I argue that this relational-performative approach opens up new possibilities for how we design robots and how they socially participate in the encounter. © Copyright © 2021 Gemeinboeck.","aesthetics; agency; design; human-robot interaction design; movement; performativity",,,,,,"Falmouth University; Seventh Framework Programme, FP7: 621403; Australian Research Council, ARC: DP160104706; Universiteit Leiden; Austrian Science Fund, FWF: AR545; Technische Universiteit Eindhoven, TU/e","I would like to thank my collaborators for their contributions to the ongoing research: Rob Saunders (Leiden University, Leiden, Netherlands), Marie-Claude Poulin (kondition pluriel, Montreal, Canada; University of Applied Arts Vienna, Vienna, Austria), Maaike Bleeker (Utrecht University, Utrecht, Netherlands), Roos van Berkel (Eindhoven University of Technology, Eindhoven, Netherlands), Rochelle Haley (University of New South Wales, Sydney, Australia), and Audrey Rochette (kondition pluriel, Montreal, Canada). I would also like to thank members of De Quincey Co. (Sydney, Australia) for their contributions to the project in its earlier stages, in particular, director and choreographer Tess de Quincey, and performers Linda Luke and Kirsten Packham. Finally, I would like to thank the performers Sarah Levinsky and Katrina Brown from Falmouth University (Falmouth, United Kingdom) for their contributions to the workshops in 2018.","The research presented in this article has been partly supported by several funding bodies: the Australian Government through the Australian Research Council (DP160104706); the EU Framework Program (FP7) European Research Area Chairs Scheme project (621403); and the Austrian Government through the Austrian Science Fund (FWF, AR545).",,,,,,,,,"Airenti, G., The cognitive bases of anthropomorphism: from relatedness to empathy (2015) Int. J. Soc. Robot, 7, pp. 117-127; Alač, M., Social robots: things or agents? 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Berlin (2003) Theater der Zeit; Turkle, S., (2011) Alone together: why we expect more from technology and less from each other, , New York, NY, Basic Books; Turkle, S., (2005) The second self: computers and the human spirit, , 20th Edn, Cambridge, MA, The MIT Press; Uexküll, J.V., A stroll through the worlds of animals and men: a picture book of invisible worlds (1957) Instinctive behavior: the Development of a modern concept, pp. 5-80. , Schiller C.H., (ed), New York, NY, International Universities Press, Editor; Varela, F.J., Thompson, E., Rosch, E., (1991) The embodied mind: cognitive science and human experience, , Cambridge, MA, MIT Press; Vlachos, E., Jochum, E., Demers, L.-P., The effects of exposure to different social robots on attitudes toward preferences (2016) Interaction Studies, 17 (3), pp. 390-404; Wright, P., Reconsidering the H, the C, and the I: some thoughts on reading suchman’s human-machine reconfigurations (2011) Interactions, 18 (5), pp. 28-31; Yang, S., Mok, B.K.J., SirkinIve, D.H.P., Maheshwari, R., Fischer, K., Ju, W., Experiences developing socially acceptable interactions for a robotic trash barrel (2015) The 24th IEEE international symposium on robot and human interactive communication, pp. 277-284. , Kobe, Japan, August, 2015, New York, NY, IEEE; Ziemke, T., Introduction to the special issue on situated and embodied cognition (2002) Cognit. Syst. Res, 3, pp. 271-274; Ziemke, T., Sharkey, N., A stroll through the worlds of robots and animals (2001) Semiotica, 13 (1–4), pp. 701-746; Ziemke, T., The body of knowledge: on the role of the living body in grounding embodied cognition (2016) Biosystems, 148, pp. 4-11; Zivanovic, A., Elegant, natural motion of robots: lessons from an artist” in Gemeinboeck (2019) Proceedings of the AISB 2019 Symposium on Movement that shapes behaviour (MTSB’19), , http://aisb2019.machinemovementlab.net/MTSB2019_Proceedings.pdf, Gemeinboeck P., Saunders R., (eds), :, Accessed, June, 21, 2020, Editors","Gemeinboeck, P.; Department of Media Theory, Austria; email: petra.gemeinboeck@uni-ak.ac.at
Gemeinboeck, P.; Centre for Transformative Media Technologies, Australia; email: petra.gemeinboeck@uni-ak.ac.at",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103832305
"Luria M., Hoggenmüller M., Lee W.-Y., Hespanhol L., Jung M., Forlizzi J.","56895782800;56422380000;57204952765;55295736000;15073123400;6602845027;","Research through design approaches in human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444868","685","687",,1,"10.1145/3434074.3444868","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102777898&doi=10.1145%2f3434074.3444868&partnerID=40&md5=c8c19fc6275147e8320253e04dea465f","Human-Computer Interaction Intitute, Carnegie Mellon University, United States; Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Mechanical and Aerospace Engineering, Cornell University, United States; Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Information Science, Cornell University, United States","Luria, M., Human-Computer Interaction Intitute, Carnegie Mellon University, United States; Hoggenmüller, M., Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Lee, W.-Y., Mechanical and Aerospace Engineering, Cornell University, United States; Hespanhol, L., Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Jung, M., Information Science, Cornell University, United States; Forlizzi, J., Human-Computer Interaction Intitute, Carnegie Mellon University, United States","This workshop sets out to bring researchers across the Human-Robot Interaction (HRI), Human-Computer Interaction, and Design fields that use, or are interested in using Research through Design (RtD) in their work. RtD is a research approach that uses design practices, such as ideation and prototyping, to generate knowledge. RtD focuses on understanding what is the right thing to design, and has the potential to bring new perspectives that break through fixation within a field. In our workshop, we will attempt to classify current HRI practices of RtD, identify under-explored topics and methods, and discuss challenges on conducting this type of work in the HRI field. The workshop will result in defining next steps for better integration of RtD approaches in the HRI community and guidelines to include more researchers in RtD practice. © 2021 Owner/Author.","Design research; Human-robot interaction; Research through design","Agricultural robots; Human computer interaction; Machine design; Design approaches; Design practice; Hri communities; Human robot Interaction (HRI); Research approach; Human robot interaction",,,,,"National Science Foundation, NSF: SES-1734456","This workshop is supported by the National Science Foundation, grant SES-1734456.",,,,,,,,,,"Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Yolo, a robot for creativity: A co-design study with children (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 423-429; Auger, J., Living with robots: A speculative design approach (2014) Journal of Human-Robot Interaction, 3 (1), pp. 20-42. , 2014; Bødker, S., When second wave hci meets third wave challenges (2006) Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing Roles (Oslo, Norway) (NordiCHI '06), pp. 1-8. , https://doi.org/10.1145/1182475.1182476, Association for Computing Machinery, New York, NY, USA; Buchanan, R., Wicked problems in design thinking (1995) The Idea of Design, pp. 3-20. , Victor Margolin and Richard Buchanan (Eds.) MIT Press; Buxton, B., (2010) Sketching User Experiences: Getting the Design Right and the Right Design, , Morgan Kaufmann; Dunne, A., Raby, F., Technological dreams series no. 1 (2007) Robots, , http://dunneandraby.co.uk/content/projects/10/0; Gaver, W., What should we expect from research through design? (2012) Proc. Of the SIGCHI Conference on Human Factors in Computing Systems, pp. 937-946; Gaver, W.W., Bowers, J., Boucher, A., Gellerson, H., Pennington, S., Schmidt, A., Steed, A., Walker, B., The drift table: Designing for ludic engagement (2004) CHI'04 Extended Abstracts on Human Factors in Computing Systems. ACM, pp. 885-900; Hoggenmueller, M., Hespanhol, L., Tomitsch, M., Stop and smell the chalk flowers: A robotic probe for investigating urban interaction with physicalised displays (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-14; Koskinen, I., Zimmerman, J., Binder, T., Redstrom, J., Wensveen, S., (2011) Design Research through Practice: From the Lab, Field, and Showroom, , Elsevier; Lee, W., Jung, M., Ludic-hri: Designing playful experiences with robots (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 582-584; Luce Lupetti, M., Shybo-design of a research artefact for human-robot interaction studies (2017) Journal of Science and Technology of the Arts, 9 (1), pp. 57-69. , 2017; Luce Lupetti, M., Zaga, C., Cila, N., (2020) First International Workshop on Designerly HRI Knowledge; Luria, M., Reig, S., Zhi Tan, X., Steinfeld, A., Forlizzi, J., Zimmerman, J., Re-embodiment and co-embodiment: Exploration of social presence for robots and conversational agents (2019) Proceedings of the 2019 on Designing Interactive Systems Conference. ACM, pp. 633-644; Luria, M., Sheriff, O., Boo, M., Forlizzi, J., Zoran, A., Destruction, catharsis, and emotional release in human-robot interaction (2020) ACM Transactions on Human-Robot Interaction (THRI), 9 (4), pp. 1-19. , 2020; Luria, M., Zheng, R., Huffman, B., Shuangni, H., Zimmerman, J., Forlizzi, J., Social boundaries for personal agents in the interpersonal space of the home (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM; Luria, M., Zimmerman, J., Forlizzi, J., Championing research through design in hri (2019) 2019 CHI Conference on Human Factors in Computing Systems, , 2019; Odom, W., Selby, M., Sellen, A., Kirk, D., Banks, R., Regan, T., Photobox: On the design of a slow technology (2012) Proceedings of the Designing Interactive Systems Conference, pp. 665-668; Odom, W., Zimmerman, J., Forlizzi, J., Teenagers and their virtual possessions: Design opportunities and issues (2011) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 1491-1500; Suguitan, M., Hoffman, G., Blossom: A handcrafted open-source robot (2019) ACM Transactions on Human-Robot Interaction (THRI), 8 (1), pp. 1-27. , 2019; Yang, Q., Banovic, N., Zimmerman, J., Mapping machine learning advances from hci research to reveal starting places for design innovation (2018) Proc. Of the CHI Conference on Human Factors in Computing Systems, pp. 1-11; Zimmerman, J., Forlizzi, J., Research through design in hci (2014) Ways of Knowing in HCI, pp. 167-189. , Springer; Zimmerman, J., Forlizzi, J., Evenson, S., Research through design as a method for interaction design research in hci (2007) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 493-502",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102777898
"Williams M.-A.","7410005152;","Designing human-robot interaction with social intelligence",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444865","iii","iv",,,"10.1145/3434073.3444865","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102776321&doi=10.1145%2f3434073.3444865&partnerID=40&md5=c1cff2f29a5d121c716a609efb825251","University of New South Wales, Australia","Williams, M.-A., University of New South Wales, Australia","Robots are a distributive technology that promotes change. Human-Robot Interaction is an emerging field that has exciting prospects for widespread transformation. Today, embedding social intelligence into human-robot interaction design is a moonshot. Moonshots can boost science and engineering by galvanizing and incentivizing research communities and industry in the pursuit of impossible challenges to accelerate progress and scale impact. Social intelligence is all about power and influence; privacy and trust; ethical decisions and happiness. What if, robots could take the initiative and purposefully persuade you to believe something new. HRI with social intelligence has the potential to create breakthrough insights and that will energize the reimagination of how humans and robots will collaborate in business and society. © 2021 IEEE Computer Society. All rights reserved.","Artificial Intelligence; Disruptive Innovation; Human-Robot Interaction; Social intelligence",,,,,,,,,,,,,,,,,"Inamura, T., Kumar Pandey, A., Kumar, S., Williams, M., Cabibihan, J.-J., Behera, L., Special issue on robot and human interactive communication 2020 (2020) Adv. Robotics, 34 (20), p. 1279; Raza, S.A., Williams, M.-A., Human feedback as action assignment in interactive reinforcement learning (2020) ACM Trans. Auton. Adapt. Syst., 14 (4), pp. 141-1424; Ojha, S., Vitale, J., Williams, M.-A., (2020) EEGS: A Transparent Model of Emotions, , CoRR abs/2011.02573; Skillicorn, D.B., Alsadhan, N., Billingsley, R., Williams, M.-A., Measuring human emotion in short documents to improve social robot and agent interactions (2019) Canadian Conference on AI, pp. 29-41; Tonkin, M., Vitale, J., Herse, S., Ali Raza, S., Madhisetty, S., Kang, L., Duc Vu, T., Williams, M.-A., (2019) Privacy First: Designing Responsible and Inclusive Social Robot Applications for in the Wild Studies, pp. 1-8; Ojha, S., Williams, M., Johnston, B., The essence of ethical reasoning in robot-emotion processing (2018) Int. J. Soc. Robotics, 10 (2), pp. 211-223; Williams, M.-A., Decision-theoretic human-robot interaction: Designing reasonable and rational robot behavior (2016) ICSR, pp. 72-82; Wang, X., Williams, M., Gärdenfors, P., Vitale, J., Abidi, S.R., Johnston, B., Kuipers, B., Huang, A., (2014) Directing Human Attention with Pointing, pp. 174-179; Novianto, R., Johnston, B., Williams, M.-A., Habituation and sensitisation learning in ASMO cognitive architecture (2013) ICSR, pp. 249-259; Williams, M.-A., Robot social intelligence (2012) ICSR, pp. 45-55","Williams, M.-A.; University of New South WalesAustralia; email: mary-anne.williams@unsw.edu.au",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Editorial,"Final","",Scopus,2-s2.0-85102776321
[No author name available],[No author id available],"HRI 2021 - Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",424,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102774951&partnerID=40&md5=f34b00429735c9e951e70950f2be7ed8",,"","The proceedings contain 42 papers. The topics discussed include: lane change decision making for automated driving; aerial flight paths for communication: how participants perceive and intend to respond to drone movements; human-inspired motion planning for omni-directional social robots; charting sequential person transfers between devices, agents, and robots; smooth operator: tuning robot perception through artificial movement sound; using trust to determine user decision making & task outcome during a human-agent collaborative task; influencing moral behavior through mere observation of robot work: video-based survey on littering behavior; and assessing and addressing ethical risk from anthropomorphism and deception in socially assistive robots.",,,,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85102774951
"Saheb Jam G., Rhim J., Lim A.","57222470882;55619493200;36801929900;","Developing a data-driven categorical taxonomy of emotional expressions in real world human robot interactions",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447218","479","483",,1,"10.1145/3434074.3447218","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102773751&doi=10.1145%2f3434074.3447218&partnerID=40&md5=ace723a641f6e074e089dfa391ed803d","School of Computing Science, Simon Fraser University, Burnaby, BC, Canada","Saheb Jam, G., School of Computing Science, Simon Fraser University, Burnaby, BC, Canada; Rhim, J., School of Computing Science, Simon Fraser University, Burnaby, BC, Canada; Lim, A., School of Computing Science, Simon Fraser University, Burnaby, BC, Canada","Emotions are reactions that can be expressed through a variety of social signals. For example, anger can be expressed through a scowl, narrowed eyes, a long stare, or many other expressions. This complexity is problematic when attempting to recognize a human's expression in a human-robot interaction: categorical emotion models used in HRI typically use only a few prototypical classes, and do not cover the wide array of expressions in the wild. We propose a data-driven method towards increasing the number of known emotion classes present in human-robot interactions, to 28 classes or more. The method includes the use of automatic segmentation of video streams into short (<10s) videos, and annotation using the large set of widely-understood emojis as categories. In this work, we showcase our initial results using a large in-the-wild HRI dataset (UE-HRI), with 61 clips randomly sampled from the dataset, labeled with 28 different emojis. In particular, our results showed that the ""skeptical""emoji was a common expression in our dataset, which is not often considered in typical emotion taxonomies. This is the first step in developing a rich taxonomy of emotional expressions that can be used in the future as labels for training machine learning models, towards more accurate perception of humans by robots. © 2021 ACM.","Emojis; Emotion categories; Emotion-recognition; In-the-wild; Social signals","Agricultural robots; Behavioral research; Educational robots; Large dataset; Man machine systems; Taxonomies; Automatic segmentations; Data driven; Data-driven methods; Emotion models; Emotional expressions; Real-world; Social signals; Training machines; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC","This work was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grants program.",,,,,,,,,,"Admoni, H., Nonverbal communication for human-robot interaction (2014) ACM Transactions on Embedded Computing Systems, 10 (10), pp. 1-3. , 2014; Al-Halah, Z., Aitken, A., Shi, W., Caballero, J., Smile, be happy:) emoji embedding for visual sentiment analysis (2019) IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 4491-4500; Feldman Barrett, L., Adolphs, R., Marsella, S., Martinez, A.M., Pollak, S.D., Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements (2019) Psychological Science in the Public Interest, 20 (1), pp. 1-68. , 2019; Ben-Youssef, A., Clavel, C., Essid, S., Bilac, M., Chamoux, M., Lim, A., Ue-hri: A new dataset for the study of user engagement in spontaneous human-robot interactions (2017) Proceedings of the ACM International Conference on Multimodal Interaction, pp. 464-472; Ben-Youssef, A., Varni, G., Essid, S., Clavel, C., On-the-fly detection of user engagement decrease in spontaneous human-robot interaction using recurrent and deep neural networks (2019) International Journal of Social Robotics, 11 (5), pp. 815-828. , 2019; Bloem, A.C., Barakova, E., Hootsmans, I.M., Opheij, L.M., Toebosch, R.H.A., Kerzel, M., Barros, P., Improving emotional expression recognition of robots using regions of interest from human data (2020) Companion of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 142-144; Bohus, D., Horvitz, E., Managing human-robot engagement with forecasts and. um. hesitations (2014) Proceedings of the 16th International Conference on Multimodal Interaction, pp. 2-9; Celiktutan, O., Skordos, E., Gunes, H., Multimodal human-human-robot interactions (mhhri) dataset for studying personality and engagement (2019) IEEE Transactions on Affective Computing, 10 (4), pp. 484-497. , 2019; Chen, L., Zhou, M., Su, W., Wu, M., She, J., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Information Sciences, 428, pp. 49-61. , 2018; Cohen, J., A coefficient of agreement for nominal scales (1960) Educational and Psychological Measurement, 20 (1), pp. 37-46. , 1960; Cowen, A.S., Keltner, D., Self-report captures 27 distinct categories of emotion bridged by continuous gradients (2017) Proceedings of the National Academy of Sciences, 114, pp. E7900-E7909; Coyne, A.K., Murtagh, A., McGinn, C., Using the geneva emotion wheel to measure perceived affect in human-robot interaction (2020) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 491-498; 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Krumhuber, E.G., Kappas, A., Manstead, A.S.R., Effects of dynamic aspects of facial expressions: A review (2013) Emotion Review, 5 (1), pp. 41-46. , 2013; Laukka, P., Neiberg, D., Forsell, M., Karlsson, I., Elenius, K., Expression of affect in spontaneous speech: Acoustic correlates and automatic detection of irritation and resignation (2011) Computer Speech & Language, 25 (1), pp. 84-104. , 2011; Leite, I., McCoy, M., Ullman, D., Salomons, N., Scassellati, B., Comparing models of disengagement in individual and group interactions (2015) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 99-105; Lemaignan, S., Edmunds, C.E.R., Senft, E., Belpaeme, T., The pinsoro dataset: Supporting the data-driven study of child-child and child-robot social dynamics (2018) PloS One, 13 (10), p. 205999. , 2018; Li, T.S., Kuo, P., Tsai, T., Luan, P., Cnn and lstm based facial expression analysis model for a humanoid robot (2019) IEEE Access, 7, pp. 93998-94011. , 2019; 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Mollahosseini, A., Hasani, B., Mahoor, M.H., Affectnet: A database for facial expression, valence, and arousal computing in the wild (2017) IEEE Transactions on Affective Computing, 10 (1), pp. 18-31. , 2017; Montáns, F.J., Chinesta, F., Gómez-Bombarelli, R., Nathan Kutz, J., Data-driven modeling and learning in science and engineering (2019) Comptes Rendus Mécanique, 347 (11), pp. 845-855. , 2019; Ed Plutchik, R., Conte, H.R., (1997) Circumplex Models of Personality and Emotions, , American Psychological Association; Querci, T., Duan, Y., Ince, R.A.A., Chen, C., Peussa, L.K., Garrod, O.G.B., Schyns, P.G., Jack, R.E., Distinct face movements boost recognition of facial expressions of emotions (2020) Journal of Vision, 20 (11), p. 1039. , 2020; Russell, J.A., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), p. 1161. , 1980; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608. , 2019; Sharma, G., Dhall, A., A survey on automatic multimodal emotion recognition in the wild (2020) Advances in Data Science: Methodologies and Applications, pp. 35-64. , Springer; Vaufreydaz, D., Johal, W., Combe, C., Starting engagement detection towards a companion robot using multimodal features (2016) Robotics and Autonomous Systems, 75, pp. 4-16. , 2016; Viera, A.J., Garrett, J.M., Understanding interobserver agreement: The kappa statistic (2005) Fam Med, 37 (5), pp. 360-363. , 2005; Watson, S.G., Judgment of emotion from facial and contextual cue combinations (1972) Journal of Personality and Social Psychology, 24 (3), p. 334. , 1972; Wimmer, M., MacDonald, B.A., Jayamuni, D., Arpit, Yadav., Facial expression recognition for human-robot interaction-a prototype (2008) International Workshop on Robot Vision, pp. 139-152; Yu, C., Tapus, A., Interactive robot learning for multimodal emotion recognition (2019) International Conference on Social Robotics, pp. 633-642",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102773751
"Pelikan H., Robinson F.A., Keevallik L., Velonaki M., Broth M., Bown O.","57193572237;57215966842;25936365200;24073920300;23970080000;27867522200;","Sound in human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444871","706","708",,,"10.1145/3434074.3444871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102771870&doi=10.1145%2f3434074.3444871&partnerID=40&md5=e901c85a5b9927a67c48545e6a7c5554","Linköping University, Linköping, Sweden; Creative Robotics Lab, University of New South Wales, Sydney, Australia; Interactive Media Lab, University of New South Wales, Sydney, Australia","Pelikan, H., Linköping University, Linköping, Sweden; Robinson, F.A., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Keevallik, L., Linköping University, Linköping, Sweden; Velonaki, M., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Broth, M., Linköping University, Linköping, Sweden; Bown, O., Interactive Media Lab, University of New South Wales, Sydney, Australia","Robot sound spans a wide continuum, from subtle motor hums, through music, bleeps and bloops, to human-inspired vocalizations, and can be an important means of communication for robotic agents. This first workshop on sound in HRI aims to bring together interdisciplinary perspectives on sound, including design, conversation analysis, (computational) linguistics, music, engineering and psychology. The goal of the workshop is to stimulate interdisciplinary exchange and to form a more coherent overview of perspectives on how sound can facilitate human-robot interaction. During the half-day workshop, we will explore (1) the diverse application opportunities of sound in human-robot interaction, (2) strategies for designing sonic human-robot interactions, and (3) methodologies for the evaluation of robot sound. Workshop outcomes will be documented on a dedicated website and are planned to be collected in a special issue. © 2021 Owner/Author.","Human-robot interaction; Non-lexical sounds; Robot sound; Robotic musicianship; Semantic-free utterances; Sonic interaction design","Agricultural robots; Man machine systems; Conversation analysis; Diverse applications; Half-day workshop; Robotic agents; Social robots",,,,,,,,,,,,,,,,"Hoffman, G., Bauman, S., Vanunu, K., Robotic experience companionship in music listening and video watching (2016) Personal and Ubiquitous Computing, 20 (1), pp. 51-63. , https://doi.org/10.1007/s00779-015-0897-1, Feb 2016; Pelikan, M.H.R., Broth, M., Keevallik, L., Are you sad, cozmo?"": How humans make sense of a home robot's emotion displays (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 461-470. , https://doi.org/10.1145/3319502.3374814, Association for Computing Machinery, New York, NY, USA; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 928-935. , https://doi.org/10.1109/ROMAN.2017.8172414, IEEE, Lisbon; Weinberg, G., Bretan, M., Hoffman, G., Driscoll, S., Robotic musicianship: Embodied artificial creativity and mechatronic musical expression (2020) Springer Nature, 8; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32 (1), pp. 63-85. , https://doi.org/10.1080/10447318.2015.1093856, Jan 2016",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102771870
"Kamino W., Joshi S., Šabanovic S.","57220182215;57201549303;14036199300;","'Food' for human robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447144","131","135",,,"10.1145/3434074.3447144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102769591&doi=10.1145%2f3434074.3447144&partnerID=40&md5=4834d412ab76e61b2b08c7aeaa2f66e0","Indiana University Bloomington, Bloomington, IN, United States","Kamino, W., Indiana University Bloomington, Bloomington, IN, United States; Joshi, S., Indiana University Bloomington, Bloomington, IN, United States; Šabanovic, S., Indiana University Bloomington, Bloomington, IN, United States","'Food', when mentioned in Human-Robot Interaction (HRI) research, is most often in the context of functional applications of automation, delivery, and assistance. Food has, however, not been explored as a medium for social expression or building relationships with social robots. Using web-based examples of robot food and our pilot collection of LOVOT and AIBO robot user's Tweets about their practices of feeding their robots, we show how food has the potential to sustain interactions, increase enjoyment, sociability and companionship in HRI, enhance life-likeness, autonomy, and agency for robots, and open up opportunities for community building among robot users. We present design implications of food for HRI, and urge HRI researchers to envision food as a facet of Human-Robot relationships and < - Human-Food-Robot - > interaction as a celebratory, provocative, and promising domain for HRI and social robot design. © 2021 ACM.","Food; Human-robot interaction; Social robot","Agricultural robots; Machine design; Man machine systems; AIBO robot; Community buildings; Design implications; Functional applications; Human robot Interaction (HRI); Human robots; Web based; Social robots",,,,,,,,,,,,,,,,"Beardsworth, A., Keil, T., (2002) Sociology on the Menu: An Invitation to the Study of Food and Society, , Routledge; Bell, D., Valentine, G., (1997) Consuming Geographies: We Are Where We Eat, , Psychology Press; Bhattacharjee, T., Gordon, E.K., Scalise, R., Cabrera, M.E., Caspi, A., Cakmak, M., Srinivasa, S.S., Is more autonomy always better? exploring preferences of users with mobility impairments in robot-assisted feeding (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-190; Bugmann, G., Copleston, S.N., What can a personal robot do for you? (2011) Conference towards Autonomous Robotic Systems, pp. 360-371. , Springer; Cahlander, R.L., Carroll, D.W., Hanson, R.A., Hollingsworth, A., Reinertsen, J.O., (1990) Food Preparation Robot; (2012) Leisure Food Series (Cycad Leaf/Sugar Cane/Chunk of Ice), , https://www.pleoworld.com/pleo_rb/eng/product.php?&c1id=2&id=5, Innvo Labs Corporation; (2012) Mint Leaf, , https://www.pleoworld.com/pleo_rb/eng/product.php?&c1id=2&id=26, Innvo Labs Corporation; (2012), www.pleoworld.com, Innvo Labs Corporation; (2020), https://us.aibo.com/, Sony Corporation; Cruz-Maya, A., Tapus, A., Teaching nutrition and healthy eating by using multimedia with a kompai robot: Effects of stress and user's personality (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 644-649; Dubé, L., LeBel, J.L., Lu, J., Affect asymmetry and comfort food consumption (2005) Physiology & Behavior, 86 (4), pp. 559-567. , 2005; Evers, C., Marijn Stok, F., De Ridder, D.T.D., Feeding your feelings: Emotion regulation strategies and emotional eating (2010) Personality and Social Psychology Bulletin, 36 (6), pp. 792-804. , 2010; Friedman, B., Kahn, P.H., Jr., Hagman, J., Hardware companions? what online aibo discussion forums reveal about the human-robotic relationship (2003) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 273-280; Jeff Goldblatt, J., (1990) Special Events: The Art and Science of Celebration, , Van Nostrand Reinhold; Grimes, A., Harper, R., Celebratory technology: Newdirections for food research in hci (2008) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 467-476; Esther Hamburg, M., Finkenauer, C., Schuengel, C., Food for love: The role of food offering in empathic emotion regulation (2014) Frontiers in Psychology, 5, p. 32. , 2014; Iqbal, J., Hameed Khan, Z., Khalid, A., Prospects of robotics in food industry (2017) Food Science and Technology, 37 (2), pp. 159-165. , 2017; Kyung Lee, M., Forlizzi, J., Rybski, P.E., Crabbe, F., Chung, W., Finkle, J., Glaser, E., Kiesler, S., The snackbot: Documenting the design of a robot for long-term human-robot interaction (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 7-14; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) International Journal of Social Robotics, 5 (2), pp. 291-308. , 2013; Lupton, D., Food, memory and meaning: The symbolic and social nature of food events (1994) The Sociological Review, 42 (4), pp. 664-685. , 1994; Lupton, D., (1996) Food, the Body and the Self, , Sage; Nussey, S., (2020) SoftBank Brings Food Service Robot to Labour-strapped Japan, , https://www.reuters.com/article/softbank-group-robotics-idUSKBN26J0GC; Palinko, O., Fischer, K., Ruiz Ramirez, E., Damsgaard Nissen, L., Langedijk, R.M., A drink-serving mobile social robot selects who to interact with using gaze (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 384-385; Park, D., Hoshi, Y., Kemp, C.C., A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 1544-1551. , 2018; Randall, N., Joshi, S., Liu, X., Health-e-eater: Dinnertime companion robot and magic plate for improving eating habits in children from low-income families (2018) Companion of the 2018 ACM/IEEE International Conference on Human-robot Interaction, pp. 361-362; Randall, N., Joshi, S., Liu, X., Health-e-eater: Dinnertime companion robot and magic plate for improving eating habits in children from low-income families (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Chicago, IL, USA) (HRI '18), pp. 361-362. , https://doi.org/10.1145/3173386.3177828, Association for Computing Machinery, New York, NY, USA; (2019) IEEE Spectrum., , https://robots.ieee.org/robots/starship/; Troisi, J.D., Gabriel, S., Chicken soup really is good for the soul ""comfort food"" fulfills the need to belong (2011) Psychological Science, 22 (6), pp. 747-753; Yamazaki, A., Masuda, R., Autonomous foods handling by chopsticks for meal assistant robot (2012) ROBOTIK 2012; 7th German Conference on Robotics. VDE, pp. 1-6",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102769591
"Bartlett M.E., Stewart T.C., Thill S.","57208306544;15835430800;36624312800;","Estimating levels of engagement for social human-robot interaction using legendre memory units",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447193","362","366",,,"10.1145/3434074.3447193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102767962&doi=10.1145%2f3434074.3447193&partnerID=40&md5=91c49c463896737f8def34102d84fd9f","CRNS, University of Plymouth, Plymouth, United Kingdom; National Research Council of Canada, University of Waterloo Collaboration Centre, Waterloo, ON, Canada; Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands","Bartlett, M.E., CRNS, University of Plymouth, Plymouth, United Kingdom; Stewart, T.C., National Research Council of Canada, University of Waterloo Collaboration Centre, Waterloo, ON, Canada; Thill, S., Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands","In this study, we examine whether the data requirements associated with training a system to recognize multiple 'levels' of an internal state can be reduced by training systems on the 'extremes' in a way that allows them to estimate ""intermediate""classes as falling in-between the trained extremes. Specifically, this study explores whether a novel recurrent neural network, the Legendre Delay Network, added as a pre-processing step to a Multi-Layer Perception, produces an output which can be used to separate an untrained intermediate class of task engagement from the trained extreme classes. The results showed that identifying untrained classes after training on the extremes is feasible, particularly when using the Legendre Delay Network. © 2021 ACM.","Activity recognition; Intensity; Legendre memory units; MLP; Task engagement","Agricultural robots; Man machine systems; Multilayer neural networks; Recurrent neural networks; Data requirements; Delay networks; Internal state; Memory units; Multi-layer perception; Pre-processing step; Social human-robot interactions; Training Systems; Social robots",,,,,,,,,,,,,,,,"Bartlett, M., Hernandez Garcia, D., Thill, S., Belpaeme, T., (2019) Recognizing Human Internal States: A Conceptor-Based Approach, , 2019; Belpaeme, T., Kennedy, J., Baxter, P., Vogt, P., Krahmer, E.E.J., Kopp, S., Bergmann, K., Göksun, T., L2tor-second language tutoring using social robots (2015) Proceedings of the ICSR 2015 WONDER Workshop; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954. , 2018; Blouw, P., Choo, X., Hunsberger, E., Eliasmith, C., Benchmarking keyword spotting efficiency on neuromorphic hardware (2019) Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop, pp. 1-8; Blouw, P., Malik, G., Morcos, B., Voelker, A.R., Eliasmith, C., (2020) Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware, , 2020; Hood, D., Lemaignan, S., Dillenbourg, P., The cowriter project: Teaching a robot how to write (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, p. 269; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization, , 2014; Lemaignan, S., Edmunds, C., Belpaeme, T., (2017) The PInSoRo Dataset, , https://doi.org/10.5281/zenodo.1043507; Lemaignan, S., Edmunds, C.E.R., Senft, E., Belpaeme, T., The pinsoro dataset: Supporting the data-driven study of child-child and child-robot social dynamics (2018) PloS One, 13 (10). , 2018; Ramachandran, A., Huang, C., Scassellati, B., Give me a break! personalized timing strategies to promote learning in robot-child tutoring (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 146-155; Senft, E., Lemaignan, S., Baxter, P.E., Bartlett, M., Belpaeme, T., Teaching robots social autonomy from in situ human guidance (2019) Science Robotics, 4 (35). , 2019; Voelker, A., Kajic, I., Eliasmith, C., Legendre memory units: Continuous-time representation in recurrent neural networks (2019) Advances in Neural Information Processing Systems, pp. 15544-15553; Wang, R., Huang, E., Chandrasekaran, U., Yu, R., (2020) Aortic Pressure Forecasting with Deep Sequence Learning, , 2020",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102767962
"Schmitt J., Hillenbrand A., Kranz P., Kaupp T.","36022926800;57222471462;57222466199;6506976411;","Assisted human-robot-interaction for industrial assembly: Application of spatial augmented reality (SAR) for collaborative assembly tasks",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447127","52","56",,,"10.1145/3434074.3447127","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102766393&doi=10.1145%2f3434074.3447127&partnerID=40&md5=aa918e7cf57b08bcf69fdb9f445b3555","University of Applied Sciences, Würzburg-Schweinfurt, Germany; Institute Digital Engineering","Schmitt, J., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering; Hillenbrand, A., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering; Kranz, P., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering; Kaupp, T., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering","Human-robot collaboration is increasingly applied to industrial assembly sequences due to the growing need for flexibility in manufacturing. Assistant systems are able help to support shared assembly sequences to facilitate collaboration. This contribution shows a workplace installation of a collaborative robot (Cobot) and a spatial augmented reality (SAR) assistant system applied to an assembly use-case. We demonstrate a methodology for the distribution of the assembly sequence between the worker, the Cobot, and the SAR. © 2021 Owner/Author.","Cobot; Industrial assembly; Spatial augmented reality; Worker assistance","Agricultural robots; Augmented reality; Industrial robots; Man machine systems; Assembly sequence; Collaborative assembly; Collaborative robots; Flexibility in manufacturing; Human-robot collaboration; Industrial assemblies; Spatial augmented realities; Social robots",,,,,"Bayerische Forschungsstiftung: AZ-1401-19","The authors gratefully acknowledge the support by the state of Bavaria (Germany). The Bayerische Forschungsstiftung funds this research project InKoMo under the grant no. AZ-1401-19.",,,,,,,,,,"Gattullo, M., Dammacco, L., Francesca Ruospo, F., Evangelista, A., Fiorentino, M., Schmitt, J., Uva, A.-E., Design preferences on industrial augmented reality: A survey with potential technical writers (2020) IEEE International Symposium on Mixed and Augmented Reality (ISMAR), p. 2020. , Nov; Schuster, F., Sponholz, U., Engelmann, B., Schmitt, J., A user study on arassisted industrial assembly (2020) IEEE International Symposium on Mixed and Augmented Reality (ISMAR), , Nov 2020; Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) CIRP Annals, 68 (2), pp. 701-726; Huber, A., Weiss, A., Developing human-robot interaction for an industry 4. 0 robot: How industry workers helped to improve remote-hri to physical-hri (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 137-138. , March; Hegenberg, J., Schimpf, D.W., Fischer, N., Schmidt, L., Fallstudie zur Roboterunterstützung des Menschen bei manueller Montage (2018) Zeitschrift Für Arbeitswissenschaft, 72 (4), pp. 239-251; Kremer, D., Hermann, S., Arbeitsplätze Für Die Mensch-Roboter-Kollaboration Inklusionsförderlich und Wirtschaftlich Gestalten; Funk, M., Kosch, T., Schmidt, A., Interactive worker assistance: Comparing the effects of in-situ projection, head-mounted displays, tablet, and paper instructions (2016) Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 934-939. , September; Makris, S., Pintzos, G., Rentzos, L., Chryssolouris, G., Assembly support using ar technology based on automatic sequence generation (2013) CIRP Annals, 62 (1), pp. 9-12; Tang, A., Owen, C., Biocca, F., Mou, W., Comparative effectiveness of augmented reality in object assembly (2003) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 73-80. , April; Blaga, A., Tamas, L., Augmented reality for digital manufacturing (2018) 2018 26th Mediterranean Conference on Control and Automation (MED), pp. 173-178. , June IEEE; Makris, S., Karagiannis, P., Koukas, S., Matthaiakis, A.S., Augmented reality system for operator support in human-robot collaborative assembly (2016) CIRP Annals, 65 (1), pp. 61-64; Charoenseang, S., Tonggoed, T., Human-robot collaboration with augmented reality (2011) International Conference on Human-Computer Interaction, pp. 93-97. , July Springer, Berlin, Heidelberg; Ender, J., Wagner, J.C., Kunert, G., Guo, F.B., Larek, R., Pawletta, T., Concept of a self-learning workplace cell for worker assistance while collaboration with a robot within the self-adapting-production-planning-system (2019) Informatyka, Automatyka, Pomiary W Gospodarce i Ochronie Srodowiska, 9 (4), pp. 4-9; Vysocky, A.L.E.S., Novak, P.E.T.R., Human-robot collaboration in industry (2016) MM Science Journal, 9 (2), pp. 903-906; Gopinath, V., Ore, F., Grahn, S., Johansen, K., Safety-focussed design of collaborative assembly station with large industrial robots (2018) Procedia Manufacturing, 25, pp. 503-510; Bobka, P., Germann, T., Heyn, J.K., Gerbers, R., Dietrich, F., Dröder, K., Simulation platform to investigate safe operation of human-robot collaboration systems (2016) Procedia CIRP, 44, pp. 187-192; Matheson, E., Minto, R., Zampieri, E.G., Faccio, M., Rosati, G., Human-robot collaboration in manufacturing applications: A review (2019) Robotics, 8 (4), p. 100; Kemény, Z., Beregi, R., Nacsa, J., Kardos, C., Horváth, D., Human-robot collaboration in the mta sztaki learning factory facility at gyr (2018) Procedia Manufacturing, 23, pp. 105-110; Johannsmeier, L., Haddadin, S., A hierarchical human-robot interactionplanning framework for task allocation in collaborative industrial assembly processes (2016) IEEE Robotics and Automation Letters, 2 (1), pp. 41-48; Cesta, A., Orlandini, A., Umbrico, A., Fostering robust human-robot collaboration through ai task planning (2018) Procedia CIRP, 72, pp. 1045-1050; Koppenborg, M., Lungfiel, A., Naber, B., Nickel, P., (2013) Auswirkung von Autonomie und Geschwindigkeit in der Virtuellen Mensch-Roboter-Kollaboration, pp. 417-420. , Gesellschaft für Arbeitswissenschaft (Hrsg.), Chancen durch Arbeits-, Produkt-und Systemgestaltung; Matheson, E., Minto, R., Zampieri, E.G., Faccio, M., Rosati, G., Human-robot collaboration in manufacturing applications: A review (2019) Robotics, 8 (4), p. 100; Lee, E., Barthelmey, A., Reckelkamm, T., Kang, H., Son, J., A study on human-robot collaboration based hybrid assembly system for flexible manufacturing (2019) IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, 1, pp. 4197-4202. , October IEEE; Hegenberg, J., Schimpf, D.W., Fischer, N., Schmidt, L., Fallstudie zur Roboterunterstützung des Menschen bei manueller Montage (2018) Zeitschrift Für Arbeitswissenschaft, 72 (4), pp. 239-251; Kremer, D., Hermann, S., Arbeitsplätze Für Die Mensch-Roboter-Kollaboration Inklusionsförderlich und Wirtschaftlich Gestalten; Funk, M., Kosch, T., Schmidt, A., Interactive worker assistance: Comparing the effects of in-situ projection, head-mounted displays, tablet, and paper instructions (2016) Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 934-939. , September; Schenk, M., Haase, T., Keller, A., Berndt, D., Herausforderungen der Mensch-Technik-Interaktion für die Gestaltung zukünftiger Arbeitssysteme (2016) Megatrend Digitalisierung. Potentiale der Arbeits-und Betriebsorganisation. Neue Ausgabe, pp. 131-140. , Schlick, C. (Hg.). Berlin: Gito (Schriftenreihe der Wissenschaftliche Gesellschaft für Arbeits-und Betriebsorganisation (WGAB) eV, 1); Zäh, M., Wiesbeck, M., Engstler, F., Friesdorf, F., Schubö, A., Stork, S., Wallhoff, F., Kognitive Assistenzsysteme in der manuellen Montage (2007) Wt Werkstattstechnik Online, 97 (9), pp. 644-650; Lupinetti, K., Bonino, B., Giannini, F., Monti, M., Exploring the benefits of the virtual reality technologies for assembly retrieval applications (2019) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 43-59. , June Springer, Cham; Nilsson, S., Johansson, B., Acceptance of augmented reality instructions in a real work setting (2008) CHI'08 Extended Abstracts on Human Factors in Computing Systems, pp. 2025-2032; Hou, L., Wang, X., Truijens, M., Using augmented reality to facilitate piping assembly: An experiment-based evaluation (2015) Journal of Computing in Civil Engineering, 29 (1), p. 05014007; Danielsson, O., Syberfeldt, A., Brewster, R., Wang, L., Assessing instructions in augmented reality for human-robot collaborative assembly by using demonstrators (2017) Procedia CIRP, 63, pp. 89-94","Schmitt, J.; University of Applied SciencesGermany; email: jan.schmitt@fhws.de",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85102766393
"Langholf L., Battefeld D., Henning K., Zatrib R., Groß A., Richter B., Vollmer A.-L., Schneider S.","57222466504;57222470125;57222470515;57222465026;57222471618;57222464964;26424485900;36148533200;","Testing the elaboration likelihood model of persuasion on the acceptance of health regulations in a video human-robot interaction study",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447142","121","125",,,"10.1145/3434074.3447142","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102763477&doi=10.1145%2f3434074.3447142&partnerID=40&md5=c128aecf19d9bc85db95a25ed39aa806","Bielefeld University, Bielefeld, Germany","Langholf, L., Bielefeld University, Bielefeld, Germany; Battefeld, D., Bielefeld University, Bielefeld, Germany; Henning, K., Bielefeld University, Bielefeld, Germany; Zatrib, R., Bielefeld University, Bielefeld, Germany; Groß, A., Bielefeld University, Bielefeld, Germany; Richter, B., Bielefeld University, Bielefeld, Germany; Vollmer, A.-L., Bielefeld University, Bielefeld, Germany; Schneider, S., Bielefeld University, Bielefeld, Germany","Social robots in public places could be a useful tool to guide and remind people to adhere to general regulations (e.g., wearing a mask, keeping social distance during a pandemic). Additionally, robots could be a useful assistive tool for public order offices, such as reducing risks of infection for employees. However, it is uncertain whether and how robots could enhance regulation adherence. To this extent, we present the results of a 2 (distraction: yes/no) between- by 2 (argument: strong/weak) within-mixed HRI video study (n=83) investigating the argument's persuasiveness based on the Elaboration Likelihood Model of persuasion (ELM). Participants watched a video of a robot persuading people to wear a mask using either a strong or a weak argument. As a distraction, participants had to either count the word mask in the video or not. Our results show that the distraction had no influence, while the argument's strength significantly influences the perceived robot's persuasiveness. © 2021 ACM.","Elaboration likelihood model; Healthcare; Persuasive robots; Video hri","Acceptance tests; Agricultural robots; End effectors; Laws and legislation; Man machine systems; Public risks; Wear of materials; Assistive tool; Elaboration likelihood models; Health regulations; Public places; Social distance; Social robots",,,,,,,,,,,,,,,,"Pepper the Humanoid and Programmable Robot | SoftBank Robotics, , https://www.softbankrobotics.com/emea/en/pepper; Chidambaram, V., Chiang, Y., Mutlu, B., Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (Boston, Massachusetts, USA) (HRI '12), pp. 293-300. , https://doi.org/10.1145/2157689.2157798, Association for Computing Machinery, New York, NY, USA; Cooper, J., (2007) Cognitive Dissonance: 50 Years of A Classic Theory, , Sage; Geiskkovitch, D.Y., Cormier, D., Seo, S.H., Young, J.E., Please continue, we need more data: An exploration of obedience to robots (2016) Journal of Human-Robot Interaction, 5 (1), pp. 82-99. , 2016; Ghazali, A.S., Ham, J., Ivanova Barakova, E., Markopoulos, P., Persuasive robots acceptance model (pram): Roles of social responses within the acceptance model of persuasive robots (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-019-00611-1, 2020; (2012) LimeSurvey: An Open Source Survey Tool, , http://www.limesurvey.org, LimeSurvey Project Team/Carsten Schmitz. LimeSurvey Project, Hamburg, Germany; Midden, C., Ham, J., Using negative and positive social feedback from a robotic agent to save energy (2009) Proceedings of the 4th International Conference on Persuasive Technology (Claremont, California, USA) (Persuasive '09), , https://doi.org/10.1145/1541948.1541966, Association for Computing Machinery, New York, NY, USA; Modic, D., Anderson, R., Palomäki, J., We will make you like our research: The development of a susceptibility-to-persuasion scale (2018) PLOS ONE, 13 (3), p. 194119. , https://doi.org/10.1371/journal.pone.0194119, 2018; Orsinger, R.R., (2011) The Role of Reasoning in Constructing A Persuasive Argument; Petty, R., Cacioppo, J., The elaboration likelihood model of persuasion (1986) Advances in Experimental Social Psychology, 19, pp. 123-205. , https://doi.org/10.1016/S0065-2601(08)60214-2, 12 1986; Sherif, M., Hovland, C.I., (1961) Social Judgment: Assimilation and Contrast Effects in Communication and Attitude Change, , 1961; Tsui, K.M., Desai, M., Yanco, H.A., Cramer, H., Kemper, N., Using the ""negative attitude toward robots scale"" with telepresence robots (2010) PerMIS '10: Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop, pp. 243-250. , https://doi.org/10.1145/2377576.2377621, 2010; Winkle, K., Lemaignan, S., Caleb-Solly, P., Leonards, U., Turton, A., Bremner, P., Effective persuasion strategies for socially assistive robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 277-285. , https://doi.org/10.1109/HRI.2019.8673313, IEEE, Daegu, Korea (South)",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102763477
"Choi B.J., Park J., Park C.H.","57222469899;57192667984;36080995200;","Formal verification for human-robot interaction in medical environments",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447155","181","185",,,"10.1145/3434074.3447155","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102761207&doi=10.1145%2f3434074.3447155&partnerID=40&md5=e8b98c59ac8302d4c7b7f8f09188f8b8","Potomac School, McLean, VA, United States; George Washington University, Washington, DC, United States","Choi, B.J., Potomac School, McLean, VA, United States; Park, J., George Washington University, Washington, DC, United States; Park, C.H., George Washington University, Washington, DC, United States","We present a formal verification method that provides a model-based approach to human-robot interaction (HRI) in medical settings by utilizing linear temporal logic (LTL). We define high-level HRI procedures with an LTL-based framework to create algorithmically sound robots that can function independently in dynamic HRI environments. Our approach's theoretical infallibility confers particular advantages for medical robots, where safety and informative communications are crucial. In order to establish the viability of our proposed method, and with the ongoing COVID-19 pandemic in mind, we developed an LTL knowledge base for a medical robot tasked with HRI-intensive roles of patient reception and triage. We designed robotic simulations based on our LTL architecture to test our approach, employing randomized inputs to generate unpredictable HRI environments. We then conducted formal verification via an automata-theoretic approach by evaluating our simulated robot against generalized Büchi automata. We hope our LTL-based approach can enable future achievements in HRI. © 2021 ACM.","Formal verification; Human-robot interaction; Linear temporal logic; Robotic medical receptionist","Agricultural robots; Automata theory; Formal verification; Knowledge based systems; Man machine systems; Medical robotics; Temporal logic; Automata-theoretic approach; Formal verification methods; Human robot Interaction (HRI); Informative communication; Linear temporal logic; Medical settings; Model based approach; Robotic simulation; Human robot interaction",,,,,"National Science Foundation, NSF: 1846658","This research is partially funded by the National Science Foundation (NSF) under the grant #1846658.",,,,,,,,,,"Alimohamadi, Y., Sepandi, M., Taghdir, M., Hosamirudsari, H., Determine the most common clinical symptoms in COVID-19 patients: A systematic review and meta-analysis (2020) Journal of Preventive Medicine and Hygiene, 61 (3), p. E304. , 2020; Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F., Safer-hrc: Safety analysis through formal verification in human-robot collaboration (2016) International Conference on Computer Safety, Reliability, and Security, pp. 283-295. , Springer; Bhatia, A., Kavraki, L.E., Vardi, M.Y., Sampling-based motion planning with temporal goals (2010) 2010 IEEE International Conference on Robotics and Automation. IEEE, pp. 2689-2696; Bordini, R.H., Fisher, M., Sierhuis, M., Formal verification of human-robot teamwork (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 267-268; Fainekos, G.E., Kress-Gazit, H., Pappas, G.J., Temporal logic motion planning for mobile robots (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, pp. 2020-2025; Finucane, C., Jing, G., Kress-Gazit, H., Ltlmop: Experimenting with language, temporal logic and robot control (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 1988-1993; Karaman, S., Frazzoli, E., Complex mission optimization for multiple-uavs using linear temporal logic (2008) 2008 American Control Conference. IEEE, pp. 2003-2009; Hameed Khan, Z., Siddique, A., Won Lee, C., Robotics utilization for healthcare digitization in global COVID-19 management (2020) International Journal of Environmental Research and Public Health, 17 (11), p. 3819. , 2020; Lestingi, L., Askarpour, M., Bersani, M.M., Rossi, M., Formal verification of human-robot interaction in healthcare scenarios (2020) International Conference on Software Engineering and Formal Methods, pp. 303-324. , Springer; Palmieri, J.J., Stern, T.A., Lies in the doctor-patient relationship (2009) Primary Care Companion to the Journal of Clinical Psychiatry, 11 (4), p. 163. , 2009; Pnueli, A., The temporal logic of programs (1977) 18th Annual Symposium on Foundations of Computer Science (Sfcs 1977), pp. 46-57; Porfirio, D., Sauppé, A., Albarghouthi, A., Mutlu, B., Authoring and verifying human-robot interactions (2018) Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pp. 75-86; Porfirio, D., Sauppé, A., Albarghouthi, A., Mutlu, B., Computational tools for human-robot interaction design (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 733-735; Rozier, K.Y., Linear temporal logic symbolic model checking (2011) Computer Science Review, 5 (2), pp. 163-203. , 2011; Vogel, L., (2019) Why Do Patients Often Lie to Their Doctors?; Webster, M., Salem, M., Dixon, C., Fisher, M., Dautenhahn, K., (2014) Formal Verification of An Autonomous Personal Robotic Assistant, , 2014; Xu, N., Li, J., Niu, Y., Shen, L., An ltl-based motion and action dynamic planning method for autonomous robot (2016) IFAC-PapersOnLine, 49 (5), pp. 91-96. , 2016",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102761207
"Fischer K., Naik L., Langedijk R.M., Baumann T., Jelínek M., Palinko O.","24491633100;57193054899;57208909016;36141006400;57222463186;35099082400;","Initiating human-robot interactions using incremental speech adaptation",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447205","421","425",,1,"10.1145/3434074.3447205","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102760788&doi=10.1145%2f3434074.3447205&partnerID=40&md5=dd20f02b010979d74afc799b0d565144","Department of Design and Communication, University of Southern Denmark, Sonderborg, Denmark; Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark; Natural Language Systems, University of Hamburg, Germany","Fischer, K., Department of Design and Communication, University of Southern Denmark, Sonderborg, Denmark; Naik, L., Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark; Langedijk, R.M., Department of Design and Communication, University of Southern Denmark, Sonderborg, Denmark; Baumann, T., Natural Language Systems, University of Hamburg, Germany; Jelínek, M., Department of Design and Communication, University of Southern Denmark, Sonderborg, Denmark; Palinko, O., Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark","In this paper, we present a study in which a robot initiates interactions with people passing by in an in-the-wild scenario. The robot adapts the loudness of its voice dynamically to the distance of the respective person approached, thus indicating who it is talking to. It furthermore tracks people based on information on body orientation and eye gaze and adapts the text produced based on people's distance autonomously. Our study shows that the adaptation of the loudness of its voice is perceived as personalization by the participants and that the likelihood that they stop by and interact with the robot increases when the robot incrementally adjusts its behavior. © 2021 ACM.","Gaze; Incrementality; Initiating interaction; Personalization; Voice","Agricultural robots; Man machine systems; Eye-gaze; On-body; Personalizations; Human robot interaction",,,,,"Innovationsfonden, IFD","This project was funded by the Innovation Fund Denmark.",,,,,,,,,,"Admoni, H., Bank, C., Tan, J., Toneva, M., Scassellati, B., Robot gaze does not reflexively cue human attention (2011) Proceedings of the Cognitive Science Society, 33; Alku, P., Vintturi, J., Vilkman, E., Measuring the effect of fundamental frequency raising as a strategy for increasing vocal intensity in soft, normal and loud phonation (2002) Speech Communication, 38 (3), pp. 321-334. , https://doi.org/10.1016/S0167-6393(01)00072-3, 2002; Baumann, T., Lindner, F., Incremental speech production for polite and natural personal-space intrusion (2015) Social Robotics, Proceedings of the 7th International Conference, ICSR 2015, 9388, pp. 72-82. , https://doi.org/10.1007/978-3-319-25554-5_8, Springer, Paris, France LNAI; Baumann, T., Schlangen, D., Inpro-iss: A component for just-in-time incremental speech synthesis (2012) Procs of ACL 2012 System Demos, , (Jeju, Korea) urn: nbn: de: 0070-pub-25145619; Carlmeyer, B., Schlangen, D., Wrede, B., Look at me!: Self-interruptions as attention booster? (2016) Proceedings of the Fourth International Conference on Human Agent Interaction. ACM, pp. 221-224; Chromik, M., Carlmeyer, B., Wrede, B., Ready for the next step?: Investigating the effect of incremental information presentation in an object fetching task (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 95-96; Clark, H.H., Communal lexicons (1998) Context in Language Learning and Language Understanding, pp. 63-87. , K. Malmkjaer and J. Williams (Eds.) Cambridge: Cambridge University Press; Colledanchise, M., Ögren, P., How behavior trees modularize hybrid control systems and generalize sequential behavior compositions, the subsumption architecture, and decision trees (2016) IEEE Transactions on Robotics, 33 (2), pp. 372-389. , 2016; Fischer, K., (2016) Designing Speech for A Recipient, , Amsterdam: John Benjamins; Fischer, K., Soto, B., Pantofaru, C., Takayama, L., Initiating interactions in order to get help: Effects of social framing on people's responses to robots' requests for assistance (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 999-1005; Fischer, K., Yang, S., Mok, B., Maheshwari, R., Sirkin, D., Ju, W., Initiating interactions and negotiating approach: A robotic trash can in the field (2015) AAAI Symposium on Turn-taking and Coordination in Human-Machine Interaction, pp. 10-16. , AAAI Press; Frobenius, M., Audience design in monologues: How vloggers involve their viewers (2014) Journal of Pragmatics, 72, pp. 59-72. , 2014; Gehle, R., Pitsch, K., Dankert, T., Wrede, S., How to open an interaction between robot and museum visitor? strategies to establish a focused encounter in hri (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 187-195; Christian Jensen, L., Melissa Langedijk, R., Fischer, K., Understanding the perception of incremental robot response in human-robot interaction (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 41-47; Kristian Juel, W., Haarslev, F., Krüger, N., Bodenhagen, L., An integrated object detection and tracking framework for mobile robots (2020) Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics-Volume 1: ICINCO. SCITEPRESS Digital Library, pp. 513-520; Juel, W.K., Haarslev, F., Ramírez, E.R., Marchetti, E., Fischer, K., Shaikh, D., Manoonpong, P., Krüger, N., Smooth robot: Design for a novel modular welfare robot (2020) Journal of Intelligent & Robotic Systems, 98 (1), pp. 19-37. , 2020; Kato, Y., Kanda, T., Ishiguro, H., May i help you?-design of human-like polite approaching behavior (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 35-42; Pan, Y., Okada, H., Uchiyama, T., Suzuki, K., On the reaction to robot's speech in a hotel public space (2015) International Journal of Social Robotics, 7 (5), pp. 911-920. , 2015; Pitsch, K., Kuzuoka, H., Suzuki, Y., Sussenbach, L., Luff, P., Heath, C., The first five seconds"": Contingent stepwise entry into an interaction as a means to secure sustained engagement in hri (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 985-991; Golam Rashed, M., Das, D., Kobayashi, Y., Kuno, Y., A study on proactive methods for initiating interaction with human by social robots (2018) Asian Journal for Convergence in Technology (AJCT), , 2018; Rottschäfer, S., Buschmeier, H., Van Welbergen, H., Kopp, S., Online lombard-adaptation in incremental speech synthesis (2015) Proceedings of INTERSPEECH 2015, pp. 80-84. , Dresden, Germany; Sacks, H., Notes on methodology (1984) Structures of Social Action: Studies in Conversation Analysis, pp. 21-27. , 1984; Sacks, H., Schegloff, E.A., Jefferson, G., A simplest systematics for the organization of turn-taking for conversation (1974) Language, pp. 696-735. , 1974; Schlangen, D., Skantze, G., A general, abstract model of incremental dialogue processing (2011) Dialogue & Discourse, 2 (1). , 2011; Schulman, R., Articulatory dynamics of loud and normal speech (1989) The Journal of the Acoustical Society of America, 85 (1), pp. 295-312. , https://doi.org/10.1121/1.397737, 1989 arXiv https://doi. org/10. 1121/1. 397737; Shi, C., Satake, S., Kanda, T., Ishiguro, H., A robot that distributes flyers to pedestrians in a shopping mall (2018) International Journal of Social Robotics, 10 (4), pp. 421-437. , 2018; Sidner, C.L., Lee, C., Kidd, C., Lesh, N., Rich, C., Explorations in engagement for humans and robots (2005) Artificial Intelligence, 166 (1-2), pp. 140-164. , 2005; Yang, S., Ka-Jun Mok, B., Sirkin, D., Ive Page, H., Maheshwari, R., Fischer, K., Ju, W., Experiences developing socially acceptable interactions for a robotic trash barrel (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 277-284",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102760788
"Hannibal G.","56512012200;","Focusing on the vulnerabilities of robots through expert interviews for trust in human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447178","288","293",,,"10.1145/3434074.3447178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102759582&doi=10.1145%2f3434074.3447178&partnerID=40&md5=b351c81ed2e7ba96a3bcaf78acc9cbb1","Institute of Visual Computing and Human-Centered Technology, TU Wien, Austria","Hannibal, G., Institute of Visual Computing and Human-Centered Technology, TU Wien, Austria","The practical value of trust in human-robot Interaction (HRI) has been to strength human long-term collaboration and interaction with robots. While much work focuses on determining the various factors influencing trust in HRI, vulnerability as a precondition of trust has not yet been explored from a robot-centered perspective. Based on eight semi-structured interviews with experts, I set out to identify robot vulnerabilities for then to present a systematic overview that resulted in a total of 13 categories grouped into four different themes. In the discussion, I specifically focus on how the experts interpreted the notion of vulnerability as it would relate to robots and how malicious human behavior can be problematic when aiming to ensure mutual trust in HRI. © 2021 ACM.","Concept; Ethics; Expert interviews; Trust; Vulnerability","Agricultural robots; Behavioral research; Man machine systems; Human behaviors; Human robot Interaction (HRI); Mutual trust; Semi structured interviews; Social robots",,,,,"E056-05","Many thanks to Dr. Mateusz Tokarski for transcribing the interviews, Dr. Astrid Weiss for the helpful comments on previous versions, and the reviewers for their feedback. This work was supported by the Trust Robot Doctoral College (E056-05) at TU Wien.",,,,,,,,,,"Baier, A., Trust and antitrust (1986) Ethics, 96 (2), pp. 231-260. , 1986; Booth, S., Tompkin, J., Pfister, H., Waldo, J., Gajos, K., Nagpal, R., Piggybacking robots: Human-robot overtrust in university dormitory security (2017) Proceedings of the 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI '17), pp. 426-434. , ACM Press, Vienna, Austria; Braun, V., Clarke, V., Hayfield, N., Terry, G., Thematic analysis (2019) Handbook of Research Methods in Health Social Sciences, 48, pp. 843-860. , P. Liamputtong (Ed.) 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(2018) Ethics and Information Technology, 20 (2), pp. 87-99. , 2018; Hancock, P.A., Kessler, T.T., Kaplan, A.D., Brill, J.C., Szalma, J.L., Evolving trust in robots: Specification through sequential and comparative meta-analyses (2020) Human Factors, pp. 1-34. , https://doi.org/10.1177/0018720820922080; Hoffman, R.R., Cognition, M., Lee, J.D., Madison, W., Trust in automation (2013) IEEE Intelligent Systems, 28 (1), pp. 84-88. , 2013; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Frontiers in Psychology, 9 (861). , https://doi.org/10.3389/FPSYG.2018.00861, 2018; Ishak, D., Nathan-Roberts, D., Analysis of elderly human-robot team trust models (2015) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59, pp. 65-69. , SAGE Publications, Los Angeles (CA), USA; Kaniarasu, P., Steinfeld, A.M., Effects of blame on trust in human robot interaction (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 850-855. , IEEE, Edinburgh, UK; Kwon, M., Jung, M.F., Knepper, R.A., Human expectations of social robots (2016) Proceedings of the 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI'16), pp. 463-464. , IEEE, Christchurch, New Zealand; Latour, B., (1993) We Have Never Been Modern, p. 168. , Harvard University Press, Cambridge, MA; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors: The Journal of the Human Factors and Ergonomics Society, 46 (1), pp. 50-80. , 2004; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations of Trusted Autonomy (Studies in Systems, Decision and Control, 117, pp. 135-159. , Hassein A. 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"Szafr D., Szafr D.A.","57222471065;57222471064;","Connecting human-robot interaction and data visualization",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444683","281","292",,1,"10.1145/3434073.3444683","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102756048&doi=10.1145%2f3434073.3444683&partnerID=40&md5=a08f24fe0d63252e41066b86430b101c","ATLAS Institute and Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States","Szafr, D., ATLAS Institute and Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States; Szafr, D.A., ATLAS Institute and Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States","Human-robot interaction (HRI) research frequently explores how to design interfaces that enable humans to efectively teleoperate and supervise robots. One of the principle goals of such systems is to support data collection, analysis, and human decision making, which requires representing robot data in ways that support fast and accurate analyses by humans. However, the interfaces for these systems do not always use best-practice principles for efectively visualizing data. We present a new framework to scafold reasoning about robot interface design that emphasizes the need to consider data visualization for supporting analysis and decision-making pro-cesses, detail several data visualization best practices relevant to HRI, identify a set of core data tasks that commonly occur in HRI, and highlight several promising opportunities for further synergis-tic activities at the intersection of these two research areas. © 2021 IEEE Computer Society. All rights reserved.","Data Visualization; Human-Robot Interaction (HRI); Information Visualization; InfoVis; Robot Interface Design; VIS","Agricultural robots; Data visualization; Decision making; Machine design; Man machine systems; Visualization; Accurate analysis; Best practices; Data collection; Design interface; Human decision making; Human robot Interaction (HRI); Robot interface; Supporting analysis; Social robots",,,,,"National Science Foundation, NSF: 1764092","This work was supported by the NSF under Award #1764092.",,,,,,,,,,"Adamides, G., Katsanos, C., Constantinou, I., Christou, G., Xenos, M., Hadzilacos, T., Edan, Y., Design and devel-opment of a semi-autonomous agricultural vineyard sprayer: Human-robot interaction aspects (2017) Journal of Field Robotics, 34 (8), pp. 1407-1426; Adams, J.A., Critical considerations for human-robot interface develop-ment (2002) Proceedings of 2002 AAAI Fall Symposium., pp. 1-8; Adams, J.A., Unmanned vehicle situation awareness: A path forward (2007) Human Systems Integration Symposium., pp. 31-89; Albers, D., Correll, M., Gleicher, M., Task-driven evaluation of aggregation in time series visualization (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems., pp. 551-560; Amar, R., Eagan, J., Stasko, J., Low-level components of analytic activity in information visualization (2005) IEEE Symposium on Information Visualization, 2005. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85102756048
"Part J.L., Hernández Garciá D., Yu Y., Gunson N., Dondrup C., Lemon O.","57193651604;56520258500;56991624700;36188108600;56086338900;6603015795;","Towards visual dialogue for human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447278","670","672",,1,"10.1145/3434074.3447278","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102752735&doi=10.1145%2f3434074.3447278&partnerID=40&md5=3c5e65c0c435ab3c36bc4ac22a749898","Heriot-Watt University, Edinburgh, United Kingdom","Part, J.L., Heriot-Watt University, Edinburgh, United Kingdom; Hernández Garciá, D., Heriot-Watt University, Edinburgh, United Kingdom; Yu, Y., Heriot-Watt University, Edinburgh, United Kingdom; Gunson, N., Heriot-Watt University, Edinburgh, United Kingdom; Dondrup, C., Heriot-Watt University, Edinburgh, United Kingdom; Lemon, O., Heriot-Watt University, Edinburgh, United Kingdom","The goal of the EU H2020-ICT funded SPRING project is to develop a socially pertinent robot to carry out tasks in a gerontological healthcare unit. In this context, being able to perceive its environment and have coherent and relevant conversations about the surrounding world is critical. In this paper, we describe current progress towards developing the necessary integrated visual and conversational capabilities for a robot to operate in such environments. Concretely, we introduce an architecture for conversing about objects and other entities present in an environment. The work described in this paper has applications that extend well beyond healthcare and can be used on any robot that requires to interact with its visual and spatial environment in order to be able to perform its duties. © 2021 Owner/Author.","Conversational systems; Robotics; Visual dialogue","Agricultural robots; Health care; Man machine systems; Integrated visual; Spatial environments; Human robot interaction",,,,,"871245","This work has been supported by the European Union’s H2020-ICT Research and Innovation Action SPRING – Grant Agreement No. 871245 (https://spring-h2020.eu/).",,,,,,,,,,"Cercas Curry, A., Papaioannou, I., Suglia, A., Agarwal, S., Shalyminov, I., Xinnuo, X., Dušek, O., Lemon, O., Alana v2: Entertaining and informative open-domain social dialogue using ontologies and entity linking (2018) Proceedings of the 2nd Alexa Prize; Dondrup, C., Papaioannou, I., Lemon, O., (2019) Petri Net Machines for Human-Agent Interaction; Kirillov, A., Girshick, R., He, K., Dollár, P., Panoptic feature pyramid networks (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6399-6408; Lai, K., Bo, L., Fox, D., Unsupervised feature learning for 3d scene labeling (2014) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 3050-3057; Yi Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Lawrence Zitnick, C., Microsoft coco: Common objects in context (2014) Proceedings of the European Conference on Computer Vision (ECCV), pp. 740-755. , Springer Verlag; Wu, Y., Kirillov, A., Massa, F., Lo, W., Girshick, R., (2019), https://github.com/facebookresearch/detectron2",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102752735
"Irfan B., Ramachandran A., Spaulding S., Kalkan S., Parisi G.I., Gunes H.","57194725723;56480082900;56085336700;23027462100;56028608900;7005067251;","Lifelong learning and personalization in long-term human-robot interaction (LEAP-HRI)",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444881","724","727",,,"10.1145/3434074.3444881","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102743925&doi=10.1145%2f3434074.3444881&partnerID=40&md5=10f5cc370949acc9831f97bff9ddfa89","University of Plymouth, Plymouth, United Kingdom; Van Robotics, Columbia, SC, United States; Massachusetts Institute of Technology, Cambridge, MA, United States; Middle East Technical University, Ankara, Turkey; University of Hamburg, Hamburg, Germany; University of Cambridge, Cambridge, United Kingdom","Irfan, B., University of Plymouth, Plymouth, United Kingdom; Ramachandran, A., Van Robotics, Columbia, SC, United States; Spaulding, S., Massachusetts Institute of Technology, Cambridge, MA, United States; Kalkan, S., Middle East Technical University, Ankara, Turkey; Parisi, G.I., University of Hamburg, Hamburg, Germany; Gunes, H., University of Cambridge, Cambridge, United Kingdom","While most of the research in Human-Robot Interaction (HRI) focuses on short-term interactions, long-term interactions require bolder developments and a substantial amount of resources, especially if the robots are deployed in the wild. Robots need to incrementally learn new concepts or abilities in a lifelong fashion to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The ""Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)""Workshop aims to take a leap from the traditional HRI approaches towards addressing the developments and challenges in these areas and create a medium for researchers to share their work in progress, present preliminary results, learn from the experience of invited researchers and discuss relevant topics. The workshop extends the topics covered in the ""Personalization in Long-Term Human-Robot Interaction (PLOT-HRI)""Workshop at the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) and ""Lifelong Learning for Long-term Human-Robot Interaction (LL4LHRI)""Workshop at the 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), and focuses on studies on lifelong learning and adaptivity to users, context, environment, and tasks in long-term interactions in a variety of fields (e.g., education, rehabilitation, elderly care, collaborative tasks, customer-oriented service and companion robots). © 2021 Owner/Author.","Adaptation; Continual learning; Human-robot interaction; Lifelong learning; Long-term interaction; Long-term memory; Personalization; User modeling; User recognition","Agricultural robots; Man machine systems; Social robots; Collaborative tasks; Customer-oriented services; Human robot Interaction (HRI); Interactive communications; Life long learning; Long-term interaction; Personalizations; Work in progress; Educational robots",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R030782/1; Bilim Akademisi","Hatice Gunes is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through the ARoEQ Project (EP/R030782/1). S. Kalkan was supported by the BAGEP Award of the Science Academy, Turkey.",,,,,,,,,,"Belpaeme, T., Baxter, P., Read, R., Wood, R., Cuayáhuitl, H., Kiefer, B., Racioppa, S., Humbert, R., Multimodal child-robot interaction: Building social bonds (2013) J. Hum.-Robot Interact., 1 (2), pp. 33-53. , Jan 2013; Chen, Z., Liu, B., Lifelong machine learning (2016) Synthesis Lectures on Artificial Intelligence and Machine Learning, 10 (3), pp. 1-145. , 2016; Churamani, N., Gunes, H., Clifer: Continual learning with imagination for facial expression recognition (2020) 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 693-699; Churamani, N., Kalkan, S., Gunes, H., Continual learning for affective robotics: Why, what and how? (2020) 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, pp. 425-431. , https://doi.org/10.1109/RO-MAN47096.2020.9223564, Naples, Italy, August 31-September 4, 2020; Clabaugh, C., Mahajan, K., Jain, S., Pakkar, R., Becerra, D., Shi, Z., Deng, E., Mataric, M., Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders (2019) Frontiers in Robotics and AI, 6, p. 110. , https://doi.org/10.3389/frobt.2019.00110, 2019; Dautenhahn, K., Robots we like to live with ?!-a developmental perspective on a personalized, life-long robot companion (2004) 2004 IEEE International Workshop on Robot and Human Interactive Communication, pp. 17-22; Graaf De, A.M.M., Ben Allouch, S., Klamer, T., Sharing a life with harvey: Exploring the acceptance of and relationship-building with a social robot (2015) Computers in Human Behavior, 43, pp. 1-14. , https://doi.org/10.1016/j.chb.2014.10.030, 2015; Glas, D.F., Wada, K., Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Personal greetings: Personalizing robot utterances based on novelty of observed behavior (2017) International Journal of Social Robotics, 9 (2), pp. 181-198. , 2017; Gordon, G., Spaulding, S., Kory Westlund, J., Joo Lee, J., Plummer, L., Martinez, M., Das, M., Breazeal, C., Affective personalization of a social robot tutor for children's second language skills (2016) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona) (AAAI'16), pp. 3951-3957. , AAAI Press; Irfan, B., Céspedes Gomez, N., Casas, J., Senft, E., Gutiérrez, L.F., Rincon-Roncancio, M., Munera, M., Cifuentes, C.A., Using a personalised socially assistive robot for cardiac rehabilitation: A long-term case study (2020) 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 124-130. , https://doi.org/10.1109/RO-MAN47096.2020.9223491; Irfan, B., Hellou, M., Mazel, A., Belpaeme, T., Challenges of a real-world hri study with non-native english speakers: Can personalisation save the day? (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 272-274. , https://doi.org/10.1145/3371382.3378278, Association for Computing Machinery, New York, NY, USA; Irfan, B., Ramachandran, A., Spaulding, S., Glas, D.F., Leite, I., Lee Koay, K., Personalization in long-term human-robot interaction (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 685-686. , https://doi.org/10.1109/HRI.2019.8673076; Jones, A., Castellano, G., Adaptive robotic tutors that support self-regulated learning: A longer-term investigation with primary school children (2018) International Journal of Social Robotics, 10, pp. 357-370. , https://doi.org/10.1007/s00371-011-0630-7, 2018; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) IEEE Transactions on Robotics, 26 (5), pp. 897-913. , https://doi.org/10.1109/TRO.2010.2062550, Oct 2010; Lee, M.K., Forlizzi, J., Kiesler, S., Rybski, P., Antanitis, J., Savetsila, S., Personalization in hri: A longitudinal field experiment (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 319-326; Leyzberg, D., Spaulding, S., Scassellati, B., Personalizing robot tutors to individuals' learning differences (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction (Bielefeld, Germany) (HRI '14), pp. 423-430. , https://doi.org/10.1145/2559636.2559671, Association for Computing Machinery, New York, NY, USA; Lomonaco, V., Maltoni, D., (2017) CORe50: A New Dataset and Benchmark for Continuous Object Recognition, , 2017 arXiv: arXiv: 1705. 03550v1; Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S., Continual lifelong learning with neural networks: A review (2019) Neural Networks, 113, pp. 54-71. , 2019; Parisi, G.I., Tani, J., Weber, C., Wermter, S., Lifelong learning of humans actions with deep neural network self-organization (2017) Neural Networks, 96, pp. 137-149. , 2017; Pentina, A., Lampert, C.H., Lifelong learning with non-iid tasks (2015) Advances in Neural Information Processing Systems, pp. 1540-1548; Ramachandran, A., Huang, C., Scassellati, B., Give me a break! personalized timing strategies to promote learning in robot-child tutoring (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (Vienna, Austria) (HRI '17), pp. 146-155. , https://doi.org/10.1145/2909824.3020209, Association for Computing Machinery, New York, NY, USA; Ring, M., (1994) Continual Learning in Reinforcement Environments, p. 78712. , Ph. D. Dissertation. University of Texas at Austin Austin, Texas; Rudovic, O., Lee, J., Dai, M., Schuller, B., Picard, R.W., Personalized machine learning for robot perception of affect and engagement in autism therapy (2018) Science Robotics, 3 (19). , https://doi.org/10.1126/scirobotics.aao6760, 2018; Saunders, J., Syrdal, D.S., Koay, K.L., Burke, N., Dautenhahn, K., Teach me-show me-end-user personalization of a smart home and companion robot (2016) IEEE Transactions on Human-Machine Systems, 46 (1), pp. 27-40. , https://doi.org/10.1109/THMS.2015.2445105, Feb 2016; Scassellati, B., Boccanfuso, L., Huang, C., Mademtzi, M., Qin, M., Salomons, N., Ventola, P., Shic, F., Improving social skills in children with asd using a long-term, in-home social robot (2018) Science Robotics, 3 (21). , https://doi.org/10.1126/scirobotics.aat7544, 2018; Sung, J., Grinter, R.E., Christensen, H.I., Pimp my roomba"": Designing for personalization (2009) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI '09), pp. 193-196. , https://doi.org/10.1145/1518701.1518732, Association for Computing Machinery, New York, NY, USA; Tapus, A., Tapus, C., Mataric, M., Long term learning and online robot behavior adaptation for individuals with physical and cognitive impairments (2010) Field and Service Robotics, pp. 389-398. , Andrew Howard, Karl Iagnemma, and Alonzo Kelly (Eds.) Springer Berlin Heidelberg, Berlin, Heidelberg; Thrun, S., Mitchell, T.M., Lifelong robot learning (1995) Robotics and Autonomous Systems, 15 (1-2), pp. 25-46. , 1995; Tjomsland, J., Kalkan, S., Gunes, H., (2020) Mind Your Manners! A Dataset and A Continual Learning Approach for Assessing Social Appropriateness of Robot Actions, , https://arxiv.org/abs/2007.12506, CoRR abs/2007. 12506 2020",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102743925
"Savery R.","57190783994;","Machine learning driven musical improvisation for mechanomorphic human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446351","559","561",,2,"10.1145/3434074.3446351","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102742995&doi=10.1145%2f3434074.3446351&partnerID=40&md5=792b96c5869a99b706e7b2873b0c3f17","Georgia Tech Center for Music Technology, Atlanta, GA, United States","Savery, R., Georgia Tech Center for Music Technology, Atlanta, GA, United States","As industrial robots and social robots become prevalent in commercial and home settings it is crucial to improve forms of communication with human collaborators and companions. In this work, I describe the use of musical improvisation to generate emotional musical prosody for improved human-robot interaction. This aims to develop a canny approach, where robots perform in a mechanomorphic manner improving collaboration opportunities with humans. I have currently collected a new 12-hour dataset and developed a Conditional Variational Autoencoder to generate new phrases. Generations have then been used to compare the impact of prosody on anthropomorphism, animacy, likeability, perceived intelligence, and trust. Future work will incorporate prosody into groups of robots and humans, using personality to drive emotional decisions and emotion contagion. © 2021 Owner/Author.","Audio; Emotion; Music; Prosody; Robotics; Sound","Agricultural robots; Industrial robots; Machine learning; Man machine systems; Animacy; Auto encoders; Likeability; Social robots",,,,,,,,,,,,,,,,"Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Bates, J., The role of emotion in believable agents (1994) Commun. ACM, 37 (7), pp. 122-125. , https://doi.org/10.1145/176789.176803, July 1994; Ellis Benson, B., (2003) The Improvisation of Musical Dialogue: A Phenomenology of Music, , Cambridge University Press; Bethel, C.L., Murphy, R.R., Review of human studies methods in hri and recommendations (2010) International Journal of Social Robotics, 2 (4), pp. 347-359. , 2010; Breazeal, C., Aryananda, L., Recognition of affective communicative intent in robot-directed speech (2002) Autonomous Robots, 12 (1), pp. 83-104. , 2002; Briot, J., Hadjeres, G., Pachet, F., (2017) Deep Learning Techniques for Music Generation-a Survey, , 2017; Crumpton, J., Bethel, C.L., A survey of using vocal prosody to convey emotion in robot speech (2016) International Journal of Social Robotics, 8 (2), pp. 271-285. , 2016; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may i serve you? A robot companion approaching a seated person in a helping context (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction (Salt Lake City, Utah, USA) (HRI '06), pp. 172-179. , https://doi.org/10.1145/1121241.1121272, Association for Computing Machinery, New York, NY, USA; Ferreira, L., Whitehead, J., Learning to generate music with sentiment (2019) ISMIR, pp. 384-390; Hausen, M., Torppa, R., Salmela, V.R., Vainio, M., Särkämö, T., Music and speech prosody: A common rhythm (2013) Frontiers in Psychology, 4, p. 566. , 2013; Marie Higgins, K., (2011) The Music of Our Lives, , 2011; Jones, R., Communication in the real world: An introduction to communication studies (2013) The Saylor Foundation; Mateas, M., (1999) Artificial Intelligence Today, pp. 297-328. , http://dl.acm.org/citation.cfm?id=1805750.1805762, Springer-Verlag, Berlin, Heidelberg, Chapter An Oz-centric Review of Interactive Drama and Believable Agents; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35. , 2015; Moore, R.K., Appropriate voices for artefacts: Some key insights (2017) 1st International Workshop on Vocal Interactivity In-and-between Humans, Animals and Robots; Moore, R.K., Is spoken language all-or-nothing? (2017) Implications for Future Speech-based Human-machine Interaction. In Dialogues with Social Robots, pp. 281-291. , Springer; Norberto Pires, J., Azar, A.S., Advances in robotics for additive/hybrid manufacturing: Robot control, speech interface and path planning (2018) Industrial Robot: An International Journal, , 2018; Sacharin, V., Schlegel, K., Scherer, K.R., (2012) Geneva Emotion Wheel Rating Study (Report), , Geneva, Switzerland: University of Geneva. Swiss Center for Affective Sciences (2012); Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608. , 2019; Savery, R., Rose, R., Weinberg, G., Establishing human-robot trust through music-driven robotic emotion prosody and gesture (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7; Savery, R., Rose, R., Weinberg, G., Finding shimi's voice: Fostering human-robot communication with music and a NVIDIA jetson tx2 (2019) Proceedings of the 17th Linux Audio Conference, p. 5; Savery, R., Weinberg, G., A survey of robotics and emotion: Classifications and models of emotional interaction (2020) Proceedings of the 29th International Conference on Robot and Human Interactive Communication; Savery, R., Zahray, L., Weinberg, G., Emotional musical prosody for the enhancement of trust in robotic arm communication (2020) Trust, Acceptance and Social Cues in Human-Robot Interaction, RO-MAN 2020; Savery, R., Zahray, L., Weinberg, G., Prosodycvae: A conditional convolutionalvariational autoencoder for real-timeemotional music prosody generation (2020) 2020 Joint Conference on AI Music Creativity. CSMC+MUME; Schaefer, K.E., Measuring trust in human robot interactions: Development of the (2016) Trust Perception Scale-HRI, pp. 191-218. , https://doi.org/10.1007/978-1-4899-7668-0_10, Springer US, Boston, MA; Sloboda, J., Music: Where cognition and emotion meet (1999) Conference Proceedings: Opening the Umbrella; An Encompassing View of Music Education; Australian Society for Music Education, XII National Conference, p. 175. , University of Sydney, NSW, Australia 09-13 July Australian Society for Music Education; Sturm, B.L., Felipe Santos, J., Ben-Tal, O., Korshunova, I., (2016) Music Transcription Modelling and Composition Using Deep Learning, , 2016; Forde Thompson, W., Glenn Schellenberg, E., Husain, G., Decoding speech prosody: Do music lessons help? (2004) Emotion, 4 (1), p. 46. , 2004; Wennerstrom, A., (2001) The Music of Everyday Speech: Prosody and Discourse Analysis, , Oxford University Press; Wu, J., Hu, C., Wang, Y., Hu, X., Zhu, J., A hierarchical recurrent neural network for symbolic melody generation (2019) IEEE Transactions on Cybernetics, 50 (6), pp. 2749-2757. , 2019; Zhao, K., Li, S., Cai, J., Wang, H., Wang, J., An emotional symbolic music generation system based on lstm networks (2019) 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 2039-2043","Savery, R.; Georgia Tech Center for Music TechnologyUnited States; email: rsavery3@gatech.edu",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102742995
"Friedman N., Love K., Bremers A., Parry A.J., Lc R., Amgalan B., Liu J., Ju W.","57215417038;57222463840;57222471955;57222463816;57215536959;57222466975;57202049558;7005898423;","Designing functional clothing for human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444870","703","705",,,"10.1145/3434074.3444870","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102742936&doi=10.1145%2f3434074.3444870&partnerID=40&md5=af276c9546dd0907f4e0dce732a5a655","Cornell Tech, New York City, NY, United States; New York University, New York, NY, United States; Bay Area Artist Collective, Mixed Baggage, Los Angeles, CA, United States; City University of Hong Kong, Hong Kong, Hong Kong; Parsons School of Design, New York, NY, United States; Cornell University, Ithaca, NY, United States","Friedman, N., Cornell Tech, New York City, NY, United States; Love, K., New York University, New York, NY, United States; Bremers, A., Cornell Tech, New York City, NY, United States; Parry, A.J., Bay Area Artist Collective, Mixed Baggage, Los Angeles, CA, United States; Lc, R., City University of Hong Kong, Hong Kong, Hong Kong; Amgalan, B., Parsons School of Design, New York, NY, United States; Liu, J., Cornell University, Ithaca, NY, United States; Ju, W., Cornell Tech, New York City, NY, United States","We believe that we can design robot clothes to help robots become better robots-help them to be useful in a wider array of contexts, or to better adapt or function in the contexts they are already in. We propose that robot clothing should avoid mere mimicry of human apparel, and instead be motivated by what robots need. While we have seen robots dressed in clothes in the last few decades, we believe that robot clothes can be designed with thoughtful intention and should be studied as its own field. In this workshop, we explore this new area within human robot interaction by bringing together HRI researchers, designers, fashion and costume designers, and artists. We will focus on potential functions of robot clothes, discuss potential trends, and design clothes for robots together in an interactive prototyping session. Through this workshop, we hope to build a community of people who will push forward the field of robot clothing design. © 2021 Owner/Author.","Design; Fashion; Human-robot interaction","Agricultural robots; Clothes; Machine design; Man machine systems; Can design; Clothing design; Interactive prototyping; On potentials; Push forwards; Human robot interaction",,,,,,,,,,,,,,,,"(2014) Big Hero, p. 6; (2020) Robots: Your Guide to the World of Robotics: Sophia, , https://robots.ieee.org/robots/sophia/, Accessed: 2020-09-10",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102742936
[No author name available],[No author id available],"HRI 2021 - Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",755,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102741405&partnerID=40&md5=df0652de36636aca7d61d69bcc324feb",,"","The proceedings contain 181 papers. The topics discussed include: sex robots in care: setting the stage for a discussion on the potential use of sexual robot technologies for persons with disabilities; robots as moral advisors: the effects of deontological, virtue, and Confucian role ethics on encouraging honest behavior; boosting robot credibility and challenging gender norms in responding to abusive behavior: a case for feminist robots; who wants to grant robots rights?; infants respond to robot’s need for assistance in pursuing action-based goals; assisted human-robot-interaction for industrial assembly: application of spatial augmented reality (SAR) for collaborative assembly tasks; looks can permit deceiving: how reward or punishment decisions are influenced by robot embodiment; perceptual effects of ambient sound on an artificial agent’s rate of speech; the haunted desk: exploring non-volitional behavior change with everyday robotics; and a user-centered agile approach to the development of a real-world social robot application for reception areas.",,,,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85102741405
"Sandoval E.B., Shi J., Cruz-Sandoval D., Li B., Cappuccio M., Rosenbaum S.","37057915200;57222464111;57193760366;8576468400;56524371200;45761309400;","A prototype of a robot memory game: Exploring the technical limitations of human-robot interaction in a playful context",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447158","195","199",,,"10.1145/3434074.3447158","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102739105&doi=10.1145%2f3434074.3447158&partnerID=40&md5=57eb2a6a477591b481118ce2206579d9","University of New South Wales, School of Arts and Design, Sydney, NSW, Australia; Independent Researcher, Sydney, NSW, Australia; CICESE, Ensenda BC, Mexico; University of New South Wales, School of Minerals and Energy Resources, Sydney, NSW, Australia; University of New South Wales, School of Engineering and Information Technology, Canberra, ACT, Australia; University of New South Wales, School of Psychiatry, Sydney, NSW, Australia","Sandoval, E.B., University of New South Wales, School of Arts and Design, Sydney, NSW, Australia; Shi, J., Independent Researcher, Sydney, NSW, Australia; Cruz-Sandoval, D., CICESE, Ensenda BC, Mexico; Li, B., University of New South Wales, School of Minerals and Energy Resources, Sydney, NSW, Australia; Cappuccio, M., University of New South Wales, School of Engineering and Information Technology, Canberra, ACT, Australia; Rosenbaum, S., University of New South Wales, School of Psychiatry, Sydney, NSW, Australia","We explored the feasibility and limitations of designing and developing a Robot-Human interactive board game known as memory, a turn-based game of matching card pairs. Our analysis of this case study suggests significant limitations and further interactive improvements before exposing the prototype to the users. In terms of technical limitations, the variability of light and the lack of sharp camera imaging makes it challenging to identify cards uniquely. Open MANIPULATOR-X's (robotic arm used) dexterity is limited and could not mimic the interaction of card flipping. For interactive design terms, we analysed the robot morphology, expressiveness and modifications in the cards. We suggest running comparative studies with well-known humanoid robots and humans. This project is the initial step to developing more engaging and interactive games between Humans and Robots. Future experiments aim to explore the emotional, physical and mental benefit users could obtain from playing games with robots. © 2021 ACM.","Memory game; Robot expressiveness; Robot games; Robot morphology","Agricultural robots; Anthropomorphic robots; Industrial manipulators; Machine design; Man machine systems; Manipulators; Board games; Comparative studies; Humanoid robot; Interactive design; Interactive games; Robot morphology; Technical limitations; Human robot interaction",,,,,,,,,,,,,,,,"Adams, E., (2010) Fundamentals of Game Design, , (2 edition ed.) New Riders; Anderson, L., (2010) Games Bible, , Workman Publishing, New York, NY; Brooks, A.G., Gray, J., Hoffman, G., Lockerd, A., Lee, H., Breazeal, C., Robot's play: Interactive games with sociable machines (2004) Computers in Entertainment (CIE), 2 (3), p. 10. , 2004; Caillois, R., (1958) Les Jeux et les Hommes, , 1958; Cappuccio, M.L., Sandoval, E.B., Mubin, O., Obaid, M., Velonaki, M., Can robots make us better humans? (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-020-00700-6, Oct 2020; Chorianopoulos, K., Giannakos, M., Design principles for serious video games in mathematics education: From theory to practice (2014) International Journal of Serious Games, 1 (3), p. 2014. , https://doi.org/10.17083/ijsg.v1i3.12, Sept; Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., Paiva, A., Just follow the suit! trust in human-robot interactions during card game playing (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 507-512; Correia, F., Alves-Oliveira, P., Ribeiro, T., Melo, F.S., Paiva, A., A social robot as a card game player (2017) Thirteenth Artificial Intelligence and Interactive Digital Entertainment Conference; Crawford, C., Chris crawford on game design (2003) New Riders; De Graaf, M., Ben Allouch, S., Van Dijk, J., Why do they refuse to use my robot?: Reasons for non-use derived from a long-term home study (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 224-233; De Graaf, M.M., Ben Allouch, S., Van Dijk, J.A., Longterm acceptance of social robots in domestic environments: Insights from a user's perspective (2016) 2016 AAAI Spring Symposium Series; De Graaf, A.M.M., Ben Allouch, S., Van Dijk, M.G.J.A., Why would i use this in my home? A model of domestic social robot acceptance (2019) Human-Computer Interaction, 34 (2), pp. 115-173. , https://doi.org/10.1080/07370024.2017.1312406, March 2019; Engelstein, G., (2020) GameTek, , HarperCollins, Sydney; Fischbach, M., Lugrin, J., Brandt, M., Erich Latoschik, M., Zimmerer, C., Lugrin, B., Follow the white robot-a role-playing game with a robot game master (2018) Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '18) International Foundation for Autonomous Agents and Multiagent Systems, pp. 1812-1814. , Richland, SC; Goodrich, M.A., Olsen, D.R., Seven principles of efficient human robot interaction (2003) SMC'03 Conference Proceedings, 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), 4, pp. 3942-3948. , https://doi.org/10.1109/ICSMC.2003.1244504; Jones, L.T., Howden, A., Knighton, M.S., Sims, A., Kittinger, D.L., Hollander, R.E., (1983) Robot Computer Chess Game; Matuszek, C., Mayton, B., Aimi, R., Deisenroth, M.P., Bo, L., Chu, R., Kung, M., Fox, D., Gambit: An autonomous chess-playing robotic system (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 4291-4297. , https://doi.org/10.1109/ICRA.2011.5980528; Ribeiro, T., Leite, I., Kedziersski, J., Oleksy, A., Paiva, A., Expressing emotions on robotic companions with limited facial expression capabilities (2011) InternationalWorkshop on Intelligent Virtual Agents, pp. 466-467. , Springer; Ribeiro, T., Paiva, A., The illusion of robotic life: Principles and practices of animation for robots (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 383-390; Robert, D., Wistorrt, R., Gray, J., Breazeal, C., Exploring mixed reality robot gaming (2010) Proceedings of the Fifth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '11), pp. 125-128. , https://doi.org/10.1145/1935701.1935726, Association for Computing Machinery, New York, NY, USA; Sandoval, E.B., (2016) Reciprocity in Human Robot Interaction, , Ph. D. Dissertation. University of Canterbury; Benítez Sandoval, E., Brandstatter, J., Yalcin, U., Bartneck, C., Robot likeability and reciprocity in human robot interaction: Using ultimatum game to determinate reciprocal likeable robot strategies (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-020-00658-5, June 2020; Benítez Sandoval, E., Brandstetter, J., Bartneck, C., Can a robot bribe a human? The measurement of the negative side of reciprocity in human robot interaction (2016) Human-Robot Interaction (HRI), 2016 11th ACM/IEEE International Conference on; Benítez Sandoval, E., Brandstetter, J., Obaid, M., Bartneck, C., Reciprocity in human-robot interaction: A quantitative approach through the prisoner's dilemma and the ultimatum game (2015) International Journal of Social Robotics, pp. 1-15. , https://doi.org/10.1007/s12369-015-0323-x; Schell, J., (2014) The Art of Game Design: A Book of Lenses, , Second Edition (2 edition ed.) a K Peters/CRC Press, Boca Raton; Salen Tekinbas, K., Zimmerman, E., (2003) Rules of Play: Game Design Fundamentals, , The MIT Press, Cambridge, Mass; Tomitsch, M., Wrigley, C., Borthwick, M., Ahmadpour, N., Frawley, J., Baki Kocaballi, A., Núñez-Pacheco, C., Loke, L., (2018) Design. Think. Make. Break. Repeat.: A Handbook of Methods, , BIS Publishers, Amsterdam; Yanow, D., Schwartz-Shea, P., (2015) Interpretation and Method: Empirical Research Methods and the Interpretive Turn, , Routledge. Google-Books-ID: w_TqBgAAQBAJ",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102739105
"Teh N., Hu S., Soh H.","57222468891;57196120781;56001754300;","A theoretical framework for large-scale human-robot interaction with groups of learning agents",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447220","489","493",,,"10.1145/3434074.3447220","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102737037&doi=10.1145%2f3434074.3447220&partnerID=40&md5=f285b0f4a06c52f48a6088334ba3360d","Dept of Computer Science, National University of Singapore, Singapore","Teh, N., Dept of Computer Science, National University of Singapore, Singapore; Hu, S., Dept of Computer Science, National University of Singapore, Singapore; Soh, H., Dept of Computer Science, National University of Singapore, Singapore","Recent advances in robot capabilities have led to a growing consensus that robots will eventually be deployed at scale across numerous application domains. An important open question is how humans and robots will adapt to one another over time. In this paper, we introduce the model-based Theoretical Human-Robot Scenarios (THuS) framework, capable of elucidating the interactions between large groups of humans and learning robots. We formally establish THuS, and consider its application to a human-robot variant of the n-player coordination game, demonstrating the power of the theoretical framework as a tool to qualitatively understand and quantitatively compare HRI scenarios that involve different agent types. We also discuss the framework's limitations and potential. Our work provides the HRI community with a versatile tool that permits first-cut insights into large-scale HRI scenarios that are too costly or challenging to carry out in simulations or in the real-world. © 2021 ACM.","Game theory; Human-robot interaction; Multi-agent learning","Agricultural robots; Educational robots; End effectors; Intelligent agents; Man machine systems; Hri communities; Human robots; ITS applications; Large groups; Learning agents; Model-based OPC; Theoretical framework; Versatile tools; Human robot interaction",,,,,"National Research Foundation Singapore, NRF: AISG-RP-2019-011","This research is supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2019-011).",,,,,,,,,,"Fischinger, D., Einramhof, P., Papoutsakis, K., Wohlkinger, W., Mayer, P., Panek, P., Hofmann, S., Vincze, M., Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016) Robotics and Autonomous Systems, 75, pp. 60-78. , 2016; Gu, N., Peng, Z., Wang, D., Shi, Y., Wang, T., Antidisturbance coordinated path following control of robotic autonomous surface vehicles: Theory and experiment (2019) IEEE/ASME Transactions on Mechatronics, 24 (5), pp. 2386-2396. , 2019; Hu, S., Leung, C., Leung, H., Soh, H., (2020) The Evolutionary Dynamics of Independent Learning Agents in Population Games, , 2020; Huang, M., Malhamé, R.P., Caines, P.E., Large population stochastic dynamic games: Closed-loop mckean-vlasov systems and the nash certainty equivalence principle (2006) Communications in Information & Systems, 6 (3), pp. 221-252. , 2006; Khalvati, K., Park, S., Mirbagheri, S., Philippe, R., Sestito, M., Dreher, J., Rao, N.R.P., Modeling other minds: Bayesian inference explains human choices in group decision-making (2019) Science Advances, 5 (11). , 2019; Cai Kok, B., Soh, H., Trust in robots: Challenges and opportunities (2020) Current Robotics Reports, 1 (4), pp. 297-309. , https://doi.org/10.1007/s43154-020-00029-y, 2020; Lalmuanawma, S., Hussain, J., Chhakchhuak, L., Applications of machine learning and artificial intelligence for COVID-19 (sars-cov-2) pandemic: A review (2020) Chaos, Solitons & Fractals, 139, p. 110059. , 2020; Lasry, J., Lions, P., Mean field games (2007) Japanese Journal of Mathematics, 2 (1), pp. 229-260. , 2007; Woo Lee, K., Hwang, J., Human-robot interaction as a cooperative game (2008) Trends in Intelligent Systems and Computer Engineering, pp. 91-103. , Springer; Li, Y., Peng Tee, K., Yan, R., Liang Chan, W., Wu, Y., A framework of human-robot coordination based on game theory and policy iteration (2016) IEEE Transactions on Robotics, 32 (6), pp. 1408-1418. , 2016; Liu, Z., Wu, B., Lin, H., Coordinated robot-assisted human crowd evacuation (2018) Proceedings of the 57th IEEE Conference on Decision and Control (CDC), pp. 4481-4486; Nikolaidis, S., Nath, S., Procaccia, A., Srinivasa, S., Game-theoretic modeling of human adaptation in human-robot collaboration (2017) Proceedings of the 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 323-331; Paeng, E., Wu, J., Boerkoel, J., Human-robot trust and cooperation through a game theoretic framework (2016) Proceedings of the 13th AAAI Conference on Artificial Intelligence (AAAI), pp. 4246-4247; Rescorla, M., Bayesian modeling of the mind: From norms to neurons (2021) WIREs Cognitive Science, 12 (1), p. e1540. , 2021; Sandholm, W., Chapter 13-population games and deterministic evolutionary dynamics (2015) Handbook of Game Theory with Economic Applications, 4, pp. 703-778; Shiri, H., Park, J., Bennis, M., (2019) Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach, , 2019; Soh, H., Xie, Y., Chen, M., Hsu, D., Multi-task trust transfer for human-robot interaction (2020) The International Journal of Robotics Research, 39 (2-3), pp. 233-249. , 2020; Sutton, R.S., Barto, A.G., (2018) Reinforcement Learning: An Introduction; Xie, Y., Bodala, I.P., Ong, D.C., Hsu, D., Soh, H., Robot capability and intention in trust-based decisions across tasks (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 39-47",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85102737037
"Rosen E., Groechel T., Walker M.E., Chang C.T., Forde J.Z.","57195424425;57215285175;57201144448;57222472876;57219478857;","Virtual, augmented, and mixed reality for human-robot interaction (VAM-HRI)",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444879","721","723",,,"10.1145/3434074.3444879","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102736758&doi=10.1145%2f3434074.3444879&partnerID=40&md5=015acdc0d591abcb0ab07ba135e5b016","Brown University, Providence, RI, United States; University of Southern California, Los Angeles, CA, United States; University of Colorado Boulder, Boulder, CO, United States","Rosen, E., Brown University, Providence, RI, United States; Groechel, T., University of Southern California, Los Angeles, CA, United States; Walker, M.E., University of Colorado Boulder, Boulder, CO, United States; Chang, C.T., University of Colorado Boulder, Boulder, CO, United States; Forde, J.Z., Brown University, Providence, RI, United States","The 4th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) will bring together HRI, robotics, and mixed reality researchers to address challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of augmented reality interfaces that mediate communication between humans and robots, the investigations of mixed reality interfaces for robot learning, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. Special topics of interest this year include VAM-HRI research during the COVID-19 pandemic as well as the ethical implications of VAM-HRI research. VAM-HRI 2021 will follow on the success of VAM-HRI 2018-20 and advance the cause of this nascent research community. © 2021 Owner/Author.","Augmented reality; Human-robot interaction; Mixed reality; Virtual reality","Agricultural robots; Augmented reality; Educational robots; Machine design; Man machine systems; Robot learning; Social robots; Virtual addresses; Design practice; Ethical implications; Interactive robot; International workshops; Mixed reality interfaces; Reality interface; Research communities; Virtual agent; Mixed reality",,,,,,,,,,,,,,,,"Brooks, C., Szafir, D., (2020) Visualization of Intended Assistance for Acceptance of Shared Control; Teja Chadalavada, R., Andreasson, H., Krug, R., Lilienthal, A.J., That's on my mind! robot to human intention communication through on-board projection on shared floor space (2015) ECMR; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., (2017) Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots, p. 08930. , CoRR, abs/1703; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., Projection-aware task planning and execution for human-in-the-loop operation of robots (2018) IROS; Christensen, H.I., Batzinger, T., Bekris, K., Bohringer, K., Bordogna, J., Bradski, G., Brock, O., Eastman, R., A roadmap for us robotics: From internet to robotics (2009) CCC and CRA; Diaz, C., Walker, M., Albers Szafir, D., Szafir, D., Designing for depth perceptions in augmented reality (2017) Proc. ISMAR; Groechel, T., Shi, Z., Pakkar, R., Mataric, M.J., Using socially expressive mixed reality arms for enhancing low-expressivity robots (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-8; Hedayati, H., Walker, M., Szafir, D., Improving collocated robot teleoperation with augmented reality (2018) Proc. HRI; Hetrick, R., Amerson, N., Kim, B., Rosen, E., Visser De, E.J., Phillips, E., Comparing virtual reality interfaces for the teleoperation of robots (2020) 2020 Systems and Information Engineering Design Symposium (SIEDS), pp. 1-7; Huang, B., Bayazit, D., Ullman, D., Gopalan, N., Tellex, S., Flight, camera, action! using natural language and mixed reality to control a drone (2019) ICRA; Leutert, F., Herrmann, C., Schilling, K., A spatial augmented reality system for intuitive display of robotic data (2013) Proc. HRI; Mahajan, K., Groechel, T.R., Pakkar, R., Lee, H., Cordero, J., Mataric, M.J., Adapting usability metrics for a socially assistive, kinesthetic, mixed reality robot tutoring environment (2020) International Conference on Social Robotics; Rosen, E., Kumar, N., Gopalan, N., Ullman, D., Konidaris, G., Tellex, S., Building plannable representations with mixed reality (2020) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9; Rosen, E., Whitney, D., Fishman, M., Ullman, D., Tellex, S., Mixed reality as a bidirectional communication interface for human-robot interaction (2020) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9; Sato, S., Sakane, S., A human-robot interface using an interactive hand pointer that projects a mark in the real work space (2000) Proc. ICRA; Shen, J., Jin, J., Gans, N., A multi-view camera-projector system for object detection and robot-human feedback (2013) Proc. ICRA; Szafir, D., Mutlu, B., Fong, T., Communication of intent in assistive free flyers (2014) Proceedings of HRI. ACM; Szafir, D., Mutlu, B., Fong, T., Communicating directionality in flying robots (2015) Proceedings of HRI. ACM; Turk, M., Fragoso, V., Computer vision for mobile augmented reality (2015) Mobile Cloud Visual Media Computing.; Walker, M., Hedayati, H., Lee, J., Szafir, D., Communicating robot motion intent with augmented reality (2018) Proc. HRI; Watanabe, A., Ikeda, T., Morales, Y., Shinozawa, K., Miyashita, T., Hagita, N., Communicating robotic navigational intentions (2015) IROS, pp. 5763-5769; Williams, T., Briggs, G., Oosterveld, B., Scheutz, M., Going beyond literal command-based instructions: Extending robotic natural language interaction capabilities (2015) Proceedings of AAAI; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., The 1st international workshop on virtual, augmented, and mixed reality for humanrobot interaction (2018) AI Magazine; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., Virtual, augmented, and mixed reality for human-robot interaction (2018) Comp. HRI; Zhang, Y., Sreedharan, S., Kulkarni, A., Chakraborti, T., Hankui Zhuo, H., Kambhampati, S., Plan explicability and predictability for robot task planning (2017) ICRA. IEEE",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102736758
"Hart J.W., Depalma N., Pryor M.W., Hayes B., Kruusamäe K., Mirsky R., Xiao X.","25031145200;35193719800;7004159251;55638866400;34771687000;57192390008;56742595000;","Exploring applications for autonomous nonverbal human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444882","728","729",,1,"10.1145/3434074.3444882","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102732152&doi=10.1145%2f3434074.3444882&partnerID=40&md5=3ecba1a5a2fe5733b61a6d79c90bcf0c","The University of Texas at Austin, Austin, TX, United States; Facebook AI Research (FAIR), Pittsburgh, PA, United States; The University of Colorado Boulder, Boulder, CO, United States; The University of Tartu, Tartu, Estonia","Hart, J.W., The University of Texas at Austin, Austin, TX, United States; Depalma, N., Facebook AI Research (FAIR), Pittsburgh, PA, United States; Pryor, M.W., The University of Texas at Austin, Austin, TX, United States; Hayes, B., The University of Colorado Boulder, Boulder, CO, United States; Kruusamäe, K., The University of Tartu, Tartu, Estonia; Mirsky, R., The University of Texas at Austin, Austin, TX, United States; Xiao, X., The University of Texas at Austin, Austin, TX, United States","Non-verbal Human-Robot Interaction (nHRI) encompasses the study of the exchange of human-robot gaze, gesture, touch, body language, paralinguistic, facial and affect expression. nHRI has advanced beyond theoretical and computational contributions. Progress has been made through a variety of user studies and laboratory experiments as well as practical efforts such as integration of nonverbal inputs with other HRI modalities including domain specific implementations. This workshop seeks to promote collaboration between two threads of research: experimental nHRI, and application domains that can benefit from its use. The workshop will link researchers working on new approaches to nHRI in the laboratory to applied roboticists who present challenges in specific domains, such as: service robots, field robotics, socially-assistive robotics, and human-robot collaborative work that could benefit from richer nHRI. This workshop will draw participation from diverse areas to evaluate best practices and integration efforts across different research domains. We will target a broad, cross-disciplinary audience, and provide a venue for recent efforts related to multimodal interaction, system integration, data collection, and user studies. © 2021 Owner/Author.","Human-robot interaction; Nonverbal hri","Agricultural robots; Man machine systems; Robotics; Assistive robotics; Collaborative Work; Cross-disciplinary; Laboratory experiments; Multi-Modal Interactions; Non-verbal human; Research domains; System integration; Social robots",,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102732152
"Schmitt J., Kranz P., Hillenbrand A., Kaupp T.","36022926800;57222466199;57222471462;6506976411;","Assisted human-robot-interaction for industrial assembly: Video to assisted human-robot-interaction for industrial assembly",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447276","665","667",,,"10.1145/3434074.3447276","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102726310&doi=10.1145%2f3434074.3447276&partnerID=40&md5=fb29357d519c207110323afc34651eb3","University of Applied Sciences, Würzburg-Schweinfurt, Germany; Institute Digital Engineering","Schmitt, J., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering; Kranz, P., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering; Hillenbrand, A., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering; Kaupp, T., University of Applied Sciences, Würzburg-Schweinfurt, Germany, Institute Digital Engineering","Human-robot collaboration is increasingly applied to industrial assembly sequences due to the growing need for flexibility in manufacturing. Assistant systems are able help to support shared assembly sequences to facilitate collaboration. This contribution shows a workplace installation of a collaborative robot (Cobot) and a spatial augmented reality (SAR) assistant system applied to an assembly use-case. We demonstrate a methodology for the distribution of the assembly sequence between the worker, the Cobot, and the SAR. © 2021 Owner/Author.","Cobot; Industrial assembly; Spatial augmented reality; Worker assistance","Agricultural robots; Augmented reality; Industrial robots; Man machine systems; Assembly sequence; Collaborative robots; Flexibility in manufacturing; Human-robot collaboration; Industrial assemblies; Spatial augmented realities; Social robots",,,,,"Bayerische Forschungsstiftung: AZ-1401-19","The authors gratefully acknowledge the support by the state of Bavaria (Germany). The Bayerische Forschungsstiftung funds this research project InKoMo under the grant no. AZ-1401-19.",,,,,,,,,,"Gattullo, M., Dammacco, L., Francesca Ruospo, F., Evangelista, A., Fiorentino, M., Schmitt, J., Uva, A.-E., Design preferences on industrial augmented reality: A survey with potential technical writers (2020) IEEE International Symposium on Mixed and Augmented Reality (ISMAR), , Nov 2020; Schuster, F., Sponholz, U., Engelmann, B., Schmitt, J., A user study on arassisted industrial assembly (2020) IEEE International Symposium on Mixed and Augmented Reality (ISMAR), , Nov 2020; Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) CIRP Annals, 68 (2), pp. 701-726","Schmitt, J.; University of Applied SciencesGermany; email: jan.schmitt@fhws.de",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102726310
"Kirschner R.J., Burr L., Porzenheim M., Mayer H., Abdolshah S., Haddadin S.","57223928721;57223932406;57223928669;13406394400;37071846300;23008499000;","Involuntary motion in human-robot interaction: Effect of interactive user training on the occurrence of human startle-surprise motion",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419526","28","32",,,"10.1109/ISR50024.2021.9419526","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106486695&doi=10.1109%2fISR50024.2021.9419526&partnerID=40&md5=f5eeff59e98d3bdfb44cd7a4994923e9","Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Munich Center of Technology and Sociology, Technical University Munich, Munich, 80797, Germany","Kirschner, R.J., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Burr, L., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Porzenheim, M., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Mayer, H., Munich Center of Technology and Sociology, Technical University Munich, Munich, 80797, Germany; Abdolshah, S., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Haddadin, S., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany","In human-robot interaction (HRI) high relative velocity between robot and human at collision may lead to severe injury. Human rapid involuntary motion (IM) may therefore be a safety hazard resulting from startle or surprise (StSu). The arousal that may cause such motion can be reduced by habituation effects, indicating that user application training leads to improved safety in HRI. We suggest using an introductory hands-on user training and investigate whether it can influence the occurrence of IM. An exploratory experiment was performed with 21 participants. During the experiment a robot frontally approached the human six times. The experiment was repeated before and after a hands-on training session. We observe whether the participants show cues of StSu at the approach and compare the quantity of these cues occurring before and after hands-on robot introductory training. Our findings show promising results for the hands-on introductory training to positively effect the psychological and physical safety in HRI. © 2021 IEEE.",,"Agricultural robots; Educational robots; Man machine systems; Robotics; Hands-on-trainings; Human robot Interaction (HRI); Relative velocity; Safety hazards; User training; Human robot interaction",,,,,"5140951; 5140953; Horizon 2020 Framework Programme, H2020: 732737, KoBo34, V5ARA202; Bundesministerium für Bildung und Forschung, BMBF: 16SV7985","ACKNOWLEDGMENT The authors would like to thank Jan Schreiber for supporting the experiments and Nico Mansfeld for proof reading. We gratefully acknowledge the funding of the Lighthouse Initiative Geriatronics by StMWi Bayern (Project X, grant no. 5140951) and LongLeif GaPa gGmbH (Project Y, grant no. 5140953), the European Union’s Horizon 2020 research and innovation programme as part of the project ILIAD under grant no. 732737 and the funding of project KoBo34 (Verbundprojektnummer V5ARA202) by the BMBF (grant no. 16SV7985). The support by the Bavarian Institute for Digital Transformation is gratefully acknowledged. Please note that S. Haddadin has a potential conflict of interest as shareholder of Franka Emika GmbH.",,,,,,,,,,"Haddadin, S., Haddadin, S., Khoury, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research., 31 (13), pp. 1578-1602; (2017) Robots and Robotic Devices-Collaborative Robots, , ISO/TS 15066, Beuth-Verlag, Berlin; Dhillon, B.S., (2012) Robot Reliability and Safety, , Springer Science and Business Media; Martin, W.L., Murray, P.S., Bates, P.R., Fear-potentiated startle: A review from an aviation perspective (2015) The International Journal of Avaiation Psychology, 25 (2), pp. 97-107; Hattori, T., Yamada, Y., Mori, S., Okamoto, S., Hara, S., Psychological experiments on avoidance action characteristics for estimating avoidability of harm to eyes from robots (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5400-5405. , Vilamoura; Kirschner, R.J., Burr, L., Mayer, H., Mansfeld, N., Abdolshah, S., Haddadin, S., Towards preventing involuntary human motion in hri: Parameter identification and control concept (2020) , IEEE Transactions on Cognitive and Developmental Systems, , to be published; Grillon, C., Fear-potentiated startle in humans: Effects of anticipatory anxiety on the acoustic blink reflex (1991) Psychophysiology, 28 (5), pp. 588-595; Kulic, D., Croft, E., Anxiety detection during human-robot interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 616-621. , Edmonton, Alta; Haddadin, S., Johannsmeier, L., Becker, M., Schappler, M., Lilge, T., Haddadin, S., Roboterfabrik: A pilot to link and unify German robotics education to match industrial and societal demands (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, p. 375; Share, P., Pender, J., Preparing for a robot future? Social professions, social robotics and the challenges ahead (2018) Irish Journal of Applied Social Studies, 18 (1), p. 4; De Lange, F.P., Heilbron, M., Kok, P., How do expectations shape perception? (2018) Trends in Cognitive Sciences, 22 (9), pp. 764-779; Haddadin, S., Albu-Schffer, A., Hirzinger, G., Requirements for safe robots: Measurements, analysis and new insights (2009) The International Journal of Robotics Research, 28 (11-12), pp. 1507-1527; Burke, M.J., Sarpy, S.A., Smith-Crowe, K., Chan-Serafin, S., Salvador, R.O., Islam, G., Relative effectiveness of worker safety and health training methods (2006) American Journal of Public Health, 96 (2), pp. 315-324. , February 1; Parsons, H.M., Human factors in industrial robot safety (1986) Journal of Occupational Accidents, 8 (1-2), pp. 25-47; Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H., Safe and efficient human-robot collaboration Part I: Estimation of human arm motions (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1993-1999. , Brisbane, QLD; Grillon, C., Baas, J., A review of the modulation of the startle reflex by affective states and its application in psychiatry (2003) Clinical Neurophysiology, 114 (9), pp. 1557-1579; Davis, M., The mammalian startle response (1984) Neural Mechanism of Startle Behavior, pp. 287-351. , R.C. Eaton (ed.), New York: Plenum Press; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139; Ekman, P., Friesen, W.V., Simons, R.C., Is the startle reaction an emotion (1985) Journal of Personality and Social Psychology, 49 (5), pp. 1416-1426; Allwood, J., Cerrato, L., Jokinen, K., Navarretta, C., Paggio, P., The MUMIN coding scheme for the annotation of feedback, turn management and sequencing phenomena (2007) Language Resources and Evaluation, 41 (3-4), pp. 273-287; Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H., ELAN: A professional framework for multimodality research? (2006) Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), pp. 1556-1559",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106486695
"Liu J., Yamada Y., Akiyama Y.","57218350116;35410760400;55516094900;","Calculating the supplied energy for physical human-robot interaction",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419517","157","160",,,"10.1109/ISR50024.2021.9419517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106482164&doi=10.1109%2fISR50024.2021.9419517&partnerID=40&md5=eeb8bdf6dc27fefe2d6cf7b0c21724ac","Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan","Liu, J., Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan; Yamada, Y., Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan; Akiyama, Y., Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan","As the safety of robotics is highly demanded, injury criteria are widely investigated and employed in control strategies because of their guiding roles, in which the energy (density) tolerances are of priority owing to unification of other physical variables for injury criteria. However, the calculation method for the supplied energy of the physical Human-Robot Interaction (pHRI) is not addressed by considering the viscoelasticity of the human soft tissue. This paper presents four collision modes for classifying the pHRI and proposes a generic method for calculating the supplied energy of each collision mode. Consequently, specific calculation instances are given by treating the human soft tissue as a three-element Maxwell model with maximum/allowable compressive displacement. The feasibility and precision of the proposed calculation method are verified by comparing the computational and simulation results. This calculation method is intended to provide a way of directly determining or indirectly converting the allowable velocity or mass of an approaching robot. © 2021 IEEE.","Calculation method; Collision mode; Human soft tissue; Physical Human-Robot Interaction; Supplied energy; Viscoelasticity","Agricultural robots; Man machine systems; Robotics; Tissue; Tissue engineering; Generic method; In-control; Injury criteria; Maxwell models; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical variables; Soft tissue; Human robot interaction",,,,,,,,,,,,,,,,"Haddadin, S., (2013) Towards Safe Robots: Approaching Asimov's 1st Law, 90. , Springer; Versace, J., (1971) A Review of the Severity Index, , tech. rep., SAE Technical Paper; Bicchi, A., Tonietti, G., Fast and"" soft-arm"" tactics [robot arm design] (2004) IEEE Robotics & Automation Magazine, 11 (2), pp. 22-33; Oberer, S., Schraft, R.D., Robot-dummy crash tests for robot safety assessment (2007) Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 2934-2939; Yamada, Y., Umetani, Y., Hirasawa, Y., Proposal of a psychophysiological experiment system applying the reaction of human pupillary dilation to frightening robot motions (1999) IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028), 2, pp. 1052-1057; Alami, R., Albu-Schäffer, A., Bicchi, A., Bischoff, R., Chatila, R., De Luca, A., De Santis, A., Hirzinger, G., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1-16; Povse, B., Koritnik, D., Kamnik, R., Bajd, T., Munih, M., Industrial robot and human operator collision (2010) 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 2663-2668; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schäffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31 (13), pp. 1578-1602; Sugiura, R., Fujikawa, T., Nishikata, R., Nishimoto, T., Soft tissue bruise injury by blunt impact in human-robot interaction-difference of tolerance between chest and extremities (2019) 2019 19th International Conference on Control, Automation and Systems (ICCAS), pp. 792-797; (2016) Ts 15066: 2016: Robots and Robotic Devices-collaborative Robots, , I. ISO Geneva, Switzerland: International Organization for Standardization; ISO-ISO 12100:2010-Safety of machinery-General principles for design-Risk assessment and risk reduction.""; Tadele, T.S., De Vries, T.J., Stramigioli, S., Combining energy and power based safety metrics in controller design for domestic robots (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1209-1214; Raiola, G., Cardenas, C.A., Tadele, T.S., De Vries, T., Stramigioli, S., Development of a safety-and energy-aware impedance controller for collaborative robots (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 1237-1244; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) 2017 17th International Conference on Control, Automation and Systems (ICCAS), pp. 867-872; Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363; Iki, Y., Yamada, Y., Akiyama, Y., Okamoto, S., Niwa, K., Liu, J., Evaluation of contact between a human hand and a robot end tip for designing a dummy (2019) The Proceedings of JSME Annual Conference on Robotics and Mechatronics (Robomec), 2019, pp. 201-208; Fung, Y., (2013) Biomechanics: Mechanical Properties of Living Tissues, , Springer Science & Business Media; Winter, D.A., (2009) Biomechanics and Motor Control of Human Movement, , John Wiley & Sons","Liu, J.; Nagoya University, Japan; email: liu.jian@k.mbox.nagoya-u.ac.jp",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106482164
"Ramadoss V., Sagar K., Ikbal M.S., Zlatanov D., Zoppi M.","57192715243;57192683963;57207710331;6602800066;35838978700;","Modeling and stiffness evaluation of tendon-driven robot for collaborative human-robot interaction",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419387","233","238",,,"10.1109/ISR50024.2021.9419387","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106476640&doi=10.1109%2fISR50024.2021.9419387&partnerID=40&md5=8efbb2a34d7fa512bdaef38ec40a9882","PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy","Ramadoss, V., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Sagar, K., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Ikbal, M.S., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Zlatanov, D., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Zoppi, M., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy","This paper presents a clear and detailed mathematical analysis of the stiffness evaluation of tendon-driven serial kinematic chains which allow single-point and multi-point routing with bundling per rigid link. The kinematic analysis of tendon driven serial chain with cable routing between links, re-routing within links and bundling is presented. Targeting a musculoskeletal two-link system with different tendon arrangements as a case study, the stiffness of each system is evaluated using a stiffness ellipse. The stiffness performance is analyzed to provide a holistic overview of passive stiffness and force production capabilities that have a pivotal role in the rehabilitation paradigm and human robot-interaction. © 2021 IEEE.",,"Agricultural robots; Kinematics; Man machine systems; Redundant manipulators; Robotics; Stiffness; Tendons; Cable routing; Kinematic Analysis; Kinematic chain; Mathematical analysis; Passive stiffness; Production capabilities; Serial chains; Tendon-driven robots; Human robot interaction",,,,,,,,,,,,,,,,"Kino, H., Kikuchi, S., Yahiro, T., Tahara, K., Basic study of biarticular muscle's effect on muscular internal force control based on physiological hypotheses (2009) 2009 IEEE International Conference on Robotics and Automation. IEEE, pp. 4195-4200; Junius, K., Moltedo, M., Cherelle, P., Rodriguez-Guerrero, C., Vanderborght, B., Lefeber, D., Biarticular elements as a contributor to energy efficiency: Biomechanical review and application in bioinspired robotics (2017) Bioinspiration & Biomimetics, 12 (6), p. 061001; Salvucci, V., Kimura, Y., Oh, S., Hori, Y., Biwi: Bi-articularly actuated and wire driven robot arm (2011) 2011 IEEE International Conference on Mechatronics. IEEE, pp. 827-832; Mitobe, K., Togashi, J., Seki, T., Capi, G., Motion generation and stability of the marionette style elastic tendon driven robot arm (2016) 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). IEEE, pp. 40-45; Inouye, J., Valero-Cuevas, F.J., Asymmetric routings with fewer tendons can offer both flexible endpoint stiffness control and high force-production capabilities in robotic fingers (2012) 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, pp. 1273-1280; Nakanishi, Y., Asano, Y., Kozuki, T., Mizoguchi, H., Motegi, Y., Osada, M., Shirai, T., Inaba, M., Design concept of detail musculoskeletal humanoid ""kenshiro""-toward a real human body musculoskeletal simulator (2012) 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). IEEE, pp. 1-6; Lau, D., Oetomo, D., Halgamuge, S.K., Generalized modeling of multilink cable-driven manipulators with arbitrary routing using the cable-routing matrix (2013) IEEE Transactions on Robotics, 29 (5), pp. 1102-1113; Lau, D., Eden, J., Tan, Y., Oetomo, D., Caspr: A comprehensive cable-robot analysis and simulation platform for the research of cabledriven parallel robots (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 3004-3011; Ramadoss, V., Zlatanov, D., Zoppi, M., Kinematic and workspace analysis of minimally routed cable driven open chains (2019) IFToMM World Congress on Mechanism and Machine Science, pp. 2841-2851. , Springer; Eden, J., Song, C., Tan, Y., Oetomo, D., Lau, D., Caspr-ros: A generalised cable robot software in ros for hardware (2018) Cable-Driven Parallel Robots, pp. 50-61. , Springer; Zlatanov, D., Agrawal, S., Gosselin, C.M., Convex cones in screw spaces (2005) Mechanism and Machine Theory, 40 (6), pp. 710-727; Behzadipour, S., Sohi, M.A., Antagonistic stiffness in cabledriven mechanisms (2007) IFToMM World Congress, , Besançon, France, In; Bryson, J.T., Agrawal, S.K., Methodology to identify and analyze optimal cable configurations in the design of cable-driven serial manipulators (2013) ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, p. 69; Sanjeevi, N., Vashista, V., On the stiffness analysis of a cable driven leg exoskeleton (2017) 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, pp. 455-460; Panchal, N., Sanjeevi, N., Vashista, V., Lower limb musculoskeletal stiffness analysis during swing phase as a cable-driven serial chain system (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). IEEE, pp. 934-939; Jeong, H., Cho, Y., Kang, B., Cheong, J., Son, Y., Stiffness characteristics of new modular type antagonistic tendon-driven joint systems (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 5972-5977; Matsutani, Y., Tahara, K., Kino, H., Ochi, H., Stiffness evaluation of a tendon-driven robot with variable joint stiffness mechanisms (2017) 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). IEEE, pp. 213-218; Calles, J.H.L., Ramadoss, V., Zoppi, M., Cannata, G., Molfino, R., Modeling of a cable-based revolute joint using biphasic media variable stiffness actuation (2019) 2019 Third IEEE International Conference on Robotic Computing (IRC). IEEE, pp. 618-623; Ramadoss, V., Lau, D., Zlatanov, D., Zoppi, M., Analysis of planar multilink cable driven robots using internal routing scheme (2020) International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, p. 029. , American Society of Mechanical Engineers 83990; Megaro, V., Knoop, E., Spielberg, A., Levin, D.I., Matusik, W., Gross, M., Thomaszewski, B., Bacher, M., Designing cable-driven actuation networks for kinematic chains and trees (2017) Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, p. 15; Zlatanov, D., Serial kinematic chains with unilateral external force constraints (2011) IFToMM World Congress in Mechanism and Machine Science, , Guanajuato, Mexico; Inouye, J.M., Valero-Cuevas, F.J., Anthropomorphic tendondriven robotic hands can exceed human grasping capabilities following optimization (2014) The International Journal of Robotics Research, 33 (5), pp. 694-705; Alamdari, A., Haghighi, R., Krovi, V., Stiffness modulation in an elastic articulated-cable leg-orthosis emulator: Theory and experiment (2018) IEEE Transactions on Robotics, 34 (5), pp. 1266-1279; Gouttefarde, M., Gosselin, C.M., Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms (2006) IEEE Transactions on Robotics, 22 (3), pp. 434-445; Workspace Analysis of Fully Routed Two-link Tendon-driven Manipulators, , https://youtu.be/u97miAZklc; Modeling and Stiffness Evaluation of Tendon Driven Robot for Collaborative Human-robot Interaction, , https://youtu.be/bdbEHdSaCQA",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106476640
"Higuchi Y.-K., Fujikawa T., Sugiura R., Nishimoto T., Sato F.","57217171311;7102532161;14007993500;7102868497;35620890000;","Development of a porcine thigh finite element model for evaluating the soft-tissue injuries caused by blunt impacts during human-robot interactions",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419540","301","305",,,"10.1109/ISR50024.2021.9419540","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106464141&doi=10.1109%2fISR50024.2021.9419540&partnerID=40&md5=24025c135ef6b08beecc397e8a4f3364","Japan Automobile Research Institute (JARI), 2530, Karima, Tsukuba, Ibaraki, 305-0822, Japan; Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; College of Engineering, Nihon University, Koriyama, 963-8642, Japan","Higuchi, Y.-K., Japan Automobile Research Institute (JARI), 2530, Karima, Tsukuba, Ibaraki, 305-0822, Japan; Fujikawa, T., Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; Sugiura, R., College of Engineering, Nihon University, Koriyama, 963-8642, Japan; Nishimoto, T., College of Engineering, Nihon University, Koriyama, 963-8642, Japan; Sato, F., Japan Automobile Research Institute (JARI), 2530, Karima, Tsukuba, Ibaraki, 305-0822, Japan","The data concerning the tolerance of slight injuries during human-robot interactions is essential for setting necessary safety measures for robots. In this study, to provide a tool for investigating bruise tolerance, a porcine thigh finite element (FE) model was developed based on quasi-static and dynamic compression tests with living porcine subjects. The FE model demonstrated that strain concentrations occurred in the corresponding region, where the haematoxylin-eosin staining showed blood leaks in the harvested soft-tissue specimens from the impacted porcine subjects. Based on this correlation, the maximum principal strain has potential of being a tissue-level bruise injury metric. © 2021 IEEE.",,"Agricultural robots; Compression testing; Finite element method; Man machine systems; Robotics; Tissue; Blunt impacts; Dynamic compression tests; Haematoxylin; Maximum principal strain; Safety measures; Soft-tissue injury; Strain concentration; Tissue levels; Human robot interaction",,,,,,,,,,,,,,,,"Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety 'Kyozon' system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proc. 1995 IEEE Int. Conf. Robot. Autom.; Saito, T., Ikeda, H., Measuring system and analytical method of pain tolerance to mechanical stimulus for safe design of humancollaborative robot (2005) Proc. 4th Int. Conf. Saf. Ind. Autom. Syst.; Muttray, A., Melia, M., Geibler, B., König, J., Letzel, S., (2014) Experimental Assessment of Pain Thresholds in Major Parts of the Human Body Due to Mechanical Exposure in Human-machine Interface, Final Report of Project No. FF-FP 0317, , https://www.dguv.de/ifa/forschung/projektverzeichnis/ff-fp0317.jsp; Sugiura, R., Fujikawa, T., Nishikata, R., Nishimoto, T., Soft tissue bruise injury by blunt impact in human-robot interaction-Difference of tolerance between chest and extremities (2019) Int. Conf. Control. Autom. Syst.; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) Int. Conf. Control. Autom. Syst.; Gayzik, F.S., Hoth, J.J., Stitzel, J.D., Finite element-based injury metrics for pulmonary contusion via concurrent model optimization (2011) Biomech. Model. Mechanobiol., 10, pp. 505-520; Takhounts, E.G., Craig, M.J., Moorhouse, K., McFadden, J., Hasija, V., Development of Brain Injury Criteria (BrIC) (2013) Stapp Car Crash J., 57, pp. 243-266; Ní Annaidh, A., Bruyère, K., Destrade, M., Gilchrist, M.D., Otténio, M., Characterization of the anisotropic mechanical properties of excised human skin (2012) J. Mech. Behav. Biomed. Mater., 5, pp. 139-148; Comley, K., Fleck, N., The compressive response of porcine adipose tissue from low to high strain rate (2012) Int. J. Impact Eng., 46, pp. 1-10; Alkhouli, N., The mechanical properties of human adipose tissues and their relationships to the structure and composition of the extracellular matrix (2013) Am. J. Physiol.-Endocrinol. Metab., 305, pp. 1427-1435; Van Loocke, M., Lyons, C.G., Simms, C.K., Viscoelastic properties of passive skeletal muscle in compression: Stress-relaxation behaviour and constitutive modelling (2008) J. Biomech., 41, pp. 1555-1566; Takaza, M., Moerman, K.M., Gindre, J., Lyons, G., Simms, C.K., The anisotropic mechanical behaviour of passive skeletal muscle tissue subjected to large tensile strain (2013) J. Mech. Behav. Biomed. Mater., 17, pp. 209-220; Ogden, R.W., Large deformation isotropic elasticity-on the correlation of theory and experiment for incompressible rubberlike solids (1972) Proc. R. Soc. London. A. Math. Phys. Sci., 326 (1567), pp. 565-584; Fung, Y., (2013) Biomechanics: Mechanical Properties of Living Tissues, , New York: Springer Science & Business Media; Hallquist, J.O., (2006) LS-DYNA Theory Manual. California: Livermore Software Technology Corporation; Sato, F., Hyper-viscoelastic response of perfused liver under dynamic compression and estimation of tissue strain thresholds with a liver finite element model (2013) IRCOBI Conf. 2013; Tang, K., Determining Bruise etiology in muscle tissue using finite element analysis (2014) J. Forensic Sci., 59 (2), pp. 371-374; Grosse, I.R., Huang, L., Davis, J.L., Cullinane, D., A multilevel hierarchical finite element model for capillary failure in soft tissue (2014) J. Biomech. Eng., 136 (8), p. 305",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106464141
"Kim D., Han S., Kim T., Kim C., Lee D., Kang D., Koh J.-S.","57208222058;24337917700;57201124770;57221217109;57222432566;36682500000;35491056200;","Design of a sensitive balloon sensor for safe human–robot interaction",2021,"Sensors","21","6","2163","1","12",,1,"10.3390/s21062163","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102705064&doi=10.3390%2fs21062163&partnerID=40&md5=96090d257ec39377a2e95886dcbf951a","Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea","Kim, D., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Han, S., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Kim, T., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Kim, C., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Lee, D., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Kang, D., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Koh, J.-S., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea","As the safety of a human body is the main priority while interacting with robots, the field of tactile sensors has expanded for acquiring tactile information and ensuring safe human–robot interaction (HRI). Existing lightweight and thin tactile sensors exhibit high performance in detecting their surroundings. However, unexpected collisions caused by malfunctions or sudden external collisions can still cause injuries to rigid robots with thin tactile sensors. In this study, we present a sensitive balloon sensor for contact sensing and alleviating physical collisions over a large area of rigid robots. The balloon sensor is a pressure sensor composed of an inflatable body of low‐density polyethylene (LDPE), and a highly sensitive and flexible strain sensor laminated onto it. The mechanical crack‐based strain sensor with high sensitivity enables the detection of extremely small changes in the strain of the balloon. Adjusting the geometric parameters of the balloon allows for a large and easily customizable sensing area. The weight of the balloon sensor was approximately 2 g. The sensor is employed with a servo motor and detects a finger or a sheet of rolled paper gently touching it, without being damaged. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human–robot interaction; Pressure sensor; Soft robot","Balloons; Machine design; Contact sensing; High sensitivity; Inflatable bodies; Robot interactions; Strain sensors; Tactile information; Tactile sensors; Without being damaged; Human robot interaction; aircraft; finger; human; robotics; touch; Aircraft; Fingers; Humans; Robotics; Touch",,,,,"Ajou University; Ministry of Trade, Industry and Energy, MOTIE: 20000512; National Research Foundation of Korea, NRF: 2019R1F1A1063066, 2021R1C1C1011872","Funding: This work is supported in parts by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No. 20000512, Basic Science Research Pro‐ gram through the National Research Foundation of Korea (NRF) funded by the Korea government (2019R1F1A1063066, 2021R1C1C1011872). Daeshik Kang, Seungyong Han, and Je‐sung Koh acknowledge financial support by the new faculty research fund of Ajou University and the Ajou University research fund.",,,,,,,,,,"Heinzmann, J., Zelinsky, A., Quantitative Safety Guarantees for Physical Human‐Robot Interaction (2003) Int. J. Rob. Res, 22, pp. 479-504; Haddadin, S., Albu‐Schäffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human‐robot interaction (2008) IEEE RSJ Int. Conf. Intell. Robot. Syst. Iros, pp. 3356-3363. , doi.org; Michalos, G., Makris, S., Tsarouchi, P., Guasch, T., Kontovrakis, D., Chryssolouris, G., Design considerations for safe human-robot collaborative workplaces (2015) Proc. CiRP, 37, pp. 248-253; Lasota, P.A., Fong, T., Shah, J.A., A Survey of Methods for Safe Human‐Robot Interaction (2017) Found. Trends Robot, 5, pp. 261-349; Zou, L., Ge, C., Wang, Z.J., Cretu, E., Li, X., Novel tactile sensor technology and smart tactile sensing systems: A review (2017) Sensors, 17, pp. 1-24; Argall, B.D., Billard, A.G., A survey of Tactile Human‐Robot Interactions (2010) Robot. Auton. Syst, 58, pp. 1159-1176; Dahiya, R.S., Mittendorfer, P., Valle, M., Cheng, G., Lumelsky, V.J., Directions toward effective utilization of tactile skin: A review (2013) IEEE Sens. J, 13, pp. 4121-4138; Luo, S., Bimbo, J., Dahiya, R., Liu, H., Robotic tactile perception of object properties: A review (2017) Mechatronics, 48, pp. 54-67; Huet, L.A., Rudnicki, J.W., Hartmann, M.J.Z., Tactile sensing with whiskers of various shapes: Determining the three‐dimen-sional location of object contact based on mechanical signals at the whisker base (2017) Soft Robot, 4, pp. 88-102; Wu, Y., Liu, Y., Zhou, Y., Man, Q., Hu, C., Asghar, W., Li, F., Liu, G., A skin‐inspired tactile sensor for smart prosthetics (2018) Sci. Robot, 3, p. eaat0429; Xie, H., Liu, H., Luo, S., Seneviratne, L.D., Althoefer, K., Fiber optics tactile array probe for tissue palpation during minimally invasive surgery (2013) IEEE Int. Conf. Intell. Robot. Syst, pp. 2539-2544. , doi.org; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human‐robot interaction (2016) IEEE Robot. Autom. Lett, 1, pp. 41-48; Kaltenbrunner, M., Sekitani, T., Reeder, J., Yokota, T., Kuribara, K., Tokuhara, T., Drack, M., Bauer‐ Gogonea, S., An ultra‐lightweight design for imperceptible plastic electronics (2013) Nature, 499, pp. 458-463; Ohmura, Y., Kuniyoshi, Y., Nagakubo, A., Conformable and scalable tactile sensor skin for a curved surfaces (2006) Proc. IEEE Int. Conf. Robot. Autom, 2006, pp. 1348-1353; Mittendorfer, P., Cheng, G., Humanoid multimodal tactile‐sensing modules (2011) IEEE Trans. Robot, 27, pp. 401-410; O’Neill, J., Lu, J., Dockter, R., Kowalewski, T., Practical, stretchable smart skin sensors for contact‐aware robots in safe and collaborative interactions (2015) Proc. IEEE Int. Conf. Robot. Autom, 2015, pp. 624-629; Kim, T., Park, J., Yoon, S.J., Kong, D.H., Park, H.W., Park, Y.L., Design of a lightweight inflatable sensing sleeve for increased adaptability and safety of legged robots (2019) IEEE Int. Conf. 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Rep, 7, pp. 1-8; Kang, D., Pikhitsa, P.V., Choi, Y.W., Lee, C., Shin, S.S., Piao, L., Park, B., Choi, M., Ultrasensitive mechanical crack‐based sensor inspired by the spider sensory system (2014) Nature, 516, pp. 222-226; Lee, E., Kim, T., Suh, H., Kim, M., Pikhitsa, P.V., Han, S., Koh, J.S., Kang, D., Effect of metal thickness on the sensitivity of crack-based sensors (2018) Sensors, 18, pp. 1-8; Kim, T., Lee, T., Lee, G., Choi, Y.W., Kim, S.M., Kang, D., Choi, M., Polyimide encapsulation of spider‐inspired crack‐based sensors for durability improvement (2018) Appl. Sci, 8, p. 367","Kang, D.; Department of Mechanical Engineering, San 5, Woncheon‐dong, Yeongtong‐gu, South Korea; email: dskang@ajou.ac.kr
Koh, J.-S.; Department of Mechanical Engineering, San 5, Woncheon‐dong, Yeongtong‐gu, South Korea; email: jskoh@ajou.ac.kr",,,"MDPI AG",,,,,14248220,,,"33808860","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102705064
"Murnane M., Higgins P., Saraf M., Ferraro F., Matuszek C., Engel D.","57210704318;57219786637;57211500135;53979619700;10239806100;7102518553;","A simulator for human-robot interaction in virtual reality",2021,"Proceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021",,,"9419217","470","471",,1,"10.1109/VRW52623.2021.00117","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105967066&doi=10.1109%2fVRW52623.2021.00117&partnerID=40&md5=45fb368e39d236f2d9e7b3e295f156b6","University of Maryland, Baltimore County","Murnane, M., University of Maryland, Baltimore County; Higgins, P., University of Maryland, Baltimore County; Saraf, M., University of Maryland, Baltimore County; Ferraro, F., University of Maryland, Baltimore County; Matuszek, C., University of Maryland, Baltimore County; Engel, D., University of Maryland, Baltimore County","We present a suite of tools to model a robot, its sensors, and the surrounding environment in VR, with the goal of collecting training data for real-world robots. The virtual robot observes a rigged avatar created in our photogrammetry facility and embodying a VR user. We are particularly interested in verbal human/robot interactions, which can be combined with the robot's sensor data for grounded language learning. Because virtual scenes, tasks, and robots are easily reconfigured compared to their physical analogs, our approach proves extremely versatile in preparing a wide range of robot scenarios for an array of use cases. © 2021 IEEE.","Robotics; ROS; Unity; Virtual Reality","Abstracting; Educational robots; End effectors; User interfaces; Virtual reality; Language learning; Physical analog; Real-world; Sensor data; Surrounding environment; Training data; Virtual robots; Virtual scenes; Human robot interaction",,,,,"National Science Foundation, NSF: 1428204, 1531491, 1637614, 1637937, 1657469, 1940931","This material is based upon work supported by the National Science Foundation under Grant Nos. 1428204, 1531491, 1637614, 1657469, 1637937, and 1940931. We would like to thank Adam Berlier for extensive, helpful feedback on this manuscript, and our reviewers for their thoughtful suggestions.",,,,,,,,,,"Bansal, M., Matuszek, C., Andreas, J., Artzi, Y., Bisk, Y., Proceedings of the first workshop on language grounding for robotics (2017) Proceedings of the First Workshop on Language Grounding for Robotics; Bischoff, M., (2019) ROS#, , Dec; Hermann, K.M., Hill, F., Green, S., Wang, F., Faulkner, R., Soyer, H., Szepesvari, D., Teplyashin, D., (2017) Grounded Language Learning in A Simulated 3D World, , Technical report, arXiv preprint; Inamura, T., Mizuchi, Y., (2020) Sigverse: A Cloud-based VR Platform for Research on Social and Embodied Human-robot Interaction; Jo, E.S., Gebru, T., Lessons from archives: Strategies for collecting sociocultural data in machine learning (2020) Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 306-316; Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Gordon, D., Farhadi, A., (2017) Ai2-thor: An Interactive 3D Environment for Visual Ai; Murnane, M., Breitmeyer, M., Ferraro, F., Matuszek, C., Engel, D., Learning from human-robot interactions in modeled scenes (2019) ACM SIGGRAPH 2019 Posters, SIGGRAPH '19, , Association for Computing Machinery, New York, NY, USA; Murnane, M., Breitmeyer, M., Matuszek, C., Engel, D., Virtual reality and photogrammetry for improved reproducibility of humanrobot interaction studies (2019) 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 1092-1093; Robertson, A., Facebook Is Making Oculus' Worst Feature Unavoidable, , http://tiny.cc/oculus-privacy, Accessed: 22/1/2021; Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., Fox, D., Alfred: A benchmark for interpreting grounded instructions for everyday tasks (2020) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Robotic Operating System, , Stanford Artificial Intelligence Laboratory et al","Murnane, M.; University of Maryland, email: mark25@umbc.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021","27 March 2021 through 3 April 2021",,168770,,9780738113678,,,"English","Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW",Conference Paper,"Final","",Scopus,2-s2.0-85105967066
"Su H., Di Lallo A., Murphy R.R., Taylor R.H., Garibaldi B.T., Krieger A.","55059793600;57207876575;7403470480;7405756438;16432483300;57201238593;","Physical human–robot interaction for clinical care in infectious environments",2021,"Nature Machine Intelligence","3","3",,"184","186",,1,"10.1038/s42256-021-00324-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102767462&doi=10.1038%2fs42256-021-00324-z&partnerID=40&md5=55c40d02850b9f5705ca94357a3b048e","Department of Mechanical Engineering, The City University of New York, City College, New York, NY, United States; Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, United States; Division of Pulmonary and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Mechanical Engineering, The John Hopkins University, Baltimore, MD, United States","Su, H., Department of Mechanical Engineering, The City University of New York, City College, New York, NY, United States; Di Lallo, A., Department of Mechanical Engineering, The City University of New York, City College, New York, NY, United States; Murphy, R.R., Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States; Taylor, R.H., Department of Computer Science, The Johns Hopkins University, Baltimore, MD, United States; Garibaldi, B.T., Division of Pulmonary and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States; Krieger, A., Department of Mechanical Engineering, The John Hopkins University, Baltimore, MD, United States",[No abstract available],,,,,,,,,,,,,,,,,,"Fraher, E.P., (2020) New Engl. J. Med., 382, pp. 2181-2183; Erdem, H., Lucey, D.R., (2021) Int. J. Infect. Dis., 102, pp. 239-241; Fu, S.J., George, E.L., Maggio, P.M., Hawn, M., Nazerali, R., (2020) Ann. Surg., 272, pp. e79-e80; Hollander, J.E., Carr, B.G., (2020) New Engl. J. Med., 382, pp. 1679-1681; (2020) Nat. Mach. Intell, 2, p. 553; Li, S.-Q., (2020) Eur. Respir. J., 56, p. 2001912; Ye, R., (2021) Chest, 159, pp. 270-281; Shih, B., (2020) Sci. Robot., 5, p. eaaz9239; Hauser, K., Shaw, R., (2020) IEEE Spectrum, , https://spectrum.ieee.org/automaton/robotics/medical-robots/medical-robots-future-outbreak-response; Yang, G., (2020) Chin. J. Mech. Eng., 33, p. 47; Vagvolgyi, B.P., (2020), https://arxiv.org/abs/2010.05247; Hemmerling, T., (2012) Brit. J. Anaesth., 108, pp. 1011-1016; Hawkes, E.W., Blumenschein, L.H., Greer, J.D., Okamura, A.M., (2017) Sci. Robot., 2, p. eaan3028; Mahler, J., (2019) Sci. Robot., 4, p. eaau4984; Zemmar, A., Lozano, A.M., Nelson, B.J., (2020) Nat. Mach. Intell., 2, pp. 566-572; Zheng, J., (2020) Surg. Endosc., 34, pp. 5172-5180; Yang, G.-Z., (2017) Sci. Robot., 2, p. eaam8638; Shademan, A., (2016) Sci. Trans. Med., 8, p. 337ra64; Hager, G., Kumar, V., Murphy, R., Rus, D., Taylor, R., (2020), https://arxiv.org/abs/2010.09909; Mathur, B., (2019) 2019 IEEE 19Th International Conference on Bioinformatics and Bioengineering, pp. 649-656. , (IEEE","Su, H.; Department of Mechanical Engineering, United States; email: hao.su@ccny.cuny.edu
Krieger, A.; Department of Mechanical Engineering, United States; email: axel@jhu.edu",,,"Nature Research",,,,,25225839,,,,"English","Nat. Mach. Intell.",Note,"Final","All Open Access, Bronze",Scopus,2-s2.0-85102767462
"Anagnostis A., Benos L., Tsaopoulos D., Tagarakis A., Tsolakis N., Bochtis D.","57190282809;56115151500;13806237900;54397639400;55921449500;24484566500;","Human activity recognition through recurrent neural networks for human–robot interaction in agriculture",2021,"Applied Sciences (Switzerland)","11","5","2188","1","21",,5,"10.3390/app11052188","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102521457&doi=10.3390%2fapp11052188&partnerID=40&md5=84f3bde8a14704c0bff6353d78a6dfa4","Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece; farmB Digital Agriculture, Doiraniis 17, Thessaloniki, GR54639, Greece; Department of Computer Science & Telecommunications, University of Thessaly, Lamia, GR35131, Greece","Anagnostis, A., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece, farmB Digital Agriculture, Doiraniis 17, Thessaloniki, GR54639, Greece, Department of Computer Science & Telecommunications, University of Thessaly, Lamia, GR35131, Greece; Benos, L., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece; Tsaopoulos, D., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece; Tagarakis, A., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece; Tsolakis, N., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece; Bochtis, D., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR 57001, Greece, farmB Digital Agriculture, Doiraniis 17, Thessaloniki, GR54639, Greece","The present study deals with human awareness, which is a very important aspect of hu-man–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0– 96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Deep learn-ing; Gyroscope; Long short-term memory networks; Machine learning; Magnetometer; Robotics; Safe human-robot interaction; Sensor fusion, accelerometer; Situation awareness",,,,,,"General Secretariat for Research and Technology, GSRT: 2386","Acknowledgments: This research has been partly supported by the Project: “Human-Robot Synergetic Logistics for High Value Crops” (project acronym: SYNERGIE), funded by the General Secretariat for Research and Technology (GSRT) under reference no. 2386.",,,,,,,,,,"Sørensen, C.A.G., Kateris, D., Bochtis, D., ICT Innovations and Smart Farming (2019) Proceedings of the Communications in Computer and Information Science, 953, pp. 1-19. , Springer Verlag; Moysiadis, V., Tsolakis, N., Katikaridis, D., Sørensen, C.G., Pearson, S., Bochtis, D., Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects (2020) Appl. 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Eng, 184, pp. 111-121; Sainath, T.N., Vinyals, O., Senior, A., Sak, H., Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks (2015) Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, 2015, pp. 4580-4584. , Institute of Electrical and Electronics Engineers Inc., August; Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., Woo, W., Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (2015) Adv. Neural Inf. Process. Syst, pp. 802-810. , 2015-January","Bochtis, D.; Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, Greece; email: d.bochtis@certh.gr
Bochtis, D.; farmB Digital Agriculture, Doiraniis 17, Greece; email: d.bochtis@certh.gr",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102521457
"Qbilat M., Iglesias A., Belpaeme T.","57202920069;8704705100;13103663500;","A proposal of accessibility guidelines for human-robot interaction",2021,"Electronics (Switzerland)","10","5","561","1","14",,3,"10.3390/electronics10050561","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101679973&doi=10.3390%2felectronics10050561&partnerID=40&md5=2204497ebe7478f7af09e3f7876a0b65","Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, 28911, Spain; IDLab – imec, Ghent University, Ghent, 9052, Belgium","Qbilat, M., Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, 28911, Spain; Iglesias, A., Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, 28911, Spain; Belpaeme, T., IDLab – imec, Ghent University, Ghent, 9052, Belgium","We will increasingly become dependent on automation to support our manufacturing and daily living, and robots are likely to take an important place in this. Unfortunately, currently not all the robots are accessible for all users. This is due to the different characteristics of users, as users with visual, hearing, motor or cognitive disabilities were not considered during the design, imple-mentation or interaction phase, causing accessibility barriers to users who have limitations. This research presents a proposal for accessibility guidelines for human-robot interaction (HRI). The guidelines have been evaluated by seventeen HRI designers and/or developers. A questionnaire of nine five-point Likert Scale questions and 6 open-ended questions was developed to evaluate the proposed guidelines for developers and designers, in terms of four main factors: usability, social acceptance, user experience and social impact. The questions act as indicators for each factor. The majority (15 of 17 participants) agreed that the guidelines are helpful for them to design and imple-ment accessible robot interfaces and applications. Some of them had considered some ad hoc guidelines in their design practice, but none of them showed awareness of or had applied all the proposed guidelines in their design practice, 72% of the proposed guidelines have been applied by less than or equal to 8 participants for each guideline. Moreover, 16 of 17 participants would use the proposed guidelines in their future robot designs or evaluation. The participants recommended the importance of aligning the proposed guidelines with safety requirements, environment of interaction (indoor or outdoor), cost and users’ expectations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Accessibility; Guidelines; Human-robot interaction; Inclusive design",,,,,,"European Commission, EC: AT17-5509-UMA, FP7-ICT-601116, UMA18-FEDERJA-074","This work has been partially funded by the EU ECHORD++ project (FP7-ICT-601116), and the UMA18-FEDERJA-074, the AT17-5509-UMA and the CSO2017-86747-R Spanish projects.",,,,,,,,,,"Feil-Seifer, D., Matarić, M.J., Toward Socially Assistive Robotics for Augmenting Interventions for Children with Autism Spec-trum Disorders (2009) Springer Tracts Adv. Robot, 54, pp. 201-210. , doi.org; Heerink, M., Kröse, B., Wielinga, B., Evers, V., Measuring the Influence of Social Abilities on Acceptance of an Interface Robot and a Screen Agent by Elderly Users (2009) People Comput. XXIII Celebr. People Technol. Proc. 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Care, pp. 185-203; Visual Disabilities-Introduction, , https://webaim.org/articles/visual/, WebAIM: (accessed on 6 June 2020); (2015) Blindness and Deafness, , WHO WHO: Geneva, Switzerland; Change the Definition of Blindness, , https://www.who.int/blindness/Change%20the%20Defini-tion%20of%20Blindness.pdf, (accessed on 3 February 2021); Priority eye diseases, , https://www.who.int/blindness/causes/priority/en/index4.html, WHO (accessed on 14 June 2020); Blindness: Causes, Type, Treatment & Symptoms, , https://www.medicinenet.com/blindness/article.htm, (ac-cessed on 29 November 2020); Kim, E., Paul, R., Shic, F., Scassellati, B., Bridging the Research Gap: Making HRI Useful to Individuals with Autism (2012) J. Human-Robot Interact, 1, pp. 26-54; Fernaeus, Y., Håkansson, M., Jacobsson, M., Ljungblad, S., How Do You Play with a Robotic Toy Animal? A Long-Term Study of Pleo (2010) Proc. IDC2010 9th Int. Conf. Interact. Des. Child, pp. 39-48; Deafness and Hearing Loss, , https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss, (accessed on 19 June 2020); https://www.w3.org/WAI/people-use-web/abilities-barriers/#auditory, Diverse Abilities and Barriers Web Accessibility Initiative (WAI) W3C. (accessed on 20 June 2020); Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Maisonnier, B., Mechatronic Design of NAO Humanoid (2009) Proceedings of the 2009 IEEE International Conference on Robotics and Automation, , Kobe, Japan, 12–17 May; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Robotics, A., Choregraphe: A Graphical Tool for Humanoid Robot Programming (2009) Proc.-IEEE Int. Work. Robot Hum. Interact. Commun, pp. 46-51; Motor Disabilities-Assistive Technologies, , https://webaim.org/articles/motor/assistive, WebAIM: (accessed on 23 June 2020); Andreasen Struijk, L.N.S., Egsgaard, L.L., Lontis, R., Gaihede, M., Bentsen, B., Wireless Intraoral Tongue Control of an Assistive Robotic Arm for Individuals with Tetraplegia (2017) J. Neuroeng. Rehabil, 14, pp. 1-8; https://www.w3.org/WAI/people-use-web/abilities-barriers/#cognitive, Diverse Abilities and Barriers Web Accessibility Initiative (WAI) W3C. (accessed on 24 June 2020); Cognitive Disabilities-Design Considerations, , https://webaim.org/articles/cognitive/design, WebAIM: (ac-cessed on 4 July 2020); Law, M., Sutherland, C., Ahn, H.S., Macdonald, B.A., Peri, K., Johanson, D.L., Vajsakovic, D.-S., Broadbent, E., Developing Assistive Robots for People with Mild Cognitive Impairment and Mild Dementia: A Qualitative Study with Older Adults and Experts in Aged Care (2019) BMJ, 9; https://www.section508.gov/manage/laws-and-policies, IT Accessibility Laws and Policies Section508.gov. (accessed on 30 July 2020); https://eur-lex.europa.eu/legal-con-tent/EN/TXT/?qid=1596098310471&uri=CELEX:32016L2102, EUR-Lex-32016L2102-EN-EUR-Lex. (accessed on 30 July 2020); Internet Accessibility | Samuelson-Glushko Canadian Internet Policy and Public Interest Clinic (CIPPIC), , https://cippic.ca/fr/node/128422#canadian, (accessed on 30 July 2020); https://www.w3.org/TR/WCAG21/#wcag-2-layers-of-guidance, Web Content Accessibility Guidelines (WCAG) 2.1. (accessed on 30 July 2020); Mobile Guidelines-Funka, , https://www.funka.com/en/our-assignments/research-and-innovation/archive---research-projects/mobile-guidelines/, (accessed on 26 February 2020); https://www.bbc.co.uk/guide-lines/futuremedia/accessibility/, BBC-Future Media Standards & Guidelines-Accessibility Guidelines v2.0. (accessed on 30 July 2020); IBM Human Ability and Accessibility Center | Developer Guidelines, , https://www.ibm.com/able/guide-lines/index.html, (accessed on 30 July 2020); 2014-Robots and Robotic Devices—Safety Requirements for Personal Care Robots, , https://www.iso.org/standard/53820.html, ISO-ISO 13482:. 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Iglesias, A.; Department of Computer Science and Engineering, Spain; email: aiglesia@inf.uc3m.es",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85101679973
"Wu M., Su W., Chen L., Liu Z., Cao W., Hirota K.","57218664297;57196287282;55774663200;56101506800;7402082944;7402829702;","Weight-Adapted Convolution Neural Network for Facial Expression Recognition in Human-Robot Interaction",2021,"IEEE Transactions on Systems, Man, and Cybernetics: Systems","51","3","8663434","1473","1484",,13,"10.1109/TSMC.2019.2897330","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101063006&doi=10.1109%2fTSMC.2019.2897330&partnerID=40&md5=612ef7ead8da44095cd633c31ef3f435","School of Automation, China University of Geosciences, Wuhan, 430074, China; Tokyo Institute of Technology, Tokyo, 226-8502, Japan","Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China; Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China; Liu, Z., School of Automation, China University of Geosciences, Wuhan, 430074, China; Cao, W., School of Automation, China University of Geosciences, Wuhan, 430074, China; Hirota, K., Tokyo Institute of Technology, Tokyo, 226-8502, Japan","The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network's (CNN's) potential performance in avoiding local optima and speeding up convergence by the hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human-robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using a principal component analysis. Finally, the high-level expression semantic features are extracted and recognized by WACNN which is optimized by HGA. In order to evaluate the effectiveness of WACNN, experiments on JAFFE, CK+, and static facial expressions in the wild 2.0 databases are carried out by using {k} -fold cross validation, and experimental results show the recognition accuracies of the proposal are superior to that of the state-of-the-art, such as local directional ternary pattern and weighted mixture deep neural network (DNN), which aim to extract discriminative and are the DNN-based methods. Moreover, recognition accuracies of the proposal are also higher than the deep CNN without HGA, which indicates that the proposal has better global optimization ability. Meanwhile, preliminary application experiments are also carried out by using the proposed algorithm on the emotional social robot system, where nine volunteers and two-wheeled robots experience the scenario of emotion understanding. Application results indicate that the wheeled robots can recognize basic expressions, such as happy, surprise, and so on. © 2013 IEEE.","Accelerated gradient descent (AGD); convolution neural network (CNN); facial expression recognition (FER); genetic algorithm (GA)","Convolution; Deep neural networks; Face recognition; Genetic algorithms; Global optimization; Man machine systems; Semantics; Social robots; Convolution neural network; Directional ternary patterns; Emotion understanding; Facial expression recognition; High level expression; Hybrid genetic algorithms; Optimization ability; Recognition accuracy; Neural networks",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016; China University of Geosciences, Wuhan, CUG: 201839; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","Manuscript received August 6, 2018; revised November 17, 2018; accepted January 20, 2019. Date of publication March 8, 2019; date of current version February 17, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61603356 and Grant 61733016, in part by the 111 Project under Grant B17040, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, under Grant 201839. This paper was recommended by Associate Editor S. Nahavandi. (Corresponding author: Luefeng Chen.) M. Wu, W. J. Su, L. F. Chen, Z. T. Liu, and W. H. Cao are with the School of Automation, China University of Geosciences, Wuhan 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China (e-mail: wumin@cug.edu.cn; suwanjuan@cug.edu.cn; chenluefeng@cug.edu.cn; liuzhentao@cug.edu.cn; weihuacao@cug.edu.cn).",,,,,,,,,,"Jang, J., Cho, H., Kim, J., Lee, J., Yang, S., Facial attribute recognition by recurrent learning with visual fixation (2019) IEEE Trans. Cybern., 49 (2), pp. 616-625. , Feb; Chen, L.F., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model IEEE Trans. Syst., Man, Cybern., Syst., , to be published; Poria, S., Cambria, E., Bajpai, R., Hussain, A., A review of affective computing: From unimodal analysis to multimodal fusion (2017) Inf. Fusion, 37, pp. 98-125. , Sep; Halder, A., General and interval type-2 fuzzy face-space approach to emotion recognition (2013) IEEE Trans. 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Fuzzy Syst., 26 (5), pp. 2524-2538. , Oct","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682216,,,,"English","IEEE Trans. Syst. Man Cybern. Syst.",Article,"Final","",Scopus,2-s2.0-85101063006
"Fratczak P., Goh Y.M., Kinnell P., Justham L., Soltoggio A.","57212485104;8626315800;57204794040;6506661924;15046088100;","Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction",2021,"International Journal of Industrial Ergonomics","82",,"103078","","",,2,"10.1016/j.ergon.2020.103078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099353931&doi=10.1016%2fj.ergon.2020.103078&partnerID=40&md5=aa1c1622d3f8812b6d4d6ce69222057f","Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Computer Science Department, Loughborough University, Loughborough, LE11 3TP, United Kingdom","Fratczak, P., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Goh, Y.M., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Kinnell, P., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Justham, L., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Soltoggio, A., Computer Science Department, Loughborough University, Loughborough, LE11 3TP, United Kingdom","Due to safety requirements for Human-Robot Interaction (HRI), industrial robots have to meet high standards of safety requirements (ISO 10218). However, even if robots are incapable of causing serious physical harm, they still may influence people's mental and emotional wellbeing, as well as their trust, behaviour and performance in close collaboration. This work uses an HTC Vive Virtual Reality headset to study the potential of using robot control strategies to positively influence human post-accident behaviour. In the designed scenario, a virtual industrial robot first makes sudden unexpected movements, after which it either does or does not attempt to apologise for them. The results show that after the robot tries to communicate with the participants, the robot is reported to be less scary, more predictable and easier to work with. Furthermore, postural analysis shows that the participants who were the most affected by the robot's sudden movement recover 74% of their postural displacement within 60 s after the event if the robot apologised, and only 34% if it did not apologise. It is concluded, that apologies, which are commonly used as a trust-recovery strategy in social robotics, can positively influence people engaged with industrial robotics as well. Relevance to industry: Findings can be used as guidelines for designing robot behaviour and trust-recovery control strategies meant to speed up human recovery after a trust-violating event in industrial Human-Robot Interaction. © 2021","Human-robot interaction; Industrial robots; Trust recovery; Virtual reality","Accident prevention; Accidents; Behavioral research; Industrial robots; Man machine systems; Recovery; Robotics; Social robots; Virtual reality; Emotional wellbeing; Human robot Interaction (HRI); Industrial robotics; Postural analysis; Recovery control; Recovery strategies; Safety requirements; Virtual-reality headsets; Economic and social effects; accidental injury; adult; Article; body position; control strategy; controlled study; experimental study; female; human; human relation; interpersonal communication; male; measurement; occupational accident; physical parameters; prediction; priority journal; questionnaire; robotics; trust; virtual reality",,,"Vive, HTC","HTC","2229424, EP/N509516/1","The authors are grateful to the Engineering and Physical Science Research Council (EPSRC) for the financial support extended to this research through the Doctoral Training Program (DTP) ( EP/N509516/1 , Project Reference number: 2229424).",,,,,,,,,,"Atkinson, D.J., Clark, M.H., Methodology for study of human-robot social interaction in dangerous situations (2014) Proceedings of the Second International Conference on Human-Agent Interaction, pp. 371-376. , October 2014, Tsukuba, Japan; Bailenson, J.N., Blascovich, J., Beall, A.C., Loomis, J.M., Interpersonal distance in immersive virtual environments (2003) Pers. 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J. Ind. Ergon.",Article,"Final","",Scopus,2-s2.0-85099353931
"Zhang T., Zeng Y., Pan R., Shi M., Lu E.","56181288200;55509735800;56939949600;57205546527;57205584475;","Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction",2021,"Cognitive Computation","13","2",,"381","393",,1,"10.1007/s12559-020-09753-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087991999&doi=10.1007%2fs12559-020-09753-1&partnerID=40&md5=3c23c9b0134715f264672dd613d1150a","Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Research Center for Intelligent Security Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Beijing, China","Zhang, T., Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Zeng, Y., Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China; Pan, R., Research Center for Intelligent Security Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Beijing, China; Shi, M., Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China; Lu, E., Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China","Improving robots with self-learning ability is one of the critical challenges for the researchers in the area of cognitive robotics and artificial general intelligence. This robot will decide when, where, and what to learn in a continuous visual environment by itself. Here we focus on the procedural knowledge learning, which is sequential and considered harder to understand compared with declarative knowledge in the cognitive system. Inspired by the architecture of the human brain which has integrated well different kinds of cognitive functions, a Brain-inspired Active Learning Architecture (BALA) is proposed for procedural knowledge understanding based on Baxter robot and human interaction. The BALA model contains four main parts: inspired by Primary Visual Pathway, a Convolutional Neural Network (CNN) is constructed for spatial information abstraction; inspired by the Hippocampus Pathway (especially the recurrent loops in CA3 sub-region), a Recurrent Neural Network (RNN) is built for sequential information processing related with procedural knowledge; inspired by the Prefrontal Cortex, a Knowledge Graph based on Bag Of Words (BOW) is constructed for declarative knowledge generation and association; inspired by the Basal Ganglia Pathway, we select Q matrix for Reinforcement Learning (RL). The CNN and RNN parts will be firstly pre-trained on ImageNet dataset and standard Youtube Video-Scene dataset respectively. Then, the RNN, Knowledge Graph, and Q matrix will be dynamically updated in the Baxter robot’s interactive learning procedure with human cooperators. The BALA could actively and incrementally recognize different kinds of procedural knowledge. In 22-type daily-life videos with procedure knowledge (e.g., opening the door, wiping the table, or taking the phone), the BALA model gets the best performance compared with standard CNN, RNN, RL, and other integrative methods. The BALA model is a small step on integrative intelligence interaction between the Baxter robot and human cooperator. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.","Brain-inspired architecture; Deep neural network; Knowledge graph; Procedural knowledge; Reinforcement learning","Brain; Cognitive systems; Convolutional neural networks; Graphic methods; Information retrieval; Intelligent robots; Knowledge representation; Learning systems; Network architecture; Reinforcement learning; Social robots; Artificial general intelligences; Declarative knowledge; Interactive learning; Procedural knowledge; Recurrent neural network (RNN); Self-learning ability; Sequential information; Spatial informations; Recurrent neural networks",,,,,"2018CXGC1503; National Natural Science Foundation of China, NSFC; Chinese Academy of Sciences, CAS: XDB32070100; Natural Science Foundation of Beijing Municipality; Beijing Municipal Natural Science Foundation: 4184103; Beijing Municipal Science and Technology Commission, BMSTC: Z181100001518006; Young Scientists Fund: 61806195; China Electronics Technology Group Corporation, CETC: 6141B08010103","This study is supported by National Natural Science Foundation of China (No. 61806195), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB32070100), the Beijing Municipality of Science and Technology (Grant No. Z181100001518006), the Major Research Program of Shandong Province (Grant No. 2018CXGC1503) and the CETC Joint Fund (Grant No. 6141B08010103).",,,,,,,,,,"Shen, Y.-Y., Liu, C.-L., Incremental adaptive learning vector quantization for character recognition with continuous style adaptation (2018) Cognitive Computation, 10 (2), pp. 334-346; Reyes, O., Altalhi, A.H., Ventura, S., Statistical comparisons of active learning strategies over multiple datasets (2018) Knowl-Based Syst, 145, pp. 274-288; Zhu, Z., Hu, H., Robot learning from demonstration in robotic assembly: A survey (2018) Robotics, 7 (2), p. 17; Bhat, A.A., Mohan, V., Goal-directed reasoning and cooperation in robots in shared workspaces: an internal simulation based neural framework (2018) Cognitive Computation, 10 (4), pp. 558-576; Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S., Continual lifelong learning with neuralnetworks: a review (2019) Neural Networks; Hao, W., Fan, J., Zhang, Z., Zhu, G., End-to-end lifelong learning: a framework to achieve plasticities of both the feature and classifier constructions (2018) Cognitive Computation, 10 (2), pp. 321-333; Kaliukhovich, D.A., Beeck, H.O., Hierarchical stimulus processing in rodent primary and lateral visual cortex as assessed through neuronal selectivity and repetition suppression (2018) Journal of Neurophysiology, 120 (3), pp. 926-941; He, K., Zhang, X., Ren, S., Sun, J., Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 1026-1034; Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database (2009) 2009 IEEE Conference on Computer Vision and Pattern Recognition. 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IEEE, pp. 002301-002306; Zhang, T., Zeng, Y., Zhao, D., Shi, M., A plasticity-centric approach to train the non-differential spiking neural networks (2018) Thirty-Second AAAI Conference on Artificial Intelligence; Zhang, T., Yi, Z., Zhao, D., Xu, B., Brain-inspired balanced tuning for spiking neural networks (2018) IJCAI, pp. 1653-1659; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural computation, 9 (8), pp. 1735-1780; Chen, D.L., Dolan, W.B., Collecting highly parallel data for paraphrase evaluation (2011) Proceedings of the 49Th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 190-200. , Association for Computational Linguistics; Torabi, A., Pal, C., Larochelle, H., Courville, A., (2015) Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research; Rohrbach, A., Rohrbach, M., Tandon, N., Schiele, B., A dataset for movie description (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3202-3212","Zhang, T.; Research Center for Brain-inspired Intelligence, China; email: tielin.zhang@ia.ac.cn
Zeng, Y.; University of Chinese Academy of SciencesChina; email: yi.zeng@ia.ac.cn",,,"Springer",,,,,18669956,,,,"English","Cognitive Comput.",Article,"Final","",Scopus,2-s2.0-85087991999
"Céspedes N., Raigoso D., Múnera M., Cifuentes C.A.","57210636629;57222344332;6505875786;36717243800;","Long-Term Social Human-Robot Interaction for Neurorehabilitation: Robots as a Tool to Support Gait Therapy in the Pandemic",2021,"Frontiers in Neurorobotics","15",,"612034","","",,3,"10.3389/fnbot.2021.612034","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102393650&doi=10.3389%2ffnbot.2021.612034&partnerID=40&md5=98e3b51cc0fdcb3a850fcd2b757f2309","Departament of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia","Céspedes, N., Departament of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia; Raigoso, D., Departament of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia; Múnera, M., Departament of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia; Cifuentes, C.A., Departament of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia","COVID-19 pandemic has affected the population worldwide, evidencing new challenges and opportunities for several kinds of emergent and existing technologies. Social Assistive Robotics could be a potential tool to support clinical care areas, promoting physical distancing, and reducing the contagion rate. In this context, this paper presents a long-term evaluation of a social robotic platform for gait neurorehabilitation. The robot's primary roles are monitoring physiological progress and promoting social interaction with human distancing during the sessions. A clinical validation with ten patients during 15 sessions were conducted in a rehabilitation center located in Colombia. Results showed that the robot's support improves the patients' physiological progress by reducing their unhealthy spinal posture time, with positive acceptance. 65% of patients described the platform as helpful and secure. Regarding the robot's role within the therapy, the health care staff agreed (>95%) that this tool can promote physical distancing and it is highly useful to support neurorehabilitation throughout the pandemic. These outcomes suggest the benefits of this tool to be further implemented in the pandemic. © Copyright © 2021 Céspedes, Raigoso, Múnera and Cifuentes.","biofeedback; COVID-19; gait rehabilitation; Lokomat; long-term human-robot interaction; socially assistive robotics","End effectors; Neuromuscular rehabilitation; Physiology; Robotics; Assistive robotics; Clinical validations; Long-term evaluation; Neurorehabilitation; Potential tool; Social human-robot interactions; Social interactions; Social robotics; Social robots; adult; Article; biofeedback; body position; Borg Scale; cerebrovascular accident; cervical spine; clinical article; Colombia; coronavirus disease 2019; disease course; female; gait; gait therapy; health care personnel; heart rate; human; human robot interaction; long term care; male; neurorehabilitation; pandemic; patient attitude; patient monitoring; rating scale; social distancing; social interaction; socially assistive robotics; spinal cord injury; spinal posture time; thoracic spine; validation study",,,"IMU BNO055; NAO V6; Surface Pro, Microsoft, United States; Windows, Microsoft, United States; Zephyr HxM, Medtronic, New Zealand","Medtronic, New Zealand; Microsoft, United States","801-2017; Royal Academy of Engineering, RAENG: EXPP20211\1\183","This work was carried out in collaboration with the Mobility group rehabilitation center, where the experiments and protocols were performed. The authors acknowledge the work of patients and therapist who were involved in this study. Funding. This work was financially supported by the Ministerio de Ciencia, Tecnolog?a e Innovaci?n (Grant 801-2017) and the Royal Academy of Engineering - Pandemic Preparedness (Grant EXPP20211\1\183).",,,,,,,,,,"Agrigoroaie, R.M., Tapus, A., “Developing a healthcare robot with personalized behaviors and social skills for the elderly,” (2016) International Conference on Human Robot Interaction, , Christchurch, in; Aymerich-Franch, L., Why it is time to stop ostracizing social robots (2020) Nat. Mach. Intell, 2, p. 364; Bickmore, T.W., Picard, R.W., Establishing and maintaining long-term human- computer relationships (2005) ACM Trans. Comput. Hum. Interact, 2, pp. 617-638; Campa, R., Campa, R., The rise of social robots: a review of the recent literature (2016) J. Evol. 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Bull, 1, p. 80; Winkle, K., Caleb-Solly, P., Turton, A., Bremner, P., “Social robots for engagement in rehabilitative therapies: design implications from a study with therapists,” (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 289-297. , New York, NY, Association for Computing Machinery; Yang, L., Cheng, H., Hao, J., Ji, Y., Kuang, Y., “A survey on media interaction in social robotics,” (2015) Lecture Notes in Computer Science, pp. 181-190. , Gwangju, Springer Verlag; Zhang, J., Litvinova, M., Liang, Y., Wang, Y., Wang, W., Zhao, S., Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China (2020) Science, 368, pp. 1481-1486. , 32350060","Cifuentes, C.A.; Departament of Biomedical Engineering, Colombia; email: carlos.cifuentes@escuelaing.edu.co",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102393650
"Vinanzi S., Cangelosi A., Goerick C.","57195222288;6701387796;8589247000;","The collaborative mind: intention reading and trust in human-robot interaction",2021,"iScience","24","2","102130","","",,1,"10.1016/j.isci.2021.102130","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100668259&doi=10.1016%2fj.isci.2021.102130&partnerID=40&md5=7b0b827bd84e5d1966dce35793b8b91c","Cognitive Robotics Lab, The University of Manchester, Manchester, M13 9PL, United Kingdom; AIST-AIRC, Tokyo, Japan; Honda Research Institute Europe GmbH, Offenbach am Main, 63073, Germany","Vinanzi, S., Cognitive Robotics Lab, The University of Manchester, Manchester, M13 9PL, United Kingdom; Cangelosi, A., Cognitive Robotics Lab, The University of Manchester, Manchester, M13 9PL, United Kingdom, AIST-AIRC, Tokyo, Japan; Goerick, C., Honda Research Institute Europe GmbH, Offenbach am Main, 63073, Germany","Robots are likely to become important social actors in our future and so require more human-like ways of assisting us. We state that collaboration between humans and robots is fostered by two cognitive skills: intention reading and trust. An agent possessing these abilities would be able to infer the non-verbal intentions of others and to evaluate how likely they are to achieve their goals, jointly understanding what kind and which degree of collaboration they require. For this reason, we propose a developmental artificial cognitive architecture that integrates unsupervised machine learning and probabilistic models to imbue a humanoid robot with intention reading and trusting capabilities. Our experimental results show that the synergistic implementation of these cognitive skills enable the robot to cooperate in a meaningful way, with the intention reading model allowing a correct goal prediction and with the trust component enhancing the likelihood of a positive outcome for the task. © 2021 The Author(s)","Artificial Intelligence; Human-Centered Computing; Human-Computer Interaction",,,,,,"Air Force Office of Scientific Research, AFOSR; Air Force Materiel Command, AFMC; U.S. Air Force, USAF: FA9550-19-1-7002","This work was supported by a project funded by the Honda Research Institute Europe (DeCIFER project) and the material is in part based upon work supported by the Air Force Office of Scientific Research, Air Force Materiel Command, USAF under Awards FA9550-19-1-7002.",,,,,,,,,,"Baldwin, D.A., Baird, J.A., Discerning intentions in dynamic human action (2001) Trends Cogn. 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Developmental robotics model of trust and theory of mind (2019) Philos. Trans. R. Soc. Lond. B Biol. Sci., 374, p. 20180032; Wellman, H.M., Cross, D., Watson, J., Meta-analysis of theory-of-mind development: the truth about false belief (2001) Child Development, 72, pp. 655-684; Woodward, A.L., Sommerville, J.A., Gerson, S., Henderson, A.M., Buresh, J., The emergence of intention attribution in infancy (2009) Psychol. Learn. Motiv., 51, pp. 187-222; Zanatto, D., When do We Cooperate with Robots? (2019), PhD thesis University of Plymouth","Vinanzi, S.; Cognitive Robotics Lab, United Kingdom; email: samuele.vinanzi@manchester.ac.uk",,,"Elsevier Inc.",,,,,25890042,,,,"English","iScience",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100668259
"Li Z., Mu Y., Sun Z., Song S., Su J., Zhang J.","57200183907;57222047047;25637891100;57207760100;57045140900;57202953338;","Intention Understanding in Human–Robot Interaction Based on Visual-NLP Semantics",2021,"Frontiers in Neurorobotics","14",,"610139","","",,1,"10.3389/fnbot.2020.610139","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101079714&doi=10.3389%2ffnbot.2020.610139&partnerID=40&md5=e2a50659a2eb9dc860a94c56bd81605a","Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China; School of Statistics, Southwestern University of Finance and Economics, Chengdu, China; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China; Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China; School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, China; Research Center on Special Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","Li, Z., Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China; Mu, Y., School of Statistics, Southwestern University of Finance and Economics, Chengdu, China; Sun, Z., School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China; Song, S., Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China; Su, J., School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, China; Zhang, J., Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China, Research Center on Special Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","With the rapid development of robotic and AI technology in recent years, human–robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human–robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures. © Copyright © 2021 Li, Mu, Sun, Song, Su and Zhang.","human–robot interaction; intention estimation; scene understanding; semantics; visual-NLP","Anthropomorphic robots; Artificial intelligence; Economic and social effects; Object recognition; Semantics; Visual languages; Intention understanding; Language semantics; Multiple objects; Natural languages; Robot interactions; Sentence structures; Similarity computation; Visual object recognition; Social robots; adult; algorithm; article; female; human; human experiment; language; male; recognition; robotics; semantics",,,,,"2019-INT020; Science, Technology and Innovation Commission of Shenzhen Municipality: JCYJ20170410172100520, JCYJ20170818104502599","This work was partially supported by the Shenzhen Science and Technology Innovation Commission (Grants Nos. JCYJ20170410172100520 and JCYJ20170818104502599). This work was also partially supported by the Shenzhen Institute of Artificial Intelligence and Robotics for Society (Grants No. 2019-INT020).",,,,,,,,,,"Alonso-Martín, F., Castro-González, A., de Gorostiza Luengo, F.J.F., Salichs, M.Á., Augmented robotics dialog system for enhancing human-robot interaction (2015) Sensors, 15, pp. 15799-15829. , 26151202; Dzifcak, J., Scheutz, M., Baral, C., Schermerhorn, P., What to do and how to do it: translating natural language directives into temporal and dynamic logic representation for goal management and action execution (2009) 2009 IEEE International Conference on Robotics and Automation, pp. 4163-4168. , Kobe, IEEE; Eppe, M., Trott, S., Feldman, J., Exploiting deep semantics and compositionality of natural language for human-robot-interaction (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 731-738. , Daejeon, IEEE; Fang, B., Sun, F., Liu, H., Liu, C., 3d human gesture capturing and recognition by the immu-based data glove (2018) Neurocomputing, 277, pp. 198-207; Fang, B., Wei, X., Sun, F., Huang, H., Yu, Y., Liu, H., Skill learning for human-robot interaction using wearable device (2019) Tsinghua Sci. 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IEEE, 64, pp. 501-531; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: towards real-time object detection with region proposal networks (2015) IEEE transactions on pattern analysis and machine intelligence, 39, pp. 1137-1149. , 27295650; Richards, L.E., Matuszek, C., Learning to understand non-categorical physical language for human robot interactions (2019) From the RSS Workshop on AI and its Alternatives in Assistive and Collaborative Robotics, , Messe Freiburg, in; Shridhar, M., Mittal, D., Hsu, D., INGRESS: interactive visual grounding of referring expressions (2020) Int. J. Robot. Res, 39, pp. 217-232; Trask, A., Michalak, P., Liu, J., sense2vec-a fast and accurate method for word sense disambiguation in neural word embeddings (2015) arXiv[Preprint].arXiv: 1511.06388; Uzkent, B., Yeh, C., Ermon, S., Efficient object detection in large images using deep reinforcement learning (2020) The IEEE Winter Conference on Applications of Computer Vision, pp. 1824-1833. , Snowmass Village, CO; Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Polarmask: single shot instance segmentation with polar representation (2020) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12193-12202. , Seattle, WA","Zhang, J.; Institute of Robotics and Intelligent Manufacturing, China; email: zhangjiaming@cuhk.edu.cn
Zhang, J.; Research Center on Special Robots, China; email: zhangjiaming@cuhk.edu.cn",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85101079714
"Ghonasgi K., Yousaf S.N., Esmatloo P., Deshpande A.D.","57190752341;57212314873;57195415034;7202168327;","A modular design for distributed measurement of human–robot interaction forces in wearable devices",2021,"Sensors","21","4","1445","1","17",,2,"10.3390/s21041445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100940696&doi=10.3390%2fs21041445&partnerID=40&md5=25b9471455dd1a4190f13c675a8b83a8","Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States","Ghonasgi, K., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States; Yousaf, S.N., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States; Esmatloo, P., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States; Deshpande, A.D., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States","Measurement of interaction forces distributed across the attachment interface in wearable devices is critical for understanding ergonomic physical human–robot interaction (pHRI). The main challenges in sensorization of pHRI interfaces are (i) capturing the fine nature of force transmission from compliant human tissue onto rigid surfaces in the wearable device and (ii) utilizing a low-cost and easily implementable design that can be adapted for a variety of human interfaces. This paper addresses both challenges and presents a modular sensing panel that uses force-sensing resistors (FSRs) combined with robust electrical and mechanical integration principles that result in a reliable solution for distributed load measurement. The design is demonstrated through an upper-arm cuff, which uses 24 sensing panels, in conjunction with the Harmony exoskeleton. Validation of the design with controlled loading of the sensorized cuff proves the viability of FSRs in an interface sensing solution. Preliminary experiments with a human subject highlight the value of distributed interface force measurement in recognizing the factors that influence ergonomic pHRI and elucidating their effects. The modular design and low cost of the sensing panel lend themselves to extension of this approach for studying ergonomics in a variety of wearable applications with the goal of achieving safe, comfortable, and effective human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Exoskeleton attachment design; Force sensing; Physical human robot interaction","Costs; Ergonomics; Exoskeleton (Robotics); Machine design; Wearable technology; Distributed interfaces; Distributed measurements; Force sensing resistor; Force transmission; Interaction forces; Mechanical integration; Robot interactions; Wearable applications; Human robot interaction; electronic device; ergonomics; exoskeleton (rehabilitation); human; robotics; Ergonomics; Exoskeleton Device; Humans; Robotics; Wearable Electronic Devices",,,,,"National Science Foundation, NSF: 1941260, 2019704, DGE-1610403","Funding: This research was funded, in part, by the National Science Foundation (NSF) grant numbers 2019704 and 1941260, as well as the NSF Graduate Research Fellowship Program (GRFP) grant number DGE-1610403.",,,,,,,,,,"Viteckova, S., Kutilek, P., Jirina, M., Wearable lower limb robotics: A review (2013) Biocybern. Biomed. Eng, 33, pp. 96-105. , [CrossRef]; Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S., A survey on robotic devices for upper limb rehabilitation (2014) J. Neuroeng. Rehabil, 11, p. 3. , [CrossRef]; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art (2008) IEEE Trans. Rob, 24, pp. 144-158. , [CrossRef]; Rocon, E., Ruiz, A., Raya, R., Schiele, A., Pons, J.L., Belda-Lois, J., Poveda, R., Moreno, J., Human-robot physical interaction (2008) Wearable Robots: Biomechatronic Exoskeletons, pp. 127-163. , Wiley: Hoboken, NJ, USA; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mech. Mach. Theory, 43, pp. 253-270. , [CrossRef]; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) Int. J. Rob. 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Pain, 8, pp. 650-656. , [CrossRef] [PubMed]; Varghese, R.J., Mukherjee, G., King, R., Keller, S., Deshpande, A.D., Designing Variable Stiffness Profiles to Optimize the Physical Human Robot Interface of Hand Exoskeletons Proceedings of the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 1101-1108. , Enschede, The Netherlands, 26–29 August 2018; Kendall, F.P., McCreary, E.K., Provance, P.G., Rodgers, M., Romani, W.A., (1993) Muscles, Testing and Function: With Posture and Pain, , Williams & Wilkins Baltimore: Philadelphia, PA, USA; Islam, M.R.U., Bai, S., Intention detection for dexterous human arm motion with FSR sensor bands Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 139-140. , Vienna, Austria, 6–9 March 2017; https://www.tekscan.com/products-solutions/systems/i-scan-system, I-Scan System. (accessed on 23 November 2020); Pliance System, , https://www.novelusa.com/pliance, (accessed on 7 December 2020); Pezent, E., Fani, S., Clark, J., Bianchi, M., O’Malley, M.K., Spatially separating haptic guidance from task dynamics through wearable devices (2019) IEEE Trans. Haptic, 12, pp. 581-593. , [CrossRef] [PubMed]; Sengeh, D.M., Herr, H., A variable-impedance prosthetic socket for a transtibial amputee designed from magnetic resonance imaging data (2013) JPO, 25, pp. 129-137. , [CrossRef]","Deshpande, A.D.; Rehabilitation and Neuromuscular Robotics Lab, United States; email: ashish@austin.utexas.edu",,,"MDPI AG",,,,,14248220,,,"33669615","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100940696
"Audet J.M., Gosselin C.","57203313801;57210720538;","Rotational low-impedance physical human-robot interaction using underactuated redundancy",2021,"Journal of Mechanisms and Robotics","13","1","014503","","",,1,"10.1115/1.4048258","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107981194&doi=10.1115%2f1.4048258&partnerID=40&md5=293e6ce863ad979b496a5c649a7a8144","Department of Mechanical Engineering, Université Laval, Québec, QC  G1V0A6, Canada","Audet, J.M., Department of Mechanical Engineering, Université Laval, Québec, QC  G1V0A6, Canada; Gosselin, C., Department of Mechanical Engineering, Université Laval, Québec, QC  G1V0A6, Canada","This paper extends the concept of underactuated redundancy for physical human-robot interaction (pHRI) in a context of industrial assembly by introducing a novel 1-dof gravity balanced rotational manipulator. The proposed architecture consists of a rotational active counterweight with a passive joint equipped with an encoder. The proposed architecture is first described, and the static equilibrium conditions are used to describe the operation of the mechanism. Then, alternative architectures are briefly introduced. Finally, an experimental validation is provided to demonstrate the viability of the concept for rotational low impedance pHRI. Copyright © 2020 by ASME","Industrial robots; Mechanical impedance; Passive-active mechanism; Physical human-robot interaction; Underactuated robot","Agricultural robots; Architecture; Industrial manipulators; Industrial robots; Man machine systems; Manipulators; Redundancy; Experimental validations; Industrial assemblies; Low impedance; Passive joints; Physical human-robot interactions; Physical humanrobot interaction (phri); Proposed architectures; Static equilibrium conditions; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Fonds de recherche du Québec – Nature et technologies, FRQNT","This study was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and by the Fonds de recherche du Québec – Nature et Technologies (FRQNT). The authors would like to acknowledge the help of Thierry Laliberté and Simon Foucault with the experimental validation of the prototype.",,,,,,,,,,"Krüger, J., Lien, T., Verl, A., Cooperation of Human and Machines in Assembly Lines (2009) CIRP Ann. Manuf. Technol, 58 (2), pp. 628-646; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative Manufacturing with Physical Human-robot Interaction (2016) Robotics Comput.-Integrated Manuf, 40, pp. 1-13; Bonev, I., (2014) Should We Fence the Arms of Universal Robots?, , http://coro.etsmtl.ca/blog/?p=299, [Online]. Available: accessed on October 17, 2018; van der Linde, R., Lammertse, P., Hapticmaster-A Generic Force Controlled Robot for Human Interaction (2003) Industrial Robot: Int. 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Robotics Automation, 4 (6), pp. 677-686; Colgate, E., Hogan, N., An Analysis of Contact Instability in Terms of Passive Physical Equivalents (1989) IEEE International Conference on Robotics and Automation (ICRA), 1, pp. 404-409. , May Scottsdale, AZ; Lamy, X., Colledani, F., Geffard, F., Measson, Y., Morel, G., Achieving Efficient and Stable Comanipulation Through Adaptation to Changes in Human Arm Impedance (2009) IEEE International Conference on Robotics and Automation (ICRA), pp. 265-271. , May Kobe, Japan; Colgate, J. E., Hogan, N., Robust Control of Dynamically Interacting Systems (1988) Int. J. Control, 48 (1), pp. 65-88; Colgate, J. E., Coupled Stability of Multiport Systems-Theory and Experiments (1994) J. Dyn. Syst. Meas. Control, 116 (3), pp. 419-428; Labrecque, P. D., Laliberté, T., Foucault, S., Abdallah, M. E., Gosselin, C., uMan: A Low Impedance Manipulator for Human-robot Cooperation Based on Underactuated Redundancy (2017) IEEE/ASME Trans. Mechatronics, 22 (3), pp. 1401-1411; Badeau, N., Gosselin, C., Foucault, S., Laliberté, T., Abdallah, M. E., Intuitive Physical Human-Robot Interaction Using a Passive Parallel Mechanism (2018) IEEE Robot. Autom. Mag, 25 (2), pp. 28-38; Ebert-Uphoff, I., Gosselin, C. M., Laliberté, T., Static Balancing of Spatial Parallel Platform Mechanisms-Revisited (2000) ASME J. Mech. Des, 122 (1), pp. 43-51; Alamdari, A., Krovi, V. N., Design of Articulated Leg-Wheel Subsystem by Kinetostatic Optimization (2016) Mech. Mach. Theory, 100, pp. 222-234; van Dorsser, W. D., Barents, R., Wisse, B. M., Herder, J. L., Gravity-Balanced Arm Support With Energy-Free Adjustment (2007) ASME J. Med. Devices, 1 (2), pp. 151-158; Kawamura, A., Hisatsune, T., Matsusaka, K., Uemura, M., Kawamura, S., Adaptive Motion Control of a Robotic Arm With Movable Counterweights (2014) Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 882-887. , Besancon, France; Kawamura, A., Gang, B., Uemura, M., Kawamura, S., Mechanism and Control of Robotic Arm Using Rotational Counterweights (2015) Proceedings of the IEEE International Conference. on Robotics and Automation (ICRA), pp. 2716-2721. , Seatle, WA","Gosselin, C.; Department of Mechanical Engineering, Canada; email: gosselin@gmc.ulaval.ca",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85107981194
"Seibt J., Vestergaard C., Damholdt M.F.","25636250400;57000193600;38861389400;","The Complexity of Human Social Interactions Calls for Mixed Methods in HRI: Comment on ""a Primer for Conducting Experiments in Human-robot Interaction,"" by G. Hoffman and X. Zhao",2021,"ACM Transactions on Human-Robot Interaction","10","1","3439715","","",,,"10.1145/3439715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100920990&doi=10.1145%2f3439715&partnerID=40&md5=65c62c3be7c967bf88613c627f57fa9c","School of Culture and Society, Aarhus University, Research Unit for Robophilosophy and Integrative Social Robotics, Jens Christian Skousvej 7, Aarhus, 8000, Denmark; Department of Psychology and Behavioural Sciences, Bartholins Allé 11, Aarhus C, 8000, Denmark","Seibt, J., School of Culture and Society, Aarhus University, Research Unit for Robophilosophy and Integrative Social Robotics, Jens Christian Skousvej 7, Aarhus, 8000, Denmark; Vestergaard, C., School of Culture and Society, Aarhus University, Research Unit for Robophilosophy and Integrative Social Robotics, Jens Christian Skousvej 7, Aarhus, 8000, Denmark; Damholdt, M.F., Department of Psychology and Behavioural Sciences, Bartholins Allé 11, Aarhus C, 8000, Denmark","In this research note, we offer a comment on the ""A Primer for Conducting Experiments in Human-robot Interaction,""by G. Hoffman and X. Zhao, suggesting that due to the complexity of human social reality quantitative methods should be integrated into a mixed method approach. © 2021 ACM.","Empirical research in HRI; mixed methods; responsible robotics","Agricultural robots; Man machine systems; Human social interactions; Mixed method; Quantitative method; Social reality; Social robots",,,,,"Carlsbergfondet: CF2016-0004","The research activities leading to this publication have been sponsored by the Carlsberg Foundation, Grant CF2016-0004. Authors’ addresses: J. Seibt and C. Vestergaard, School of Culture and Society, Aarhus University, Jens Christian Skousvej 7, 8000 Aarhus, Denmark; emails: filseibt@cas.au.dk, etnov@cas.au.dk; M. Damholdt, Department of Psychology and Behavioural Sciences, Bartholins Allé 11, 8000 Aarhus C, Denmark; email: malenefd@psy.au.dk. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. 2573-9522/2021/02-ART10 https://doi.org/10.1145/3439715",,,,,,,,,,"Turkle, S., (2011) Alone Together, , Basic Books; Damholdt, M.F., Vestergaard, C., Kryvous, A., Smedegaard, C.V., Seibt, J., What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life (2019) Paladyn J. Behav. Robot., 10 (1), pp. 438-453. , 2019; Zlotowski, J., Sumioka, H., Eyssel, F., Nishio, S., Bartneck, C., Ishiguro, H., Model of dual anthropomorphism: The relationship between the media equation efect and implicit anthropomorphism (2018) Int. J. Soc. Robot., 10 (5), pp. 701-714. , 2018; Seibt, J., Vestergaard, C., Damholdt, M.F., Sociomorphing, not anthropomorphizing: Towards a typology of experienced sociality (2020) Culturally Sustainable Social Robotics-Proceedings of Robophilosophy 2020, (2020), pp. 51-67. , M. Nørskov, J. Seibt, O. Quick. IOS Press, Amsterdam; Wykowska, A., Metta, G., Becchio, C., Hortensius, R., Cross, E., Cognitive and social neuroscience methods for HRI (2018) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction., pp. 405-406; Wiese, E., Metta, G., Wykowska, A., Robots as intentional agents: Using neuroscientifc methods to make robots appear more social (2017) Front. Psychol., 8, p. 1663. , 2017; Smedegaard, C.V., Reframing the role of novelty within social HRI: From noise to information (2019) Proceedings of the 14th ACM/IEEE International Conference on Human-robot Interaction (HRI'19)., pp. 411-420; Seibt, J., Damholdt, M.F., Vestergaard, C., Integrative social robotics, value-driven design, and transdisci-plinarity (2020) Interact. Stud., 21 (1), pp. 111-144. , 2020; (2018) The Ieee Global Initiative on Ethics of Autonomous and Intelligent Systems, , https://standards.ieee.org/industry-connections/ec/autonomous-systems.html, IEEE. IEEE SA; Jost, C., Podevic, B., Grandgeorge, M., (2019) Methods in Human Robot Interaction, , Springer; Cheon, E., Su, N.M., Futuristic autobiographies: Weaving participant narratives to elicit values around robots (2018) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI'18)., pp. 388-397",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100920990
"Sheron P.S.F., Sridhar K.P., Baskar S., Shakeel P.M.","57218371092;55608797500;56396373800;57211443039;","Projection-dependent input processing for 3D object recognition in human robot interaction systems",2021,"Image and Vision Computing","106",,"104089","","",,3,"10.1016/j.imavis.2020.104089","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098654356&doi=10.1016%2fj.imavis.2020.104089&partnerID=40&md5=3902f0935d63908decb9ab766128101f","Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia","Sheron, P.S.F., Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Sridhar, K.P., Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Baskar, S., Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Shakeel, P.M., Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia","Human-Robot Interaction (HRI) provides assisted services in different real-time applications. The robotic systems identify objects through digital visualization wherein a three-dimensional (3D) image is converged to a plane-based projection. The projection is analyzed using the co-ordinates and identification points for recognizing the object. In such a converging process, the misidentification of projections in different planes results in recognition errors. This article proposes projection-dependent input processing (PDIP) method to reduce the misidentifications in object recognition. In this method, the input is the visualizing image projected in all the possible dimensions to identify the conjoining indices. The conjoined indices without intersection are segregated using labeled analysis. The non-correlating indices are identified in the possible dimension projections to prevent errors. The deviations in planes and indices matching are prevented by correlating the input with similar stored inputs with labels. The proposed method is verified using the metrics recognition ratio (96.4%), time (630.36 ms), complexity (5.93), and error (0.605). © 2020 Elsevier B.V.","3D images; Dimension modeling; HRI; Principle component analysis; Space projection","Errors; Man machine systems; Object recognition; Three dimensional computer graphics; 3d object recognition; Digital visualization; Human robot Interaction (HRI); Identification point; Real-time application; Recognition error; Recognition ratio; Three-dimensional (3D) image; Social robots",,,,,"Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; Science and Engineering Research Board, SERB; Department of Science and Technology, Government of West Bengal, DST","The authors would like to thank SERB, Science and Engineering Research Board, and Department of Science and Technology(DST), New Delhi, India for the funding to carry out the research work at our institution.","The authors would like to thank SERB, Science and Engineering Research Board , and Department of Science and Technology(DST) , New Delhi, India for the funding to carry out the research work at our institution.",,,,,,,,,"Boutteau, R., Rossi, R., Qin, L., Merriaux, P., Savatier, X., A vision-based system for robot localization in large industrial environments (2019) J. 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Remote Sens., 161, pp. 13-26; Rahman, M.M., Tan, Y., Xue, J., Shao, L., Lu, K., 3D object detection: learning 3D bounding boxes from scaled-down 2D bounding boxes in RGB-D images (2019) Inf. Sci., 476, pp. 147-158; Ren, Y., Chen, C., Li, S., Kuo, C.-C.J., Context-assisted 3D (C3D) object detection from RGB-D images (2018) J. Vis. Commun. Image Represent., 55, pp. 131-141; Wang, D., He, Y., Liu, Y., Li, D., Wu, S., Qin, Y., Xu, Z., 3D object detection algorithm for panoramic images with multi-scale convolutional neural network (2019) IEEE Access., 7, pp. 171461-171470; Guo, B., Dai, H., Li, Z., Huang, W., Efficient planar surface-based 3D mapping method for mobile robots using stereo vision (2019) IEEE Access., 7, pp. 73593-73601; Berz, E.L., Tesch, D.A., Hessel, F.P., Machine-learning-based system for multi-sensor 3D localization of stationary objects (2018) IET Cyber-Phys. Syst. Theory Applic., 3 (2), pp. 81-88; Crivellaro, A., Rad, M., Verdie, Y., Yi, K.M., Fua, P., Lepetit, V., Robust 3D object tracking from monocular images using stable parts (2018) IEEE Trans. Pattern Anal. Mach. Intell., 40 (6), pp. 1465-1479; Zhang, J., Sun, J., Instance-based object recognition in 3D point clouds using discriminative shape primitives (2017) Mach. Vis. Appl., 29 (2), pp. 285-297; Yang, Y., Qiu, F., Li, H., Zhang, L., Wang, M.-L., Fu, M.-Y., Large-scale 3D semantic mapping using stereo vision (2018) Int. J. Autom. Comput., 15 (2), pp. 194-206; https://shape.cs.princeton.edu/benchmark/","Sheron, P.S.F.; Department of Electronics and Communication, India; email: febinsheronpps@gmail.com",,,"Elsevier Ltd",,,,,02628856,,IVCOD,,"English","Image Vision Comput",Article,"Final","",Scopus,2-s2.0-85098654356
"Du Z., Liang Y., Yan Z., Sun L., Chen W.","8698649300;57195986873;55414380400;57204292698;57198583932;","Human-robot interaction control of a haptic master manipulator used in laparoscopic minimally invasive surgical robot system",2021,"Mechanism and Machine Theory","156",,"104132","","",,1,"10.1016/j.mechmachtheory.2020.104132","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092351200&doi=10.1016%2fj.mechmachtheory.2020.104132&partnerID=40&md5=b1f7579d99f7fd37e5484a1d7af4bced","State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Shenzhen polytechnic, Shenzhen, Guangdong, China","Du, Z., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Liang, Y., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Yan, Z., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Sun, L., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Chen, W., Shenzhen polytechnic, Shenzhen, Guangdong, China","As an interface for physical human-robot interaction (pHRI), the master manipulator of robot-assisted surgery system is critical to the success rate of surgical procedures. In this paper, a pHRI control scheme of the haptic master manipulator used in laparoscopic surgical robots is presented for enhancing the precision and comfort of operations. Firstly, the inverse dynamic equations of the master manipulator are derived and a modified friction model is proposed to improve the accuracy of friction torque calculation. Then a torque observer based on generalized momentum is designed. The proposed observer can precisely track the joint driving torque needed by the motion state and comply with the operator's intentions. Thirdly, the compensator based on time-delay neural network (TDNN) is proposed to improve the performance of pHRI. Finally, the pHRI control strategy which can adjust the degree of operation compliance actively is introduced. The results of theoretical analysis and comparative experiments indicate that the proposed strategy can substantially reduce the force and torque applied to surgeons by the master manipulator during the operation. © 2020","Generalized momentum; Master manipulator; Physical human-robot interaction; Surgical robots; Torque observer","Compliance control; Delay control systems; Friction; Laparoscopy; Man machine systems; Manipulators; Social robots; Surgical equipment; Torque; Transplantation (surgical); Comparative experiments; Human-robot interaction control; Inverse dynamic equations; Laparoscopic surgical robots; Minimally invasive surgical robot; Physical humanrobot interaction (phri); Robot-assisted surgery; Time delay neural networks; Robotic surgery",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1304202","This work was supported by the National Key R&D Program of China [grant number 2017YFB1304202 ].",,,,,,,,,,"Liang, Y., Du, Z., Wang, W., Sun, L., A novel position compensation scheme for cable-pulley mechanisms used in laparoscopic surgical robots (2017) Sensors, 17, p. 2257; Liang, Y., Du, Z., Wang, W., Yan, Z., Sun, L., An improved scheme for eliminating the coupled motion of surgical instruments used in laparoscopic surgical robots (2019) Rob. Autom. Syst., 112, pp. 49-59; Xue, R., Du, Z., Yan, Z., Ren, B., An estimation method of grasping force for laparoscope surgical robot based on the model of a cable-pulley system (2019) Mech. Mach. Theory, 134, pp. 440-454; Nisar, S., Endo, T., Matsuno, F., Design and optimization of a 2-degree-of-freedom planar remote center of motion mechanism for surgical manipulators with smaller footprint (2018) Mech. Mach. Theory, 129, pp. 148-161; Liang, Y., Sun, L., Du, Z., Yan, Z., Wang, W., Mechanism design and optimization of a haptic master manipulator for laparoscopic surgical robots (2019) IEEE Access, 7, pp. 147808-147824; Xue, R., Ren, B., Yan, Z., Du, Z., A cable-pulley system modeling based position compensation control for a laparoscope surgical robot (2017) Mech. Mach. Theory, 118, pp. 283-299; Yaşır, A., Kiper, G., Dede, M.İ.C., Kinematic design of a non-parasitic 2R1T parallel mechanism with remote center of motion to be used in minimally invasive surgery applications (2020) Mech. Mach. Theory, 153; Kim, W., Choi, Y.R., Cho, J.Y., Yoon, Y.S., Han, H.S., Robot single incision left lateral sectionectomy via da Vinci Xi Single Site & vaginal extraction of the specimen (2020) Surg. Oncol., 33, pp. 254-255; Agarwal, D.K., Sharma, V., Toussi, A., Viers, B.R., Tollefson, M.K., Gettman, M.T., Frank, I., Initial experience with da Vinci single-port robot-assisted radical prostatectomies (2019) Eur. Urol.; Freschi, C., Ferrari, V., Melfi, F., Ferrari, M., Mosca, F., Cuschieri, A., Technical review of the da Vinci surgical telemanipulator (2013) Int. J. Med. Robot. Comput. Assist. Surgery, 9, pp. 396-406; Ballantyne, G.H., Moll, F., The da Vinci telerobotic surgical system: the virtual operative field and telepresence surgery (2003) Surgical Clinics, 83, pp. 1293-1304; Du, Z., Wang, W., Yan, Z., Dong, W., Wang, W., Variable admittance control based on fuzzy reinforcement learning for minimally invasive surgery manipulator (2017) Sensors, 17, p. 844; Li, Z., Bo, H., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robotic exoskeleton by admittance control (2018) IEEE Trans. Indust. Electron., p. 1. , -1; Chandrasekaran, K., Sivaraman, S., Thondiyath, A., (2015), Static balancing and inertia compensation of a master manipulator for tele-operated surgical robot application; Ugurlu, B., Oshima, H., Sariyildiz, E., Narikiyo, T., Babic, J., Active compliance control reduces upper body effort in exoskeleton-supported walking (2020) IEEE Trans. Human Mach. 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"Bolotnikova A., Gergondet P., Tanguy A., Courtois S., Kheddar A.","57190802530;36801684700;57193646277;57204948230;56276683900;","Task-Space Control Interface for SoftBank Humanoid Robots and its Human-Robot Interaction Applications",2021,"2021 IEEE/SICE International Symposium on System Integration, SII 2021",,,"9382685","560","565",,,"10.1109/IEEECONF49454.2021.9382685","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103741993&doi=10.1109%2fIEEECONF49454.2021.9382685&partnerID=40&md5=900a7ac78022d9dedd6566b6983f5c9a","SoftBank Robotics Europe, Paris, France; University of Montpellier-CNRS Lirmm, Interactive Digital Humans, Montpellier, France; CNRS-AIST Joint Robotics Faboratory, Irf, Tsukuba, 3218, Japan; Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China","Bolotnikova, A., SoftBank Robotics Europe, Paris, France, University of Montpellier-CNRS Lirmm, Interactive Digital Humans, Montpellier, France; Gergondet, P., Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China; Tanguy, A., CNRS-AIST Joint Robotics Faboratory, Irf, Tsukuba, 3218, Japan; Courtois, S., SoftBank Robotics Europe, Paris, France; Kheddar, A., University of Montpellier-CNRS Lirmm, Interactive Digital Humans, Montpellier, France, CNRS-AIST Joint Robotics Faboratory, Irf, Tsukuba, 3218, Japan, Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China","We present an open-source software interface, called mc-naoqi, that allows to perform whole-body task-space Quadratic Programming based control, implemented in mc rtc framework, on the SoftBank Robotics Europe humanoid robots. We describe the control interface, associated robot description packages, robot modules and sample whole-body controllers. We demonstrate the use of these tools in simulation for a robot interacting with a human model. Finally, we showcase and discuss the use of the developed open-source tools for running the human-robot close contact interaction experiments with real human subjects inspired from assistance scenarios. © 2021 IEEE.",,"Anthropomorphic robots; End effectors; Open source software; Open systems; Quadratic programming; Robot programming; Contact interaction; Control interfaces; Human Model; Human robots; Human subjects; Humanoid robot; Open source tools; Task-space control; Human robot interaction",,,,,,,,,,,,,,,,"Escande, A., Mansard, N., Wieber, P.-B., Hierarchical quadratic programming: Fast online humanoid-robot motion generation (2014) The International Journal of Robotics Research, 33, pp. 1006-1028. , 1 May; Cisneros, R., Benallegue, M., Morisawa, M., Kanehiro, F., Qpbased task-space hybrid/parallel control for multi-contact motion in a torque-controlled humanoid robot (2019) IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), pp. 663-670. , (Toronto, Canada), 15-17 October; Bouyarmane, K., Chappellet, K., Vaillant, J., Kheddar, A., Quadratic programming for multirobot and task-space force control (2019) IEEE Transactions on Robotics, 35, pp. 64-77. , February; Kheddar, A., Caron, S., Gergondet, P., Comport, A., Tanguy, A., Ott, C., Henze, B., Roa, M.A., Humanoid robots in aircraft manufacturing: The airbus use cases (2019) IEEE Robotics & Automation Magazine, 26, pp. 30-45. , December; Otani, K., Bouyarmane, K., Ivaldi, S., Generating assistive humanoid motions for co-manipulation tasks with a multi-robot quadratic program controller (2018) IEEE International Conference on Robotics and Automation (ICRA), pp. 3107-3113. , (Brisbane, Australia), 21-25 May; Bolotnikova, A., Courtois, S., Kheddar, A., Autonomous initiation of human physical assistance by a humanoid (2020) IEEE 29th International Conference on Robot and Human Interactive Communication (RO-MAN), , (virtual conference), 31 August-4 September; Hirukawa, H., Kanehiro, F., Kaneko, K., Kajita, S., Fujiwara, K., Kawai, Y., Tomita, F., Fukase, Y., Humanoid robotics platforms developed in hrp (2004) Robotics and Autonomous Systems, 48, pp. 165-175. , 31 October; Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Maisonnier, B., Mechatronic design of nao humanoid (2009) IEEE International Conference on Robotics and Automation (ICRA), pp. 769-774. , (Kobe, Japan), 12-17 May; Pandey, A., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) IEEE Robotics & Automation Magazine, 25, pp. 40-48. , September; Escande, A., Miossec, S., Benallegue, M., Kheddar, A., A strictly convex hull for computing proximity distances with continuous gradients (2014) IEEE Transactions on Robotics, 30, pp. 666-678. , June; Kam, H.R., Lee, S.-H., Park, T., Kim, C.-H., Rviz: A toolkit for real domain data visualization (2015) Telecommunication Systems, 60, pp. 337-345. , October",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/SICE International Symposium on System Integration, SII 2021","11 January 2021 through 14 January 2021",,168053,,9781728176581,,,"English","IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85103741993
"Hafi L.E., Nakamura H., Taniguchi A., Hagiwara Y., Taniguchi T.","57188836029;57212144620;57189575562;26648361300;35585520600;","Teaching System for Multimodal Object Categorization by Human-Robot Interaction in Mixed Reality",2021,"2021 IEEE/SICE International Symposium on System Integration, SII 2021",,,"9382607","320","324",,1,"10.1109/IEEECONF49454.2021.9382607","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103740027&doi=10.1109%2fIEEECONF49454.2021.9382607&partnerID=40&md5=62c344047ca18cda822a04837d3503e0","Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan","Hafi, L.E., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Nakamura, H., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Taniguchi, A., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Hagiwara, Y., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Taniguchi, T., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan","As service robots are becoming essential to support aging societies, teaching them how to perform general service tasks is still a major challenge preventing their deployment in daily-life environments. In addition, developing an artificial intelligence for general service tasks requires bottom-up, unsupervised approaches to let the robots learn from their own observations and interactions with the users. However, compared to the top-down, supervised approaches such as deep learning where the extent of the learning is directly related to the amount and variety of the pre-existing data provided to the robots, and thus relatively easy to understand from a human perspective, the learning status in bottom-up approaches is by their nature much harder to appreciate and visualize. To address these issues, we propose a teaching system for multimodal object categorization by human-robot interaction through Mixed Reality (MR) visualization. In particular, our proposed system enables a user to monitor and intervene in the robot's object categorization process based on Multimodal Latent Dirichlet Allocation (MLDA) to solve unexpected results and accelerate the learning. Our contribution is twofold by 1) describing the integration of a service robot, MR interactions, and MLDA object categorization in a unified system, and 2) proposing an MR user interface to teach robots through intuitive visualization and interactions. © 2021 IEEE.",,"Deep learning; Man machine systems; Mixed reality; Mobile robots; Statistics; User interfaces; Visualization; Aging societies; Bottom up approach; Human perspectives; Latent Dirichlet allocation; Object categorization; Teaching systems; Unified system; Unsupervised approaches; Social robots",,,,,"Ministry of Education, Culture, Sports, Science and Technology, Monbusho: 17H06379, 4903","This study was supported by the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas “Integrative Studies of Language Evolution for Co-Creative Human Communication” (Area no. 4903, Task no. 17H06379). 1Lotf El Hafi, Hitoshi Nakamura, Akira Taniguchi, Yoshinobu Hagiwara, and Tadahiro Taniguchi are with Ritsumeikan University; 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan. { "" l o t f i . e l h a f i , nakam ura.hitoshi, a.taniguchi, yhagiwara, t a n i g u c h i .Z @ e m . c i . r i t s u m e i . a c . j p",,,,,,,,,,"Nakamura, T., Nagai, T., Iwahashi, N., Grounding of word meanings in multimodal concepts using lda (2009) Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), p. 39433948. , St. Louis, United States, Oct; Araki, T., Nakamura, T., Nagai, T., Long-term learning of concept and word by robots: Interactive learning framework and preliminary results (2013) Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pp. 2280-2287. , Tokyo, Japan, Nov; Liu, H., Zhang, Y., Si, W., Xie, X., Zhu, Y., Zhu, S.-C., Interactive robot knowledge patching using augmented reality (2018) Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA 2018), pp. 1947-1954. , Brisbane, Australia, May; (2016) HoloLens, , https://www.microsoft.com/en-us/hololens, Microsoft; El Hafi, L., Isobe, S., Tabuchi, Y., Katsumata, Y., Nakamura, H., Fukui, T., Matsuo, T., Taniguchi, T., System for augmented human-robot interaction through mixed reality and robot training by non-experts in customer service environments (2020) RSJ Advanced Robotics, 34 (3-4), pp. 157-172. , Feb; Wada, K., New robot technology challenge for convenience store (2017) Proceedings of 2017 IEEE/SICE International Symposium on System Integration (SII2017), pp. 1086-1091. , Taipei, Taiwan, Dec; (2005) Unity Technologies, , https://unity3d.com/, Unity; (2018) Sciurus17, , https://rt-net.jp/products/sciurus17/, RT Corporation; Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Ng, A., Ros: An open-source robot operating system (2009) Proceedings of 2009 IEEE Workshop on Open Source Software, , Kobe, Japan, May; Lamb, P., (2017) ARToolKt5, , https://github.com/artoolkit/ARToolKit5; Bischoff, M., (2017) ROS#, , https://github.com/siemens/ros-sharp; (2000) OpenCV: Open Source Computer Vision Library, , https://github.com/opencv/opencv, Intel Corporation; Alcantarilla, P.F., Nuevo, J., Bartoli, A., Fast explicit diffusion for accelerated features in nonlinear scale spaces (2013) Proceedings of 24th British Machine Vision Conference (BMVC 2013), 13, pp. 1-11. , Bristol, United Kingdoms, Sept",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/SICE International Symposium on System Integration, SII 2021","11 January 2021 through 14 January 2021",,168053,,9781728176581,,,"English","IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85103740027
"Kim D.H., Lee D., Kim Y., Kim S., Shin D.","57207193276;57205745198;55662738300;57221790286;55449807100;","A power assistant algorithm based on human–robot interaction analysis for improving system efficiency and riding experience of e‐bikes",2021,"Sustainability (Switzerland)","13","2","768","1","19",,1,"10.3390/su13020768","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100187764&doi=10.3390%2fsu13020768&partnerID=40&md5=b41818b342bad304fc9a29228d37e62a","Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea","Kim, D.H., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Lee, D., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Kim, Y., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Kim, S., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Shin, D., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea","As robots are becoming more accessible in our daily lives, the interest in physical human– robot interaction (HRI) is rapidly increasing. An electric bicycle (E‐bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E‐bike in close proximity. Most commercially available E‐bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E‐bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E‐bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E‐bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Electric bicycles (E‐bikes); Energy consumption; Human analysis; Leg kinematics; Muscular dynamics; Riding experience","algorithm; biomechanics; detection method; efficiency measurement; electric vehicle; methodology; parameter estimation",,,,,"Ministry of Trade, Industry and Energy, MOTIE; Ministry of Science, ICT and Future Planning, MSIP: 20007058, 2018R1C1B6008549; National Research Foundation of Korea, NRF","Funding: This study was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.NRF‐2018R1C1B6008549). Also, this study was supported by the Industrial Technology Innovation Program (No. 20007058, Development of safe and comfortable human augmentation hybrid robot suit) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). And, this study was supported by the Chung‐Ang University Graduate Research Scholarship in 2017.","This study was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.NRF?2018R1C1B6008549). Also, this study was supported by the Industrial Technology Innovation Program (No. 20007058, Development of safe and comfortable human augmentation hybrid robot suit) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). And, this study was supported by the Chung?Ang University Graduate Research Scholarship in 2017.The authors would like to thank people in the Human?Centered Robotics Laboratory at Chung?Ang University for their valuable comments and feedback.",,,,,,,,,"Shin, D., Sardellitti, I., Khatib, O., Cutkosky, M., Design and control of a bio‐inspired human‐friendly robot (2010) Int. J. Robot. Res, 29, pp. 571-584; Wang, P., (2018) Dynamics and control of rider‐bicycle systems, , Ph.D. dissertation. Rutgers University, Piscataway, NJ, USA, October; Wang, P., Gong, Y., Yi, J., Liu, T., An integrated stationary/moving balance control of an autonomous bikebot Proceedings of the IEEE American Control Conference, pp. 3273-3278. , Philadelphia, PA, USA, 10–12 July 2019; Kim, N., Yun, S., Shin, D., A bio‐inspired lightweight wrist for high‐dof robotic prosthetic arms (2019) Ieee/Asme Trans. Mechatron, 24, pp. 2674-2683; Lee, D., Kim, D.H., Che, C.H., In, J.B., Shin, D., Highly durable bidirectional joint with twisted string actuators and variable radius pulley (2020) Ieee/Asme Trans. Mechatron, 25, pp. 2674-2683; Goodrich, M.A., Schultz, A.C., Human–robot interaction: A survey (2007) Found. Trends® Hum.–Comput. Interact, 1, pp. 203-275; Kim, N. H., Kim, J. M., Khatib, O., Shin, D., Design optimization of hybrid actuation combining macro‐mini actuators (2017) Int. J. Precis. Eng. Manuf, 18, pp. 519-527; Hansen, K. B., Nielsen, T. A. S., Exploring characteristics and motives of long distance commuter cyclists (2014) Transp. Policy, 35, pp. 57-63; Ton, D., Duives, D. C., Cats, O., Hoogendoorn‐Lanser, S., Hoogendoorn, S. P., Cycling or walking? Determinants of mode choice in the Netherlands (2019) Transp. Res. Part A: Policy Pract, 123, pp. 7-23; Koszowski, C., Gerike, R., Hubrich, S., Götschi, T., Pohle, M., Wittwer, R., Active mobility: bringing together transport planning, urban planning, and public health (2019) Towards User‐Cent. Transp. Eur. Springer: Chamswitzerland, pp. 149-171; Hatada, K., Hirata, K., Energy‐efficient power assisting methods for periodic motions and its experimental verification (2012) Proceedings of the 2012 IEEE International Conference on Industrial Technology, pp. 858-863. , Athens, Greece, 19–21 March; Spagnol, P., Alli, G., Spelta, C., Lisanti, P., Todeschini, F., Savaresi, S.M., Morelli, A., A full hybrid electric bike: How to increase human efficiency (2012) Proceedings of the 2012 American Control Conference (ACC), pp. 2761-2766. , Montreal, QC, Canada. 27–29 June; Hull, M.L., Gonzalez, H., Bivariate optimization of pedaling rate and crank arm length in cycling (1988) J. Biomech, 21, pp. 839-849; Too, D., Williams, C., Determination of the optimal crank arm length to maximize peak power production in an upright cycling position (2018) Hum. Power Ej, 25, pp. 1-26; Cafolla, D., Chen, I, Ceccarelli, M., An experimental characterization of human torso motion (2015) Front. Mech. Eng, 10, pp. 311-325. , M; Gorenflo, C., Rios, I., Golab, L., Keshav, S., Usage patterns of electric bicycles: an analysis of the WeBike project (2017) J. Adv. Transp, pp. 1-14; Hsu, R. C., Liu, C. T., Chan, D. Y., A reinforcement‐learning‐based assisted power management with QoR provisioning for human–electric hybrid bicycle (2012) Trans. Ind. Electronics, 59, pp. 3350-3359; Lee, J. S., Jiang, J. W., Enhanced fuzzy‐logic‐based power‐assisted control with user‐adaptive systems for human‐electric bikes (2019) Iet Intell. Transp. Systems, 13, pp. 1492-1498; Liu, C. T., Hsu, R. C., A fuzzy Q‐learning based assisted power management method for comfortable riding of pedelec (2015) IEEE International Conference on Automation, Robotics and Applications, pp. 580-585. , Queenstown, New Zealand. 17–19 February; Bhargava, L. J., Pandy, M. G., Anderson, F. C., A phenomenological model for estimating metabolic energy consumption in muscle contraction (2004) J. Biomechanics, 37, pp. 81-88; Marks, R., The effect of isometric quadriceps strength training in mid‐range for osteoarthritis of the knee (1993) Arthritis Rheum.: Off. J. Am. Coll. Rheumatology, 6, pp. 52-56; Thelen, D. G., Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults (2003) J. Biomechanics. Eng, 125, pp. 70-77; Ting, L. H., Kautz, S. A., Brown, D. A., Zajac, F. E., Phase reversal of biomechanical functions and muscle activity in backward cycling (1999) J. Neurophysiology, 81, pp. 544-551; Childers, W. L., Kistenberg, R. S., Gregor, R. J., The biomechanics of cycling with a transtibial amputation: recommendations for prosthetic design and direction for future research (2009) Prosthet. Orthot. Intl, 33, pp. 256-271; Lai, A. K., Arnold, A. S., Wakeling, J. M., Why are antagonist muscles co‐activated in my simulation? A musculoskeletal model for analysing human locomotor tasks (2017) Ann. Biomed. Engineering, 45, pp. 2762-2774; Shi, F., Improving urban non‐motorized mobility for public affairs trips: a survey and analysis of innovative official bicycles in Nanjing city, China (2019) J. Urban Management, 8, pp. 396-407; Jensen, P., Rouquier, J. B., Ovtracht, N., Robardet, C., Characterizing the speed and paths of shared bicycle use in Lyon (2010) Transp. Res. Part D: Transp. Environment, 15, pp. 522-524; Rossi, R., Mantuano, A., Pascucci, F., Rupi, F., Fitting time headway and speed distributions for bicycles on separate bicycle lanes (2017) Transp. Res. Procedia, 27, pp. 19-26; Szecsi, J., Krause, P., Krafczyk, S., Brandt, T., Straube, A., Functional output improvement in FES cycling by means of forced smooth cycling (2007) Med. Sci. Sports Exercise, 39, pp. 764-780; Zhang, L., Diraneyya, M. M., Ryu, J., Haas, C. T., Abdel‐Rahman, E. M., Jerk as an indicator of physical exertion and fatigue (2019) Autom. Construction, 104, pp. 120-128","Shin, D.; Department of Mechanical Engineering, South Korea; email: djshin@cau.ac.kr",,,"MDPI",,,,,20711050,,,,"English","Sustainability",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85100187764
"Shamonsky D.","57363866100;","User experience best practices for human-robot interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12763 LNCS",,,"318","328",,,"10.1007/978-3-030-78465-2_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120711089&doi=10.1007%2f978-3-030-78465-2_24&partnerID=40&md5=2585d7f8de05ce6015bb2051a4c7030c","Brandeis University, 415 South Street, Waltham, MA  02453, United States","Shamonsky, D., Brandeis University, 415 South Street, Waltham, MA  02453, United States","User experience (UX) design of human-robot interaction (HRI) is an emerging practice [1]. Best practices for this discipline are actively evolving as robotics expands into commercial markets. As is typical of emerging technologies, the technology itself takes center stage, continuing to present challenges to proper functioning as it is tested in real world applications. Also, deployment comes at a high price until the market is competitive enough to drive hardware and development prices down. All these aspects preclude an emphasis on UX design, until the technology and associated market reaches a tipping point and good user experience is in demand. If robots continue to be deployed at rates the industry currently predicts, the need for user experience design knowledge and best practices for HRI is eminent. Best practices are a collection of methods, specifically principles, heuristic evaluators and taxonomies [2]. Principles embody high level guidance for design direction and design processes [3]. Heuristics ensure measurable “must have” base-functionality [4–6]. Taxonomies provide a conceptual understanding of possible categories of interactivity [7–9]. This paper focuses on two aspects of best practices, 1.) proposing a robustly user-centric set of emerging technology principles for HRI, which is the area of best practices least explored by the literature, and 2.) proposing a design matrix as a beginning step in addressing the complexity of HRI. © Springer Nature Switzerland AG 2021.","Emerging technology principles; HRI best practices; HRI heuristics; Human-robot interaction","Commerce; Heuristic methods; Machine design; Man machine systems; Best practices; Emerging technologies; Emerging technology principle; Human-robot interaction best practice; Human-robot interaction heuristic; Humans-robot interactions; Technology principles; Users' experiences; Human robot interaction",,,,,,,,,,,,,,,,"Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Advances in Intelligent Systems and Computing, 490, pp. 267-277. , https://doi.org/10.1007/978-3-319-41697-7_24, Schlick, C., Trzcieliński, S. (eds.), vol., pp. , Springer, Cham; Best Practice: https://en.wikipedia.org/wiki/Best_practice. Accessed 12 Feb 2021; Tognazzini, B.: First Principles, HCI Design, Human Computer Interaction (HCI), Principles of HCI Design, Usability Testing. https://asktog.com/atc/principles-of-interaction-design/. Accessed 12 Feb 2021; Clarkson, E., Arkin, R.: Applying heuristic evaluation to human-robot interaction systems. In: GVU Tech Report No. GIT-GVU-06–08 (2006). ftp://ftp.cc.gatech.edu/pub/gvu/tr/2006/ 06-08.pdf; Powers, A., FEATURE, what robotics can learn from HCI (2008) Interactions, 15 (2), pp. 67-69. , https://doi.org/10.1145/1340961.1340978; Weiss, A., Wurhofer, D., Bernhaupt, R., Altmaninger, M., Tscheligi, M., A methodological adaptation for heuristic evaluation of HRI (2010) 19Th International Symposium in Robot and Human Interactive Communication, pp. 1-6. , https://doi.org/10.1109/ROMAN.2010.5598735, Viareggio, pp; Malik, A.A., Bilberg, A., Developing a reference model for human–robot interaction (2019) Int. J. Interact. Des. Manufact. (Ijidem), 13 (4), pp. 1541-1547. , https://doi.org/10.1007/s12008-019-00591-6; Yanco, H.A., Drury, J.L., Classifying human-robot interaction: An updated taxonomy (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics, the Hague, Vol. 3, P. 2841, , https://doi.org/10.1109/ICSMC.2004.1400763; Yanco, H.A., Drury, J.L., A taxonomy for human-robot interaction (2002) Proceedings of the AAAI Fall Symposium on Human-Robot Interaction, pp. 111-119. , , pp; Rosenzweig, E., (2015) Successful User Experience: Strategies and Roadmaps, , https://doi.org/10.1016/C2013-0-19353-1, Morgan Kaufmann Publishers Inc., San Francisco; Wigdor, D., Wixon, D., Brave NUI World (2011) Designing Natural User Interfaces for Touch and Gesture. Morgan Kaufman, Boston; Shneiderman, B., (1987) Designing the User Interface: Strategies for Effective Human-Computer Interaction, , Addison-Wesley Publishing Co., Reading; Scholtz, J., Evaluation methods for human-system performance of intelligent systems (2002) Proceedings of PERMIS’02; Nielsen, J., Finding usability problems through heuristic evaluation (1992) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 373-380. , https://doi.org/10.1145/142750.142834, , pp. , Association for Computing Machinery, Monterey; Nielsen, J., Enhancing the explanatory power of usability heuristics (1994) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 152-158. , https://doi.org/10.1145/191666.191729, , pp. , Association for Computing Machinery, Boston; Mankoff, J., Dey, A.K., Hsieh, G., Kientz, J., Ames, M., Lederer, S., Heuristic evaluation of ambient displays (2003) Proceedings of CHI’03, pp. 169-176. , , pp; Onnasch, L., Roesler, E., A taxonomy to structure and analyze human–robot interaction (2020) Int. J. Soc. Robot., pp. 1-17. , https://doi.org/10.1007/s12369-020-00666-5; Weiss, A., Bernhaupt, R., Lankes, M., Tscheligi, M., The USUS evaluation framework for human-robot interaction. Proc (2009) AISB, 9 (4), pp. 11-26; Nielsen, J.: 10 Usability Heuristics for Interface Design. https://www.nngroup.com/articles/ten-usability-heuristics/. Accessed 12 Feb 2021; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2018) Int. J. Ind. Ergon., 68, pp. 355-367. , https://doi.org/10.1016/j.ergon.2017.02.004; Sheikholeslami, S., Moon, A., Croft, E.A., Cooperative gestures for industry: Exploring the efficacy of robot hand configurations in expression of instructional gestures for human–robot interaction (2017) Int. J. Robot. Res., 36, pp. 699-720. , https://doi.org/10.1177/027836491770 9941; Getting Started with the Guide | Alexa Skills Kit: https://developer.amazon.com/en-US/docs/alexa/alexa-design/get-started.html. Accessed 12 Feb 2021; (2021) Principles of Leap Motion Interaction Design, , https://blog.leapmotion.com/6-principles-of-interaction-design/. Accessed 12, Feb; Our Principles – Google AI: https://ai.google/principles/. Accessed 12 Feb 2021; Campana, J.R., Quaresma, M., The importance of specific usability guidelines for robot user interfaces (2017) DUXU 2017. LNCS, Vol. 10289, pp. 471-483. , https://doi.org/10.1007/978-3-319-58637-3_37, Marcus, A., Wang, W. (eds.), pp. , Springer, Cham; Shamonsky, D., (2021) User Experience Design Principles for a Natural User Interface (NUI, , https://www.ics.com/blog/user-experience-design-principles-natural-user-interf ace-nui. Accessed 12, Feb","Shamonsky, D.; Brandeis University, 415 South Street, United States; email: dshamonsky@ics.com","Kurosu M.",,"Springer Science and Business Media Deutschland GmbH","Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262079,03029743,9783030784645,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85120711089
"Alenljung B., Lindblom J.","24402835300;7006805787;","Analysing action and intention recognition in human-robot interaction with anemone",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12763 LNCS",,,"181","200",,,"10.1007/978-3-030-78465-2_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120694190&doi=10.1007%2f978-3-030-78465-2_14&partnerID=40&md5=843b3a88b135d4a135d1944894794848","University of Skövde, Skövde, Sweden","Alenljung, B., University of Skövde, Skövde, Sweden; Lindblom, J., University of Skövde, Skövde, Sweden","The ANEMONE is a methodological approach for user experience (UX) evaluation of action and intention recognition in human-robot interaction that has activity theory as its theoretical lens in combination with the seven stages of action model and UX evaluation methodology. ANEMONE has been applied in a case where a prototype has been evaluated. The prototype was a workstation in assembly in manufacturing consisting of a collaborative robot, a pallet, a tablet, and a workbench, where one operator is working in the same physical space as one robot. The purpose of this paper is to provide guidance on how to use ANEMONE, with a particular focus on the data analysis part, through describing a real example together with lessons learned and recommendations. © Springer Nature Switzerland AG 2021.","Action Recognition; Activity Theory; Evaluation; Human-Robot Collaboration; Human-Robot Interaction; Intention Recognition; Seven Stages of Action Model; User Experience (UX); User-centered","Activity coefficients; Collaborative robots; Human computer interaction; Action modeling; Action recognition; Activity Theory; Evaluation; Human-robot collaboration; Humans-robot interactions; Intention recognition; Seven stage of action model; User experience (UX); User-centred; Users' experiences; Man machine systems",,,,,"Horizon 2020 Framework Programme, H2020: 637107; European Commission, EC; Stiftelsen för Kunskaps- och Kompetensutveckling, KKS: 20140220","Acknowledgements. We wish to thank the participants that have contributed to our UX evaluation. We also want to thank our colleagues Wei Wang, Magnus Holm, Richard Sennington, and Bernard Schmidt who have provided assistance during data collection and configured the robot platform used in the present study. We also thank Christine Lindblom for designing Fig. 4. This work was financially supported by the Knowledge Foundation, Stockholm, under SIDUS grant agreement no. 20140220 (AIR, action and intention recognition in human interaction with autonomous systems) as well as the European Commission under the Horizon 2020 Programme under grant agreement no. 637107 (SYMBIO-TIC).",,,,,,,,,,"Lindblom, J., Alenljung, B., The ANEMONE: Theoretical foundations for UX evaluation of action and intention recognition in human-robot interaction (2020) Sensors, 20, p. 4284. , https://doi.org/10.3390/s20154284; Nouwen, R., van Rooij, R., Sauerland, U., Schmitz, H.-C., Introduction (2011) VIC 2009. LNCS, Vol. 6517, pp. 1-12. , https://doi.org/10.1007/978-3-642-18446-8_1, Nouwen, R., van Rooij, R., Sauerland, U., Schmitz, H.-C. (eds.), pp. , Springer, Heidelberg; Kulichenko, A.D., Polovko, S.A., Yu Smirnova, E., Shubin, P.K., Ulanov, V.N., Ontology for group of rescuing robots (2020) IOP Conference Series: Earth and Environmental Science, 539. , https://doi.org/10.1088/1755-1315/539/1/012121; Lytridis C., et al.: Social robots as cyber-physical actors in entertainment and education. In: International Conference on Software, Telecommunications and Computer Networks (Soft-COM), Split, Croatia, pp. 1–6. IEEE (2019). https://doi.org/10.23919/SOFTCOM.2019.890 3630; Mankodiya, K., Gandhi, R., Narasimhan, P.: Challenges and Opportunities for Embedded Computing in Retail Environments. In: Martins, F., Lopes, L., Paulino, H. (eds.) S-CUBE 2012. LNICSSITE, vol. 102, pp. 121–136. Springer, Heidelberg (2012). https://doi.org/10. 1007/978-3-642-32778-0_10; Esteban, P.G., How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder (2017) Paladyn J. Behav. Robot., 8, pp. 18-38. , https://doi.org/10.1515/pjbr-2017-0002; Boden, M., Principles of robotics: Regulating robots in the real world (2017) Connect. Sci., 29, pp. 124-129. , https://doi.org/10.1080/09540091.2016.1271400; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Trust-aware decision making for human-robot collaboration: Model learning and planning (2020) ACM Trans. Hum.-Robot Interact., 9 (9). , https://doi.org/10.1145/3359616; Kahn, P.H., Jr., et al.: What is a human? Toward psychological benchmarks in the field of human–robot interaction. Interact. Stud. 8, 363–390 (2007). https://doi.org/10.1075/is.8.3. 04kah; Stephanidis, C., Seven HCI grand challenges (2019) Int. J. Hum.-Computer Interact., 35, pp. 1229-1269. , https://doi.org/10.1080/10447318.2019.1619259; Vernon, D., Thill, S., Ziemke, T., The Role of Intention in Cognitive Robotics (2016) Toward Robotic Socially Believable Behaving Systems-Volume I. ISRL, 105, pp. 15-27. , https://doi.org/10.1007/978-3-319-31056-5_3, Esposito, A., Jain, L.C. (eds.), vol., pp. , Springer, Cham; Engeström, Y., (2015) Learning by Expanding: An Activity-Theoretical Approach to Developmental Research, , 2nd edn. Cambridge University Press, New York, NY, USA; Leontiev, A.N., Activity, Consciousness, and Personality. Prentice-Hall, Englewood Cliffs, NJ (1978) USA; Leontiev, A.N.: The problem of activity in psychology. In: Wertsch, J.V. (ed.) The Concept of Activity in Soviet Psychology, pp. 37–71. M.E. Sharpe, Armonk, NY, USA (1972/1981); Vygotsky, L.S., (1978) Mind in Society: The Development of Higher Psychological Processes, , Harvard University Press, Cambridge, MA, USA; Norman, D., Cognitive engineering (1986) Norman, D.A., Draper, S.W, pp. 31-61. , (eds.) User Centred System Design, pp. , Lawrence Erlbaum, Hillsdale, NJ, USA; Norman, D., (2013) The Design of Everyday Things, , Basic Books, New York, NY, USA; Dumas, J.S., Redish, J., (1999) A Practical Guide to Usability Testing, , Ablex Publishing, Norwood, NJ, USA; Hartson, R., Pyla, P., (2018) The UX Book: Agile UX Design for Quality User Experience, , Morgan Kaufmann, Amsterdam, The Netherlands; Hassenzahl, M., User experience and experience design (2013) The Encyclopedia of Human-Computer Interaction, , https://www.interaction-design.org/literature/book/the-enc yclopedia-of-human-computerinteraction-2nd-ed/user-experience-and-experience-design, Soegaard, M., Dam, R.F., (eds.) , 2nd edn. The Interaction Design Foundation, Aarhus, Denmark; Lindblom, J., Alenljung, B., Billing, E., Evaluating the User Experience of Human–Robot Interaction (2020) Human-Robot Interaction. SSBN, Vol. 12, pp. 231-256. , https://doi.org/10.1007/978-3-030-42307-0_9, Jost, C., et al. (eds.), pp. , Springer, Cham; Wallström, J., Lindblom, J., Design and Development of the USUS Goals Evaluation Framework (2020) Human-Robot Interaction. SSBN, 12, pp. 177-201. , https://doi.org/10.1007/978-3-030-42307-0_7, Jost, C., et al. (eds.), vol., pp. , Springer, Cham; Halverson, C.A., Activity theory and distributed cognition: Or what does CSCW need to do with theories? (2002) Comput. Support. Coop. Work, 11, pp. 243-267. , https://doi.org/10.1023/A:1015298005381; Kaptelinin, V., The Interaction Design Foundation (2013) The Encyclopedia of Human-Computer Interaction, , https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-com puter-interaction-2nd-ed/user-experience-and-experience-design, Activity theory. In: Soegaard, M., Dam, R.F., (eds.) , 2nd edn, Aarhus, Denmark; Law, E.L.C., Sun, X., Evaluating user experience of adaptive digital educational games with activity theory (2012) Int. J. Hum Comput Stud., 70, pp. 478-497. , https://doi.org/10.1016/j.ijhcs. 2012.01.007","Alenljung, B.; University of SkövdeSweden; email: beatrice.alenljung@his.se","Kurosu M.",,"Springer Science and Business Media Deutschland GmbH","Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262079,03029743,9783030784645,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85120694190
"Jacobs A., Jewell C.I.C., Elprama S.A.","24504454100;57191645732;55634489200;","The use of a sex doll as proxy technology to study human-robot interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12763 LNCS",,,"241","250",,,"10.1007/978-3-030-78465-2_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120674142&doi=10.1007%2f978-3-030-78465-2_18&partnerID=40&md5=189e81745eeaee856a67b93675ea09db","imec-SMIT-Vrije Universiteit Brussel, Pleinlaan 9, Brussels, 1050, Belgium; BruBotics, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium","Jacobs, A., imec-SMIT-Vrije Universiteit Brussel, Pleinlaan 9, Brussels, 1050, Belgium, BruBotics, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium; Jewell, C.I.C., imec-SMIT-Vrije Universiteit Brussel, Pleinlaan 9, Brussels, 1050, Belgium, BruBotics, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium; Elprama, S.A., imec-SMIT-Vrije Universiteit Brussel, Pleinlaan 9, Brussels, 1050, Belgium, BruBotics, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium","We studied via a survey (n = 187) how people think about the interaction with sex robots by using a sex doll as a proxy technology. An interactive public installation was created centered around a female sex doll. People passing by could participate in the survey while interacting with the installation and the doll. The findings suggest that no gender differences were found, but that we did find a successful way to study a sensitive and uncommon topic. Finally, we also propose alternative ways to elicit responses about sex robots in future research. © Springer Nature Switzerland AG 2021.","Human-Robot interaction; Proxy technology assessment; Relationships; Robots; Sex; Sex doll","Man machine systems; Surveys; Gender-differences; Humans-robot interactions; Proxy technology assessment; Relationship; Sex; Sex doll; Human robot interaction",,,,,,,,,,,,,,,,"Döring, N., Pöschl, S., Sex toys, sex dolls, sex robots: Our under-researched bed-fellows (2018) Sexologies, 27 (3), pp. 51-55; Döring, N., Rohangis Mohseni, M., Walter, R., Design, use, and effects of sex dolls and sex robots: Scoping review (2020) J. Med. Internet Res., 22 (7); Su, N.M., Lazar, A., Bardzell, J., Bardzell, S., Of dolls and men: Anticipating sexual intimacy with robots (2019) ACM Trans. Comput.-Hum. Interact., 26 (3); Szczuka, J., Krämer, N., Influences on the intention to buy a sex robot: An empirical study on influences of personality traits and personal characteristics on the intention to buy a sex robot (2017) Love and Sex with Robots: Second International Conference, pp. 72-83. , https://doi.org/10.1007/978-3-319-57738-8_7, Cheok, A.D., Devlin, K., Levy, D. (eds.), pp. , Springer, New York, USA; Pierson, J., Jacobs, A., Dreessen, K., van den Broeck, I., Lievens, B., Walking the interface: uncovering practices through ‘proxy technology assessment’ (2006) Proceedings of EPIC 2006: The Second Annual Ethnographic Praxis in Industry Conference, pp. 40-54. , , pp. , National Association for the Practice of Anthropology; Portland, Oregon; Jewell, C.I.C., Jacobs, A., Will you fall head over heels in love with a robot? (2019) 30 Questions and Answers on Man, , Jacobs, A., Tytgat, L., Maus, M., Meeusen, R., Vanderborght, B. (eds.) , Technology, Science & Art. VubPress; (2015) Laerd Statistics: Statistical Tutorials and Software Guides, , https://statistics.laerd.com/, Accessed 25 Feb 2021; Kellman, S.G., Promiscuous tongues: Erotics of translingualism and translation (2013) Neohelicon, 40 (1), pp. 35-45; Allen, C., (2015) The Artificially Scented Ape: Investigating the Role of Fragrances and Body Odours in Human Interactions; Scheutz, M., Arnold, T., Are we ready for sex robots? (2016) 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 351–358. IEEE","Elprama, S.A.; imec-SMIT-Vrije Universiteit Brussel, Pleinlaan 9, Belgium","Kurosu M.",,"Springer Science and Business Media Deutschland GmbH","Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262079,03029743,9783030784645,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85120674142
"Jutinico A.L., Flórez-Cediel O., Siqueira A.A.G.","54929830800;57205201618;7003788864;","Complementary stability of Markovian systems: Series elastic actuators and human-robot interaction",2021,"IFAC-PapersOnLine","54","4",,"112","117",,,"10.1016/j.ifacol.2021.10.019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120656080&doi=10.1016%2fj.ifacol.2021.10.019&partnerID=40&md5=14d1d30150dccf3345ca9ac39ba5eeef","Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; University of São Paulo, São Carlos, Brazil","Jutinico, A.L., Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; Flórez-Cediel, O., Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; Siqueira, A.A.G., University of São Paulo, São Carlos, Brazil","This paper presents an extension of the complementary stability analysis for discrete-time Markovian systems applied in interaction control. The control strategy considers an internal loop force control for Series Elastic Actuators based on the Robust Regulator for Discrete-time Markov Jump Linear systems and extern loop impedance control to regulate the interaction between the human and robot. Two examples show the complementary stability analysis of the human-robot interaction system, where is regarded as different values of the virtual impedances, different levels for the robustness of the force control, uncertainties, and abrupt changes for human parameters. Simulation results show the capacity of control strategy to deal with active interaction models, where the Mean Square Stability guarantees safe for the user. Additionally, from results it is understood the transmissibility of impedance achieved is a consequence of the robust performance given by the force controller. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)","Complementary stability; Markovian jump linear systems; Robotic rehabilitation; Robust control","Actuators; Control system stability; Discrete time control systems; Force control; Human robot interaction; Linear systems; Man machine systems; Robust control; Uncertainty analysis; Virtual reality; Complementary stability; Control strategies; Discrete time; Humans-robot interactions; Interaction controls; Markovian; Markovian jump linear systems; Robotic rehabilitation; Series elastic actuators; Stability analyze; Markov processes",,,,,,,,,,,,,,,,"Ajoudani, A., Zanchettin, A., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42, pp. 957-975; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244; Cerri, J.P., Terra, M.H., Recursive robust regulator for discrete-time Markovian jump linear systems (2017) IEEE Transactions on Automatic Control; Costa, O.L., Fragoso, M.D., Marques, R., Discrete-time Markov jump linear systems (2005) Probability and Its Applications, , Springer, London; Escalante, F.M., Jutinico, A.L., Jaimes, J.C., Terra, M.H., Siqueira, A.A.G., Markovian robust filtering and control applied to rehabilitation robotics (2021) IEEE/ASME Transactions on Mechatronics, 26 (1), pp. 491-502; Hogan, N., Impedance control: An approach to manipulation (1985) Journal of Dynamic Systems, Measurement and Control, 107 (1), pp. 1-24. , Parts 1-3; Jutinico, A.L., Escalante, F.M., Jaimes, J.C., Terra, M.H., Siqueira, A.A.G., Markovian robust compliance control based on electromyographic signals (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 1218-1223; Jutinico, A.L., Jaimes, J.C., Escalante, F.M., Perez-Ibarra, J.C., Terra, M.H., Siqueira, A.A.G., Impedance control for robotic rehabilitation: A robust Markovian approach (2017) Frontiers in Neurorobotics, 11, p. 43; Lee, H., Rouse, E.J., Krebs, H.I., Summary of human ankle mechanical impedance during walking (2016) IEEE Journal of Translational Engineering in Health and Medicine, 4, pp. 1-7; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, pp. 399-406",,,,"Elsevier B.V.","4th IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control CESCIT 2021","5 July 2021 through 7 July 2021",,146688,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85120656080
"Galin R.R., Meshcheryakov R.V., Mamchenko M.V.","57209776926;23035794100;57212309079;","Analysis of Intersection of Working Areas Within the Human-Robot Interaction in a Shared Workspace",2021,"Lecture Notes in Networks and Systems","232 LNNS",,,"749","759",,,"10.1007/978-3-030-90318-3_58","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120536413&doi=10.1007%2f978-3-030-90318-3_58&partnerID=40&md5=f9eee1cb407658838376b0686e4d4ed5","V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Galin, R.R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Meshcheryakov, R.V., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Mamchenko, M.V., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","This paper addresses the issues of modeling of individual workspaces of a human and a collaborative robot, as well as their intersections, taking into account the requirements of the safety of their interaction without losses in the efficiency of collaboration. Methods of safe human-robot collaboration have been considered. A review of a human functioning in a workspace within both the easy-to-access, and the optimum area depending on his motor functions has been undertaken. The approaches to solving the problem of human movement using a discrete cellular automata method, and the problem of intersection of the working bodies of a robot and a human have been considered. A model in the MATLAB simulation environment has been formed, which allows to create clouds of points of workspaces for both a robot and a human, taking into account the anthropometric parameters of a human, the dimensional characteristics of a collaborative robot, and their relative locations. In addition, the initial stage of numerical modelling has been carried out in order to form a separate cloud of points of intersection of the working areas of a collaborative robot and a human worker, as well as to calculate its approximate volume. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Collaboration efficiency; Collaborative robot; Human-robot interaction; Shared workspace; Workspace intersection",,,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331","Acknowledgements. The reported study was partially funded by RFBR according to the research project No. 19-08-00331.",,,,,,,,,,"Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput. Integr. Manufa., 40, pp. 1-13; Galin, R., Meshcheryakov, R., Kamesheva, S., Samoshina, A., Cobots and the benefits of their implementation in intelligent manufacturing (2020) Proceedings of the 2Nd International Conference “MIP: Engineering-2020: Modernization, Innovations, Progress”, Vol. 862 (032075), pp. 1-5. , pp., IOP Publishing Ltd, Bristol; Ermishin, K.V., Yuschenko, A.S., Collaborative mobile robots – a new stage of development of service robotics (2016) Robot. Tech. Cybern., 3 (12), pp. 3-9; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2015) IEEE Trans. Autom. Sci. 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(eds.), pp., Springer, Cham; Galin, R.R., Serebrenniy, V.V., Tevyashov, G.K., Shirokiy, A.A.: Human-robot Interaction in Collaborative Robotic Systems. In: Emelyanov, S.G., et al. (eds.) Proceedings of the Southwest State University, vol. 24(4), pp. 180–199. Southwest State University, Kursk (2020) (in Russian); Kebria, P.M., Al-Wais, S., Abdi, H., Nahavandi, S., Kinematic and dynamic modelling of UR5 manipulator (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 004229-004234. , pp; Galin, R., Meshcheryakov, R., Samoshina, A., Mathematical modelling and simulation of human-robot collaboration (2020) 2020 International Russian Automation Conference (Rusautocon), pp. 1058-1062. , pp., IEEE, New York; Lasota, P.A., Shah, J.A., Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration (2015) Human Factors. J. Hum. Factors Ergon. Soc., 57 (1), pp. 21-33; GOST 12.2.033-78: Occupational safety standards system. Operator’s location in a standing position. General ergonomic requirements. https://docs.cntd.ru/document/1200005187. Last accessed 1 July 2021; Korovin, I.S., Klimenko, A.B., Kalyaev, I.A., Safronenkova, I.B., An experience of the cognitive map-based classifier usage in astronaut’s emotional state monitoring (2021) Acta Astronaut, 181, pp. 537-543; Byner, C., Matthias, B., Ding, H., Dynamic speed and separation monitoring for collaborative robot applications–concepts and performance (2019) Robot. Comput. Integr. Manuf., 58, pp. 239-252; ISO/TS 15066:2016: Robots and robotic devices – collaborative robots. https://www.iso.org/ru/standard/62996.html. Last accessed 1 July 2021; Galin, R.R., Meshcheryakov, R.V., Human-Robot Interaction Efficiency and Human-Robot Collaboration (2020) Robotics: Industry 4.0 Issues & New Intelligent Control Paradigms. SSDC, 272, pp. 55-63. , https://doi.org/10.1007/978-3-030-37841-7_5, Kravets, A.G. (ed.), vol., pp., Springer, Cham; Mihelj, M., (2019) Collaborative Robots. Robotics, , 2nd ed. Springer International Publishing AG, Cham; Franklin, C.S., Dominguez, E.G., Fryman, J.D., Lewandowski, M.L., Collaborative robotics: New era of human–robot cooperation in the workplace (2020) J. Saf. Res., 73, pp. 153-160; Konz, S., Johnson, S., (1999) Work Design: Industrial Ergonomics, , 5th ed. Holcomb Hathaway Pubs, Scottsdale; Paluch, S., Wirtz, J., Kunz, W.H., Service robots and the future of services (2020) Marketing Weiterdenken, pp. 423-435. , Bruhn, M., Burmann, C., Kirchgeorg, M. (eds.), pp., Springer Gabler, Wiesbaden","Galin, R.R.; V.A. Trapeznikov Institute of Control Sciences of Russian Academy of SciencesRussian Federation; email: grr@ipu.ru","Silhavy R.Silhavy P.Prokopova Z.",,"Springer Science and Business Media Deutschland GmbH","5th Computational Methods in Systems and Software, CoMeSySo 2021","1 October 2021 through 1 October 2021",,268789,23673370,9783030903176,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85120536413
"Kang S.-H., Han J.-H.","57220190890;37048749200;","Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00842-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120323280&doi=10.1007%2fs12369-021-00842-1&partnerID=40&md5=c8a25d0bfdfa98eab0ff45d8649142a0","Seoul National University of Science and Technology, Seoul, South Korea","Kang, S.-H., Seoul National University of Science and Technology, Seoul, South Korea; Han, J.-H., Seoul National University of Science and Technology, Seoul, South Korea","Robot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system © 2021, The Author(s).","Deep learning; Egocentric video; Human robot interaction; Video captioning","Computer vision; Deep learning; Man machine systems; Visual languages; Deep learning; Egocentric video; Global actions; Global interaction; Humans-robot interactions; Learning models; Natural languages; New approaches; Video captioning; Video-clips; Human robot interaction",,,,,"National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT: 2018R1C1B6007230","This work was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (No. 2018R1C1B6007230).",,,,,,,,,,"Kongyun, Y., (2018) Human Action Recognition and Prediction: A Survey, , arXiv preprint arXiv:1806.11230; McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A survey of autonomous human affect detection methods for social robots engaged in natural hri (2016) J Intell Robot Syst, 82 (1), pp. 101-133; Ji, Y., Yangfumin, S., (2019) A Survey of Human Action Analysis in Hri Applications, IEEE Transactions on Circuits and Systems for Video Technology, , Heng Tao Shen, and Xuelong Li; Lunghi, G., Marin, R., Di Castro, M., Masi, A., Sanz Pedro, J., Multimodal human-robot interface for accessible remote robotic interventions in hazardous environments (2019) IEEE Access, 7, pp. 127290-127319; Ramos, R.A.Y., Figueroa, R.L.J., Balasubramaniyan, C., A sensor fusion based robotic system architecture using human interaction for motion control (2019) In: 2019 IEEE 9Th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0095-0100. , IEEE; Dizan, V., Procópio, S., Jorge, R.-M., Arturo, E., Anne, S., Christian, L., Human aware navigation for assistive robotics (2013) Experimental Robotics, pp. 449-462. , Springer; Gonçalves Duarte, M.F., Santana Pedro, B.J., Human-aware navigation for autonomous mobile robots for intra-factory logistics (2017) International Workshop on Symbiotic Interaction, pp. 79-85. , Springer; Moghadas, M., Moradi, H., Analyzing human-robot interaction using machine vision for autism screening (2018) 2018 6Th RSI International Conference on Robotics and Mechatronics (Icrom), pp. 572-576. , IEEE; Miao, L., Siyuli, T., Rehg James M (2020) Forecasting human-object interaction: Joint prediction of motor attention and actions in first person video European Conference on Computer Vision, pp. 704-721. , In, Springer; Anh, N., Dimitrios, K., Lucacaldwell Darwin, M., Tsagarakis Nikos, G.G., Translating videos to commands for robotic manipulation with deep recurrent neural networks (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-9. , IEEE; Silvia, C., Gabriele, C., Ciarfuglia Thomas, A., Paolo, V., Fravolini Mario, L., Full-gru natural language video description for service robotics applications (2018) IEEE Robot Autom Lett, 3 (2), pp. 841-848; Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K., Sequence to sequence-video to text (2015) Proceedings of the IEEE International Conference on Computer Vision, Pages, pp. 4534-4542; Ashish, V., Noam, S., Niki, P., Jakob, U., Lliongomez Aidan, J., Łukasz, N., Polosukhin Illia (2017) Attention is all you need Advances in Neural Information Processing Systems, pp. 5998-6008; Kishore, P., Salim, R., Todd, W., Wei-Jing, Z., Bleu: A method for automatic evaluation of machine translation (2002) Proceedings of the 40Th Annual Meeting of the Association for Computational Linguistics, pp. 311-318. , In; Yi, B., Yang, Y., Fumin, S., Ning, X., Tao, S.H., Xuelong, L., Describing video with attention-based bidirectional lstm (2018) IEEE Trans Cybernet, 49 (7), pp. 2631-2641; Zhao Bin, L.X., Xiaoqiang, L., Mam-rnn: Multi-level attention model based rnn for video captioning (2017) IJCAI, pp. 2208-2214; Yang Yang, B.Y., Xu Xing, S.F., Tao, S.H., Bidirectional long-short term memory for video description (2016) Proceedings of the 24Th ACM International Conference on Multimedia, pp. 436-440; Fang, K., Zhou, L., Jin, C., Zhang, Y., Weng, K., Zhang, T., Fan, W., Fully convolutional video captioning with coarse-to-fine and inherited attention (2019) proceedings of the AAAI conference on artificial intelligence, 33, pp. 8271-8278. , (,),. In; Sheng, L., Zhou, R., Junsong, Y., Sibnet: Sibling convolutional encoder for video captioning (2020) IEEE Trans Pattern Analy Mach Intell; Chenyoucrandall David, F., Deepdiary: Automatically captioning lifelogging image streams (2016) European Conference on Computer Vision, pp. 459-473. , Springer; Bolaños, M., Peris, Á., Casacuberta, F., Soler, S., Radeva, P., Egocentric video description based on temporally-linked sequences (2018) J Vis Commun Image Represent, 50, pp. 205-216; Tran, D., Wang, H., Torresani, L., Ray, J., Lecun, Y., Paluri, M., A closer look at spatiotemporal convolutions for action recognition (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450-6459. , In; Ji, L., Chuang, G., Song, H., Tsm: Temporal shift module for efficient video understanding (2019) Proceedings of the IEEE International Conference on Computer Vision, pp. 7083-7093; Bairuima, W., Wei, L., Wei, L., Reconstruction network for video captioning (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , June; Lianli, G., Zhao, G., Zhang Hanwang, X., Shen, H.T., Video captioning with attention-based lstm and semantic consistency (2017) IEEE Trans Multimedia, 19 (9), pp. 2045-2055; Yang, Y., Jie, Z., Jiangbo, A., Yi, B., Alan, H., Tao, S.H., Yanli, J., Video captioning by adversarial lstm (2018) IEEE Trans Image Process, 27 (11), pp. 5600-5611; Ryoo, M.S., Fuchs Thomas, J., Xia, L., Aggarwal Jakelarry, K., Robot-centric activity prediction from first-person videos: What will they do to me? (2015) 2015 10Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 295-302. , IEEE; Koppula Hema, S., Ashutosh, S., Anticipating human activities using object affordances for reactive robotic response (2015) IEEE Trans Pattern Anal Mach Intell, 38 (1), pp. 14-29; Jae, L.Y., Joydeep, G., Kristen, G., Discovering important people and objects for egocentric video summarization (2012) 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1346-1353. , IEEE; Lu, Z., Grauman, K., Story-driven summarization for egocentric video (2013) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, P, pp. 2714-2721; Alireza, F., Xiaofengrehg James, R., Learning to recognize objects in egocentric activities (2011) CVPR 2011, pp. 3281-3288. , IEEE; Ye Zhefanrehg James, L.Y., Delving into egocentric actions (2015) In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 287-295; Alirezali, F., Rehg James, Y., (2012) Learning to recognize daily actions using gaze. In: European conference on computer vision, pp. 314-327. , Springer; La, T.F.D., Jessica, H., Adam, B., Xavier, M., Justin, M., (2008) Collado Alex, , Beltran Pep, Guide to the carnegie mellon university multimodal activity (cmu-mmac) database; Ryoo Michael, S., Matthies, L., First-person activity recognition: What are they doing to me? (2013) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2730-2737; Stefano, A., Giuseppe, S., Simone, C., Francesco, S., Rita, C., From ego to nos-vision: Detecting social relationships in first-person views (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 580-585; Sibo, S., Vijay, C., Ngai-Man, C., Sanathli, N., Joo-Hwee, L., Activity recognition in egocentric life-logging videos (2014) Asian Conference on Computer Vision, pp. 445-458. , In, Springer; Doughty Hazel, D.D., Maria, F.G., Sanja, F., Antonino, F., Evangelos, K., Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray (2018) Scaling Egocentric Vision: The Epic-Kitchens Dataset, , In: European conference on computer vision (ECCV)","Han, J.-H.; Seoul National University of Science and TechnologySouth Korea; email: jhhan@seoultech.ac.kr",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85120323280
"Timmerman R.H., Hsieh T.-Y., Henschel A., Hortensius R., Cross E.S.","57352101600;57216353872;57214603084;35519731900;56497637800;","Individuals Expend More Effort to Compete Against Robots Than Humans After Observing Competitive Human–Robot Interactions",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"685","696",,,"10.1007/978-3-030-90525-5_60","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119870250&doi=10.1007%2f978-3-030-90525-5_60&partnerID=40&md5=3cdc251c6fdbab527728b0287209c7bd","Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom; Department of Psychology, Utrecht University, Heidelberglaan 1, Utrecht, 3584 CS, Netherlands; Department of Cognitive Science, Macquarie University, Sydney, Australia","Timmerman, R.H., Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom; Hsieh, T.-Y., Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom; Henschel, A., Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom; Hortensius, R., Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom, Department of Psychology, Utrecht University, Heidelberglaan 1, Utrecht, 3584 CS, Netherlands; Cross, E.S., Institute of Neuroscience and Psychology, University of Glasgow, United Kingdom, Department of Cognitive Science, Macquarie University, Sydney, Australia","In everyday life, we often observe and learn from interactions between other individuals—so-called third-party encounters. As robots are poised to become an increasingly familiar presence in our daily lives, third-party encounters between other people and robots might offer a valuable approach to influence people’s behaviors and attitudes towards robots. Here, we conducted an online experiment where participants (n = 48) watched videos of human—robot dyads interacting in a cooperative or competitive manner. Following this observation, we measured participants’ behavior and attitudes towards the human and robotic agents. First, participants played a game with the agents to measure whether their behavior was affected by their observed encounters. Second, participants’ attitudes toward the agents were measured before and after the game. We found that the third-party encounters influenced behavior during the game but not attitudes towards the observed agents. Participants showed more effort towards robots than towards humans, especially when the human and robot agents were framed as competitive in the observation phase. Our study suggests that people’s behaviors towards robots can be shaped by the mere observation of third-party encounters between robots and other people. © 2021, Springer Nature Switzerland AG.","Artificial agents; Competition; Cooperation; Human—robot interaction; Social cognition; Social robotics; Third-party encounters","Behavioral research; Robotics; Artificial agents; Cooperation; Daily lives; Humans-robot interactions; Learn+; On-line experiments; Social cognition; Social robotics; Third parties; Third-party encounter; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 677270; Leverhulme Trust: PLP-2018–152; European Research Council, ERC; Fundação Bial","Funding. Research supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement number 677270 to E.S.C.), the Leverhulme Trust (PLP-2018–152 to E.S.C), and the BIAL Foundation (to R.H.).",,,,,,,,,,"Johnson, J.D., Ashburn-Nardo, L., (2014) Soc. Psychol. Personal. Sci., 5 (3), pp. 369-376. , https://doi.org/10.1177/1948550613499938; Mazziotta, A., (2011) Group Process. Intergroup Relat., 14 (2), pp. 255-274. , https://doi.org/10.1177/1368430210390533; Quadflieg, S., Penton-Voak, I.S.: Curr. Dir. Psychol. Sci. 26(4), 383–389 (2017). https://doi. org/10.1177/0963721417694353; Quadflieg, S., Westmoreland, K., Making sense of other people’s encounters: Towards an integrative model of relational impression formation (2019) J. Nonverbal Behav., 43 (2), pp. 233-256. , https://doi.org/10.1007/s10919-019-00295-1; Shapiro, J.R., et al.: J. Exp. Soc. 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Accessed 20 Feb 2021; Scheunemann, M.M., et al.: 2020 29th IEEE International Conference on Robot and Human Interactive Communication, pp. 1340–1347, August 2020. https://doi.org/10.1109/RO-MAN 47096.2020.9223478; Mutlu, B., (2006) Proceedings of the 1St ACM SIGCHI/SIGART Conference on HRI, pp. 351-352. , https://doi.org/10.1145/1121241.1121311, New York, NY, USA, pp., March; Hsieh, T.-Y., https://doi.org/10.31234/osf.io/q6pv7, PsyArXiv, July 2020; Hortensius,R., Cross, E.S.: Ann. N. Y. Acad. Sci. 1426(1), August 2018. Art. no. 1. https://doi.org/10.1111/nyas.13727; Bridges, D., (2020) Peerj, 8. , https://doi.org/10.7717/peerj.9414; de Leeuw, J.R., Motz, B.A., Psychophysics in a Web browser? Comparing response times collected with JavaScript and psychophysics toolbox in a visual search task (2015) Behav. Res. Methods, 48 (1), pp. 1-12. , https://doi.org/10.3758/s13428-015-0567-2; Miller, R., Schmidt, K., Kirschbaum, C., Enge, S., Comparability, stability, and reliability of internet-based mental chronometry in domestic and laboratory settings (2018) Behav. Res. Methods, 50 (4), pp. 1345-1358. , https://doi.org/10.3758/s13428-018-1036-5; Wainer, J., ROMAN 2006-The 15th IEEE International Symposium on Robot & Human Interactive (2006) Communication, pp. 117-122. , https://doi.org/10.1109/ROMAN.2006.314404, pp., September; Cross, E.S., Ramsey, R.: Trends Cog Sci. 25(3), 200–2012 (2021). https://doi.org/10.1016/j. tics.2020.11.009; Henschel, A., et al.: Trends Neurosci. 43(6), 373–384 (2020). https://doi.org/10.1016/j.tins. 2020.03.013","Cross, E.S.; Institute of Neuroscience and Psychology, United Kingdom; email: emily.cross@glasgow.ac.uk","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119870250
"Yapici N.B., Tuglular T., Basoglu N.","57352230300;14627984700;15847775100;","Application of Human-Robot Interaction Features to Design and Purchase Processes of Home Robots",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"808","813",,,"10.1007/978-3-030-90525-5_76","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119861687&doi=10.1007%2f978-3-030-90525-5_76&partnerID=40&md5=1f2a288f40e3f15870acc0458fb0e26f","Izmir Institute of Technology, Izmir, 35430, Turkey","Yapici, N.B., Izmir Institute of Technology, Izmir, 35430, Turkey; Tuglular, T., Izmir Institute of Technology, Izmir, 35430, Turkey; Basoglu, N., Izmir Institute of Technology, Izmir, 35430, Turkey","Production of home robots, such as robotic vacuum cleaners, currently focuses more on the technology and its engineering than the needs of people and their interaction with robots. An observation supporting this view is that the home robots are not customizable. In other words, buyers cannot select the features and built their home robots to order. Stemmed from this observation, the paper proposes an approach that starts with a classification of features of home robots. This classification concerns robot interaction with humans and the environment, a home in our case. Following the classification, the proposed approach utilizes a new hybrid model based on a built-to-order model and dynamic eco-strategy explorer model, enabling designers to develop a production line and buyers to customize their home robots with the classified features. Finally, we applied the proposed approach to robotic vacuum cleaners. We developed a feature model for robotic vacuum cleaners, from which we formed a common uses scenario model. © 2021, Springer Nature Switzerland AG.","Build-to-order; Dynamic eco-strategy explorer model; Human-robot interaction process design; Robot customization","Classification (of information); Cleaning; Machine design; Man machine systems; Purchasing; Robotics; Sales; Build to orders; Customisation; Dynamic eco-strategy explorer model; Home robot; Human-robot interaction process design; Humans-robot interactions; Interaction features; Interaction process; Robot customization; Robotic vacuum cleaners; Human robot interaction",,,,,,,,,,,,,,,,"Robotic Vacuum Cleaner-Block Diagram, Design Solutions – STMicroelectronics. https://www.st.com/en/applications/home-and-professional-appliances/robot-vacuum-cleaners.html. Accessed 24 May 2021; Mendelson, H., Parlaktürk, A.K., Product-line competition: Customization vs proliferation (2008) Manag. Sci., 54, pp. 2039-2053. , https://doi.org/10.1287/mnsc.1080.0935; Serna-Mansoux, L., Popoff, A., Millet, D., A simplified model to include dynamic product-user interaction in the eco-design process (2014) J. Ind. Ecol., 18, pp. 529-544. , https://doi.org/10. 1111/jiec.12160; Kathawala, Y., Wilgen, A., The evolution of build-to-order supply chain and its implications with selected case studies (2005) IJSOM, 1, p. 268. , https://doi.org/10.1504/IJSOM.2005.006578; Behance: RoboCuD:WebDesignProject. https://www.behance.net/gallery/123180461/Rob oCuDWebDesignProject. Accessed 6 Sept 2021","Yapici, N.B.; Izmir Institute of TechnologyTurkey; email: nuryapici@iyte.edu.tr","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119861687
"Gaitán-Padilla M., Maldonado-Mejía J.C., Fonseca L., Pinto-Bernal M.J., Casas D., Múnera M., Cifuentes C.A.","57351939500;57219163203;57226019037;57215347962;57195469005;6505875786;36717243800;","Physical Human-Robot Interaction Through Hugs with CASTOR Robot",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"814","818",,,"10.1007/978-3-030-90525-5_77","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119853150&doi=10.1007%2f978-3-030-90525-5_77&partnerID=40&md5=1a017389995f8a61438a0a3bc5ad75d9","Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia; Corporación Universitaria del Caribe, Sincelejo, Colombia","Gaitán-Padilla, M., Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia; Maldonado-Mejía, J.C., Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia; Fonseca, L., Corporación Universitaria del Caribe, Sincelejo, Colombia; Pinto-Bernal, M.J., Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia; Casas, D., Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia; Múnera, M., Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia; Cifuentes, C.A., Colombian School of Engineering Julio Garavito, Bogota D.C., Colombia","Hugs play an essential role in social bonding between people. This study evaluates the hug interactions with a robot identifying the perception. Four hug release methods in adults were applied, a short-time hug, a long-time hug, a touch-controlled hug, and a pressure-controlled hug. The social robot CASTOR was integrated into this study, a modification was made in its arms to perform the hugging action, and a pressure sensor in its upper back. 12 adults (5 females and 7 males) participated in the study. Results showed that the perception of friendliness comparing the short-time hug and the pressure-controlled hug had differences (p= 0.036 ), making the pressure-controlled hug more friendly. In the case of natural perception, the touch-controlled hug was more natural comparing with the short-time hug (p= 0.047 ). This study presents the feasibility of implementing CASTOR in hugging interactions. © 2021, Springer Nature Switzerland AG.","Physical human-robot interaction; Robotic hug; Socially assistive robotics","Man machine systems; Robotics; Assistive robotics; Physical humanrobot interaction (phri); Release methods; Robotic hug; Social robots; Socially assistive robotic; Human robot interaction",,,,,,,,,,,,,,,,"Gleeson, M., Higgins, A., Touch in mental health nursing: An exploratory study of nurses’ views and perceptions (2009) J. Psychiatric Mental Health Nurs., 16 (4), pp. 382-389; Forsell, L.M., Åström, J.A., Meanings of hugging: From greeting behavior to touching implications (2012) Compr. Psychol., 1, pp. 02-17; Light, K.C., Grewen, K.M., Amico, J.A., More frequent partner hugs and higher oxytocin levels are linked to lower blood pressure and heart rate in premenopausal women (2005) Biol. Psychol., 69 (1), pp. 5-21; Hedayati, H., Bhaduri, S., Sumner, T., Szafir, D., Gross, M.D., HugBot: A soft robot designed to give human-like hugs (2019) Proceedings of the 18Th ACM International Conference on Interaction Design and Children, pp. 556-561. , pp; Block, A.E., Kuchenbecker, K.J., Emotionally supporting humans through robot hugs (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 293-294. , pp; Block, A.E., Christen, S., Gassert, R., Hilliges, O., Kuchenbecker, K.J., The six hug commandments: Design and evaluation of a human-sized hugging robot with visual and haptic perception (2021) Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 380-388. , pp; Casas-Bocanegra, D., An open-source social robot based on compliant soft robotics for therapy with children with ASD (2020) Actuators, Vol. 9, P. 91. Multidis-Ciplinary Digital Publishing Institute; Ramírez-Duque, A.A., Collaborative and inclusive process with the autism community: A case study in Colombia about social robot design (2021) Int. J. Soc. Robot., 13 (2), pp. 153-167; Block, A.E., Kuchenbecker, K.J., Softness, warmth, and responsiveness improve robot hugs (2019) Int. J. Soc. Robot., 11 (1), pp. 49-64","Gaitán-Padilla, M.; Colombian School of Engineering Julio GaravitoColombia; email: maria.gaitan-p@mail.escuelaing.edu.co","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119853150
"Tran N., Grant T., Phung T., Hirshfield L., Wickens C., Williams T.","57209796697;57205024437;57222465654;16303680200;7006097259;7404172627;","Robot-Generated Mixed Reality Gestures Improve Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"768","773",,,"10.1007/978-3-030-90525-5_69","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119846556&doi=10.1007%2f978-3-030-90525-5_69&partnerID=40&md5=3f20931fa5a1b5868e2911af1740a624","Department of Computer Science, Colorado School of Mines, Golden, CO  80401, United States; Institute for Cognitive Science, University of Colorado Boulder, Boulder, CO  80309, United States; Department of Psychology, Colorado State University, Fort Collins, CO  80523, United States","Tran, N., Department of Computer Science, Colorado School of Mines, Golden, CO  80401, United States; Grant, T., Institute for Cognitive Science, University of Colorado Boulder, Boulder, CO  80309, United States; Phung, T., Department of Computer Science, Colorado School of Mines, Golden, CO  80401, United States; Hirshfield, L., Institute for Cognitive Science, University of Colorado Boulder, Boulder, CO  80309, United States; Wickens, C., Department of Psychology, Colorado State University, Fort Collins, CO  80523, United States; Williams, T., Department of Computer Science, Colorado School of Mines, Golden, CO  80401, United States","We investigate the effectiveness of robot-generated mixed reality gestures. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time, and that robots can pair long referring expressions with mixed reality gestures without cognitively overloading users. © 2021, Springer Nature Switzerland AG.",,"Human robot interaction; Humans-robot interactions; Mixed reality; Referring expressions; User response time; Mixed reality",,,,,"National Science Foundation, NSF: CNS-1823245, IIS-1909864","This work was funded by NSF grants IIS-1909864 and CNS-1823245.",,,,,,,,,,"Hart, S., Staveland, L., Development of NASA-TLX (Task load index): Results of empirical and theoretical research (1988) Human Mental Workload; Hirshfield, L., Williams, T., Sommer, N., Grant, T., Gursoy, S.V., Workload-driven modulation of mixed-reality robot-human communication (2018) Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data; Lavie, N., The role of perceptual load in visual awareness (2006) Brain Res, 1080, pp. 91-100; Matuszek, C., Bo, L., Zettlemoyer, L., Fox, D., Learning from unscripted deictic gesture and language for human-robot interactions (2014) Proceedings of AAAI; Salem, M., Kopp, S., Wachsmuth, I., Rohlfing, K., Joublin, F., Generation and evaluation of communicative robot gesture (2012) Int. J. Soc. Robot., 4 (2), pp. 201-221; Sauppé, A., Mutlu, B., Robot deictics: How gesture and context shape referential communication (2014) Proceeding of HRI; Wickens, C.D., Processing resources and attention (1991) Multiple-Task Performance; Wickens, C.D., Multiple resources and mental workload (2008) Hum. Factors, 50 (3), pp. 449-555; Williams, T., Bussing, M., Cabrol, S., Lau, I., Boyle, E., Tran, N.: Investigating the potential effectiveness of allocentric mixed reality deictic gesture. In: Chen, J.Y.C., Fragomeni, G. (eds.) HCII 2019. LNCS, vol. 11575, pp. 178–198. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21565-1 12","Tran, N.; Department of Computer Science, United States; email: nttran@alumni.mines.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119846556
"Gruber M.E., Hancock P.A.","57226408001;7102723856;","The Self-Evaluation Maintenance Model in Human-Robot Interaction: A Conceptual Replication",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"268","280",,,"10.1007/978-3-030-90525-5_23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119845566&doi=10.1007%2f978-3-030-90525-5_23&partnerID=40&md5=593f205074ce4d33ce128cf74462f43a","University of Central Florida, Orlando, United States","Gruber, M.E., University of Central Florida, Orlando, United States; Hancock, P.A., University of Central Florida, Orlando, United States","Understanding human-robot social comparison is critical for creating psychologically safe robots (i.e., robots that do not cause psychological discomfort). However, there has been limited research examining social comparison processes in human-robot interaction (HRI). We aimed to conceptually replicate prior research suggesting that the Self-Evaluation Maintenance (SEM) model of social comparison applies to HRI. In short, the SEM model describes the mechanisms in which others can impact one’s self-evaluation. We applied the model to an online presentation of a humanoid robot, RUDY. We predicted that task relevance would moderate the relationship between the robot’s performance level and participant evaluations of the robot. When RUDY engaged in a low-relevance task (guessing someone’s age), participants would evaluate RUDY accurately (i.e., they would rate RUDY more positively when it performed well than when it performed poorly). However, when RUDY engaged in a high-relevance task (understanding how people feel), participants would evaluate RUDY inaccurately (i.e., they would rate RUDY negatively regardless of its actual performance). Contrary to our hypothesis, we found that participants in both the high- and low-relevance conditions evaluated RUDY accurately. Our results suggest that SEM effects may not generalize to all types of tasks and robots. A “highly relevant” task might mean something different depending on the exact nature of the human-robot relationship. Given the inconsistency between these findings and past research, discerning the boundary conditions for SEM effects may be crucial for developing psychologically safe robots. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Self-evaluation; Social comparison","Anthropomorphic robots; Man machine systems; Human robots; Humanoid robot; Humans-robot interactions; Maintenance effect; Maintenance models; Performance; Performance:level; Safe robots; Self evaluation; Social comparison; Human robot interaction",,,,,,,,,,,,,,,,"Darling, K., (2012) Extending Legal Protection to Social Robots: The Effects of Anthropomorphism, Empathy, and Violent Behavior Towards Robotic Objects, pp. 213-234. , https://doi.org/10.2139/ssrn.2044797, In: Calo, R., Froomkin, M., Kerr, I. (eds.), Robot Law, pp., Edward Elgar; Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., Paiva, A., The influence of empathy in human-robot relations (2013) Int. J. Hum Comput Stud., 71 (3), pp. 250-260. , https://doi.org/10.1016/j.ijhcs.2012.09.005; Kim, B., How early task success affects attitudes toward social robots (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 287-289. , https://doi.org/10.1145/3371382.3378241; Jhangiani, R., Tarry, H., Principles of social psychology – 1st international edition (2014) Bccampus Open Education, , https://opentextbc.ca/socialpsychology/; Tesser, A., Toward a self-evaluation maintenance model of social behavior (1988) Adv. Exp. Soc. Psychol., 21 (1), pp. 181-227. , https://doi.org/10.1016/S0065-2601(08)60227-0; Gino, F., Galinsky, A.D., Vicarious dishonesty: When psychological closeness creates distance from one’s moral compass (2012) Organ. Behav. Hum. Decis. Process., 119 (1), pp. 15-26. , https://doi.org/10.1016/j.obhdp.2012.03.011; Cialdini, R.B., Borden, R.J., Thorne, A., Walker, M.R., Freeman, S., Sloan, L.R., Basking in reflected glory: Three (football) field studies (1976) J. Pers. Soc. Psychol., 34 (3), pp. 366-375. , https://doi.org/10.1037/0022-3514.34.3.366; Tesser, A., Smith, J., Some effects of task relevance and friendship on helping: You don’t always help the one you like (1980) J. Exp. Soc. Psychol., 16 (6), pp. 582-590. , https://doi.org/10.1016/0022-1031(80)90060-8; Crawford, M.T., The renegotiation of social identities in response to a threat to self-evaluation maintenance (2007) J. Exp. Soc. Psychol., 43 (1), pp. 39-47. , https://doi.org/10.1016/j.jesp.2005. 12.011; Nicholls, E., Stukas, A.A., Narcissism and the self-evaluation maintenance model: Effects of social comparison threats on relationship closeness (2011) J. Soc. Psychol., 151 (2), pp. 201-212. , https://doi.org/10.1080/00224540903510852; Pleban, R., Tesser, A., The effects of relevance and quality of another’s performance on interpersonal closeness (1981) Soc. Psychol. Quart., 44 (3), pp. 278-285. , https://doi.org/10.2307/3033841; Kamide, H., Kawabe, K., Shigemi, S., Arai, T., Social comparison between the self and a humanoid (2013) ICSR 2013. LNCS (LNAI), Vol. 8239, pp. 190-198. , https://doi.org/10.1007/978-3-319-02675-6_19, Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.), pp., Springer, Cham; (2016) Families Caring for an Aging America, , https://doi.org/10.17226/23606, Schulz, R., Eden, J. (eds.), National Academies Press, Washington, D.C; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interac. Stud. Soc. Behav. Commun. Biol. Artif. Syst., 7 (3), pp. 437-454. , https://doi.org/10.1075/is.7.3.14nom; Bender, L., Walia, G., Kambhampaty, K., Nygard, K., Nygard, T.E., Social sensitivity and classroom team projects: An empirical investigation (2012) SIGCSE ’12, pp. 403-408. , https://doi.org/10.1145/2157136.2157258, pp; Ma, D.S., Kantner, J., Wittenbrink, B., Chicago face database: Multiracial expansion (2020) Behav. Res. Methods, 53 (3), pp. 1289-1300. , https://doi.org/10.3758/s13428-020-01482-5; Tesser, A., Campbell, J., A self-evaluation maintenance approach to school behavior (1982) Educ. Psychol., 17 (1), pp. 1-12. , https://doi.org/10.1080/00461528209529240; INF Robotics [Photograph of RUDY]. INF Robotics Inc (2020); Bartneck, C., Croft, E., Kulic, D., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3; Corp, I.B.M., (2020) IBM SPSS Statistics for Macintosh. (Version, 10 (Software), p. 27. , https://www.ibm.com/analytics/spss-statistics-software; Olejnik, S.F., Algina, J., Parametric ANCOVA and the rank transform ANCOVA when the data are conditionally non-normal and heteroscedastic (1984) J. Educ. Stat., 9 (2), pp. 129-149. , https://doi.org/10.3102/10769986009002129; Damholdt, M.F., Christina, V., Kryvous, A., Smedegaard, C.V., Seibt, J., What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life (2019) Paladyn, J. Behav. Robot., 10 (1), pp. 438-453. , https://doi.org/10.1515/pjbr-2019-0034; Eyssel, F., Kuchenbrandt, D.: Social categorization of social robots: anthropomorphism as a function of robot group membership. Br. J. Soc. Psychol. 51(4), 724–731 (2012). https://doi. org/10.1111/j.2044-8309.2011.02082.x","Gruber, M.E.; University of Central FloridaUnited States; email: miraeg@knights.ucf.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119845566
"Zhang Q., Fang L., Zhang Q., Xiong C.","56888078500;57346156400;57346485000;57211738191;","Simultaneous estimation of joint angle and interaction force towards sEMG-driven human-robot interaction during constrained tasks",2021,"Neurocomputing",,,,"","",,,"10.1016/j.neucom.2021.05.113","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119412427&doi=10.1016%2fj.neucom.2021.05.113&partnerID=40&md5=f6abefe45936a89e349d435c464747dd","State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China","Zhang, Q., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; Fang, L., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; Zhang, Q., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; Xiong, C., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China","Human has excellent motor capability and performance in conducting various manipulation tasks. During some tasks such as tightening/loosening a screw with a screwdriver, the motion is accompanied by force exertion to the environment (that is, constrained motion). To obtain natural human-robot interaction (HRI) as human interacts/collaborates with the environment, decoding the human's movement intention in a way of motion and interaction force is meaningful for robots to carry out such constrained tasks. This paper proposes a long shortterm memory (LSTM) -based decoding method for the simultaneous estimation of human motion and interactive force from muscle activities represented by surface electromyography (sEMG) signals. The sEMG recorded from the muscles of forearm is used to decode human's movement intention. In order to extract smooth features from non-stationary sEMG signals, Bayesian filter is applied instead of traditional time-domain feature extraction method. From the real-time experiments on eight subjects, the LSTM-based decoding method represents high accuracy of motion estimation (91.7%) and force estimation (96.1%) despite of the existence of muscle coupling and non-stationary mapping between muscle activities and motion/interaction force during such constrained tasks. It indicates that the estimated motion and interaction force can be further applied for HRI in accomplishing constrained tasks. © 2021 Elsevier B.V.","Constrained motions; Human-robot interaction (HRI); Long short-term memory (LSTM); Simultaneous estimation; Surface electromyography (sEMG)","Decoding; Human robot interaction; Long short-term memory; Muscle; Signal encoding; Time domain analysis; Constrained motion; Constrained tasks; Human movements; Human-robot interaction; Humans-robot interactions; Interaction forces; Long short-term memory; Simultaneous estimation; Surface electromyography; Motion estimation",,,,,"National Natural Science Foundation of China, NSFC: 52027806, 52175268, U1913601, U2013213","This research was funded by the National Natural Science Foundation of China (Grant No. 52175268, U2013213, 52027806, U1913601) in part.",,,,,,,,,,"Karimi, H.R., A sliding mode approach to h∞ synchronization of master-slave time-delay systems with markovian jumping parameters and nonlinear uncertainties (2012) J. Franklin Inst., 349 (4), pp. 1480-1496; Sheridan, T.B., Human–robot interaction: status and challenges (2016) Human Factors, 58 (4), pp. 525-532; Zhu, J., Navarro, B., Passama, R., Fraisse, P., Crosnier, A., Cherubini, A., Robotic manipulation planning for shaping deformable linear objects withenvironmental contacts (2020) IEEE Robotics Automation Letters, 5 (1), pp. 16-23; Gu, N., Peng, Z., Wang, D., Zhang, F., Path-guided containment maneuvering of mobile robots: Theory and experiments (2020) IEEE Trans. Industr. Electron., p. 1; Wang, Z., Yang, S., Xiang, X., Vasilijevic, A., Miskovic, N., Nad, D., Cloud-based mission control of USV fleet: Architecture, implementation and experiments (2021) Control Eng. Practice, 106. , 104657; Karimi, H.R., Gao, H., New delay-dependent exponential h∞ synchronization for uncertain neural networks with mixed time delays (2010) IEEE Trans. Syst., Man, Cybern., Part B (Cybern.), 40 (1), pp. 173-185; Li, Z., Guiraud, D., Andreu, D., Gelis, A., Fattal, C., Hayashibe, M., (2018), Real-time closed-loop functional electrical stimulation control of muscle activation with evoked electromyography feedback for spinal cord injured patients, International Journal of Neural Systems 28 (06) 1750063, pMID: 29378445; Guo, J., Liu, Y., Qiu, Q., Huang, J., Liu, C., Cao, Z., Chen, Y., A novel robotic guidance system with eye gaze tracking control for needle based interventions (2019) IEEE Trans. Cognitive Dev. Syst., p. 1; Wang, Y., Metcalfe, B., Zhao, Y., Zhang, D., An assistive system for upper limb motion combining functional electrical stimulation and robotic exoskeleton (2020) IEEE Trans. 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Mizutani, Motion analysis for force characteristic of cooperative task between two humans (01 2007). doi:10.1109/ICCAS.2007.4406775; Ehrsson, H.H., Fagergren, A., Jonsson, T., Westling, G., Johansson, R.S., Forssberg, H., (2000), Cortical activity in precision- versus power-grip tasks: An fmri study, Journal of Neurophysiology 83 (1) 528–536, pMID: 10634893; Liu, Y., Li, Z., Liu, H., Kan, Z., Skill transfer learning for autonomous robots and human-robot cooperation: A survey (2020) Robotics Autonomous Systems, 128; Zhang, Z., Chen, Y., Zhang, D., Xie, J., Liu, M., A six-dimensional traction force sensor used for human-robot collaboration (2019) Mechatronics, 57, pp. 164-172; Son, H.I., Bhattacharjee, T., Lee, D.Y., (2010), Estimation of environmental force for the haptic interface of robotic surgery, The international journal of medical robotics + computer assisted surgery: MRCAS 6 (2) 221–230. doi:10.1002/rcs.311; Tsarouchi, P., Makris, S., Chryssolouris, G., Human–robot interaction review and challenges on task planning and programming (2016) Int. 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Robotics Res., 37 (1), pp. 155-167; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Ajoudani, A., Tsagarakis, N.G., Bicchi, A., Tele-impedance: Towards transferring human impedance regulation skills to robots (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 382-388. , IEEE; Ohno, Y., Inoue, J., Iwase, M., Hatakeyama, S., Motion and force estimation based on the narx with an emg signal (2018) AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application, pp. 288-298. , V.H. Duy T.T. Dao I. Zelinka S.B. Kim T.T. Phuong Springer International Publishing Cham; Bullock, I.M., Dollar, A.M., Classifying human manipulation behavior (2011), pp. 1-6. , 2011 IEEE International Conference on Rehabilitation Robotics; Bakshi, K., Manjunatha, M., Kumar, C., Estimation of continuous and constraint-free 3 dof wrist movements from surface electromyogram signal using kernel recursive least square tracker (2018) Biomed. Signal Process. Control, 46, pp. 104-115; Buchanan, T.S., Lloyd, D.G., Manal, K., Besier, T.F., Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command (2004) J. Appl. Biomech., 20 (4), pp. 367-395; Neumann, D.A., Kinesiology of the musculoskeletal system-e-book: foundations for rehabilitation (2013) Elsevier Health Sciences; Zhang, Q., Pi, T., Liu, R., Xiong, C., Simultaneous and proportional estimation of multijoint kinematics from emg signals for myocontrol of robotic hands (2020) IEEE/ASME Trans. 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Eng., 53 (11), pp. 2232-2239; Kwon, S., Kim, J., Real-time upper limb motion estimation from surface electromyography and joint angular velocities using an artificial neural network for human–machine cooperation (2011) IEEE Trans. Inform. Technol. Biomed., 15 (4), pp. 522-530; Li, Z., Wang, B., Sun, F., Yang, C., Xie, Q., Zhang, W., semg-based joint force control for an upper-limb power-assist exoskeleton robot (2013) IEEE J. Biomed. Health Inform., 18 (3), pp. 1043-1050; Zhuang, K., Sommer, N., Mendez, V., Aryan, S., Formento, E., D'Anna, E., Artoni, F., Micera, S., Shared human-robot proportional control of a dexterous myoelectric prosthesis, Nature (2019) Machine Intelligence, 1, pp. 400-411; Ma, C., Lin, C., Samuel, O.W., Xu, L., Li, G., Continuous estimation of upper limb joint angle from semg signals based on sca-lstm deep learning approach (2020) Biomed. Signal Process. Control, 61; Wang, C., Guo, W., Zhang, H., Guo, L., Huang, C., Lin, C., semg-based continuous estimation of grasp movements by long-short term memory network (2020) Biomed. Signal Process. Control, 59; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for emg signal classification (2012) Expert Systems Appl., 39 (8), pp. 7420-7431; Artemiadis, P.K., Kyriakopoulos, K.J., Emg-based control of a robot arm using low-dimensional embeddings (2010) IEEE Trans. Rob., 26 (2), pp. 393-398","Zhang, Q.; State Key Lab of Digital Manufacturing Equipment and Technology, China; email: qin.zhang@hust.edu.cn",,,"Elsevier B.V.",,,,,09252312,,NRCGE,,"English","Neurocomputing",Article,"Article in Press","",Scopus,2-s2.0-85119412427
"Du G., Yao G., Li C., Liu P.","36959418400;57211921067;57203273419;57354690400;","An Offline-Merge-Online Robot Teaching Method Based on Natural Human-Robot Interaction and Visual-Aid Algorithm",2021,"IEEE/ASME Transactions on Mechatronics",,,,"","",,,"10.1109/TMECH.2021.3112722","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118602230&doi=10.1109%2fTMECH.2021.3112722&partnerID=40&md5=fb9285d61de7968af6577302d0ea82b4","School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006 China (e-mail: csgldu@scut.edu.cn).; School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006 China (e-mail: 1040189529@qq.com).; School of Information Engineering, Nanchang University, Nanchang 330031 China (e-mail: lichunquan@ncu.edu.cn).; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6 Canada.","Du, G., School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006 China (e-mail: csgldu@scut.edu.cn).; Yao, G., School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006 China (e-mail: 1040189529@qq.com).; Li, C., School of Information Engineering, Nanchang University, Nanchang 330031 China (e-mail: lichunquan@ncu.edu.cn).; Liu, P., Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6 Canada.","This article proposes an offline-merge-online robot teaching method (OMORTM). Specifically, a virtual-real fusion interactive interface (VRFII) is first developed by projecting a virtual robot into the real scene with an augmented-reality (AR) device, aiming to implement offline teaching. Second, a visual-aid algorithm (VAA) is proposed to improve offline teaching accuracy. Third, a gesture and speech teaching fusion algorithm (GSTA) with the fingertip tactile force feedback is developed to obtain the natural teaching pattern and improve the interactive accuracy of teaching the real or virtual robot. More specifically, through the VRFII, the operator can use the GSTA and the VAA to teach the virtual robot naturally and safely, and then the real robot reproduces the motion of the virtual robot. Therefore, OMORTM enables the teaching results to be quickly verified while ensuring the operator&#x0027;s safety and avoiding damage to the robot or workpiece. A series of experiments were conducted to validate the practicality and effectiveness of OMORTM. The results show that by effectively combining the offline and online, OMORTM provides accurate robotic teaching processes, suitable for nonprofessionals. IEEE","Education; Gesture and speech teaching algorithm; offline-merge-online robot teaching; Robot kinematics; Robot sensing systems; Robots; Service robots; Task analysis; Trajectory; virtual-real fusion interactive interface (VRFII); visual-aid algorithm (VAA)","Augmented reality; E-learning; Human robot interaction; Job analysis; Online systems; Virtual reality; Gesture and speech teaching algorithm; Interactive interfaces; Offline; Offline-merge-online robot teaching; Online robots; Robot kinematics; Robot sensing system; Robot teaching; Service robots; Task analysis; Teaching algorithms; Virtual-real fusion interactive interface; Visual aids; Visual-aid algorithm; Teaching",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10834435,,IATEF,,"English","IEEE ASME Trans Mechatron",Article,"Article in Press","",Scopus,2-s2.0-85118602230
"Xing X., Maqsood K., Huang D., Yang C., Li Y.","57323901700;57223270782;25521811100;55671767000;35174564100;","Iterative Learning-Based Robotic Controller With Prescribed Human-Robot Interaction Force",2021,"IEEE Transactions on Automation Science and Engineering",,,,"","",,,"10.1109/TASE.2021.3119400","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118544520&doi=10.1109%2fTASE.2021.3119400&partnerID=40&md5=c1d748a49ea0c5abfc0036913b7a0afa","Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K..; School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China.; Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, U.K..; Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K. (e-mail: yl557@sussex.ac.uk)","Xing, X., Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K..; Maqsood, K., Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K..; Huang, D., School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China.; Yang, C., Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, U.K..; Li, Y., Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K. (e-mail: yl557@sussex.ac.uk)","In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human's upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user. IEEE","Dynamics; Force; Force tracking; human-robot interaction; iterative learning; Robot kinematics; robotic controller.; Robots; Task analysis; Trajectory; Uncertainty","Controllers; Human robot interaction; Iterative methods; Job analysis; Least squares approximations; Man machine systems; Robotics; Uncertainty analysis; Force; Force tracking; Humans-robot interactions; Interaction forces; Iterative learning; Robot kinematics; Robotic controller.; Robotic controllers; Task analysis; Uncertainty; Trajectories",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85118544520
"Omrani J., Moghaddam M.M.","57320257500;7004934628;","Nonlinear time delay estimation based model reference adaptive impedance control for an upper-limb human-robot interaction",2021,"Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine",,,,"","",,,"10.1177/09544119211054919","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118356777&doi=10.1177%2f09544119211054919&partnerID=40&md5=2e79e15f145e2446241da5575a4f2b95","Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran","Omrani, J., Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran; Moghaddam, M.M., Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran","A nonlinear Time Delay Estimation (TDE) based model reference adaptive impedance controller was developed for Tarbiat Modares University Upper Limbs Rehabilitation Robot (TUERR). The proposed controller uses a stable reference impedance model, which produces desired dynamic relationship between applied force and position error for the robot End-effector to track the desired trajectory. TDE based model reference adaptive controller estimates unknown system dynamics and uncertainties, and the adaption law modifies the controller gains. Using a Lyapunov function was shown trajectory tracking errors in the overall system are bounded. In addition, a performance-based velocity profile proposed to modify the pace of trajectory planning considering the deviation from the desired path. Finally, the performance of the presented controller and rehabilitation process is experimentally investigated for TUERR. © IMechE 2021.","adaptive control; Impedance control; Lyapunov stability; rehabilitation robot","Controllers; Delay control systems; End effectors; Human robot interaction; Lyapunov functions; Model reference adaptive control; Time delay; Timing circuits; Uncertainty analysis; Adaptive Control; Humans-robot interactions; Impedance control; Impedance controllers; Lyapunov stability; Model reference adaptive; Nonlinear time delays; Rehabilitation robot; Time delay estimation; Upper limbs; Trajectories",,,,,,,,,,,,,,,,"Shafiee, A., Ahmadian, M.T., Hoviattalab, M., Mechanical characterization of brain tissue in compression, , Proceedings of the ASME 2016 international design engineering technical conferences and computers and information engineering conference, Charlotte, NC, 21–24 August, 2016, New York, ASME, In; Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., A survey on robotic devices for upper limb rehabilitation (2014) J Neuroeng Rehabil, 11 (1), p. 3; Cao, F., Li, Y., Shi, J., Adaptive sliding mode impedance control in lower limbs rehabilitation robotic, pp. 310-315. , Proceedings of the 2013 Chinese automation congress, Changsha, China, 7–8 November, 2013, New York, IEEE, In; Yeung, L.F., Lau, C.C.Y., Lai, C.W.K., Effects of wearable ankle robotics for stair and over-ground training on sub-acute stroke: A randomized controlled trial (2021) J Neuro Engineering Rehabil, 18, p. 19; Drills, R., Contini, R., Bluestein, M., Body segment parameters (1964) Artif Limbs, 8 (1), pp. 44-66; Matthew, R.P., Seko, S., Kurillo, G., Reachable workspace and proximal function measures for quantifying upper limb motion (2020) IEEE J Biomed Health Inform, 24 (11), pp. 3285-3294; Ozgur, A.G., Bruno, B., Taburet, V., Design of dynamic tangible workspaces for games: application on robot-assisted upper limb rehabilitation, pp. 172-178. , Proceedings of the 2020 29th IEEE international conference on robot and human interactive communication (RO-MAN), Naples, Italy, 31 August–4 September 2020, New York, IEEE, In; Gopura, R.A., Bandara, D.S., Kiguchi, K., Developments in hardware systems of active upper-limb exoskeleton robots: a review (2016) Robot Auton Syst, 75, pp. 203-220; Rehmat, N., Zuo, J., Meng, W., Upper limb rehabilitation using robotic exoskeleton systems: a systematic review (2018) Int J Intell Robot Appl, 2 (3), pp. 283-295; Gupta, A., O’Malley, M.K., Patoglu, V., Design, control and performance of RiceWrist: a force feedback wrist exoskeleton for rehabilitation and training (2008) Int J Rob Res, 27 (2), pp. 233-251; Krebs, H.I., Hogan, N., Aisen, M.L., Robot-aided neurorehabilitation (1998) IEEE Trans Rehabil Eng, 6 (1), pp. 75-87; Swinnen, E., Duerinck, S., Baeyens, J., Effectiveness of robot-assisted gait training in persons with spinal cord injury: a systematic review (2010) J Rehabil Med, 42 (6), pp. 520-526; Tahmid, S., Yang, J., Font-Llagunes, J.M., Review of Models and Robotic Devices for Stroke Survivors Upper Extremity Rehabilitation, 1. , Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information Engineering Conference. 39th Computers and Information Engineering Conference, Anaheim, California, USA, 18–21 August, 2019, In:, p. V001T02A044. ASME; Song, A., Pan, L., Xu, G., Adaptive motion control of arm rehabilitation robot based on impedance identification (2015) Robotica, 33 (9), pp. 1795-1812; Zhou, S.-H., Fong, J., Crocher, V., Learning control in robot-assisted rehabilitation of motor skills – a review (2016) J Contr Decis, 3 (1), pp. 19-43; Krebs, H.I., Chapter 13 - Twenty + years of robotics for upper-extremity rehabilitation following a stroke (2018) Rehabilitation Robotics, pp. 175-192. , Colombo R., Sanguineti V., (eds), Academic Press, In:, (eds; Huang, X., Naghdy, F., Naghdy, G., Robot-assisted post-stroke motion rehabilitation in upper extremities: a survey (2017) Int J Disabil Hum Dev, 16 (3), pp. 233-247; Perdereau, V., Drouin, M., A new scheme for hybrid force-position control (1993) RoManSy 9. 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J Dyn Sys Meas Control, 107 (1), pp. 1-7; Hogan, N., Impedance control: an approach to manipulation: part II—implementation (1985) J Dyn Sys Meas Control, 107 (1), pp. 8-16; Hogan, N., Krebs, H.I., Sharon, A., (1995) Interactive robotic therapist, , Google Patents; Mendoza, M., Bonilla, I., Reyes, F., A Lyapunov-based design tool of impedance controllers for robot manipulators (2012) Kybernetika, 48 (6), pp. 1136-1155; Akdoğan, E., Aktan, M.E., Koru, A.T., Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: performance analysis and clinical results (2018) Mechatronics, 49, pp. 77-91; Yang, Y., Wang, L., Tong, J., (2006) Arm rehabilitation robot impedance control and experimentation, pp. 914-918. , Proceedings of the 2006 IEEE international conference on robotics and biomimetics, Kunming, China, New York, IEEE, In; Lu, W.-S., Meng, Q.-H., Impedance control with adaptation for robotic manipulations (1991) IEEE Trans Robot Autom, 7 (3), pp. 408-415; Li, Z., Huang, Z., He, W., Adaptive impedance control for an upper limb robotic exoskeleton using biological signals (2017) IEEE Trans Ind Electron, 64 (2), pp. 1664-1674; He, W., Huang, H., Ge, S.S., Adaptive neural network control of a robotic manipulator with time-varying output constraints (2017) IEEE Trans Cybern, 47 (10), pp. 3136-3147; Haninger, K., Lu, J., Tomizuka, M., Robust impedance control with applications to a series-elastic actuated system, pp. 5367-5372. , Proceedings of the 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS), Daejeon, Korea, 9–14 October 2016, New York, IEEE, In; He, W., Dong, Y., Sun, C., Adaptive neural impedance control of a robotic manipulator with input saturation (2016) IEEE Trans Syst Man Cybern Syst, 46 (3), pp. 334-344; Xu, G., Song, A., Li, H., Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network (2011) J Intell Robot Syst, 62 (3-4), pp. 501-525; Shekhar, A., Sharma, A., Review of model reference adaptive control, pp. 1-5. , Proceedings of the 2018 international conference on information, communication, engineering and technology (ICICET), Pune, India, 29–31 August 2018, New York, IEEE, In; Kang, S.H., Jin, M., Chang, P.H., A solution to the accuracy/robustness dilemma in impedance control (2009) IEEE/ASME Trans Mechatron, 14 (3), pp. 282-294; Pan, L., Song, A., Xu, G., Safety supervisory strategy for an upper-limb rehabilitation robot based on impedance control (2013) Int J Adv Robot Syst, 10 (2), p. 127; Lv, X., Han, J., Yang, C., Model reference adaptive impedance control in lower limbs rehabilitation robot, pp. 254-259. , Proceedings of the 2017 IEEE international conference on information and automation (ICIA), Macao, China, 18–20 July 2017, New York, IEEE, In; Brahmi, B., Brahmi, A., Saad, M., Robust adaptive tracking control of uncertain rehabilitation exoskeleton robot (2019) J Dyn Syst Meas Control, 141 (12). , (, ): 8; Liu, X., Tao, R., Tavakoli, M., Adaptive control of uncertain nonlinear teleoperation systems (2014) Mechatronics, 24 (1), pp. 66-78; Sharifi, M., Behzadipour, S., Vossoughi, G., Model reference adaptive impedance control of rehabilitation robots in operational space, pp. 1698-1703. , Proceedings of the 2012 4th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics (BioRob), Rome, Italy, 24–27, June 2012, New York, IEEE, In; Youcef-Toumi, K., Ito, O., A time delay controller for systems with unknown dynamics (1990) J Dyn Sys Meas Control, 112 (1), pp. 133-142; Hsia, T.C., Gao, L., Robot manipulator control using decentralized linear time-invariant time-delayed joint controllers, pp. 2070-2075. , Proceedings of the IEEE international conference on robotics and automation, Cincinnati, OH, 13–18 May 1990, New York, IEEE, In; Lee, J., Chang, P.H., Yu, B., An effective adaptive gain dynamics for time-delay control of robot manipulators (2020) IEEE Access, 8, pp. 192229-192238; Nguyen, N., Summers, E., (2011) On time delay margin estimation for adaptive control and robust modification adaptive laws, , https://doi.org/10.2514/6.2011-6438, Proceedings of the AIAA guidance, navigation, and control conference, Portland, Oregon, In:, p. 6438; Han, S., Wang, H., Tian, Y., Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a rehabilitation exoskeleton (2020) ISA Trans, 97, pp. 171-181; Tar, J., Rudas, I., Bitó, J., Analysis of the effect of backlash and joint acceleration measurement noise in the adaptive control of electro-mechanical systems, 1, pp. 286-291. , Proceedings of the 2003 IEEE international symposium on industrial electronics (cat. No. 03TH8692), Rio de Janeiro, Brazil, 9–11 June 2003, New York, IEEE, In; Slotine, J.-J.E., Li, W., (1991) Applied nonlinear control (no. 1), , Englewood Cliffs, NJ, Prentice Hall; Jin, M., Lee, J., Tsagarakis, N.G., Model-free robust adaptive control of humanoid robots with flexible joints (2017) IEEE Trans Ind Electron, 64 (2), pp. 1706-1715; Ahmed, S., Wang, H., Tian, Y., Time delay estimation based model reference adaptive control for robot manipulators, pp. 261-265. , Proceedings of the 2019 IEEE 8th data driven control and learning systems conference (DDCLS), Dali, China, 24–27 May 2019, New York, IEEE, In; Krebs, H.I., Volpe, B.T., Williams, D., Robot-aided neurorehabilitation: a robot for wrist rehabilitation (2007) IEEE Trans Neural Syst Rehabil Eng, 15 (3), pp. 327-335","Moghaddam, M.M.; Department of Mechanical Engineering, Iran; email: m.moghadam@modares.ac.ir",,,"SAGE Publications Ltd",,,,,09544119,,PIHME,,"English","Proc. Inst. Mech. Eng. Part H J. Eng. Med.",Article,"Article in Press","",Scopus,2-s2.0-85118356777
"Romeo M., Hernández García D., Han T., Cangelosi A., Jokinen K.","57204472530;56520258500;57217385990;6701387796;23397498500;","Predicting apparent personality from body language: benchmarking deep learning architectures for adaptive social human–robot interaction",2021,"Advanced Robotics","35","19",,"1167","1179",,,"10.1080/01691864.2021.1974941","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118275214&doi=10.1080%2f01691864.2021.1974941&partnerID=40&md5=afb2069bea278c3d855c78891c52b086","Department of Computer Science, The University of Manchester, Manchester, United Kingdom; Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; School of Mathematical and Computer Sciences, Herriot-Watt University, Edinburgh, United Kingdom","Romeo, M., Department of Computer Science, The University of Manchester, Manchester, United Kingdom, Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; Hernández García, D., School of Mathematical and Computer Sciences, Herriot-Watt University, Edinburgh, United Kingdom; Han, T., Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; Cangelosi, A., Department of Computer Science, The University of Manchester, Manchester, United Kingdom, Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; Jokinen, K., Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan","First impressions of personality traits can be inferred by non-verbal behaviours such as head pose, body postures, and hand gestures. Enabling social robots to infer the apparent personalities of their users based on such non-verbal cues will allow robots to gain the ability of adapting to their users, constituting a further step towards the personalisation of human–robot interactions. Deep learning architectures such as residual networks, 3D convolutional networks, and long-short time memory networks have been applied to classify human activities and actions in computer vision tasks. These same architectures are beginning to be applied to study human emotions and personality by focusing mainly on facial features in video recordings. In this work, we exploit body language cues to predict apparent personality traits for human–robot interactions. We customised four state-of-the-art neural network architectures to the task, and benchmarked them on a dataset of short side-view videos of dyadic interactions. Our results show the potential for deep learning architectures to predict apparent personality traits from body language cues. While the performance varied between models and personality traits, our results show that these models could still be able to predict sole personality traits, as exemplified by the results on the conscientiousness trait. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","adaptive robotics; deep learning; Personality computing; video classification","Deep learning; Human robot interaction; Network architecture; Robotics; Video recording; Adaptive robotic; Body language; Deep learning; First impressions; Humans-robot interactions; Learning architectures; Personality computing; Personality traits; Social human-robot interactions; Video classification; Forecasting",,,,,"Horizon 2020 Framework Programme, H2020; University of Manchester; European Research Council, ERC; New Energy and Industrial Technology Development Organization, NEDO","This work was partially supported by a grant of AIST-AIRC (Japan) for the collaboration with the University of Manchester. The study is based on the results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). The work was also supported by the EPSRC UKRI TAS Node on Trust and the European Research Council (H2020) projects PERSEO ETN and eLADDA ETN.",,,,,,,,,,"Vinciarelli, A., Mohammadi, G., A survey of personality computing (2014) IEEE Trans Affect Comput, 5 (3), pp. 273-291. , Jul; Aly, A., Tapus, A., https://doi.org/10.1109/HRI.2013.6483606, A model for synthesizing a combined verbal and nonverbal behavior based on personality traits human–robot interaction. In: Proceedings of 8th ACM/IEEE International Conference on Human–Robot Interaction (HRI); 2013 Mar. IEEE press. p. 325–332; Klug, M., Zell, A., https://doi.org/10.1109/ICCCyb.2013.6617620, Emotion-based human–robot-interaction. In: 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC); 2013. 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"Zakka G.V., Hou Z., Pang G., Yang H., Yang G.","57315895400;57224192612;57202024082;7406558218;54961736000;","IoT-Enabled Robot Skin System for Enhancement of Safe Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13014 LNAI",,,"457","468",,,"10.1007/978-3-030-89098-8_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118196788&doi=10.1007%2f978-3-030-89098-8_43&partnerID=40&md5=0f3cca76b2d17f0084745f55e1231e7a","State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW  2006, Australia","Zakka, G.V., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Hou, Z., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Pang, G., School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW  2006, Australia; Yang, H., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Yang, G., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","Collaborative robots along with skilled manpower offer flexible and intelligent services by combining the cognitive power of humans with robot’s flexibility in small-batch production. To realize this, collaborative robots are required to collaborate with humans in close range sharing the same workspace. In such a case, ensuring safe Human-Robot Collaboration (HRC) is a big challenge. Safety solutions are expected to be smart enough to monitor the robot environment, analyse the collected data, and alert the user in case of any anomalies for timely intervention. This work, therefore, proposes an Internet of Things (IoT)-enabled smart robot skin system as a safety solution to enhance safety in the collaborative environment. It is flexible and modular, based on self-capacitive technology for proximity-touch sensing. Leveraging the IoT technology, wireless communication within the skin system and remote programming of the skin network were implemented. More so, real-time data monitoring, analysis, and visualization are implemented leveraging the flexible ThingSpeak cloud service data processing capabilities. Based on the processed data, cloud-based event handling, which updates the user of the robot skin performance, was also implemented. This approach will enable remote maintenance, and enhance the safety function of the skin system for effective HRC. © 2021, Springer Nature Switzerland AG.","Human-robot collaboration; Internet of Things; Robot skin","Collaborative robots; Data handling; Data visualization; Intelligent robots; Internet of things; Man machine systems; Close range; Collaborative robots; Flexible service; Human-robot collaboration; Intelligent Services; Power; Robot skin; Safe human-robot interaction; Skilled manpower; Small batch production; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51890884, 51975513; Natural Science Foundation of Zhejiang Province, ZJNSF: LR20E050003","Acknowledgments. This work was supported in part by the National Natural Science Foundation of China under Grant 51975513 and Grant 51890884, and in part by the Natural Science Foundation of Zhejiang Province under Grant LR20E050003.",,,,,,,,,,"Göger, D., Alagi, H., Wörn, H., Tactile proximity sensors for robotic applications (2013) 2013 IEEE International Conference on Industrial Technology, pp. 978-983. , , pp. , IEEE Press, Cape Town, South Africa; Dahiya, R., Yogeswaran, N., Liu, F., Large-area soft e-skin: The challenges beyond sensor designs (2019) Proc. IEEE, 107 (10), pp. 2016-2033; Xia, F., Campi, F., Bahreyni, B., Tri-mode capacitive proximity detection towards improved safety in industrial robotics (2018) IEEE Sens. J., 18 (12), pp. 5058-5066; Ding, Y., Wilhelm, F., Faulhammer, L., With proximity servoing towards safe human-robot-interaction (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4907-4912. , , pp. , IEEE Press, Macau, China; Heng, W., Yang, G., Pang, G., Fluid-driven soft coboskin for safer human–robot collaboration: Fabrication and adaptation (2020) Adv. Intell. Syst., 3 (3); Ye, Z., Yang, G., Pang, G., Design and implementation of robot skin using highly sensitive sponge sensor (2020) IEEE Trans. Med. Robot. Bio., 2 (4), pp. 670-680; Pang, G., Yang, G., Heng, W., CoboSkin: Soft robot skin with variable stiffness for safer human–robot collaboration (2020) IEEE Trans. Ind. Electron., 68 (4), pp. 3303-3314; Yang, G., Deng, J., Pang, G., An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning (2018) IEEE J. Transl. Eng. Health Med., 6, pp. 1-10; Gbouna, Z.V., Pang, G., Yang, G., User-interactive robot skin with large-area scalability for safer and natural human-robot collaboration in future telehealthcare (2021) IEEE J. Biomed. Health Inform., , https://doi.org/10.1109/JBHI.2021.3082563; Nambiar, R., Poess, M., Industry standards for the analytics era: TPC roadmap. Lect. Notes Comput (2018) Sci, 10661, pp. 1-6; Viegas, V., Dias Pereira, J.M., Girao, P., A preliminary study of loop-time delays in IoT platforms: The thingspeak case (2019) 2019 International Conference on Sensing and Instrumentation in Iot Era, pp. 1-4. , , pp. , IEEE Press, Lisbon, Portugal; Viegas, V., Pereira, J.D., Girão, P., Study of latencies in Thingspeak (2021) Adv. Sci. Technol. Eng. Syst. J., 6 (1), pp. 342-348; Bhandari, S., Sharma, S.K., Wang, X.B., Latency minimization in wireless IoT using prioritized channel access and data aggregation (2017) 2017 IEEE Global Communications Conference, pp. 1-6. , , pp. , IEEE Press, Singapore; Ding, Y.T., Wilhelm, F., Faulhammer, L., With proximity servoing towards safe human-robot-interaction (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4907-4912. , , pp. , IEEE Press, Macau, China; Tsuji, S., Kohama, T., Self-capacitance proximity and tactile skin sensor with shock-absorbing structure for a collaborative robot (2020) IEEE Sens. J., 20 (24), pp. 15075-15084; Abah, C., Orekhov, A.L., Johnston, G.L.H., A multi-modal sensor array for safe human-robot interaction and mapping (2019) 2019 International Conference on Robotics and Automation, pp. 3768-3774. , , pp. , IEEE Press, Montreal, QC, Canada; Ma, G.G., Soleimani, M., A versatile 4D capacitive imaging array: A touchless skin and an obstacle-avoidance sensor for robotic applications (2020) Sci. Rep., 10 (1)","Yang, G.; State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: yanggeng@zju.edu.cn","Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030890971,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85118196788
"Nie Y., Ma X.","57287553500;27171307900;","Gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13015 LNAI",,,"293","303",,,"10.1007/978-3-030-89134-3_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118131809&doi=10.1007%2f978-3-030-89134-3_27&partnerID=40&md5=3707a2dd7c96ddfdbd3cb2afff0a0744","School of Control Science and Engineering, Shandong University, Jinan, 250061, China","Nie, Y., School of Control Science and Engineering, Shandong University, Jinan, 250061, China; Ma, X., School of Control Science and Engineering, Shandong University, Jinan, 250061, China","Human-robot interaction (HRI) is the key capability for assistive robots to provide support for the elders and impaired in daily activities. Implicit intention understanding is a challenge problem for natural and intuitive HRI. In this paper, an implicit intention inference framework based on gaze behavior is proposed. First, support vector machine classifier is used to classify human’s gaze behavior as intentional and unintentional visual behavior. Then, a Naive Bayes model based on implicit intention inference is presented with taking historical visual attention into accounts. The advantage of this model is that the human’s implicit intention could be inferred based on his/her gaze at multiple objects by dealing with the historical information of visual behavior. Finally, experiments are conducted in a scenario of “home care in the kitchen”. People are free to look at one or more objects among 14 kinds of objects provided in experiment. With the proposed model, four kinds of intentions could be inferred based on people’s historical gaze on objects. The experiment results validate that the proposed model, which considers the historical information of visual behavior, outperforms the previous gaze-based intention inference methods. © 2021, Springer Nature Switzerland AG.","Gaze behavior; Human-robot interaction; Implicit intention recognition","Bayesian networks; Behavioral research; Man machine systems; Robotics; Support vector machines; Assistive robots; Daily activity; Gaze behaviours; Historical information; Humans-robot interactions; Implicit intention recognition; Implicit intentions; Intention recognition; Visual Attention; Visual behavior; Human robot interaction",,,,,"Key Technology Research and Development Program of Shandong: ZR2019ZD07; National Key Research and Development Program of China, NKRDPC: 2018YFB1305803","Acknowledgement. This work was supported in part by the National Key Research and Development Plan of China under Grant 2018YFB1305803 and the Key Development Program for Basic Research of Shandong Province under Grant ZR2019ZD07.",,,,,,,,,,"Louie, W.Y.G., McColl, D., Nejat, G., Acceptance and attitudes toward a human-like socially assistive robot by older adults (2014) Assist. Technol., 26 (3), pp. 140-150; Trick, S., Koert, D., Peters, J., (2019) Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. Arxiv Preprint Arxiv, 1907, p. 02426; Bingol, M.C., Aydogmus, O., Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot (2020) Eng. Appl. Artif. Intell., 95; Pleshkova, S., Bekiarski, A., Algorithm for motion management by interaction between mobile robot and human (2019) 2019 II International Conference on High Technology for Sustainable Development (Hitech), Pp. 1–4. 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Learn., 20 (3), pp. 273-297","Ma, X.; School of Control Science and Engineering, China; email: maxin@sdu.edu.cn","Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030891336,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85118131809
"Shao S., Wang T., Li Y., Song C., Jiang Y., Yao C.","54967359700;56222883600;57300629700;23111298700;57300629800;55234317100;","Comparison Analysis of Different Time-Scale Heart Rate Variability Signals for Mental Workload Assessment in Human-Robot Interaction",2021,"Wireless Communications and Mobile Computing","2021",,"8371637","","",,,"10.1155/2021/8371637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117371752&doi=10.1155%2f2021%2f8371637&partnerID=40&md5=42e9463d6560764f81f0f7c09d5a4301","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Shenyang Ligong University, School of Automation and Electrical Engineering, Shenyang, 110159, China","Shao, S., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Wang, T., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Li, Y., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Song, C., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Jiang, Y., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, Shenyang Ligong University, School of Automation and Electrical Engineering, Shenyang, 110159, China; Yao, C., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China","Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K-nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction. © 2021 Shiliang Shao et al.",,"Electrocardiography; Heart; Human robot interaction; Machine learning; Man machine systems; Nearest neighbor search; Time measurement; Comparison analysis; Different time scale; Dual-arm robot; Electrocardiogram signal; Heart rate variability signals; Human health; Humans-robot interactions; Mental workload; Mental workload assessments; Signal length; Learning algorithms",,,,,"2020-BS-025; National Natural Science Foundation of China, NSFC: U20A20201; National Key Research and Development Program of China, NKRDPC: 2016YFE0206200; Liaoning Revitalization Talents Program: XLYC1807018","This research was funded by the National Natural Science Foundation of China (Grant number U20A20201), the Liaoning Province Doctoral Scientific Research Foundation (Grant number 2020-BS-025), the Liaoning Revitalization Talents Program (Grant number XLYC1807018), and the National Key Research and Development Program of China (Grant number 2016YFE0206200).",,,,,,,,,,"Costes, A., Danieau, F., Argelaguet, F., Guillotel, P., Lecuyer, A., Towards haptic images: A survey on touchscreen-based surface haptics (2020) Ieee Transactions on Haptics, 13 (3), pp. 530-541; Shao, S., Wang, T., Su, Y., Yao, C., Song, C., Ju, Z., Multi-IMF sample entropy features with machine learning for surface texture recognition based on robot tactile perception (2021) International Journal of Humanoid Robotics, 18 (2), p. 2150005; Zheng, W., Liu, H., Sun, F., Lifelong visual-tactile cross-modal learning for robotic material perception (2021) Ieee Transactions on Neural Networks and Learning Systems, 32 (3), pp. 1192-1203; Falco, P., Lu, S., Natale, C., Pirozzi, S., Lee, D., A transfer learning approach to cross-modal object recognition: From visual observation to robotic haptic exploration (2019) Ieee Transactions on Robotics, 35 (4), pp. 987-998. , 2-s2.0-85070506743; Jung, J., Lee, S., Myung, H., Indoor mobile robot localization and mapping based on ambient magnetic fields and aiding radio sources (2015) Ieee Transactions on Instrumentation and Measurement, 64 (7), pp. 1922-1934. , 2-s2.0-85027957500; Yuan, W., Li, Z., Su, C.-Y., Multisensor-based navigation and control of a mobile service robot (2021) Ieee Transactions on Systems, Man, and Cybernetics: Systems, 51 (4), pp. 2624-2634; Debie, E., Fernandez Rojas, R., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., Abbass, H.A., Multimodal fusion for objective assessment of cognitive workload: A review (2021) Ieee Transactions on Cybernetics, 51 (3), pp. 1542-1555; Pei, Z., Wang, H., Bezerianos, A., Li, J., EEG-based multiclass workload identification using feature fusion and selection (2021) Ieee Transactions on Instrumentation and Measurement, 704001108; Delliaux, S., Delaforge, A., Deharo, J.-C., Chaumet, G., Mental workload alters heart rate variability, lowering non-linear dynamics (2019) Frontiers in Physiology, 10565. , 2-s2.0-85068268225; Tsunoda, K., Chiba, A., Yoshida, K., Watanabe, T., Mizuno, O., Predicting changes in cognitive performance using heart rate variability (2017) Ieice Transactions on Information and Systems, 100500 (10), pp. 2411-2419. , 2-s2.0-85030218171; Shao, S., Wang, T., Song, C., Chen, X., Cui, E., Zhao, H., Obstructive sleep apnea recognition based on multi-bands spectral entropy analysis of short-time heart rate variability (2019) Entropy, 21 (8), p. 812; Shao, S.-L., Wang, T., Song, C.-H., Cui, E.N., Zhao, H., Yao, C., A novel method of heart rate variability measurement (2019) Acta Physica Sinica, 68 (17); Tiwari, A., Albuquerque, I., Parent, M., Gagnon, J.F., Lafond, D., Tremblay, S., Falk, T.H., Multi-scale heart beat entropy measures for mental workload assessment of ambulant users (2019) Entropy, 21 (8), p. 783. , 2-s2.0-85070458166; Tiwari, A., Narayanan, S., Falk, T.H., Stress and Anxiety Measurement ""in-the-Wild"" Using Quality-aware Multi-scale Hrv Features, pp. 7056-7059. , Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019 Montreal, Canada; Gilfriche, P., Arsac, L.M., Daviaux, Y., Diaz-Pineda, J., Miard, B., Morellec, O., André, J.M., Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG (2018) American Journal of Physiology- Regulatory, Integrative and Comparative Physiology, 315 (3), pp. R469-R478. , 2-s2.0-85052543327; Malik, M., Bigger, J.T., Camm, A.J., Kleiger, R.E., Malliani, A., Moss, A.J., Schwartz, P.J., Heart rate variability: Standards of measurement, physiological interpretation, and clinical use (1996) European Heart Journal, 17 (3), pp. 354-381. , 2-s2.0-0029988782; Villani, V., Righi, M., Sabattini, L., Secchi, C., Wearable devices for the assessment of cognitive effort for human-robot interaction (2020) Ieee Sensors Journal, 20 (21), pp. 13047-13056; Shao, S., Wang, T., Wang, Y., Su, Y., Song, C., Yao, C., Research of HRV as a measure of mental workload in human and dual-arm robot interaction (2020) Electronics, 9 (12); Villani, V., Capelli, B., Secchi, C., Fantuzzi, C., Sabattini, L., Humans interacting with multi-robot systems: A natural affect-based approach (2020) Autonomous Robots, 44, pp. 601-616; Castaldo, R., Montesinos, L., Melillo, P., James, C., Pecchia, L., Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life (2019) Bmc Medical Informatics and Decision Making, 19 (1). , 2-s2.0-85060153698; Pan, J., Tompkins, W.J., A real-time QRS detection algorithm (1985) Ieee Transactions on Biomedical Engineering, 32 (3), pp. 230-236. , 2-s2.0-0021892137; Chen, L., Zhang, X., Song, C., An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram (2015) Ieee Transactions on Automation Science and Engineering, 12 (1), pp. 106-115. , 2-s2.0-85027953078; Zhu, F., Yang, J., Xu, S., Gao, C., Ye, N., Yin, T., Incorporating neighbors' distribution knowledge into support vector machines (2017) Soft Computing, 21 (21), pp. 6407-6420. , 2-s2.0-84973171633; Zhu, F., Ning, Y., Chen, X.C., Zhao, Y., Gang, Y., On removing potential redundant constraints for SVOR learning (2021) Applied Soft Computing, 102 (4); Li, Y., Pan, W., Li, K., Jiang, Q., Liu, G., Sliding trend fuzzy approximate entropy as a novel descriptor of heart rate variability in obstructive sleep apnea (2019) Ieee Journal of Biomedical and Health Informatics, 23 (1), pp. 175-183. , 2-s2.0-85040612055; MacHetanz, K., Berelidze, L., Guggenberger, R., Gharabaghi, A., Brain-heart interaction during transcutaneous auricular vagus nerve stimulation (2021) Frontiers in Neuroscience, 15632697; Clifford, G.D., Tarassenko, L., Quantifying errors in spectral estimates of HRV due to beat replacement and resampling (2005) Ieee Transactions on Biomedical Engineering, 52 (4), pp. 630-638. , 2-s2.0-12344308577; Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., Yang, J., Feature selection boosted by unselected features (2021) Ieee Transactions on Neural Networks and Learning Systems, pp. 1-13","Shao, S.; State Key Laboratory of Robotics, China; email: shaoshiliang@sia.cn
Wang, T.; State Key Laboratory of Robotics, China; email: wangting@sia.cn",,,"Hindawi Limited",,,,,15308669,,,,"English","Wireless Commun. Mobile Comput.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85117371752
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","A Deployment Framework for Formally Verified Human-Robot Interactions",2021,"IEEE Access","9",,,"136616","136635",,,"10.1109/ACCESS.2021.3117852","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117238639&doi=10.1109%2fACCESS.2021.3117852&partnerID=40&md5=fca69e9276e263611216c286eceb481d","Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy; Department of Computing and Software, McMaster University, Hamilton, ON  L8S 4L8, Canada; Dipartimento di Meccanica, Politecnico di Milano, Milan, 20156, Italy","Lestingi, L., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy; Askarpour, M., Department of Computing and Software, McMaster University, Hamilton, ON  L8S 4L8, Canada; Bersani, M.M., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy; Rossi, M., Dipartimento di Meccanica, Politecnico di Milano, Milan, 20156, Italy","In the future, assistive robots will spread to everyday settings and regularly interact with humans. This paper introduces a deployment approach for assistive robotic applications where human-robot interaction is the main element. The deployment infrastructure hinges on a model-to-code transformation technique and a ROS-based middleware layer and enables deployment in real life or simulation in a virtual environment. The approach fits into a model-driven framework for the formal verification of interactive scenarios. At design-time, the application analyst estimates the most likely outcome of the robotic mission through Statistical Model Checking of a Stochastic Hybrid Automata network modeling the scenario. We introduce an innovative approach to convert a specific subset of Stochastic Hybrid Automata into executable code to control the robot and respond to human actions. Deploying or simulating the application allows analysts to validate the results obtained at design time or to refine the formal model based on runs in the real or the virtual scene. The methodology's effectiveness is tested via simulation of use cases from the healthcare setting, which can significantly benefit from this kind of approach thanks to its innovative features related to human physiology and autonomous behavior. © 2013 IEEE.","Human-robot interaction; model-driven approach; robot deployment; service robots","Automata theory; Behavioral research; Man machine systems; Middleware; Model checking; Physiology; Robotics; Stochastic models; Stochastic systems; Virtual reality; Assistive robotics; Assistive robots; Design time; Humans-robot interactions; Hybrid automatons; Model driven approach; Robot deployment; Robotics applications; Service robots; Stochastics; Human robot interaction",,,,,,,,,,,,,,,,"Gwon, T., Park, H., Seo, D., Lee, S., Kim, D., Jeon, S., A study on safety evaluation criteria of the personal carrier robot based on ISO 13482 (2019) Proc. 3rd IEEE Int. Conf. Robotic Comput. 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Eng., 25 (6), pp. 4473-4546. , Nov","Lestingi, L.; Dipartimento di Elettronica, Italy; email: livia.lestingi@polimi.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85117238639
"Roda-Sanchez L., Garrido-Hidalgo C., García A.S., Olivares T., Fernández-Caballero A.","57202112308;57193328014;16174733600;6602909884;6602230534;","Comparison of RGB-D and IMU-based gesture recognition for human-robot interaction in remanufacturing",2021,"International Journal of Advanced Manufacturing Technology",,,,"","",,,"10.1007/s00170-021-08125-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117054754&doi=10.1007%2fs00170-021-08125-9&partnerID=40&md5=040856967e70e4a15f003b547cb1a8b9","Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","Roda-Sanchez, L., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Garrido-Hidalgo, C., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; García, A.S., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Olivares, T., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Fernández-Caballero, A., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","With product life-cycles getting shorter and limited availability of natural resources, the paradigm shift towards the circular economy is being impulsed. In this domain, the successful adoption of remanufacturing is key. However, its associated process efficiency is to date limited given high flexibility requirements for product disassembly. With the emergence of Industry 4.0, natural human-robot interaction is expected to provide numerous benefits in terms of (re)manufacturing efficiency and cost. In this regard, vision-based and wearable-based approaches are the most extended when it comes to establishing a gesture-based interaction interface. In this work, an experimental comparison of two different movement-estimation systems—(i) position data collected from Microsoft Kinect RGB-D cameras and (ii) acceleration data collected from inertial measurement units (IMUs)—is addressed. The results point to our IMU-based proposal, OperaBLE, having recognition accuracy rates up to 8.5 times higher than these of Microsoft Kinect, which proved to be dependent on the movement’s execution plane, subject’s posture, and focal distance. © 2021, The Author(s).","Computer vision; Human-robot interaction; Internet of Things; Remanufacturing; Wearables","Computer vision; Efficiency; Gesture recognition; Human robot interaction; Life cycle; Man machine systems; Motion estimation; Wearable technology; Circular economy; Gestures recognition; Humans-robot interactions; Inertial measurements units; Microsoft's kinect; Paradigm shifts; Process efficiency; Product life cycles; Remanufacturing; Unit-based; Internet of things",,,,,"European Regional Development Fund, ERDF: RTI2018-098156-B-C52; Electronic Components and Systems for European Leadership, ECSEL: 876852","Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministerio de Ciencia e Innovación (FEDER, EU funds) under RTI2018-098156-B-C52 project. This work was also partially supported by VALU3S, a European co-funded innovation project that has been granted by the ECSEL Joint Undertaking (JU) [grant number 876852]. The funding of the project comes from the Horizon 2020 research programme and participating countries. National funding is provided by Germany, including the Free States of Saxony and Thuringia, Austria, Belgium, Finland, France, Italy, the Netherlands, Slovakia, Spain, Sweden, and Turkey. This work was also partially supported by Grupos I+D funding under 2021-GRIN-31042. The publication is part of the project PCI2020-112001, funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR. Luis Roda-Sanchez is pursuing his Industrial PhD under DIN2018-010177 fellowship from Spanish Ministerio de Ciencia e Innovación. Celia Garrido-Hidalgo holds 2019-PREDUCLM-10703 fellowship from FEDER, UE at Universidad de Castilla-La Mancha.",,,,,,,,,,"Ramírez, F.J., Castellani, M., Xu, W., Autonomous remanufacturing (2020) The International Journal of Advanced Manufacturing Technology, , (,),.,., https://doi.org/10.1007/s00170-020-05559-5; Zlamparet, G.I., Ijomah, W., Miao, Y., Awasthi, A.K., Zeng, X., Li, J., Remanufacturing strategies: a solution for WEEE problem (2017) J Clean Prod, 149, pp. 126-136; Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J., The circular economy–a new sustainability paradigm? (2017) J Clean Prod, 143, pp. 757-768; Ramírez, F.J., Aledo, J.A., Gamez, J.A., Pham, D.T., Economic modelling of robotic disassembly in end-of-life product recovery for remanufacturing (2020) Comput Indust Eng, 142, p. 106339; Matsumoto, M., Yang, S., Martinsen, K., Kainuma, Y., Trends and research challenges in remanufacturing (2016) Int J Precision Eng Manufact-Green Technol, 3 (1), pp. 129-142; Liu, J., Zhou, Z., Pham, D.T., Xu, W., Ji, C., Liu, Q., Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing (2018) Int J Prod Res, 56 (9), pp. 3134-3151; Liu, C., Zhu, Q., Wei, F., Rao, W., Liu, J., Hu, J., Cai, W., A review on remanufacturing assembly management and technology (2019) Int J Adv Manufact Technol, 105 (11), pp. 4797-4808; Zhou, Z., Liu, J., Pham, D.T., Xu, W., Ramirez, F.J., Ji, C., Liu, Q., Disassembly sequence planning: recent developments and future trends (2019) Proc Instit Mechan Eng Part B: J Eng Manufact, 233 (5), pp. 1450-1471; Singhal, D., Tripathy, S., Jena, S.K., Remanufacturing for the circular economy: study and evaluation of critical factors (2020) Resour Conserv Recycl, 156, p. 104681; Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M., Industry 4.0 (2014) Business Inform Syst Eng, 6 (4), pp. 239-242; Roda-Sanchez, L., Olivares, T., Garrido-Hidalgo, C., de La Vara, J.L., Fernández-Caballero, A., Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system (2020) Integrat Comput-Aided Eng Pre-Press(Pre-Press), pp. 1-17; Kinect, M., (2021), https://developer.microsoft.com/windows/kinect/; Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., Ruiz, M.C., OperaBLE: An IoT-based wearable to improve efficiency and smart worker care services in Industry 4.0 (2018) J Sensors, p. 2018; Bernard, S., Remanufacturing (2011) J Environ Econ Manag, 62 (3), pp. 337-351; Matsumoto, M., Ijomah, W., (2013) Remanufacturing. In: Handbook of Sustainable Engineering, pp. 389-408. , Springer Netherlands; Hazen, B.T., Mollenkopf, D.A., Wang, Y., Remanufacturing for the circular economy: an examination of consumer switching behavior (2017) Bus Strateg Environ, 26 (4), pp. 451-464; Rathore, P., Kota, S., Chakrabarti, A., Sustainability through remanufacturing in India: a case study on mobile handsets (2011) J Clean Prod, 19 (15), pp. 1709-1722; Raz, G., Ovchinnikov, A., Blass, V., Economic, environmental, and social impact of remanufacturing in a competitive setting (2017) IEEE Trans Eng Manag, 64 (4), pp. 476-490; Östlin, J., Sundin, E., Björkman, M., Product life-cycle implications for remanufacturing strategies (2009) J Clean Prod, 17 (11), pp. 999-1009; Yang, S., MR, A., Kaminski, J., Pepin, H., Opportunities for Industry 4.0 to support remanufacturing (2018) Appl Sci, 8 (7), p. 1177; Lee, C.M., Woo, W.S., Roh, Y.H., Remanufacturing: trends and issues (2017) Int J Precision Eng Manufact-Green Technol, 4 (1), pp. 113-125; Górriz, J.M., Ramírez, J., Ortíz, A., Martínez-Murcia, F.J., Segovia, F., Suckling, J., Leming, M., Ferrández, J.M., Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications (2020) Neurocomputing, 410, pp. 237-270; Kerin, M., Pham, D.T., Smart remanufacturing: A review and research framework (2020) Journal of Manufacturing Technology Management; Garrido-Hidalgo, C., Ramirez, F.J., Olivares, T., Roda-Sanchez, L., The adoption of Internet of Things in a circular supply chain framework for the recovery of WEEE: the case of lithium-ion electric vehicle battery packs (2020) Waste Manag, 103, pp. 32-44; Stock, T., Seliger, G., Opportunities of sustainable manufacturing in Industry 4.0 (2016) Procedia Cirp, 40, pp. 536-541; Garrido-Hidalgo, C., Hortelano, D., Roda-Sanchez, L., Olivares, T., Ruiz, M.C., Lopez, V., IoT heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable Industry 4.0 (2018) IEEE Access, 6, pp. 28417-28437; Sheridan, T.B., Human-robot interaction: status and challenges (2016) Hum Factors, 58 (4), pp. 525-532; Jazdi, N., Cyber physical systems in the context of Industry 4.0 (2014) 2014 IEEE International Conference on Automation, Quality and Testing, Robotics. IEEE, pp. 1-4; Gorecky, D., Schmitt, M., Loskyll, M., Zühlke, D., Human-machine interaction in the Industry 4.0 era (2014) 2014 12Th IEEE International Conference on Industrial Informatics. IEEE, pp. 289-294; Berg, J., Lu, S., Review of interfaces for industrial human-robot interaction (2020) Current Robot Rep, 1 (2), pp. 27-34; Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., Theobalt, C., Monocular 3d human pose estimation in the wild using improved CNN supervision (2017) 2017 International Conference on 3D Vision. IEEE, pp. 506-516; Kiruba, K., Shiloah, E.D., Sunil, R.R.C., Hexagonal volume local binary pattern (h-VLBP) with deep stacked autoencoder for human action recognition (2019) Cogn Syst Res, 58, pp. 71-93; Dong, J., Xia, Z., Yan, W., Zhao, Q., Dynamic gesture recognition by directional pulse coupled neural networks for human-robot interaction in real time (2019) J Vis Commun Image Represent, 63, p. 102583; Mueller, F., Davis, M., Bernard, F., Sotnychenko, O., Verschoor, M., Otaduy, M.A., Casas, D., Theobalt, C., Real-time pose and shape reconstruction of two interacting hands with a single depth camera (2019) ACM Trans Graph, 38 (4), pp. 1-13; Devine, S., Rafferty, K., Ferguson, S., Real time robotic arm control using hand gestures with multiple end effectors (2016) 2016 UKACC 11Th International Conference on Control. IEEE, pp. 1-5; Erdoğan, K., Durdu, A., Yilmaz, N., Intention recognition using leap motion controller and artificial neural networks (2016) 2016 International Conference on Control, Decision and Information Technologies. IEEE, pp. 689-693; Mazhar, O., Navarro, B., Ramdani, S., Passama, R., Cherubini, A., A real-time human-robot interaction framework with robust background invariant hand gesture detection (2019) Robot Comput Integr Manuf, 60, pp. 34-48; Arivazhagan, S., Shebiah, R.N., Harini, R., Swetha, S., Human action recognition from RGB-d data using complete local binary pattern (2019) Cogn Syst Res, 58, pp. 94-104; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) Int J Comput Integr Manuf, 29 (8), pp. 916-931; Albawab, T., Halim, I., Ahmad, N., Umar, R., Mohamed, M., Abullais, F., Basari, A., Saptari, A., Upper limb joints and motions sampling system using Kinect camera (2018) J Adv Manufact Technol, 12 (2), pp. 147-158; Vera, D., Costa, N., Roda-Sanchez, L., Olivares, T., Fernández-Caballero, A., Pereira, A., Body area networks in healthcare: a brief state of the art (2019) Appl Sci, 9 (16), p. 3248; Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human behavior and hand gesture classification for smart human-robot interaction (2017) Procedia Manufact, 11, pp. 91-98; Neto, P., Simão, M., Mendes, N., Safeea, M., Gesture-based human-robot interaction for human assistance in manufacturing (2019) Int J Adv Manufact Technol, 101 (1), pp. 119-135; Chen, C., Jafari, R., Kehtarnavaz, N., UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (2015) 2015 IEEE International Conference on Image Processing. IEEE, pp. 168-172","Roda-Sanchez, L.; Instituto de Investigación en Informática de Albacete, Spain; email: Luis.Roda@alu.uclm.es",,,"Springer Science and Business Media Deutschland GmbH",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85117054754
"Fu J., Du J., Teng X., Fu Y., Wu L.","49661360600;57210426672;57210430079;57295418300;57059587600;","Adaptive multi-task human-robot interaction based on human behavioral intention",2021,"IEEE Access","9",,,"133762","133773",,,"10.1109/ACCESS.2021.3115756","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117015793&doi=10.1109%2fACCESS.2021.3115756&partnerID=40&md5=4d3483d2fa81b8ae3b8d4eb976f5d465","School of Automation, Wuhan University of Technology, Wuhan, 430070, China; Department of Computer Science, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; School of Information, Wuhan University of Technology, Wuhan, 430070, China","Fu, J., School of Automation, Wuhan University of Technology, Wuhan, 430070, China; Du, J., School of Automation, Wuhan University of Technology, Wuhan, 430070, China; Teng, X., School of Automation, Wuhan University of Technology, Wuhan, 430070, China; Fu, Y., Department of Computer Science, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; Wu, L., School of Information, Wuhan University of Technology, Wuhan, 430070, China","Learning from demonstrations with Probabilistic Movement Primitives (ProMPs) has been widely used in robot skill learning, especially in human-robot collaboration. Although ProMP has been extended to multi-task situations inspired by the Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of the collaborative tasks in order to align with the instantaneous change of human intention. To solve this problem, we proposed an alternate learning-based parameter estimation method and an empirical minimum variation-based decomposition strategy with projection points, combining with linear interpolation strategy for weights, based on a Gaussian mixture model framework. Alternate learning of weights and parameters in multi-task ProMP (MTProMP) allows the robot to obtain a smooth composite trajectory planning which crosses expected via points. Decomposition strategy reflects how the desired via point state is projected onto the individual ProMP component, rendering the minimum total sum of deviations between each projection point with the respective prior. Linear interpolation is used to adjust the weights among sequential via points automatically. The proposed method and strategy are successfully extended to multi-task interaction ProMPs (MTiProMP). With MTProMP and MTiProMP, the robot can be applied to multiple tasks in industrial factories and collaborate with the worker to switch from one task to another according to changing intentions of the human. Classical via points trajectory planning experiments and human-robot collaboration experiments are performed on the Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better. © 2013 IEEE.","Alternate learning; Decomposition strategy; Human robot interaction; Motion planning; MTiProMP; MTProMP","Behavioral research; Gaussian distribution; Interpolation; Learning systems; Man machine systems; Motion planning; Robot programming; Alternate learning; Decomposition strategy; Humans-robot interactions; Motion-planning; Multi tasks; Multi-task interaction promp; Multi-task ProMP; Probabilistics; Via point; Human robot interaction",,,,,,,,,,,,,,,,"Haddadin, S., Croft, E., Physical human-robot interaction (2016) Springer Handbook of Robotics. Berlin, pp. 1835-1874. , Germany: Springer; Lee, J., (2017) A Survey of Robot Learning from Demonstrations for Human-robot Collaboration, , http://arxiv.org/abs/1710.08789; Abu-Dakka, F.J., Kyrki, V., Geometry-aware dynamic movement primitives (2020) Proc. Ieee Int. Conf. Robot. Autom. (ICRA), pp. 4421-4426. , May; Wang, N., Chen, C., Yang, C., A robot learning framework based on adaptive admittance control and generalizable motion modeling with neural network controller (2020) Neurocomputing, 390, pp. 260-267. , May; Peternel, L., Tsagarakis, N., Caldwell, D., Ajoudani, A., Robot adap-tation to human physical fatigue in human-robot co-manipulation (2018) Auto. Robots, 42 (5), pp. 1011-1021. , Jun; Paraschos, A., Daniel, C., Peters, J., Neumann, G., Probabilistic movement primitives (2013) Proc. Adv. Neural Inf. Process. Syst., pp. 2616-2624; Maeda, G., Ewerton, M., Lioutikov, R., Amor, H.B., Peters, J., Neumann, G., Learning interaction for collaborative tasks with probabilis-tic movement primitives (2014) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 527-534. , Nov; Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J., (2018) A Prob-abilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation, pp. 253-268. , Cham, Switzerland: Springer; Khoramshahi, M., Billard, A., A dynamical system approach to task-adaptation in physical human-robot interaction (2019) Auton. Robots, 43, pp. 927-946. , Apr; Suzuki, K., Mori, H., Ogata, T., Motion switching with sensory and instruction signals by designing dynamical systems using deep neu-ral network (2018) Ieee Robot. Autom. Lett., 3 (4), pp. 3481-3488. , Oct; Mohseni-Kabir, A., Veloso, M., Robot task interruption by learning to switch among multiple models (2018) Proc. 27th Int. Joint Conf. Artif. Intell., pp. 4943-4949. , Jul; Maeda, G.J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J., Probabilistic movement primitives for coordination of mul-tiple human-robot collaborative tasks (2017) Auto. Robots, 41 (3), pp. 593-612. , Mar; Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G., Extracting low-dimensional control variables for movement primi-tives (2015) Proc. Ieee Int. Conf. Robot. Autom. (ICRA), pp. 1511-1518. , May; Bishop, C.M., (2006) Pattern Recognition and Machine Learning, , New York, NY, USA: Springer; Paraschos, A., Rueckert, E., Peters, J., Neumann, G., Probabilistic movement primitives under unknown system dynamics (2018) Adv. Robot., 32 (6), pp. 297-310. , Mar; Barfoot, T.D., (2017) State Estimation for Robotics, , Cambridge, U. K. : Cambridge Univ. Press; Kulak, T., Girgin, H., Odobez, J.-M., Calinon, S., Active learning of Bayesian probabilistic movement primitives (2021) Ieee Robot. Autom. Lett., 6 (2), pp. 2163-2170. , Apr","Wu, L.; School of Information, China; email: wulv@whut.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85117015793
"Cantucci F., Falcone R., Castelfranchi C.","57205735711;57213828373;57204261558;","Investigating adjustable social autonomy in human robot interaction",2021,"CEUR Workshop Proceedings","2963",,,"49","60",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116911162&partnerID=40&md5=727452a20908cdafe4c89b8ba1ba0286","Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","Cantucci, F., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy; Falcone, R., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy; Castelfranchi, C., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","More and more often, Human Robot Interaction(HRI) applications require the design of robotics systems whose decision process implies the capability to evaluate not only the physical environment, but especially the mental states and the features of its human interlocutor, in order to adapt their social autonomy every time humans require the robot's help. Robots will be really cooperative and effective when they will expose the capability to consider not only the goals or interests explicitly required by humans, but also those one that are not declared and to provide help that go beyond the literal task execution. In order to improve the quality of this kind of smart help, a robot has to operate a meta-evaluation of its own predictive skills to build a model of the interlocutor and of her/his goals. The robot's capability to self-trust its skills to interpret the interlocutor and the context, is a fundamental requirement for producing smart and effective decisions towards humans. In this work we propose a simulated experiment, designed with the goal to test a cognitive architecture for trustworthy human robot collaboration. The experiment has been designed in order to demonstrate how the robot's capability to learn its own level of self-trust on its predictive abilities in perceiving the user and building a model of her/him, allows it to establish a trustworthy collaboration and to maintain an high level of user's satisfaction, with respect to the robot's performance, also when these abilities progressively degrade. © 2021 CEUR-WS. All rights reserved.","Cognitive modelling; Robot autonomy adaptation; Theory of mind; Transparency; Trustworthy HRI","Agricultural robots; Machine design; Man machine systems; Quality control; Robotics; Cognitive model; Decision process; Humans-robot interactions; Mental state; Physical environments; Robot autonomy; Robot autonomy adaptation; Robotic systems; Theory of minds; Trustworthy human robot interaction; Human robot interaction",,,,,,,,,,,,,,,,"Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Graepel, T., Mastering atari, go, chess and shogi by planning with a learned model (2020) Nature, 588, pp. 604-609; Fujita, H., Ai-based computer-aided diagnosis (ai-cad): the latest review to read first (2020) Radiological physics and technology, 13, pp. 6-19; Hardalov, M., Koychev, I., Nakov, P., Towards automated customer support (2018) International Conference on Artificial Intelligence: Methodology, Systems, and Applications, pp. 48-59. , Springer; Commission, E., White paper on artificial intelligence-a european approach to excellence and trust Com (2020) 65 Final, , (2020); Smuha, N. A., The eu approach to ethics guidelines for trustworthy artificial intelligence (2019) Computer Law Review International, 20, pp. 97-106; Levin, S., Wong, J. C., Self-driving uber kills arizona woman in first fatal crash involving pedestrian (2018) The Guardian, 19; Schlesinger, A., O'Hara, K. P., Taylor, A. S., Let's talk about race: Identity, chatbots, and ai (2018) Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14; D'Onofrio, G., Sancarlo, D., Raciti, M., Reforgiato, D., Mangiacotti, A., Russo, A., Ricciardi, F., Presutti, V., Mario project: Experimentation in the hospital setting (2017) Italian Forum of Ambient Assisted Living, pp. 289-303. , Springer; Cooper, S., Di Fava, A., Vivas, C., Marchionni, L., Ferro, F., Ari: The social assistive robot and companion (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 745-751. , IEEE; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 307-315; Kok, B. C., Soh, H., Trust in robots: Challenges and opportunities (2020) Current Robotics Reports, pp. 1-13; Park, S., Multifaceted trust in tourism service robots (2020) Annals of Tourism Research, 81, p. 102888; Castelfranchi, C., Falcone, R., Towards a theory of delegation for agent-based systems (1998) Robotics and Autonomous Systems, 24, pp. 141-157; Castelfranchi, C., Falcone, R., (2010) Trust theory: A socio-cognitive and computational model, 18. , John Wiley & Sons; Castelfranchi, C., Falcone, R., Towards a theory of delegation for agent-based systems (1998) Robotics and Autonomous Systems, 24, pp. 141-157; Hadfield-Menell, D., Dragan, A., Abbeel, P., Russell, S., (2016) The off-switch game, , arXiv preprint arXiv:1611.08219; Israelsen, B., Ahmed, N., Frew, E., Lawrence, D., Argrow, B., Machine self-confidence in autonomous systems via meta-analysis of decision processes (2019) International Conference on Applied Human Factors and Ergonomics, pp. 213-223. , Springer; Cantucci, F., Falcone, R., Towards trustworthiness and transparency in social human-robot interaction (2020) 2020 IEEE International Conference on Human-Machine Systems (ICHMS), pp. 1-6; Cantucci, F., Falcone, R., User Modeling and User-Adapted Interaction, , (Manuscript submitted for publication); Rao, A. S., Georgeff, M. P., Bdi agents: from theory to practice (1995) ICMAS, 95, pp. 312-319; Devin, S., Alami, R., An implemented theory of mind to improve human-robot shared plans execution (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 319-326. , IEEE; Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., Santi, A., Multi-agent oriented programming with jacamo (2013) Science of Computer Programming, 78, pp. 747-761; Bordini, R. H., Hübner, J. F., Bdi agent programming in agentspeak using jason (2005) International Workshop on Computational Logic in Multi-Agent Systems, pp. 143-164. , Springer; Braunhofer, M., Elahi, M., Ricci, F., User personality and the new user problem in a context-aware point of interest recommender system (2015) Information and communication technologies in tourism 2015, pp. 537-549. , Springer; Mohamed, M. H., Khafagy, M. H., Ibrahim, M. H., Recommender systems challenges and solutions survey (2019) 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 149-155. , IEEE; Pazzani, M. J., A framework for collaborative, content-based and demographic filtering (1999) Artificial intelligence review, 13, pp. 393-408; Swift, H. J., Abrams, D., Drury, L., Lamont, R. A., Categorization by age (2018) Encyclopedia of Evolutionary Psychological Science",,"Calegari R.Ciatto G.Denti E.Omicini A.Sartor G.",,"CEUR-WS","22nd Workshop ""From Objects to Agents"", WOA 2021","1 September 2021 through 3 September 2021",,172186,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85116911162
"Feng Y., Perugia G., Yu S., Barakova E.I., Hu J., Rauterberg G.W.M.","57196468593;57193757433;13906567800;35614959800;56442891500;57218000808;","Context-Enhanced Human-Robot Interaction: Exploring the Role of System Interactivity and Multimodal Stimuli on the Engagement of People with Dementia",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00823-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116771414&doi=10.1007%2fs12369-021-00823-4&partnerID=40&md5=03b7512f4891ce960c05e67d8ae7481c","Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Industrial Design, Northwestern Polytechnical University, Xi’an, China; Human-Technology Interaction Group, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Information Technology, Uppsala University, Uppsala, Sweden","Feng, Y., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands, Department of Industrial Design, Northwestern Polytechnical University, Xi’an, China; Perugia, G., Human-Technology Interaction Group, Eindhoven University of Technology, Eindhoven, Netherlands, Department of Information Technology, Uppsala University, Uppsala, Sweden; Yu, S., Department of Industrial Design, Northwestern Polytechnical University, Xi’an, China; Barakova, E.I., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Hu, J., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Rauterberg, G.W.M., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands","Engaging people with dementia (PWD) in meaningful activities is the key to promote their quality of life. Design towards a higher level of user engagement has been extensively studied within the human-computer interaction community, however, few extend to PWD. It is generally considered that increased richness of experiences can lead to enhanced engagement. Therefore, this paper explores the effects of rich interaction in terms of the role of system interactivity and multimodal stimuli by engaging participants in context-enhanced human-robot interaction activities. The interaction with a social robot was considered context-enhanced due to the additional responsive sensory feedback from an augmented reality display. A field study was conducted in a Dutch nursing home with 16 residents. The study followed a two by two mixed factorial design with one within-subject variable - multimodal stimuli - and one between-subject variable - system interactivity. A mixed method of video coding analysis and observational rating scales was adopted to assess user engagement comprehensively. Results disclose that when additional auditory modality was included besides the visual-tactile stimuli, participants had significantly higher scores on attitude, more positive behavioral engagement during activity, and a higher percentage of communications displayed. The multimodal stimuli also promoted social interaction between participants and the facilitator. The findings provide sufficient evidence regarding the significant role of multimodal stimuli in promoting PWD’s engagement, which could be potentially used as a motivation strategy in future research to improve emotional aspects of activity-related engagement and social interaction with the human partner. © 2021, The Author(s).","Context-enhanced human-robot interaction; Engagement; Interactivity; Multimodal stimuli; People with dementia; Social robots","Augmented reality; Human computer interaction; Human robot interaction; Man machine systems; Sensory feedback; Video signal processing; Context-enhanced human-robot interaction; Engagement; Humans-robot interactions; Interactivity; Multi-modal; Multimodal stimulus; People with dementia; Social interactions; Social robots; User engagement; Neurodegenerative diseases",,,,,"China Scholarship Council, CSC","The authors would like to acknowledge all participated residents, caregivers and Trudy van de schans-Robijns from Vitalis for their involvement and contributions. We also thank Famke Boschman for her help in the user study and data analysis. This study is partly sponsored by China Scholarship Council.",,,,,,,,,,"Alenljung, B., Lindblom, J., Andreasson, R., Ziemke, T., User experience in social human-robot interaction (2019) Rapid Automation: Concepts, Methodologies, Tools, and Applications, pp. 1468-1490. , IGI Global; Anderiesen, H., Scherder, E., Goossens, R., Visch, V., Eggermont, L., Play experiences for people with alzheimers disease (2015) International Journal of Design, 9 (2), p. 2015; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefits of interactions with physically present robots over video-displayed agents (2011) Int J Soc Robot, 3 (1), pp. 41-52; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: A systematic review into effects and effectiveness (2012) J Am Med Dir Assoc, 13 (2), pp. 114-120; Bennett, B., McDonald, F., Beattie, E., Carney, T., Freckelton, I., White, B., Willmott, L., Assistive technologies for people with dementia: ethical considerations (2017) Bull World Health Organ, 95 (11), p. 749; 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J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85116771414
"Wang C., Zhang S., Hu J., Huang Z., Shi C.","57283939400;57202710733;57283493100;57283493200;55448007200;","Upper-Limb Muscle Synergy Features in Human-Robot Interaction with Circle-Drawing Movements",2021,"Applied Bionics and Biomechanics","2021",,"8850785","","",,,"10.1155/2021/8850785","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116351654&doi=10.1155%2f2021%2f8850785&partnerID=40&md5=c4e8c149442d74108ef8fd67c0bfe9ed","Emergency Trauma Surgical Department, Ningbo First Hospital, Zhejiang, Ningbo, 315010, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China; Rehabilitation Department, Ningbo Yinzhou No. 2 Hospital, Zhejiang, Ningbo, 315192, China","Wang, C., Emergency Trauma Surgical Department, Ningbo First Hospital, Zhejiang, Ningbo, 315010, China; Zhang, S., Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China; Hu, J., Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China; Huang, Z., Rehabilitation Department, Ningbo Yinzhou No. 2 Hospital, Zhejiang, Ningbo, 315192, China; Shi, C., Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China","The upper-limb rehabilitation robots can be developed as an efficient tool for motor function assessments. Circle-drawing has been used as a specific task for robot-based motor function measurement. The upper-limb movement-related kinematic and kinetic parameters measured by motion and force sensors embedded in the rehabilitation robots have been widely studied. However, the muscle synergies characterized by multiple surface electromyographic (sEMG) signals in upper limbs during human-robot interaction (HRI) with circle-drawing movements are rarely investigated. In this research, the robot-assisted and constrained circle-drawing movements for upper limb were used to increase the consistency of muscle synergy features. Both clockwise and counterclockwise circle-drawing tasks were implemented by all healthy subjects using right hands. The sEMG signals were recorded from six muscles in upper limb, and nonnegative matrix factorization (NMF) analysis was utilized to obtain muscle synergy information. Both synergy pattern and activation coefficient were calculated to represent the spatial and temporal features of muscle synergies, respectively. The results obtained from the experimental study confirmed that high structural similarity of muscle synergies was found among the subjects during HRI with circle-drawing movement by healthy subjects, which indicates healthy people may share a common underlying muscle control mechanism during constrained upper-limb circle-drawing movement. This study indicates the muscle synergy analysis during the HRI with constrained circle-drawing movement could be considered as a task for upper-limb motor function assessment. © 2021 Cheng Wang et al.",,"Factorization; Man machine systems; Matrix algebra; Muscle; Circle drawings; Electromyographic signal; Healthy subjects; Humans-robot interactions; Motor function; Muscle synergies; Rehabilitation robot; Specific tasks; Upper limbs; Upper-limb rehabilitation; Human robot interaction",,,,,,,,,,,,,,,,"Duret, C., Grosmaire, A.G., Krebs, H.I., Robot-assisted therapy in upper extremity hemiparesis: Overview of an evidence-based approach (2019) Frontiers in Neurology, 10, p. 412. , 2-s2.0-85067868341; Aisen, M.L., Krebs, H.I., Hogan, N., The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke (1997) Archives of Neurology, 54 (4), pp. 443-446. , 2-s2.0-0030945986 9109746; Winstein, C.J., Stein, J., Arena, R., Bates, B., Cherney, L.R., Cramer, S.C., Deruyter, F., Zorowitz, R.D., Guidelines for adult stroke rehabilitation and recovery: A guideline for healthcare professionals from the American Heart Association/American Stroke Association (2016) Stroke, 47 (6), pp. 98-169; 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What we have learned from over 25 years of research (2010) Journal of Electromyography and Kinesiology, 20 (6), pp. 1023-1035. , 2-s2.0-77957018598 20702112; Allison, G.T., Marshall, R.N., Singer, K.P., EMG signal amplitude normalization technique in stretch-shortening cycle movements (1993) Journal of Electromyography and Kinesiology, 3 (4), pp. 236-244. , 2-s2.0-0027771102 20870539","Shi, C.; Cixi Institute of Biomedical Engineering, Zhejiang, China; email: changchengshi@nimte.ac.cn",,,"Hindawi Limited",,,,,11762322,,,,"English","Appl. Bionics Biomech.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116351654
"Gan J., Wang N., Zuo L.","57284548200;55446268900;57283666100;","Surface electromyography (semg)-based intention recognition and control design for human-robot interaction in uncertain environment",2021,"Sensors and Materials","33","9",,"3153","3168",,,"10.18494/SAM.2021.3230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116323513&doi=10.18494%2fSAM.2021.3230&partnerID=40&md5=de8aade6443a4a7b5aacb8abc4ff1f96","Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Bristol Robotics Laboratory, University of West of England, Frenchay, Coldharbour Ln, BristolBS34 8QZ, United Kingdom; School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, China","Gan, J., Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Wang, N., Bristol Robotics Laboratory, University of West of England, Frenchay, Coldharbour Ln, BristolBS34 8QZ, United Kingdom; Zuo, L., School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, China","An important direction of human-robot interaction (HRI) is making robots respond to complex and dexterous tasks intelligently. To achieve this, biological signals based on surface electromyography (sEMG) have widely been used to identify human intentions rapidly and effectively. We propose an algorithm that can recognize human intentions conveyed by different hand gestures through analyzing sEMG data. This will facilitate the selection of the most appropriate interaction mode and level during HRI for the robot. We also propose an admittance control framework combining a tan-type barrier Lyapunov function (BLF) and a radial basis function neural network (RBFNN) to ensure the interaction and tracking performance and to guarantee the stability of the system in uncertain environments. Experiments performed on a Baxter robot verify the effectiveness of the proposed framework. © 2021 M Y U Scientific Publishing Division. All rights reserved.","Barrier lyapunov function; Human-robot interaction; Radial basis function neural network; Surface electromyography","Human robot interaction; Machine design; Man machine systems; Radial basis function networks; System stability; Barrier lyapunov function; Biological signals; Control design; Human intentions; Humans-robot interactions; Intention recognition; Lyapunov's functions; Radial basis function neural networks (RBF); Surface electromyography; Uncertain environments; Lyapunov functions",,,,,"National Natural Science Foundation of China, NSFC: 61803039","This work was supported in part by the National Nature Science Foundation of China (NSFC) under Grant 61803039.",,,,,,,,,,"Wan, W., Lu, F., Wu, Z., Harada, K., (2017) Neurocomputing, 259, p. 85. , https://doi.org/10.1016/j.neucom.2017.01.077; Wan, W., Harada, K., Kanehiro, F., (2019) IEEE Trans. Ind. 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Inf, 14, p. 3822. , https://doi.org/10.1109/tii.2017.2785415; Zeng, C., Yang, C., Chen, Z., Dai, S. L., (2018) Assembly Automation, , https://doi.org/10.1108/aa-02-2018-019; Kong, H., Yang, C., Li, G., Dai, S. L., (2020) IEEE Access, 8, p. 26030. , https://doi.org/10.1109/access.2020.2970468; Zeng, C., Yang, C., Cheng, H., Li, Y., Dai, S. L., (2020) IEEE Trans. Ind. Inf, 17, p. 1244. , https://doi.org/10.1109/tii.2020.2984482; Yang, C., Luo, J., Liu, C., Li, M., Dai, S. L., (2018) IEEE Trans. Autom. Sci. Eng, 16, p. 1512. , https://doi.org/10.1109/tase.2018.2874454; Murray, R. M., Li, Z., Sastry, S. S., (1994) A Mathematical Introduction to Robotic Manipulation, , https://doi.org/10.1201/9781315136370, (CRC press, Florida) Chap. 2; Lee, T. H., Harris, C. J., (1998) Adaptive Neural Network Control of Robotic Manipulators, p. 396. , https://doi.org/10.1142/3774, (World Scientific, Singapore); Jin, X., Xu, J. X., (2013) Automatica, 49, p. 2508. , https://doi.org/10.1016/j.automatica.2013.04.039; Huang, H., Yang, C., Chen, C. P., (2020) IEEE Trans. Cybern, p. 1. , https://doi.org/10.1109/tcyb.2020.2998984","Wang, N.; Key Laboratory of Autonomous Systems and Networked Control, China; email: Katie.Wang@brl.ac.uk",,,"M Y U Scientific Publishing Division",,,,,09144935,,,,"English","Sens. Mater.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85116323513
"Kaszuba S., Leotta F., Nardi D.","57224850698;37118890600;7006582655;","A Preliminary Study on Virtual Reality Tools in Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12980 LNCS",,,"81","90",,,"10.1007/978-3-030-87595-4_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115860973&doi=10.1007%2f978-3-030-87595-4_7&partnerID=40&md5=1b190fdd30e382c291be516cfa9882a1","Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy","Kaszuba, S., Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy; Leotta, F., Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy; Nardi, D., Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy","Choosing the best interaction modalities and protocols in Human-Robot Interaction (HRI) is far from being straightforward, as it strictly depends on the application domain, the tasks to be executed, the types of robots and sensors involved. In the last years, a growing number of HRI researchers exploited Virtual Reality (VR) as a mean to evaluate proposed solutions, focusing in particular on safety and correctness of collaborative tasks. This allows to prove the effectiveness and robustness of a certain approach in a simulated environment, thus permitting to converge more easily to the best solution, also avoiding to experiment potentially harmful actions in a real scenario. In this paper, we aim at reviewing existing VR based approaches targeting or embodying HRI. © 2021, Springer Nature Switzerland AG.","Communication modalities; Human-robot interaction; Speech acts; Virtual Reality","Man machine systems; Speech communication; Virtual reality; Applications domains; Collaborative tasks; Communication modalities; Humans-robot interactions; Simulated environment; Speech acts; Virtual reality tools; Human robot interaction",,,,,"101016906","Acknowledgements. The work of S. Kaszuba, F. Leotta and D. Nardi has been partly supported by the H2020 EU project CANOPIES - A Collaborative Paradigm for Human Workers and Multi-Robot Teams in Precision Agriculture Systems, Grant Agreement 101016906.",,,,,,,,,,"Arntz, A., Eimler, S.C., Experiencing AI in VR: A qualitative study on designing a human-machine collaboration scenario (2020) HCI International 2020-Late Breaking Posters, pp. 299-307. , Stephanidis, C., Antona, M., Ntoa, S. (eds.), pp; Aschenbrenner, D., Tol, D.V., Rusak, Z., Werker, C., Using virtual reality for scenario-based responsible research and innovation approach for human robot co-production (2020) IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp. 146-150. , pp., 2020; Bach, K., Harnish, R.M., (1979) Linguistic Communication and Speech Acts, , MIT Press, Cambridge; Bolano, G., Juelg, C., Roennau, A., Dillmann, R., Transparent robot behavior using augmented reality in close human-robot interaction (2019) 28Th IEEE International Conference on Robot and Human Interactive Communication, pp. 1-7. , RO-MAN), pp; Bonarini, A., Communication in human-robot interaction (2020) Current Robotics Reports, pp. 1-7. , pp; Chang, C.T., Hayes, B., (2020) A Survey of Augmented Reality for Human-Robot Collaboration; Cockburn, J., Solomon, Y., (2013) Multi-Modal Human Robot Interaction in a Simulation Environment; Dianatfar, M., Latokartano, J., Lanz, M., Review on existing vr/ar solutions in human-robot collaboration (2021) Procedia CIRP, 97, pp. 407-411; Dimitrokalli, A., Vosniakos, G.C., Nathanael, D., Matsas, E., On the assessment of human-robot collaboration in mechanical product assembly by use of virtual reality (2020) Procedia Manuf, 51, pp. 627-634; Etzi, R., Using virtual reality to test human-robot interaction during a collaborative task (2019) International Design Engineering Technical Conference and Computers and Information in Engineering Conference. 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AI, 8 (158); Kaufeld, M., Nickel, P., Level of robot autonomy and information aids in human-robot interaction affect human mental workload-an investigation in virtual reality, pp (2019) 278–291 (06; Khambhaita, H., Alami, R., Assessing the social criteria for human-robot collaborative navigation: A comparison of human-aware navigation planners (2017) 2017 26Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Pp. 1140–1145; Kostavelis, I., Gasteratos, A., Semantic maps from multiple visual cues. Expert Syst (2017) Appl, 68 (C), pp. 45-57; Kurth, J., Marcel Wagner, A., New planning methods for systems with human-robot collaboration. Carl Hanser Verlag Munich 115 (2020) 698–702 (12; Liu, D., Bhagat, K.K., Gao, Y., Chang, T.W., Huang, R., (2017) The Potentials and Trends of Virtual Reality in Education; Malik, A.A., Masood, T., Bilberg, A., Virtual reality in manufacturing: Immersive and collaborative artificial-reality in design of human-robot workspace (2020) Int. J. Comput. Integr. Manuf., 33 (1), pp. 22-37; Mara, M., (2021) Cobot Studio Vr: A Virtual Reality Game Environment for Trans-Disciplinary Research on Interpretability and Trust in Human-Robot Collaboration; Matsas, E., Vosniakos, G.C., Batras, D., Prototyping proactive and adaptive techniques for human-robot collaboration in manufacturing using virtual reality (2017) Rob. Comput.-Integr. Manuf., 50; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Rob. Auton. Syst., 63, pp. 22-35; Milliez, G., Ferreira, E., Fiore, M., Alami, R., Lefèvre, F.: Simulating human-robot interactions for dialogue strategy learning. 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Eng., 14; Murnane, M., Higgins, P., Saraf, M., Ferraro, F., Matuszek, C., Engel, D., A simulator for human-robot interaction in virtual reality (2021) 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, pp. 470-471. , pp; Nikolakis, N., Maratos, V., Makris, S., A cyber physical system (Cps) approach for safe human-robot collaboration in a shared workplace (2019) Rob. Comput.-Integr. Manuf., 56, pp. 233-243; Novitzky, M., Semmens, R., Franck, N.H., Chewar, C.M., Korpela, C.: Virtual reality for immersive human machine teaming with vehicles. In: Chen, J.Y.C., Fragomeni, G. (eds.) HCII 2020. LNCS, vol. 12190, pp. 575–590. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49695-1 39; Palmarini, R., Del Amo, I.F., Bertolino, G., Dini, G., Erkoyuncu, J.A., Roy, R., Farnsworth, M., Designing an ar interface to improve trust in human-robots collaboration (2018) Procedia CIRP, 70, pp. 350-355; Schmitz, N., Hirth, J., Berns, K., A simulation framework for human-robot interaction (2010) 2010 Third International Conference on Advances in Computer-Human Interactions, Pp. 79–84. IEEE; Shi, D., Collins, E.G., Goldiez, B., Donate, A., Liu, X., Dunlap, D., Human-aware robot motion planning with velocity constraints (2008) International Symposium on Collaborative Technologies and Systems, pp. 490-497. , pp., 2008; Shu, B., Sziebig, G., Pieters, R., (2019) Architecture for Safe Human-Robot Collaboration: Multi-Modal Communication in Virtual Reality for Efficient Task Execution, pp. 2297-2302. , pp; Wijnen, L., Bremner, P., Lemaignan, S., Giuliani, M., Performing human-robot interaction user studies in virtual reality* (2020) 29Th IEEE International Conference on Robot and Human Interactive Communication, pp. 794-794. , RO-MAN), pp; Yan, H., Ang, M.A., Poo, A., A survey on perception methods for human-robot interaction in social robots (2014) Int. J. Soc. Rob., 6, pp. 85-119; Zacharaki, A., Kostavelis, I., Gasteratos, A., Dokas, I., Safety bounds in human robot interaction: A survey (2020) Saf. Sci., 127","Leotta, F.; Department of Computer, via Ariosto 25, Italy; email: leotta@diag.uniroma1.it","De Paolis L.T.Arpaia P.Bourdot P.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on Augmented Reality, Virtual Reality and Computer Graphics, AVR 2021","7 September 2021 through 10 September 2021",,265419,03029743,9783030875947,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115860973
"Blankenbach K., Hochdorfer S., Babel F., Charrier E.","25623015000;35146014000;57204529534;57239071700;","Display evaluation for advanced human-robot interaction",2021,"Digest of Technical Papers - SID International Symposium","52","S2",,"349","353",,,"10.1002/sdtp.15119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115856542&doi=10.1002%2fsdtp.15119&partnerID=40&md5=57beb60626145f7e285ca606ebffcda5","Pforzheim University, Display Lab, Pforzheim, Germany; ADLATUS Robotics, Ulm, Germany; Ulm University, Dep of Human Factors, Ulm, Germany","Blankenbach, K., Pforzheim University, Display Lab, Pforzheim, Germany; Hochdorfer, S., ADLATUS Robotics, Ulm, Germany; Babel, F., Ulm University, Dep of Human Factors, Ulm, Germany; Charrier, E., Pforzheim University, Display Lab, Pforzheim, Germany","Autonomous robots become increasingly widespread in public spaces. Human-robot interactions (HRI) with pedestrians is essential by visualizing trajectory information. We evaluated various display technologies for bright indoor conditions such as animated projection and LED lighting as well as displays. © 2021, John Wiley and Sons Inc. All rights reserved.","Ambient light; Autonomous robots; Human-Robot Interaction (HRI); LCD; LED; Optical measurements; Projection","Human robot interaction; Light emitting diodes; Man machine systems; Optical data processing; Ambient light; Display technologies; Human-robot interaction; Humans-robot interactions; Indoor conditions; LED lighting; Optical measurement; Projection; Public space; Trajectory information; Liquid crystal displays",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7967","This research has been conducted within the interdisciplinary research project ‘'RobotKoop’', which is funded by the German Ministry of Education and Research (grant number 16SV7967).",,,,,,,,,,"(2020) IFR World Robotics, , https://ifr.org/worldrobotics/; Bremner, P., Trigoni, N., Brown, I., Gunes, H., Bevan, C., Danae, S., Levine, M., Being There: Humans and Robots in Public Spaces (2013) Int. Conference on Social Robotics (ICSR); Brandl, C., Mertens, A., Schlick, C.M., Human-Robot Interaction in Assisted Personal Services: Factors Influencing Distances That Humans Will Accept between Themselves and an Approaching Service Robot (2016) Hum. Factors Man, 26, pp. 713-727; Blankenbach, K., Isele, R., Ochs, D., Reichel, S., Led-Based Automotive Exterior Displays and Interior Signage for Autonomous Cars, , https://doi.org/10.1117/12.2543253, 113040U (26 February 2020); SID Information Display Measurements Standard, , https://www.sid.org/Publications/ICDM.aspx",,,,"John Wiley and Sons Inc","International Conference on Display Technology, ICDT 2021","30 May 2021 through 2 June 2021",,264989,0097966X,,,,"English","Dig. Tech. Pap. SID Int. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85115856542
"Sun Q., Guo S., Zhang L.","57215562805;55376199600;56567941600;","Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot",2021,"Technology and Health Care","29","5",,"1029","1045",,,"10.3233/THC-202633c","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115631470&doi=10.3233%2fTHC-202633c&partnerID=40&md5=feace4058129acb02127dd9a4de448d8","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China","Sun, Q., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Guo, S., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Zhang, L., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China","BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot. © 2021 -IOS Press.","cooperation space; dexterity; human-robot interaction; Rehabilitation robot; velocity manipulability ellipsoid intersection volume",,,,,,"National Natural Science Foundation of China, NSFC: 61973205; National Key Research and Development Program of China, NKRDPC: 2018YFC2001600","This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC2001600) and the National Natural Science Foundation of China (grant no. 61973205).",,,,,,,,,,"Johnson, W., Onuma, O., Owolabi, M., Stroke: A global response is needed (2016) Bulletin of the World Health Organization, 94 (9), pp. 634-634A; French, B., Thomas, L.H., Leathley, M.J., Repetitive task training for improving functional ability after stroke (2007) Cochrane Database Syst Rev, 17 (4), p. D6073; Rodgers, H., Bosomworth, H., Krebs, H.I., Robot assisted training for the upper limb after stroke (ratuls): A multicentre randomised controlled trial (2019) The Lancet, 394, p. 10192; Zargarbashi, S.H.H., Khan, W., Angeles, J., The jacobian condition number as a dexterity index in 6r machining robots (2012) Robotics and Computer-Integrated Manufacturing, 28 (6), pp. 694-699; Mansouri, I., Ouali, M., A new homogeneous manipulability measure of robot manipulators, based on power concept (2009) Mechatronics, 19 (6), pp. 927-944; Azad, M., Babic, J., Mistry, M., Effects of the weighting matrix on dynamic manipulability of robots (2019) Autonomous Robots; Xie, B., Zhao, J., Directional manipulability constrained by the condition number (2010) Journal of Mechanical Engineering, 46 (22), pp. 8-15; Miao, Q., Mcdaid, A., Zhang, M., A three-stage trajectory generation method for robot-Assisted bilateral upper limb training with subject-specific adaptation (2018) Robotics and Autonomous Systems, 105; Li, J., Liu, J., Zhang, L., Kinematics and dexterity analysis of the hurobot-human compatible exoskeleton mechanism for shoulder joint rehabilitation (2018) Journal of Mechanical Engineering; Chen, C.P., Wang, P.J., Wang, H., Developing industrial dual arm robot for flexible assembly through reachability map (2015) Technical Report; Chen, A., Xu, P., Li, G., Velocity directional manipulability measures of dual armed robot (2005) Journal of Nanjing University of Science and Technology (Natural Science Edition), 29 (2), pp. 202-205; Jaquier, N., Rozo, L.D., Caldwell, D.G., Geometry-Aware tracking of manipulability ellipsoids (2018) Robotics: Science and Systems.; Makris, S., Tsarouchi, P., Matthaiakis, A.S., Dual arm robot in cooperation with humans for flexible assembly (2017) Cirp Annals, 66 (1), pp. 13-16; Li, X., Sun, Q., Zhang, L., Dexterity analysis of coordination operation of dual-Arm service robot (2019) Journal of Huazhong University of Science and Technology (Natural Science Edition), 47 (5), pp. 33-38; Khalil, W., Kleinfinger, J., A new geometric notation for open and closed-loop robots (1986) Ieee International Conference on Robotics & Automation., pp. 1174-1179; Zhang, L., Guo, S., Sun, Q., Development and assist-As-needed control of an end-effector upper limb rehabilitation robot (2020) Applied Sciences, 10 (19), p. 6684; Li, X., Sheng, R., Zhang, L., Singular configuration analysis of 6-dof modular manipulator (2017) Transactions of the Chinese Society for Agricultural Machinery, 48 (7), pp. 376-382; Lee, S., Dual redundant arm configuration optimization with task-oriented dual arms manipulability (1989) Ieee Transactions on Robotics and Automation, 5 (1), pp. 78-97; Fahyan, S.E., (2010) Biomechanics and Motor Control of Human Motion, , David A. Winter. University of Waterloo Press; Fenistein, D., Lenseigne, B., Christophe, T., A fast, fully automated cell segmentation algorithm for high-Throughput and high-content screening (2010) Cytometry Part A, 73 A (10), pp. 958-964; Corke, P., (2017) Robotics, Vision and Control: Fundamental Algorithms in Matlabr Second, Completely Revised., , Springer; Kuhlemann, I., Schweikard, A., Jauer, P., Robust inverse kinematics by configuration control for redundant manipulators with seven dof (2016) International Conference on Control.; Kang, T., He, J., Tillery, S.I.H., Determining natural arm configuration along a reaching trajectory (2005) Experimental Brain Research, 167 (3), pp. 352-361; Guo, S., Xiong, X., Zhang, L., A novel algorithm of kinematics parameters measurement for upper limb base on motion capture (2020) International Journal of Mechatronics and Applied Mechanics, 7, pp. 144-151; Kim, J.H., Kim, D., Computationally efficient cooperation dynamic range-only slam based on sum of Gaussian filter (2020) Sensors, 20 (11), p. 3306","Sun, Q.; School of Mechatronic Engineering and Automation, China; email: qingsun93@163.com",,,"IOS Press BV",,,,,09287329,,THCAE,,"English","Technol. Health Care",Article,"Final","",Scopus,2-s2.0-85115631470
"Gomez Cubero C., Rehm M.","57226718958;10039896700;","Intention Recognition in Human Robot Interaction Based on Eye Tracking",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12934 LNCS",,,"428","437",,,"10.1007/978-3-030-85613-7_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115269762&doi=10.1007%2f978-3-030-85613-7_29&partnerID=40&md5=7a573dc68dc95a721f6de4147a18bd82","Technical Faculty of IT and Design, Aalborg University, Aalborg, Denmark","Gomez Cubero, C., Technical Faculty of IT and Design, Aalborg University, Aalborg, Denmark; Rehm, M., Technical Faculty of IT and Design, Aalborg University, Aalborg, Denmark","In human robot interaction any input that might help the robot to understand the human behaviour is valuable, and the eyes and their movement undoubtedly hold valuable information. In this paper we propose a novel algorithm for intention recognition using eye tracking in human robot collaboration. We first explore how the Cascade Effect hypothesis and a LSTM-based machine learning model perform to classify intent from gaze. Second, an algorithm is proposed, which can be used in a real time interaction to infer intention from the human user with a small uncertainty. A data collection with 30 participants was conducted in virtual reality to train and test the algorithm. The algorithm allows to detect the user intention up to two seconds before any user action with a success rate of up to 75%. These results open the possibility to study human robot interaction, where the robot can take the initiative based on the intention recognition. © 2021, IFIP International Federation for Information Processing.","Eye tracking; Human-robot interaction; Intention recognition","Behavioral research; Classification (of information); Eye movements; Eye tracking; Human computer interaction; Long short-term memory; Cascade effects; Data collection; Human behaviours; Human-robot collaboration; Intention recognition; Machine learning models; Novel algorithm; Real time interactions; Social robots",,,,,,,,,,,,,,,,"Anvaripour, M., Khoshnam, M., Menon, C., Saif, M., FMG-and RNN-based estimation of motor intention of upper-limb motion in human-robot collaboration (2020) Front. Robot. AI, 7. , https://doi.org/10.3389/frobt.2020.573096; Awais, M., Henrich, D., Human-robot collaboration by intention recognition using probabilistic state machines (2010) 19Th International Workshop on Robotics in Alpe-Adria-Danube Region, pp. 75-80. , https://doi.org/10.1109/RAAD.2010.5524605, RAAD 2010), pp; Bader, T., Vogelgesang, M., Klaus, E., Multimodal integration of natural gaze behavior for intention recognition during object manipulation (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 199-206. , https://doi.org/10.1145/1647314.1647350, ., pp., Association for Computing Machinery, New York; Bird, G.D., Lauwereyns, J., Crawford, M.T., The role of eye movements in decision making and the prospect of exposure effects (2012) Vision. Res., 60, pp. 16-21; Buerkle, A., Eaton, W., Lohse, N., Bamber, T., Ferreira, P., EEG based arm movement intention recognition towards enhanced safety in symbiotic human-robot collaboration (2021) Robot. Comput.-Integr. Manuf., 70; Duarte, N.F., Raković, M., Tasevski, J., Coco, M.I., Billard, A., Santos-Victor, J., Action anticipation: Reading the intentions of humans and robots (2018) IEEE Robot. Autom. Lett., 3 (4), pp. 4132-4139. , https://doi.org/10.1109/LRA.2018.2861569; Duncan, K., Sarkar, S., Alqasemi, R., Dubey, R.: Scene-dependent intention recognition for task communication with reduced human-robot interaction. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8927, pp. 730–745. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16199-0 51; Feleke, A.G., Bi, L., Fei, W., EMG-based 3D hand motor intention prediction for information transfer from human to robot (2021) Sensors, 21 (4), p. 1316. , https://doi.org/10.3390/s21041316; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput, 9, pp. 1735-1780. , https://doi.org/10.1162/neco.1997.9.8.1735; Li, J., Lu, L., Zhao, L., Wang, C., Li, J., An integrated approach for robotic sit-to-stand assistance: control framework design and human intention recognition (2021) Control Eng. Pract., 107. , https://doi.org/10.1016/j.conengprac.2020. 104680; Liu, T., Lyu, E., Wang, J., Meng, M.Q.H., Unified intention inference and learning for human-robot cooperative assembly (2021) IEEE Trans. Autom. Sci. 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Res., 21 (2), pp. 274-290; Salvucci, D., Goldberg, J., (2000) Identifying Fixations and Saccades in Eye-Tracking Protocols, pp. 71-78. , https://doi.org/10.1145/355017.355028, pp; Schlenoff, C., Kootbally, Z., Pietromartire, A., Franaszek, M., Foufou, S.: Intention recognition in manufacturing applications. Robot. Comput.-Integr. Manuf. 33, 29– 41 (2015). Special issue on knowledge driven robotics and manufacturing; Schlenoff, C., Pietromartire, A., Kootbally, Z., Balakirsky, S., Foufou, S., Ontology-based state representations for intention recognition in human-robot collaborative environments (2013) Robot. Auton. Syst., 61 (11), pp. 1224-1234. , Ubiquitous robotics; Shimojo, S., Simion, C., Shimojo, E., Scheier, C., Gaze bias both reflects and influences preference (2003) Nature Neurosci, 6 (12), pp. 1317-1322; Singh, R., Miller, T., Newn, J., Velloso, E., Vetere, F., Sonenberg, L., Combining gaze and AI planning for online human intention recognition (2020) Artif. Intell., 284; Trick, S., Koert, D., Peters, J., Rothkopf, C.A., Multimodal uncertainty reduction for intention recognition in human-robot interaction (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7009-7016. , https://doi.org/10.1109/IROS40897.2019.8968171, ., pp., 2019; Vernon, D., Thill, S., Ziemke, T.: The role of intention in cognitive robotics. In: Esposito, A., Jain, L.C. (eds.) Toward Robotic Socially Believable Behaving Systems-Volume I. ISRL, vol. 105, pp. 15–27. Springer, Cham (2016). https://doi. org/10.1007/978-3-319-31056-5 3; Wang, Y., Wang, S., A new directional-intent recognition method for walking training using an omnidirectional robot (2017) J. Intell. Robot Syst., 87, pp. 231-246. , https://doi.org/10.1007/s10846-017-0503-z; Ye, L., Xiong, G., Zeng, C., Zhang, H., Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction (2020) Sci. Progr., 103 (3)","Rehm, M.; Technical Faculty of IT and Design, Denmark; email: matthias@create.aau.dk","Ardito C.Lanzilotti R.Malizia A.Malizia A.Petrie H.Piccinno A.Desolda G.Inkpen K.",,"Springer Science and Business Media Deutschland GmbH","18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021","30 August 2021 through 3 September 2021",,264499,03029743,9783030856120,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115269762
"Le Q.D., Kang H.-J.","57190282420;57217797183;","Sensor-Less Contact Force Estimation in Physical Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12837 LNCS",,,"233","244",,,"10.1007/978-3-030-84529-2_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115256801&doi=10.1007%2f978-3-030-84529-2_20&partnerID=40&md5=b4483fa091cf30aa4f9b7001a91c9434","Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea","Le, Q.D., Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea; Kang, H.-J., Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea","In this paper, the contact force estimation in physical human-robot interaction without an explicit force sensing device based on the adaptive third order sliding mode observer is proposed. Firstly, the identification parameter of a robot manipulator is required. Then an adaptive third order sliding mode observer is designed to estimate contact force. Finally, the experimental results with the immediate stop control and admittance control using the proposed estimation contact force for 3-DOF AT2-FARA robot manipulator are shown to illustrate the capability of the proposed in the real application. © 2021, Springer Nature Switzerland AG.","Admittance control sensor-less; Physical human-robot interaction; Sliding mode observer; Third order sliding mode observer","Computation theory; Flexible manipulators; Industrial manipulators; Intelligent computing; Man machine systems; Modular robots; Robot applications; Sliding mode control; Admittance control; Contact force estimations; Force sensing devices; Identification parameters; Physical human-robot interactions; Real applications; Robot manipulator; Third order sliding modes; Human robot interaction",,,,,"National Research Foundation of Korea, NRF; Kementerian Pendidikan Malaysia, KPM: NRF-2016R1D1A3B03930496","Acknowledgment. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03930496).",,,,,,,,,,"Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety “Kyozon” system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proceedings of the 1995 IEEE International Conference on Robotics and Automation, pp. 3089-3096. , pp; Geravand, M., Flacco, F., de Luca, A., Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture (2013) IEEE International Conference on Robotics and Automation, pp. 4000-4007. , https://doi.org/10.1109/ICRA.2013.6631141, pp., 2013; Stolt, A., Robertsson, A., Johansson, R., Robotic force estimation using dithering to decrease the low velocity friction uncertainties (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 3896-3902. , pp., 2015; Duchaine, V., Lauzier, N., Baril, M., Lacasse, M.-A., Gosselin, C., A flexible robot skin for safe physical human robot interaction (2009) IEEE International Conference on Robotics and Automation, ICRA, pp. 3676-3681. , pp., 2009; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human–robot interaction (2016) IEEE Robot. 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Lett., 1, pp. 41-48; Magrini, E., Flacco, F., de Luca, A., Estimation of contact forces using a virtual force sensor (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), Pp. 2126–2133; Ebert, D., Henrich, D., (2002) Safe Human-Robot-Cooperation: Image-Based Collision Detection for Industrial Robots; Eom, K.S., Suh, I.H., Chung, W.K., Oh, S.-R., Disturbance observer based force control of robot manipulator without force sensor (1998) Proceedings of the 1998 IEEE International Conference on Robotics and Automation, pp. 3012-3017. , pp; Gaz, C., Magrini, E., de Luca, A., A model-based residual approach for human-robot collaboration during manual polishing operations (2018) Mechatronics; Magrini, E., Flacco, F., de Luca, A., Control of generalized contact motion and force in physical human-robot interaction (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 2298-2304. , pp., 2015; de Luca, A., Mattone, R., Sensorless robot collision detection and hybrid force/motion control (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, Pp. 999–1004; Chen, S., Luo, M., He, F., A universal algorithm for sensorless collision detection of robot actuator faults (2018) Adv. Mech. Eng., 10; Hu, J., Xiong, R., Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter (2018) IEEE Trans. Ind. Electron., 65, pp. 3365-3375; Khalil, H.K., Cascade high-gain observers in output feedback control (2017) Automatica, 80, pp. 110-118; Kommuri, S.K., Rath, J.J., Veluvolu, K.C., Sliding-mode-based observer-controller structure for fault-resilient control in DC servomotors (2018) IEEE Trans. Ind. Electron., 65, pp. 918-929; Fridman, L., Shtessel, Y., Edwards, C., Yan, X.-G., Higher-order sliding-mode observer for state estimation and input reconstruction in nonlinear systems (2008) Int. J. Robust Nonlinear Control IFAC Affiliated J., 18, pp. 399-412; Jung, S., Hsia, T.C., Bonitz, R.G., Force tracking impedance control of robot manipulators under unknown environment (2004) IEEE Trans. Control Syst. Technol., 12, pp. 474-483. , https://doi.org/10.1109/TCST.2004.824320; Van, M., Ge, S.S., Ren, H., Robust fault-tolerant control for a class of second-order nonlinear systems using an adaptive third-order sliding mode control (2017) IEEE Trans. Syst. Man Cybern. Syst., 47, pp. 221-228; Ortiz-Ricardez, F.A., Sánchez, T., Moreno, J.A., Smooth Lyapunov function and gain design for a second order differentiator (2015) IEEE 54Th Annual Conference on Decision and Control (CDC), pp. 5402-5407. , pp., 2015; Cruz-Zavala, E., Moreno, J.A., Lyapunov functions for continuous and discontinuous differentiators (2016) Ifac-Papersonline, 49, pp. 660-665; Bahrami, M., Naraghi, M., Zareinejad, M., Adaptive super-twisting observer for fault reconstruction in electro-hydraulic systems (2018) ISA Trans, 76, pp. 235-245; Luo, D., Xiong, X., Jin, S., Kamal, S., Adaptive gains of dual level to super-twisting algorithm for sliding mode design (2018) IET Control Theory Appl, 12, pp. 2347-2356","Kang, H.-J.; Department of Electrical, South Korea; email: hjkang@ulsan.ac.kr","Huang D.Jo K.Li J.Gribova V.Hussain A.",,"Springer Science and Business Media Deutschland GmbH","17th International Conference on Intelligent Computing, ICIC 2021","12 August 2021 through 15 August 2021",,263609,03029743,9783030845285,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115256801
"Wilkinson A., Gonzales M., Hoey P., Kontak D., Wang D., Torname N., Laderoute S., Han Z., Allspaw J., Platt R., Yanco H.","57219645725;57263443500;57263811900;35727048600;57221155274;57263812400;57263630400;57208162674;56072809000;7102023517;6601994484;","Design guidelines for human-robot interaction with assistive robot manipulation systems",2021,"Paladyn","12","1",,"392","401",,,"10.1515/pjbr-2021-0023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115127943&doi=10.1515%2fpjbr-2021-0023&partnerID=40&md5=191efe1aceabc72623a6733f67896f1a","Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Department of Electrical and Computer Engineering, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; NERVE Center, University of Massachusetts Lowell (UML), Lowell, MA  01852, United States; Khoury College of Computer Sciences, Northeastern University, Boston, MA  02115, United States","Wilkinson, A., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Gonzales, M., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Hoey, P., Department of Electrical and Computer Engineering, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Kontak, D., NERVE Center, University of Massachusetts Lowell (UML), Lowell, MA  01852, United States; Wang, D., Khoury College of Computer Sciences, Northeastern University, Boston, MA  02115, United States; Torname, N., Department of Electrical and Computer Engineering, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Laderoute, S., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Han, Z., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Allspaw, J., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Platt, R., Khoury College of Computer Sciences, Northeastern University, Boston, MA  02115, United States; Yanco, H., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States","The design of user interfaces (UIs) for assistive robot systems can be improved through the use of a set of design guidelines presented in this article. As an example, the article presents two different UI designs for an assistive manipulation robot system. We explore the design considerations from these two contrasting UIs. The first is referred to as the graphical user interface (GUI), which the user operates entirely through a touchscreen as a representation of the state of the art. The second is a type of novel UI referred to as the tangible user interface (TUI). The TUI makes use of devices in the real world, such as laser pointers and a projector-camera system that enables augmented reality. Each of these interfaces is designed to allow the system to be operated by an untrained user in an open environment such as a grocery store. Our goal is for these guidelines to aid researchers in the design of human-robot interaction for assistive robot systems, particularly when designing multiple interaction methods for direct comparison. © 2021 Alexander Wilkinson et al., published by De Gruyter.","assistive robotics; augmented reality; design guidelines; graphical user interface; human-robot interaction; tangible user interface",,,,,,"National Science Foundation, NSF: IIS-1426968, IIS-1427081, IIS-1724191, IIS-1724257, IIS-1763469; Office of Naval Research, ONR: N000141410047; National Aeronautics and Space Administration, NASA: NNX13AQ85G, NNX16AC48A; Google","Funding information : This work has been supported in part by the National Science Foundation (IIS-1426968, IIS-1427081, IIS-1724191, IIS-1724257, IIS-1763469), NASA (NNX16AC48A, NNX13AQ85G), ONR (N000141410047), Amazon through an ARA to Platt, and Google through an FRA to Platt.",,,,,,,,,,"Graf, B., Hans, M., Schraft, R.D., ""care-O-bot II-development of a next generation robotic home assistant,"" (2004) Autonom. Robot., 16 (2), pp. 193-205; Kemp, C.C., Anderson, C.D., Nguyen, H., Trevor, A.J., Xu, Z., ""a point-and-click interface for the real world: Laser designation of objects for mobile manipulation,"" (2008) 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 241-248. , in IEEE pp; Gelšvartas, J., Simutis, R., Maskeliūnas, R., ""projection mapping user interface for disabled people,"" (2018) J. Healthcare Eng., 2018. , 6916204; Wang, D., Kohler, C., Pas, A.T., Wilkinson, A., Liu, M., Yanco, H., ""towards assistive robotic pick and place in open world environments,"" (2019) Proceedings of the International Symposium on Robotics Research (ISRR), , http://arXiv.org/abs/arXiv:1809.09541, in arXiv; Martens, C., Ruchel, N., Lang, O., Ivlev, O., Graser, A., ""a friend for assisting handicapped people,"" (2001) IEEE Robot. Automat. Magazine, 8 (1), pp. 57-65. , pp; Grice, P.M., Kemp, C.C., ""assistive mobile manipulation: Designing for operators with motor impairments,"" (2016) RSS 2016 Workshop on Socially and Physically Assistive Robotics for Humanity, , in; Achic, F., Montero, J., Penaloza, C., Cuellar, F., ""hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks,"" (2016) 2016 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO), pp. 249-254. , in IEEE pp; Nickelsen, N.C.M., ""imagining and tinkering with assistive robotics in care for the disabled,"" (2019) Paladyn, J. Behav. Robot., 10 (1), pp. 128-139; Cofre, J.P., Moraga, G., Rusu, C., Mercado, I., Inostroza, R., Jimenez, C., ""developing a touchscreen-based domotic tool for users with motor disabilities,"" (2012) 2012 Ninth International Conference on Information Technology-New Generations, pp. 696-701. , in pp; Choi, Y.S., Anderson, C.D., Glass, J.D., Kemp, C.C., ""laser pointers and a touch screen: Intuitive interfaces for autonomous mobile manipulation for the motor impaired,"" (2008) Proceedings of the 10th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 225-232. , in ACM pp; Gualtieri, M., Kuczynski, J., Shultz, A.M., Ten Pas, A., Platt, R., Yanco, H., ""open world assistive grasping using laser selection,"" (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4052-4057. , in IEEE pp; Quintero, C.P., Tatsambon, R., Gridseth, M., Jägersand, M., ""visual pointing gestures for bi-directional human robot interaction in a pick-and-place task,"" (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 349-354; Rouhollahi, A., Azmoun, M., Masouleh, M.T., ""experimental study on the visual servoing of a 4-DOF parallel robot for pick-and-place purpose,"" (2018) 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 27-30. , in pp; Chacko, S.M., Kapila, V., ""an augmented reality interface for human-robot interaction in unconstrained environments,"" (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3222-3228. , in pp; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ""rOS: An open-source robot operating system,"" (2009) ICRA Workshop on Open Source Software, 3, p. 5. , in Kobe, Japan p; Ten Pas, A., Gualtieri, M., Saenko, K., Platt, R., ""grasp pose detection in point clouds,"" (2017) Int. J. Robot. Res., 36 (NO. 13-14), pp. 1455-1473. , pp; Gualtieri, M., Ten Pas, A., Saenko, K., Platt, R., ""high precision grasp pose detection in dense clutter,"" (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 598-605. , in pp; (2019) National Eye Institute, ""types of Color Blindness,"", , http://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/color-blindness/types-color-blindness, [Accessed: May 12, 2021]","Wilkinson, A.; Department of Computer Science, United States; email: Alexander_Wilkinson@student.uml.edu",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85115127943
"Chien S.-H., Wang J.-H., Cheng M.-Y.","57221287244;57221294493;56326622700;","Robotic assistance for physical human-robot interaction using a fuzzy RBF hand impedance compensator and a neural network based human motion intention estimator",2021,"IEEE Access","9",,,"126048","126057",,,"10.1109/ACCESS.2021.3111875","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114727924&doi=10.1109%2fACCESS.2021.3111875&partnerID=40&md5=56266e008b6e5bc79dd96f94763d96f1","Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan","Chien, S.-H., Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan; Wang, J.-H., Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan; Cheng, M.-Y., Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan","This paper proposes a robotic assistance control scheme for intuitive teaching tasks by integrating the motion intention of human and real-time hand impedance compensation based upon a fuzzy RBF (Radial Basis Function) compensator. The motion intention of a human is estimated using a feedforward neural network. The parameters of the proposed fuzzy RBF hand impedance compensator are adjusted by considering hand impedance and robot dynamics. Three robotic assistance control schemes are compared: 1) robot without an impedance compensator; 2) robot with a constant-parameter impedance compensator; 3) robot with a fuzzy RBF hand impedance compensator. Several experiments have been conducted to verify the effectiveness of the proposed approach by comparing the contour error, exerted force and task time spent on teaching tasks. Experimental results indicate that the proposed fuzzy RBF hand impedance compensator has the best assistance results among the tested robotic assistance control schemes. © 2013 IEEE.","impedance compensator; physical human-robot interaction; Robot manipulator","Feedforward neural networks; Motion estimation; Robotics; Constant parameters; Control schemes; Human motion intention; Impedance compensation; Motion intention; Physical human-robot interactions; RBF(radial basis function); Robot dynamics; Human robot interaction",,,,,"Industrial Technology Research Institute, ITRI; Ministry of Science and Technology, Taiwan, MOST: MOST 105-2221-E-006-105-MY3","This work was supported in part by the Industrial Technology Research Institute, and in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 105-2221-E-006-105-MY3.",,,,,,,,,,"Sebastian, G., Li, Z., Crocher, V., Kremers, D., Tan, Y., Oetomo, D., Interaction force estimation using extended state observers: An application to impedance-based assistive and rehabilitation robotics (2019) IEEE Robot. 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Cybern., 45 (6), pp. 1146-1157. , Jun; Erden, M.S., Billard, A., End-point impedance measurements at human hand during interactive manual welding with robot (2014) Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 126-133. , Hong Kong, May; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) Proc. IEEE Int. Conf. Robot. Autom., 1, pp. 644-650. , May; Mitra, S., Basak, J., FRBF: A fuzzy radial basis function network (2001) Neural Comput. Appl., 10 (3), pp. 244-252. , Dec","Cheng, M.-Y.; Department of Electrical Engineering, Taiwan; email: mycheng@mail.ncku.edu.tw",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85114727924
"Roselli C., Ciardo F., Wykowska A.","57212379782;57164931800;15924284200;","Intentions with actions: The role of intentionality attribution on the vicarious sense of agency in Human–Robot interaction",2021,"Quarterly Journal of Experimental Psychology",,,,"","",,,"10.1177/17470218211042003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114597952&doi=10.1177%2f17470218211042003&partnerID=40&md5=3219f003e3845aef526f98deee599d58","Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy; Dipartimento di Informatica, Bioingegneria, Robotica ed Ingegneria dei Sistemi (DIBRIS), Genoa, Italy","Roselli, C., Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy, Dipartimento di Informatica, Bioingegneria, Robotica ed Ingegneria dei Sistemi (DIBRIS), Genoa, Italy; Ciardo, F., Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy; Wykowska, A., Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy","Sense of Agency (SoA) is the feeling of control over one’s actions and their consequences. In social contexts, people experience a “vicarious” SoA over other humans’ actions; however, the phenomenon disappears when the other agent is a computer. This study aimed to investigate the factors that determine when humans experience vicarious SoA in Human–Robot Interaction (HRI). To this end, in two experiments, we disentangled two potential contributing factors: (1) the possibility of representing the robot’s actions and (2) the adoption of Intentional Stance towards robots. Participants performed an Intentional Binding (IB) task reporting the time of occurrence for self- or robot-generated actions or sensory outcomes. To assess the role of action representation, the robot either performed a physical keypress (Experiment 1) or “acted” by sending a command via Bluetooth (Experiment 2). Before the experiment, attribution of intentionality to the robot was assessed. Results showed that when participants judged the occurrence of the action, vicarious SoA was predicted by the degree of attributed intentionality, but only when the robot’s action was physical. Conversely, digital actions elicited the reversed effect of vicarious IB, suggesting that disembodied actions of robots are perceived as non-intentional. When participants judged the occurrence of the sensory outcome, vicarious SoA emerged only when the causing action was physical. Notably, intentionality attribution predicted vicarious SoA for sensory outcomes independently of the nature of the causing event, physical or digital. In conclusion, both intentionality attribution and action representation play a crucial role for vicarious SoA in HRI. © Experimental Psychology Society 2021.","Human-Robot Interaction; Intentional Binding; Intentional Stance; Vicarious Sense of Agency",,,,,,"Horizon 2020 Framework Programme, H2020: ERC – 2016-StG-715058; European Research Council, ERC","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has received support from the European Research Council under the European Union’s Horizon 2020 research and innovation programme, ERC Starting Grant, G.A. number: ERC – 2016-StG-715058, awarded to Agnieszka Wykowska. The content of this paper is the sole responsibility of the authors. 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"Rogers W.A., Kadylak T., Bayles M.A.","57265321400;57190182209;57212064724;","Maximizing the Benefits of Participatory Design for Human–Robot Interaction Research With Older Adults",2021,"Human Factors",,,,"","",,,"10.1177/00187208211037465","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114481103&doi=10.1177%2f00187208211037465&partnerID=40&md5=acc56e98026162d94b7f9783f5d1ae3e","University of Illinois Urbana-Champaign, United States","Rogers, W.A., University of Illinois Urbana-Champaign, United States; Kadylak, T., University of Illinois Urbana-Champaign, United States; Bayles, M.A., University of Illinois Urbana-Champaign, United States","Objective: We reviewed human–robot interaction (HRI) participatory design (PD) research with older adults. The goal was to identify methods used, determine their value for design of robots with older adults, and provide guidance for best practices. Background: Assistive robots may promote aging-in-place and quality of life for older adults. However, the robots must be designed to meet older adults’ specific needs and preferences. PD and other user-centered methods may be used to engage older adults in the robot development process to accommodate their needs and preferences and to assure usability of emergent assistive robots. Method: This targeted review of HRI PD studies with older adults draws on a detailed review of 26 articles. Our assessment focused on the HRI methods and their utility for use with older adults who have a range of needs and capabilities. Results: Our review highlighted the importance of using mixed methods and including multiple stakeholders throughout the design process. These approaches can encourage mutual learning (to improve design by developers and to increase acceptance by users). We identified key phases used in HRI PD workshops (e.g., initial interview phase, series of focus groups phase, and presentation phase). These approaches can provide inspiration for future efforts. Conclusion: HRI PD strategies can support designers in developing assistive robots that meet older adults’ needs, capabilities, and preferences to promote acceptance. More HRI research is needed to understand potential implications for aging-in-place. PD methods provide a promising approach. © Copyright 2021, Human Factors and Ergonomics Society.","aging-in-place; assistive robots; human–robot interaction; older adults; participatory design","Machine design; Assistive robots; Multiple stakeholders; Mutual learning; Participatory design; Provide guidances; Quality of life; Robot development; Robot interactions; Social robots",,,,,"National Institutes of Health, NIH; National Institute on Aging, NIA: P01 AG17211; National Institute on Disability, Independent Living, and Rehabilitation Research, NIDILRR: 90REGE0006-01-00","The authors were supported in part by grants from the National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE; www.create-center.org), as well as the National Institute on Disability, Independent Living, and Rehabilitation Research (90REGE0006-01-00) under the auspices of the Rehabilitation and Engineering Research Center on Technologies to Support Aging-in-Place for People with Long-Term Disabilities (TechSAge; www.techsagererc.org). We thank Max Chan, Leonardo Galoso, Carson Smith, and Siyao Zhang for their support on this review.",,,,,,,,,,"Bedaf, S., Marti, P., De Witte, L., What are the preferred characteristics of a service robot for the elderly? 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(2015) World report on ageing and health, , https://www.who.int/ageing/events/world-report-2015-launch/en/","Rogers, W.A.; University of Illinois Urbana-ChampaignUnited States; email: wendyr@illinois.edu",,,"SAGE Publications Inc.",,,,,00187208,,HUFAA,,"English","Hum. Factors",Review,"Article in Press","",Scopus,2-s2.0-85114481103
"Sharifi M., Azimi V., Mushahwar V.K., Tavakoli M.","55224489900;49360919000;6603751651;8668966400;","Impedance Learning-Based Adaptive Control for Human-Robot Interaction",2021,"IEEE Transactions on Control Systems Technology",,,,"","",,1,"10.1109/TCST.2021.3107483","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114368286&doi=10.1109%2fTCST.2021.3107483&partnerID=40&md5=e91eb8393c712f9553905d8eae67e9fc","Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Department of Mechanical Engineering, Auburn University, Auburn, AL 36849 USA.; Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.","Sharifi, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Azimi, V., Department of Mechanical Engineering, Auburn University, Auburn, AL 36849 USA.; Mushahwar, V.K., Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Tavakoli, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.","In this article, a new learning-based time-varying impedance controller is proposed and tested to facilitate an autonomous physical human-robot interaction (pHRI). Novel adaptation laws are formulated for online adjustment of robot impedance based on human behavior. Two other sets of update rules are defined for intelligent coping with the robot's structured and unstructured uncertainties. These rules ensure stability via Lyapunov's theorem and provide uniform ultimate boundedness (UUB) of the closed-loop system's response, without a need for HRI force/torque measurement. Accordingly, the convergence of response signals, including errors in tracking, online impedance learning, robot parameter adaptation, and controller gain variation, is proven to operate in a bounded region (compact set) in the presence of robot and human uncertainties and bounded disturbances. The performance of the developed intelligent impedance-varying control strategy is investigated through comprehensive experimental studies in a repetitive following task with a moving target. IEEE","Autonomous impedance variation; Dynamics; Force; Impedance; nonlinear adaptive control; physical human-robot interaction (pHRI); Robot sensing systems; robot stability; Robots; Stability analysis; Task analysis; uniform ultimate boundedness (UUB).","Behavioral research; Closed loop systems; Controllers; Man machine systems; Bounded disturbances; Control strategies; Controller gain variations; Impedance controllers; On-line adjustment; Physical humanrobot interaction (phri); Uniform ultimate boundedness; Unstructured uncertainty; Human robot interaction",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636536,,IETTE,,"English","IEEE Trans Control Syst Technol",Article,"Article in Press","",Scopus,2-s2.0-85114368286
"Hussain I., Albalasie A., Awad M.I., Tamizi K., Niu Z., Seneviratne L., Gan D.","56730038800;56790100600;57192432184;57226881961;57218588255;7006006173;23060288900;","Design and Control of a Discrete Variable Stiffness Actuator with Instant Stiffness Switch for Safe Human-Robot Interaction",2021,"IEEE Access","9",,"9514838","118215","118231",,,"10.1109/ACCESS.2021.3105587","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113258589&doi=10.1109%2fACCESS.2021.3105587&partnerID=40&md5=6d8587a07965f510cb65960c7eba8c3e","Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Mechanical and Mechatronics Engineering, Birzeit University, Birzeit, Palestine; Department of Mechanical Engineering, Palestine Polytechnic University, Hebron, Palestine; School of Engineering Technology, Purdue University, West Lafayette, IN, United States; Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates","Hussain, I., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Albalasie, A., Department of Mechanical and Mechatronics Engineering, Birzeit University, Birzeit, Palestine; Awad, M.I., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Tamizi, K., Department of Mechanical Engineering, Palestine Polytechnic University, Hebron, Palestine; Niu, Z., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Seneviratne, L., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Gan, D., School of Engineering Technology, Purdue University, West Lafayette, IN, United States","Variable Stiffness Actuators (VSA) have been proposed as an alternative actuation system for manipulators that are utilized for safe physical Human-Robot Interaction (pHRI). However, in the incidents of collision, the need of a fast response in stiffness tuning would rise to ensure safety. In this paper, we present a novel Discrete Variable Stiffness Actuator (DVSA) to be used in a compliant robotic manipulator for safe physical Human-Robot Interaction (pHRI). The novelty of this actuator lies in its design topology which allows the stiffness level to change swiftly among predefined levels without the need of complex stiffness tuning mechanism. Through this topology, three springs in parallel are connected serially between the motor and the link via gear train. The stiffness of the actuator is altered by adding/subtracting the number of involved springs, which can be realized through engagement/disengagement electromagnetic clutches on two of these spring's shafts. The working principle, and the detailed design of the actuator are illustrated. Moreover, the stiffness model and the dynamic model are presented and discussed thoroughly. In order to validate these mathematical models and achieve optimal control, system identification for the dynamic parameters was performed experimentally on the physical model. Furthermore, the system's ability of tracking desired trajectory was achieved through the implementation of different control techniques including PID (Proportional-Integral-Derivative), LQR (Linear Quadratic Regulator) and pole placement. The results show the high potential of utilizing the actuator in compliant manipulators. Moreover, DVSA is also characterized for safety in pHRI through Head-Injury Criterion (HIC). Finally, an application of DVSA in human augmentation task (Weight Bearing Task) is presented. © 2013 IEEE.","compliant manipulation; human-robot-interaction; Variable stiffness actuators","Actuators; Dynamics; Industrial manipulators; Machine design; Man machine systems; Manipulators; Proportional control systems; Springs (components); Stiffness; Topology; Tuning; Two term control systems; Compliant manipulators; Desired trajectories; Electromagnetic clutch; Head injury criterion; Linear quadratic regulator; Physical humanrobot interaction (phri); PID (proportionalintegral-derivative); Safe human-robot interaction; Social robots",,,,,"Harbin University of Science and Technology, HRBUST; Khalifa University of Science, Technology and Research, KU: RC1-2018-KUCARS, RC2-2018-022","This work was supported by Khalifa University of Science and Technology under Award RC1-2018-KUCARS and Award RC2-2018-022 (HEIC).",,,,,,,,,,"Tsagarakis, N.G., Sardellitti, I., Caldwell, D.G., A new variable stiffness actuator (CompAct-VSA): Design and modelling (2011) Proc. 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Syst., 120. , Oct; She, Y., Su, H.-J., Meng, D., Lai, C., Design and modeling of a continuously tunable stiffness arm for safe physical human robot interaction (2020) J. Mech. Robot., 12 (1), pp. 1-11. , Feb; Bicchi, A., Tonietti, G., Fast and soft-arm' tactics [robot arm design] (2004) IEEE Robot. Autom. Mag., 11 (2), pp. 22-33. , Jul; Braun, D.J., Chalvet, V., Chong, T.-H., Apte, S.S., Hogan, N., Variable stiffness spring actuators for low-energy-cost human augmentation (2019) IEEE Trans. Robot., 35 (6), pp. 1435-1449. , Dec; De Luca, C.J., The use of surface electromyography in biomechanics (1997) J. Appl. Biomech., 13 (2), pp. 135-163. , May","Hussain, I.; Khalifa University Center for Autonomous Robotic Systems (KUCARS), United Arab Emirates; email: irfan.hussain@ku.ac.ae
Gan, D.; School of Engineering Technology, United States; email: dgan@purdue.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85113258589
"Gasteiger N., Hellou M., Ahn H.S.","57204357716;57216352354;56218250500;","Factors for Personalization and Localization to Optimize Human–Robot Interaction: A Literature Review",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00811-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113164391&doi=10.1007%2fs12369-021-00811-8&partnerID=40&md5=ecf4dfd367b8fbae8dc42db93f37742a","Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand; School of Health Sciences, The University of Manchester, Manchester, United Kingdom; Département d’Informatique, Facultés des Sciences et Ingénieries, Sorbonne Université, Paris, France; Department of Electrical, Computer and Software Engineering, CARES, The University of Auckland, Auckland, New Zealand","Gasteiger, N., Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand, School of Health Sciences, The University of Manchester, Manchester, United Kingdom; Hellou, M., Département d’Informatique, Facultés des Sciences et Ingénieries, Sorbonne Université, Paris, France, Department of Electrical, Computer and Software Engineering, CARES, The University of Auckland, Auckland, New Zealand; Ahn, H.S., Department of Electrical, Computer and Software Engineering, CARES, The University of Auckland, Auckland, New Zealand","Social service robots are becoming increasingly pervasive in our everyday lives, including in healthcare, education and customer service settings. It is known that different cultures and individuals have an array of diverse expectations when interacting with robots. These expectations influence acceptability and willingness to engage with them. However, previous research in this field mostly focuses on a sole human-related factor that may impact interaction and the acceptability of robots both within and across groups of people. This review aims to synthesize the existing literature on human factors to consider when designing robots that can be personalized or localized (transferred to other cultures). The literature review highlights key studies in this area and synthesizes them into four overarching factors: (1) communication and language, (2) behavior and service, (3) proxemics, and (4) interface design. The review shows that personalization and localization in robotics needs to move beyond catering to simple language preferences or accents. Instead, this encompasses the intricate details of interface design, service expectations, proxemics and individual and cultural communication styles and cultural values that users may possess. This study consequently highlights key considerations when attempting to optimize human–robot interaction across individuals and cultures. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Culturalization; Design; Human–robot interaction; Localization; Personalization","Agricultural robots; Machine design; Social robots; Communication styles; Customer services; Impact interaction; Interface designs; Literature reviews; Personalizations; Robot interactions; Service expectations; Educational robots",,,,,"Ministry of Science, ICT and Future Planning, MSIP: 2020-0-00842; Institute for Information and Communications Technology Promotion, IITP","This work was supported by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-00842, Development of Cloud Robot Intelligence for Continual Adaptation to User Reactions in Real Service Environments).",,,,,,,,,,"(2020) International Federation of Robotics. 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J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85113164391
"Rodriguez-Guerra D., Sorrosal G., Cabanes I., Calleja C.","57226775119;55837381200;6505573517;36650360000;","Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments",2021,"IEEE Access","9",,"9493209","108557","108578",,,"10.1109/ACCESS.2021.3099287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112639785&doi=10.1109%2fACCESS.2021.3099287&partnerID=40&md5=c3c0d6f6402b80ef9fdef5149fc472d7","Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain; Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Bilbao, 48013, Spain","Rodriguez-Guerra, D., Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain; Sorrosal, G., Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain; Cabanes, I., Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Bilbao, 48013, Spain; Calleja, C., Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain","The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shopfloor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth. © 2013 IEEE.","Cobots; HRI challenges; human-robot interaction; industrial collaborative scenarios; industrial safety; review","Industrial robots; Man machine systems; Collaborative robots; Exposed to; Industrial environments; Industrial scenarios; Potential growth; Shop floor; Human robot interaction",,,,,,,,,,,,,,,,"Hentout, A., Mustapha, A., Maoudj, A., Akli, I., Key challenges and open issues of industrial collaborative robotics (2018) Proc. Workshop Hum.-Robot Interact., from Service Ind. (RO-MAN), pp. 1-4. , Aug; Karlsson, K., Jonsson, M., Overview of SAAB in commercial aeronautics. Clean Sky 2 and ITD AIRFRAME research and SAAB aerostructures automation projects and strategy (2019) Proc. 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Manuf., 64. , Aug; Jirgl, M., Bradac, Z., Fiedler, P., Human-in-the-loop issue in context of the cyber-physical systems (2018) IFAC-PapersOnLine, 51 (6), pp. 225-230; Garcia, M.A.R., Rojas, R., Gualtieri, L., Rauch, E., Matt, D., A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing (2019) Procedia CIRP, 81, pp. 600-605. , Jan; Ma, L.M., Fong, T., Micire, M.J., Kim, Y.K., Feigh, K., Human-robot teaming: Concepts and components for design (2017) Field and Service Robotics (Springer Proceedings in Advanced Robotics), 5. , Springer; Tsarouchi, P., Makris, S., Chryssolouris, G., On a human and dual-arm robot task planning method (2016) Procedia CIRP, 57, pp. 551-555. , Jan; Kim, W., Kim, N., Lyons, J.B., Nam, C.S., Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach (2020) Appl. 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Manuf., 48, pp. 243-253. , Dec; Bochmann, L., Bänziger, T., Kunz, A., Wegener, K., Human-robot collaboration in decentralized manufacturing systems: An approach for simulation-based evaluation of future intelligent production (2017) Procedia CIRP, 62, pp. 624-629. , Jan; Liu, C., Tomizuka, M., Algorithmic safety measures for intelligent industrial co-robots (2016) Proc. IEEE Int. Conf. Robot. Autom., pp. 3095-3102. , May; Siva, S., Zhang, H., Robot perceptual adaptation to environment changes for long-term human teammate following (2020) Int. J. Robot. Res., pp. 1-15. , Jan; Chen, X., Bojko, M., Riedel, R., Apostolakis, K.C., Zarpalas, D., Daras, P., Human-centred adaptation and task distribution utilizing levels of automation (2018) IFAC-PapersOnLine, 51 (11), pp. 54-59; Li, Q., Zhang, Z., You, Y., Mu, Y., Feng, C., Data driven models for human motion prediction in human-robot collaboration (2020) IEEE Access, 8, pp. 227690-227702; Mina, T., Kannan, S.S., Jo, W., Min, B.-C., Adaptive workload allocation for multi-human multi-robot teams for independent and homogeneous tasks (2020) IEEE Access, 8, pp. 152697-152712; Chan, C.-C., Tsai, C.-C., Collision-free speed alteration strategy for human safety in human-robot coexistence environments (2020) IEEE Access, 8, pp. 80120-80133","Rodriguez-Guerra, D.; Ikerlan Technology Research Centre, Spain; email: diego.rodriguez@ikerlan.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85112639785
"Palm R., Lilienthal A.J.","14623351700;15623407200;","Fuzzy Geometric Approach to Collision Estimation Under Gaussian Noise in Human-Robot Interaction",2021,"Studies in Computational Intelligence","922",,,"191","221",,,"10.1007/978-3-030-70594-7_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112243320&doi=10.1007%2f978-3-030-70594-7_8&partnerID=40&md5=799a10465af0d43b8a3039bf1598a28e","AASS Department of Technology Orebro University, Orebro, SE-70182, Sweden","Palm, R., AASS Department of Technology Orebro University, Orebro, SE-70182, Sweden; Lilienthal, A.J., AASS Department of Technology Orebro University, Orebro, SE-70182, Sweden","Humans and mobile robots while sharing the same work areas require a high level of safety especially at possible intersections of trajectories. An issue of the human-robot navigation is the computation of the intersection point in the presence of noisy measurements or fuzzy information. For Gaussian distributions of positions/orientations (inputs) of robot and human agent and their parameters the corresponding parameters at the intersections (outputs) are computed by analytical and fuzzy methods. This is done both for the static and the dynamic case using Kalman filters for robot/human positions and orientations and thus for the estimation of the intersection positions. For the overdetermined case (6 inputs, 2 outputs) a so-called ’energetic’ approach is used for the estimation of the point of intersection. The inverse task is discussed, specifying the parameters of the output distributions and looking for the parameters of the input distributions. For larger standard deviations (stds) mixed Gaussian models are suggested as approximation of non-Gaussian distributions. © 2021, Springer Nature Switzerland AG.","Fuzzy modeling; Gaussian noise; Human-robot systems; Kalman filters; Navigation",,,,,,,"Acknowledgements This research work has been supported by the AIR-project, Action and Intention Recognition in Human Interaction with Autonomous Systems.",,,,,,,,,,"Banelli, P., Non-linear transformations of gaussians and gaussian-mixtures with implications on estimation and information theory (2013) IEEE Trans. Inf. Theory; Bruce, J., Wawer, J., Vaughan, R., Human-robot rendezvous by co-operative trajectory (2015) Signals, pp. 1-2. , pp; Firl, J., (2014) Probabilistic Maneuver Recognition in Traffic Scenarios, , Doctoral dissertation, KIT Karlsruhe; Fraichard, T., Paulin, R., Reignier, P., Human-robot motion: Taking attention into account. Research Report (2014) RR-8487; Hellendoorn, H., Palm, R., Fuzzy system technologies at siemens R and D (1994) Fuzzy Sets Syst, 63 (3), pp. 245-259; Chen, J., Wang, C., Chou, C., Multiple target tracking in occlusion area with interacting object models in urban environments (2018) Robot. Auton. Syst, 103, pp. 68-82; Smith:, J.O.: Signal metrics. In: Mathematics of the Fiscrete Fourier Transform (DFT) with Adio Applications. Center for Computer Research in Music and Acoustics (CCRMA) Department of Music, Stanford University, Ross Moore-Free Books, Sidney (2000); Schaefer, J., Strimmer, K.: A shrinkage to large scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Mol. Biol. 4(1), Art. 32 (2005); Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) IEEE International Conference on Robotics and Automation. St. Loius, Missouri, pp. 500-505. , pp; Foulloy, L., Galichet, S., Fuzzy control with fuzzy inputs (2003) IEEE Trans. Fuzzy Syst, 11 (4), pp. 437-449; Hamid, O.H., Smith, N.L., Automation, per se, is not job elimination: How artificial intelligence forwards cooperative human-machine coexistence (2017) Proceedings IEEE 15Th International Conference on Industrial Informatics (INDIN), pp. 899-904. , pp., IEEE, Emden, Germany; Palm, R., Chadalavada, R., Lilienthal, A., Fuzzy modeling and control for intention recognition in human-robot systems (2016) 7. IJCCI (FCTA), p. 2016. , Porto, Portugal; Palm, R., Iliev, B., Learning of grasp behaviors for an artificial hand by time clustering and takagi-sugeno modeling (2006) Proceedings FUZZ-IEEE 2006—IEEE International Conference on Fuzzy Systems. IEEE, Vancouver, BC, Canada; Palm, R., Lilienthal, A., Fuzzy logic and control in human-robot systems: Geometrical and kinematic considerations (2018) WCCI 2018: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Pp. 827–834. IEEE; Pota, M., Esposito, M., Pietro, G.D., Transformation of probability distribution into a fuzzy set interpretable with likelihood view. IEEE 11th International Conference on Hybrid Intelligent Systems (HIS 2011) (2011) Pp, 91-96. , IEEE, Malacca Malaysia; Tuning of scaling factors in fuzzy controllers using correlation functions (1993) Proceedings FUZZ-IEEE’93. IEEE, San Francisco, California; (1994) Fuzzy Inputs. Fuzzy Sets and Systems—special Issue on Modern Fuzzy Control, pp. 315-335. , pp; Palm, R., Lilienthal, A.: Uncertainty and fuzzy modeling in human-robot navigation. In: Proceedings of the 11th International Joint Conference on Computer International (IJCCI 2019), pp. 296–305. IJCCI, SCITEPRESS, Wien; Tahboub, K.A., Intelligent human-machine interaction based on dynamic bayesian networks probabilistic intention recognition (2006) J. Intell. Robot. Syst, 45 (1), pp. 31-52; Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Zhao, X., Kim, T.: Multiple object tracking: a literature review. Comput. Vis. Pattern Recogn. 1409,7618, pp. 1–18 (2014); Yager, R., Filev, D.B., Reasoning with probabilistic inputs (1994) Proceedings of the Joint Conference of NAFIPS, IFIS and NASA, pp. 352-356. , pp., NAFIPS, San Antonio","Palm, R.; AASS Department of Technology Orebro UniversitySweden; email: rub.palm@t-online.de","Merelo J.J.Garibaldi J.Linares-Barranco A.Warwick K.Madani K.",,"Springer Science and Business Media Deutschland GmbH","11th International Joint Conference on Computational Intelligence, IJCCI 2019","17 September 2019 through 19 September 2019",,262269,1860949X,9783030705930,,,"English","Stud. Comput. Intell.",Conference Paper,"Final","",Scopus,2-s2.0-85112243320
"Zaballa K.N.H., Cameron L.D., Lugo A.S.","57226639019;57210193310;57226648881;","Human-Robot Interactions Design for Interview Process: Needs-Affordances-Features Perspective",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12783 LNCS",,,"645","655",,,"10.1007/978-3-030-77750-0_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112168151&doi=10.1007%2f978-3-030-77750-0_43&partnerID=40&md5=7f1028b010edf92e366f69c8452d7c63","Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States","Zaballa, K.N.H., Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States; Cameron, L.D., Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States; Lugo, A.S., Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States","Human robot interaction (HRI) offers potential in fulfilling the social needs of humans specifically, in the context of an interview setting. While human robot interaction has proven potentials to provide relatively measurable outcomes in experimentation, applications in real life are limited mainly due to primitive HRI design and implementation. Research in HRI is thus the necessary first step to the diffusion of robots and robotic technologies into social life. This study presents the results a case study on HRI design for a formal interview process. The findings are presented in a framework that elucidates the key expected robot affordances—action possibilities afforded by a humanoid robot—and their relationships with humans in the interactive interview context. This framework development has been informed by the Needs-Affordances-Features perspective. © 2021, Springer Nature Switzerland AG.","Human robot interaction; Interview; Needs-affordances-features perspective; Robots","Anthropomorphic robots; Machine design; Man machine systems; Affordances; Design and implementations; Framework development; Human robot Interaction (HRI); Humanoid robot; Robotic technologies; Social life; Social needs; Social robots",,,,,,,,,,,,,,,,"Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, Pp. 25–32. University of Wisconsin-Madison Department of Computer Sciences, , https://doi.org/10.1145/2559636.2559666; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2019) Human-Robot Interaction: An Introduction, , Cambridge University Press, Cambridge; Deci, E., Vallerand, R., Pelletier, L., Ryan, R., Motivation and education: The self-determination perspective (1991) Educ. Psychol, 26 (3-4), pp. 325-346. , https://doi.org/10.1080/00461520.1991.9653137; Edwards, A., Omilion-Hodges, L., Edwards, C., How do patients in a medical interview perceive a robot versus human physician? (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017, Vienna, Austria, Pp. 109–110. Companion; Elkins, A., Gupte, A., Cameron, L., Humanoid Robots as Interviewers for Automated Credibility Assessment. Artificial Intelligence Lab. San Diego State University (2018) USA; Evans, S.K., Pearce, K.E., Vitak, J., Treem, J.W., Explicating affordances: A conceptual framework for understanding affordances in communication research (2016) J. Comput.-Mediat. Commun, 22 (1), pp. 35-52; Fallon, M., An architecture for online affordance-based perception and whole-body planning (2015) J. Field Robot, 32 (2), pp. 229-254. , https://doi.org/10.21236/ada602904; Fayard, A.-L., Weeks, J., Affordances for practice. Inf (2014) Organ, 24 (4), pp. 236-249. , https://doi.org/10.1016/j.infoandorg.2014.10.001; Fox, J., Gambino, A., Relationship development with humanoid social robots: Applying interpersonal theories to human/robot interaction. Cyberpsychol. Behav. Soc (2021) Netw, pp. 1-5. , https://doi.org/10.1089/cyber.2020.0181; Ghazali, A.S., Ham, J., Barakova, E., Markopoulos, P.: Assessing the effect of persuasive robots interactive social cues on users’ psychological reactance, liking, trusting beliefs and compliance. Adv. Robot. 33(7–8), 325–337 (2019). https://doi.org/10.1080/01691864.2019. 1589570; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., de Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors: J. Hum. Factors Ergon. Soc, 53 (5), pp. 517-527. , https://doi.org/10.1177/0018720811417254; Harris, J., Sharlin, E., Exploring the affect of abstract motion in social human-robot interaction (2011) RO-MAN, , https://doi.org/10.1109/roman.2011.6005254; Jamone, L., Affordances in psychology, neuroscience, and robotics: A survey (2018) IEEE Trans. Cogn. Dev. Syst, pp. 4-25. , https://doi.org/10.1109/tcds.2016.2594134; Kahn, P.H., Will people keep the secret of a humanoid robot? (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2015, Portland, Oregon, Pp. 173–180; Kaptelinin, V., Nardi, B., Affordances in HCI: Toward a mediated action perspective (2012) Proceedings on Human Factors in Computing Systems, CHI 2012, Austin, Texas, Pp. 967–975; Karahanna, E., Xin Xu, S., Xu, Y., Zhang, N., The needs–affordances–features perspective for the use of social media (2018) MIS Q, 42 (3), pp. 737-756. , https://doi.org/10.25300/MISQ/2018/11492; Khan, A.N., Ihalage, A., Ma, Y., Liu, B., Liu, Y., Deep learning framework for subject-independent emotion detection using wireless signals (2021) PLOS ONE, 16 (2), pp. 1-16. , https://doi.org/10.1371/journal.pone.0242946; Onnasch, L., Roesler, E., A taxonomy to structure and analyze human–robot interaction (2020) Int. J. Soc. Robot., , https://doi.org/10.1007/s12369-020-00666-5; Onyeulo, E.B., Gandhi, V., What makes a social robot good at interacting with humans? 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Behav, 24 (5), pp. 320-334. , https://doi.org/10.1177/1059712316668238; Vallverdú, J., Trovato, G., Jamone, L., Allocentric emotional affordances in HRI: The multimodal binding. Multimodal Technol (2018) Interact, 2 (78), pp. 1-20. , https://doi.org/10.20944/preprints201808.0312.v1; Wang, S.M., Cheng, W.M.: Design thinking for developing a case-based reasoning emotion-sensing robot for interactive interview. In: Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, CHI 2020, Hawaii, pp. 13–16 (2020). https://doi.org/10. 1145/3391203.3391205; Zheng, J., Jarvenpaa, S.L.: (PDF) [Internet]: Thinking Technology as Human: Affordances, Technology Features, and Egocentric Biases in Technology Anthropomorphism. ResearchGate (2020). https://www.researchgate.net/publication/347484661_Thinking_Tec hnology_as_Human_Affordances_Technology_Features_and_Egocentric_Biases_in_Tec hnology_Anthropomorphism. Accessed 21 Feb 2021","Zaballa, K.N.H.; Artificial Intelligence Lab, United States; email: nikazaballa@gmail.com","Nah F.F.Siau K.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on HCI in Business, Government and Organizations, HCIBGO 2021, Held as Part of the 23rd HCI International Conference, HCII 2021","24 July 2021 through 29 July 2021",,262219,03029743,9783030777494,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85112168151
"Chakraborty P., Ahmed S., Yousuf M.A., Azad A., Alyami S.A., Moni M.A.","57216817907;57225877868;57188663159;36087132900;57190760454;35119094400;","A Human-Robot Interaction System Calculating Visual Focus of Human's Attention Level",2021,"IEEE Access","9",,"9462086","93409","93421",,3,"10.1109/ACCESS.2021.3091642","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112114960&doi=10.1109%2fACCESS.2021.3091642&partnerID=40&md5=42c7e47f1cd68060b7d6b4891ef7acf5","Department of Computer Science and Engineering, Comilla University, Cumilla, Bangladesh; Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh; School of Biotechnology and Biomolecular Sciences, University of New South Wales Sydney (UNSW Sydney), Sydney, NSW, Australia; Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; WHO Collaborating Centre on EHealth, UNSW Digital Health, University of New South Wales Sydney (UNSW Sydney), Sydney, NSW, Australia","Chakraborty, P., Department of Computer Science and Engineering, Comilla University, Cumilla, Bangladesh; Ahmed, S., Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh; Yousuf, M.A., Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh; Azad, A., School of Biotechnology and Biomolecular Sciences, University of New South Wales Sydney (UNSW Sydney), Sydney, NSW, Australia; Alyami, S.A., Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Moni, M.A., WHO Collaborating Centre on EHealth, UNSW Digital Health, University of New South Wales Sydney (UNSW Sydney), Sydney, NSW, Australia","Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method. © 2013 IEEE.","ANN; attention level; concentration; Human-robot interaction; RNN-LSTM; visual focus of attention","Behavioral research; Deep learning; Eye movements; Feature extraction; Image segmentation; Long short-term memory; Robotics; Visual communication; Attention level; Comparative studies; Eyeball movements; Gaze estimation; Human attention; Robotic structures; Visual Attention; Visual focus of attentions; Social robots",,,,,,,,,,,,,,,,"Eisenberg, D., Downs, M.F., Golberstein, E., Zivin, K., Stigma and help seeking for mental health among college students (2009) Med. Care Res. Rev., 66 (5), pp. 522-541. , Oct; Yilmaz-Soylu, M., Akkoyunlu, B., The effect of learning styles on achievement in different learning environments (2009) Turkish Online J. Educ. Technol., 8 (4), pp. 43-50; Henderson, J.M., Visual attention and eye movement control during reading and picture viewing (1992) Eye Movements Vision Cognition, pp. 260-283. , New York, NY, USA: Springer; Chakraborty, P., Yousuf, M.A., Faruqui, N., How can a robot calculate the level of visual focus of human's attention (2020) Proc. Int. Joint Conf. Comput. Intell, pp. 329-342. , Singapore: Springer; Chakraborty, P., Yousuf, M.A., Rahman, S., Predicting level of visual focus of human's attention using machine learning approaches (2021) Proc. Int. Conf. Trends Comput. Cognit. Eng., pp. 683-694. , M. S. Kaiser, A. Bandyopadhyay, M. Mahmud, and K. Ray, Eds. Singapore: Springer; Siriteerakul, T., Sato, Y., Boonjing, V., Estimating change in head pose from low resolution video using LBP-based tracking (2011) Proc. Int. Symp. Intell. Signal Process. Commun. Syst. (ISPACS), pp. 1-6. , Dec; Martin, S., Tran, C., Tawari, A., Kwan, J., Trivedi, M., Optical flow based head movement and gesture analysis in automotive environment (2012) Proc. 15th Int. Conf. Intell. Transp. Syst., pp. 882-887; Abbas, S.S.A., Anitha, M., Jaini, X.V., Realization of multiple human head detection and direction movement using raspberry pi (2017) Proc. Int. Conf. Wireless Commun., Signal Process. Netw. (WiSPNET), pp. 1160-1164. , Mar; Mane, S.S., Surve, A.R., Engagement detection using video-based estimation of head movement (2018) Proc. 3rd IEEE Int. Conf. Recent Trends Electron., Inf. Commun. Technol. (RTEICT), pp. 1745-1749. , May; O'Rourke, C.T., Pitlik, T., Hoover, M., Fernández-Juricic, E., Hawk eyes II: Diurnal raptors differ in head movement strategies when scanning from perches (2010) PLoS One, 5 (9). , Sep; Monkaresi, H., Hussain, M.S., Calvo, R.A., Classification of affects using head movement, skin color features and physiological signals (2012) Proc. 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Asia Pacific Region, pp. 821-824; Alam, M.S., Kwon, K.-C., Kim, N., Implementation of a character recognition system based on finger-joint tracking using a depth camera (2021) IEEE Trans. Human-Mach. Syst., 51 (3), pp. 229-241. , Jun; Alam, M.S., Kwon, K.-C., Alam, M.A., Abbass, M.Y., Imtiaz, S.M., Kim, N., Trajectory-based air-writing recognition using deep neural network and depth sensor (2020) Sensors, 20 (2), p. 376. , Jan; Mondal, A., Dutta, A., Dey, N., Sen, S., Visual traffic surveillance: A concise survey (2020) Information Technology and Intelligent Transporta-tion Systems. Amsterdam, pp. 32-41. , The Netherlands: IOS Press; Dutta, A., Mondal, A., Dey, N., Sen, S., Moraru, L., Hassanien, A.E., Vision tracking: A survey of the state-of-The-art (2020) Social Netw. Comput. Sci., 1 (1), pp. 1-19. , Jan; Ali, M.N.Y., Sarowar, M.G., Rahman, M.L., Chaki, J., Dey, N., Tavares, J.M.R.S., Adam deep learning with SOM for human sentiment classification (2019) Int. J. Ambient Comput. Intell., 10 (3), pp. 92-116. , Jul; Sivakumar, S., Rajalakshmi, R., Analysis of sentiment on movie reviews using word embedding self-attentive LSTM (2021) Int. J. Ambient Comput. Intell., 12 (2), pp. 33-52. , Apr; Yang, F.-Y., Chang, C.-Y., Chien, W.-R., Chien, Y.-T., Tseng, Y.-H., Tracking learners' visual attention during a multimedia presentation in a real classroom (2013) Comput. Educ., 62, pp. 208-220. , Mar; Sehili, M.E.A., Yang, F., Devillers, L., Attention detection in elderly people-robot spoken interaction (2014) Proc. Workshop Multimodal, Multi-Party, Real-World Hum.-Robot Interact., pp. 7-12; Frutos-Pascual, M., Garcia-Zapirain, B., Assessing visual attention using eye tracking sensors in intelligent cognitive therapies based on serious games (2015) Sensors, 15 (5), pp. 11092-11117. , May; Han, J., Chen, L., Fu, Z., Fritchman, J., Bao, L., Eye-tracking of visual attention inWeb-based assessment using the force concept inventory (2017) Eur. J. Phys., 38 (4). , Jul; Peng, W.-T., Chang, C.-H., Chu, W.-T., Huang, W.-J., Chou, C.-N., Chang, W.-Y., A real-time user interest meter and its applications in home video summarizing (2010) Proc. IEEE Int. Conf. Multimedia Expo, pp. 849-854; Bhoir, S.A., Hasanzadeh, S., Esmaeili, B., Dodd, M.D., Fardhosseini, M.S., Measuring construction workers' attention using eye-tracking technology (2015) Proc. 11th Construct. Specialty Conf., pp. 2221-22210. , Vancouver, BC, Canada; Cöster, J., Ohlsson, M., Human attention: The possibility of measuring human attention using OpenCV and the Viola-Jones face detection algorithm (2015) Dept. Comput. Sci. Eng., Kth Roy. Inst. Technol. Csc, , Degree Project, First Level, Stockholm, Sweden, Tech. Rep; Suryaprasad, J., Sandesh, D., Saraswathi, V., Swathi, D., Manjunath, S., Real time drowsy driver detection using haarcascade samples (2013) Proc. Comput. Sci. Inf. Technol., pp. 45-54; Shang, L., Zhang, C., Gao, G., Eye detection and attention recognition based on OPENCV (2017) DEStech Trans. Comput. Sci. Eng., 1, pp. 465-468. , Jun; Dasgupta, A., George, A., Happy, S.L., Routray, A., A vision-based system for monitoring the loss of attention in automotive drivers (2013) IEEE Trans. Intell. Transp. Syst., 14 (4), pp. 1825-1838. , Dec; Canedo, D., Trifan, A., Neves, A.J., Monitoring students' attention in a classroom through computer vision (2018) Proc. Int. Conf. Practical Appl. Agents Multi-Agent Syst. Wiesbaden, pp. 371-378. , Germany: Springer; Mastronardi, G., Bevilacqua, V., Depasquale, R.F., Vilardi, M.D.F., Attention control during distance learning sessions (2013) Proc. Int. Conf. Image Anal. Process. Berlin, pp. 545-549. , Germany: Springer; Jo, J., Vision-based method for detecting driver drowsiness and distraction in driver monitoring system (2011) Opt. Eng., 50 (12). , Dec; Tsai, M.-J., Hou, H.-T., Lai, M.-L., Liu, W.-Y., Yang, F.-Y., Visual attention for solving multiple-choice science problem: An eye-tracking analysis (2012) Comput. Educ., 58 (1), pp. 375-385. , Jan; Klein, P., Lichtenberger, A., Kächemann, S., Becker, S., Kekule, M., Viiri, J., Baadte, C., Kuhn, J., Visual attention while solving the test of understanding graphs in kinematics: An eye-tracking analysis (2020) Eur. J. Phys., 41 (2). , Mar; Valdestilhas, A., Masseto, F.I., Ferreira, A., An adaptive graphical user interface based on attention level and diameter of eye pupil (2014) Proc. IEEE 6th Int. Conf. Adapt. Sci. Technol. (ICAST), pp. 1-4. , Oct; Masse, B., Ba, S., Horaud, R., Tracking gaze and visual focus of attention of people involved in social interaction (2018) IEEE Trans. Pattern Anal. Mach. Intell., 40 (11), pp. 2711-2724. , Nov; Pang, Y., Yuan, Y., Li, X., Pan, J., Efficient HOG human detection (2011) Signal Process., 91 (4), pp. 773-781. , Apr","Moni, M.A.; WHO Collaborating Centre on EHealth, Australia; email: m.moni@unsw.edu.au",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85112114960
"Rosén J.","57219093157;","Expectations in Human-Robot Interaction",2021,"Lecture Notes in Networks and Systems","259",,,"98","105",,,"10.1007/978-3-030-80285-1_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112052093&doi=10.1007%2f978-3-030-80285-1_12&partnerID=40&md5=8f783b90cfb124679f1136831a5231f4","Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","Rosén, J., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","It is acknowledged that humans expect social robots to interact in a similar way as in human-human interaction. To create successful interactions between humans and social robots, it is envisioned that the social robot should be viewed as an interaction partner rather than an inanimate thing. This implies that the robot should act autonomously, being able to ‘perceive’ and ‘anticipate’ the human’s actions as well as its own actions ‘here and now’. Two crucial aspects that affect the quality of social human-robot interaction is the social robot’s physical embodiment and its performed behaviors. In any interaction, before, during or after, there are certain expectations of what the social robot is capable of. The role of expectations is a key research topic in the field of Human-Robot Interaction (HRI); if a social robot does not meet the expectations during interaction, the human (user) may shift from viewing the robot as an interaction partner to an inanimate thing. The aim of this work is to unravel the role and relevance of humans’ expectations of social robots and why it is important area of study in HRI research. Moreover, I argue that the field of HRI can greatly benefit from incorporating approaches and methods from the field of User Experience (UX) in its efforts to gain a deeper understanding of human users’ expectations of social robots and making sure that the matching of these expectations and reality is better aligned. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Expectations; Human-robot interaction; User experience",,,,,,,,,,,,,,,,,"Dautenhahn, K., Methodology & themes of human-robot interaction (2007) Int. J. Adv. Rob. Syst., 4 (1), pp. 103-108; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot. Auton. Syst., 42 (3-4), pp. 177-190; Manzi, F., Massaro, D., Di Lernia, D., Maggioni, M., Riva, G., Marchetti, A., Robots are not all the same (2020) Cyberpsychol. Behav. Soc. Network., 24, pp. 1-8; Dennett, D., (1989) The Intentional Stance, , MIT press, Cambridge; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) Am. J. Psychol., 57 (2), pp. 243-259; Alač, M., Social robots: Things or agents? (2016) AI Soc, 31 (4), pp. 519-535; Hartson, R., Pyla, P.S., (2018) The UX Book, , Morgan Kaufmann, San Diego; Borg, M., Porter, L., Following the life-course of an expectation (2010) The Psychology of Expectations, pp. 1-47. , Leon, P., Tamez, N. (eds.), pp. , Nova Science Publishers; Roese, N.J., Sherman, J.W., Expectancy (2007) Social Psychology: Handbook of Basic Principles, pp. 91-115. , Kruglanski, A.W., Higgins, E.T. (eds.), pp; Hassenzahl, M., Tractinsky, N., User experience — A research agenda (2006) Behav. Inf. Technol., 25 (2), pp. 91-97; Malle, B.F., Fischer, K., Young, J.E., Moon, A., Colling, E., (2020) Trust and the Discrepancy between Expectations and Actual Capabilities of Social Robots, , Cambridge Scholars Publishing; Billing, E., Rosén, J., Lindblom, J., Expectations of robot technology in welfare (2019) The Second Workshop on Social Robots in Therapy and Care in Conjunction with the 14Th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2019), , Daegu, Korea; Billing, E., The DREAM dataset (2020) PLOS ONE, 15 (8), pp. 1-15; Riek, L.D., Wizard of Oz studies in HRI (2012) J. Hum.-Robot Interact., 1 (1), pp. 119-136; Huang, W., (2015) When HCI Meets HRI. Virginia Polytechnic Institute and State University; Lindblom, J., Alenljung, B., Billing, E., Evaluating the user experience of human robot interaction (2020) Evaluation Methods Standardization for Human-Robot Interaction, pp. 231-256. , Jost, C., et al. (eds.), pp; Tonkin, M., Vitale, J., Herse, S., Williams, M.A., Judge, W., Wang, X., Design methodology for the UX of HRI (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415. , , pp; Wallström, J., Lindblom, J., UX Evaluation of social robots with the USUS goals framework (2020) Evaluation Methods Standardization for Human-Robot Interaction, pp. 177-201. , Jost, C., et al. (eds.), pp. , Springer; Weiss, A., Bernhaupt, R., Schwaiger, D., Altmaninger, M., Buchner, R., Tscheligi, M., User experience evaluation with a wizard of Oz approach (2009) 2009 9Th IEEE RAS International Conference on Humanoid Robots, Pp. 303–308. IEEE; Sun, Y., Sundar, S.S., Psychological importance of human agency how self-assembly affects user experience of robots (2016) 2016 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 189–196. IEEE; Edwards, A., Edwards, C., Westerman, D., Spence, P.R., Initial expectations, interactions, and beyond with social robots (2019) Comput. Hum. Behav., 90, pp. 308-314; Bardzell, S., Bardzell, J., Towards a feminist HCI methodology (2011) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 675-684. , , pp","Rosén, J.; Interaction Lab, Högskolevägen 1, Sweden; email: julia.rosen@his.se","Ayaz H.Asgher U.Paletta L.",,"Springer Science and Business Media Deutschland GmbH","AHFE Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, 2021","25 July 2021 through 29 July 2021",,262279,23673370,9783030802844,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85112052093
"Rosén J., Lindblom J., Billing E.","57219093157;7006805787;36462270500;","Reporting of Ethical Conduct in Human-Robot Interaction Research",2021,"Lecture Notes in Networks and Systems","268",,,"87","94",,,"10.1007/978-3-030-79997-7_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112020768&doi=10.1007%2f978-3-030-79997-7_11&partnerID=40&md5=f6ee34fc5091aec6a323de4af5d9e46c","Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","Rosén, J., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden; Lindblom, J., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden; Billing, E., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","The field of Human-Robot Interaction (HRI) is progressively maturing into a distinct discipline with its own research practices and traditions. Aiming to support this development, we analyzed how ethical conduct was reported and discussed in HRI research involving human participants. A literature study of 73 papers from three major HRI publication outlets was performed. The analysis considered how often the following five principles of ethical conduct were reported: ethical board approval, informed consent, data protection and privacy, deception, and debriefing. These five principles were selected as they belong to all major and relevant ethical guidelines for the HRI field. The results show that overall, ethical conduct is rarely reported, with four out of five principles mentioned in less than one third of all papers. The most frequently mentioned aspect was informed consent, which was reported in 49% of the articles. In this work, we aim to stimulate increased acknowledgment and discussion of ethical conduct reporting within the HRI field. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Ethics; Human-Robot Interaction; Methodology",,,,,,,"Special thanks to Oskar MacGregor for his valuable insight on proper ethical conduct. We would also like to thank Erik Lagerstedt and Kajsa Nalin for their support and help on parts of the analysis of the literature study.",,,,,,,,,,"Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philos. Trans. Roy. Soc. B Biol. Sci, 362 (1480), pp. 679-704; Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future, pp. 267-277. , https://doi.org/10.1007/978-3-319-41697-7_24, Schlick, C., Trzcieliński, S. (eds.), pp. , Springer, Cham; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of HRI to methodology and reporting recommendations (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 391-398. , , pp. , IEEE Press; Milgram, S., Behavioral study of obedience (1963) Psychol. Sci. Public Interest, 67 (4), pp. 371-378; Haney, C., Banks, C., Zimbardo, P., Study of prisoners and guards in a simulated prison (1973) Naval Res. Rev., 26 (9), pp. 1-17; Weiten, W., Psychology: Themes and Variations (2007) Cengage Learning; American Psychological Association: Ethical Principles of Psychologists and Code of Conduct, , https://www.apa.org/ethics/code, American Psychological Association; European Commission: Ethics in Social Science and Humanities. European Commission. https://ec.europa.eu/info/sites/info/files/6._h2020_ethics-soc-science-humanities_en.pdf; World Medical Association: Declaration of Helsinki World Medical Association, , https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-res earch-involving-human-subjects/; European Union: General Data Protection Regulation. EU. https://gdpr-info.eu; ACM/IEEE International Conference on Human-Robot Interaction. https://dl.acm.org/confer ence/hri; IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). https://www.ieee-ras.org/conferences-workshops/financially-co-sponsored/ro-man; ACM Transactions on Human-Robot Interaction, , https://dl.acm.org/journal/thri; Oates, B.J., (2005) Researching Information Systems and Computing, , Sage; Riek, L., Howard, D., A code of ethics for the human-robot interaction profession (2014) Proceedings of We Robot; Oliveira, R., Friends or foes? Socioemotional support and gaze behaviors in mixed groups of humans and robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 279-288. , , pp. , ACM; Rea, D.J., Young, J.E., It’s all in your head: Using priming to shape an operator’s perceptions and behavior during teleoperation (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human Robot Interaction, pp. 32-40. , , pp. , ACM; Schroter, S., Plowman, R., Hutchings, A., Gonzalez, A., Reporting ethics committee approval and patient consent by study design in five general medical journals (2006) J. Med. Ethics, 32 (12), pp. 718-723; Matthias, A., Robot lies in health care: When is deception morally permissible? (2015) Kennedy Inst. Ethics J., 25 (2), pp. 169-162","Rosén, J.; Interaction Lab, Högskolevägen 1, Sweden; email: julia.rosen@his.se","Zallio M.Raymundo Ibanez C.Hernandez J.H.",,"Springer Science and Business Media Deutschland GmbH","AHFE Conference on Human Factors in Robots, Drones and Unmanned Systems, and AHFE Conference on Human Factors in Cybersecurity, 2021","25 July 2021 through 29 July 2021",,261829,23673370,9783030799960,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85112020768
"Antonelli M., Digo E., Pastorelli S., Gastaldi L.","57217008868;57203841907;57192185226;7004748244;","Wearable MIMUs for the identification of upper limbs motion in an industrial context of human-robot interaction",2021,"Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2021",,,,"403","409",,,"10.5220/0010548304030409","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111783924&doi=10.5220%2f0010548304030409&partnerID=40&md5=dfd165dd147d7ab809ae020e9703d531","Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; Department of Mathematical Sciences “G. L. Lagrange”, Politecnico di Torino, Turin, Italy","Antonelli, M., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; Digo, E., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; Pastorelli, S., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; Gastaldi, L., Department of Mathematical Sciences “G. L. Lagrange”, Politecnico di Torino, Turin, Italy","The automation of human gestures is gaining increasing importance in manufacturing. Indeed, robots support operators by simplifying their tasks in a shared workspace. However, human-robot collaboration can be improved by identifying human actions and then developing adaptive control algorithms for the robot. Accordingly, the aim of this study was to classify industrial tasks based on accelerations signals of human upper limbs. Two magnetic inertial measurement units (MIMUs) on the upper limb of ten healthy young subjects acquired pick and place gestures at three different heights. Peaks were detected from MIMUs accelerations and were adopted to classify gestures through a Linear Discriminant Analysis. The method was applied firstly including two MIMUs and then one at a time. Results demonstrated that the placement of at least one MIMU on the upper arm or forearm is suitable to achieve good recognition performances. Overall, features extracted from MIMUs signals can be used to define and train a prediction algorithm reliable for the context of collaborative robotics. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved","Industry 4.0; Linear discriminant analysis; MIMU; Motion prediction; Movement classification; Upper limb","Agricultural robots; Discriminant analysis; Industrial robots; Robotics; Wearable technology; Adaptive control algorithms; Different heights; Human-robot collaboration; Industrial context; Industrial tasks; Inertial measurement unit; Linear discriminant analysis; Prediction algorithms; Social robots",,,,,,,,,,,,,,,,"Ajoudani, A., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975. , Springer US; Balbinot, A., de Freitas, J. C. R., Côrrea, D. S., Use of inertial sensors as devices for upper limb motor monitoring exercises for motor rehabilitation (2015) Health and Technology, 5 (2), pp. 91-102; Digo, E., Antonelli, M., Cornagliotto, V., Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications (2020) Robotics, 9 (2), p. 33; Digo, E., Antonelli, M., Pastorelli, S., Upper limbs motion tracking for collaborative robotic applications (2020) International Conference on Human Interaction & Emerging Technologies, pp. 391-397; Filippeschi, A., Survey of motion tracking methods based on inertial sensors: A focus on upper limb human motion (2017) Sensors (Switzerland), 17 (6), pp. 1-40; Gastaldi, L., Lisco, G., Pastorelli, S., Evaluation of functional methods for human movement modelling (2015) Acta of Bioengineering and Biomechanics, 17 (4), pp. 31-38; Hsu, Y. L., Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network (2018) IEEE Access. IEEE, 6, pp. 31715-31728; Lasota, P. A., Fong, T., Shah, J. A., A Survey of Methods for Safe Human-Robot Interaction (2017) Foundations and Trends in Robotics, 5 (3), pp. 261-349; Lopez-Nava, I. H., Angelica, M. M., Wearable Inertial Sensors for Human Motion Analysis: A review (2016) IEEE Sensors Journal, 16 (22), pp. 7821-7834; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion (2013) IEEE International Conference on Intelligent Robots and Systems, pp. 299-306. , IEEE; Melchiorre, M., Vision-based control architecture for human-robot hand-over applications (2020) Asian Journal of Control, 23 (1), pp. 105-117; Pellegrinelli, S., A probabilistic approach to workspace sharing for human-robot cooperation in assembly tasks (2016) CIRP Annals - Manufacturing Technology. CIRP, 65 (1), pp. 57-60; Pereira, A., Althoff, M., Overapproximative arm occupancy prediction for human-robot co-existence built from archetypal movements (2016) IEEE International Conference on Intelligent Robots and Systems, pp. 1394-1401. , IEEE; Perez-D'Arpino, C., Shah, J. A., Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification (2015) Proceedings - IEEE International Conference on Robotics and Automation, pp. 6175-6182; Safeea, M., Neto, P., Minimum distance calculation using laser scanner and IMUs for safe human-robot interaction (2019) Robotics and Computer-Integrated Manufacturing, 58, pp. 33-42. , Elsevier Ltd; Scimmi, L. S., Experimental Real-Time Setup for Vision Driven Hand-Over with a Collaborative Robot (2019) IEEE International Conference on Control, Automation and Diagnosis (ICCAD), pp. 2-6. , IEEE; Wang, Y., Collision-free trajectory planning in human-robot interaction through hand movement prediction from vision (2017) IEEE-RAS International Conference on Humanoid Robots, pp. 305-310; Weitschat, R., Safe and efficient human-robot collaboration part I: Estimation of human arm motions (2018) Proceedings - IEEE International Conference on Robotics and Automation, pp. 1993-1999. , IEEE",,"Gusikhin O.Nijmeijer H.Madani K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","18th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2021","6 July 2021 through 8 July 2021",,170566,,9789897585227,,,"English","Proc. Int. Conf. Informatics Control, Autom. Robot., ICINCO",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85111783924
"Marvel J.A., Zimmerman M., Bagchi S.","35074587400;57208165783;57208164628;","State-of-the-art in human-robot interaction",2021,"Manufacturing In The Era Of 4th Industrial Revolution: A World Scientific Reference (In 3 Volumes)",,,,"43","86",,,"10.1142/9789811210679_0003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109605263&doi=10.1142%2f9789811210679_0003&partnerID=40&md5=b77f8b968d4cfe12f3816d35adcc4db7","U.S. National Institute of Standards and Technology (NIST), Gaithersburg, MD, United States; Johns Hopkins University, United States; National Institute of Standards and Technology, Gaithersburg, MD, United States","Marvel, J.A., U.S. National Institute of Standards and Technology (NIST), Gaithersburg, MD, United States; Zimmerman, M., Johns Hopkins University, United States; Bagchi, S., National Institute of Standards and Technology, Gaithersburg, MD, United States","The following sections are included: • Introduction • Humans and Robots in Industry • Human-Machine Interfaces • Human-Robot Interaction • Performance Metrics for HRI and HMI • Natural HRI • Conclusion • References. © 2020 by World Scientific Publishing Co. 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"Rabb N., Law T., Chita-Tegmark M., Scheutz M.","57222470528;57212389268;56700303500;6603548841;","An Attachment Framework for Human-Robot Interaction",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00802-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109345996&doi=10.1007%2fs12369-021-00802-9&partnerID=40&md5=e1d08f1754866e83b8281d7b2dd862c4","HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States","Rabb, N., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Law, T., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Chita-Tegmark, M., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Scheutz, M., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States","Attachment theory is a research area in psychology that has enjoyed decades of successful study, and has subsequently become explored in realms beyond that of the original infant-caregiver bonds. Now, attachment is studied in relation to pets, symbols (such as deities), objects, technologies, and notably for our purposes, robots. When we discuss attachment in Human-Robot Interaction (HRI), is “attachment” to a robot the same as being attached to a pet? Or does it more closely resemble attachment to a technology device such as a smartphone? Through untangling the concept of attachment in HRI, we summarize a breadth of the existing attachment literature in a unified spectrum. We present a notion of weak attachment, and strong attachment before setting both as distinct ends of a spectrum of attachment. We motivate this spectrum by teasing out the underlying theoretical basis for strong attachment, and how capabilities of the attachment figure could lead to stronger or weaker attachment. This more nuanced, multi-dimensional representation of attachment allows us to present a clarified categorization of where various human-robot bonds explored in HRI studies fit on the spectrum, where robots in general could place, and how a clearer definition of human-robot attachment can benefit future HRI studies. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Attachment; Emotional bond; Human-robot interaction; Social bond","Agricultural robots; Social robots; Human robot Interaction (HRI); Human robots; Multi dimensional; Man machine systems",,,,,,,,,,,,,,,,"Ainsworth Mary, D.S., Mary, B., Sally, W., (1978) Patterns of Attachment, , Everett Waters; Ainsworth Mary, S., Attachments beyond infancy (1989) Am Psychol, 44 (4), p. 709; Ali, S., Moroso, T., Breazeal, C., Can children learn creativity from a social robot? 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Transitional objects and transitional phenomena-a study of the first not-me possession (1953) Int J Psycho-Anal, 34, pp. 89-97; Wood, L., Christian, H., Dog walking as a catalyst for strengthening the social fabric of the community (2011) The Health Benefits of Dog Walking for Pets and People, pp. 51-74; Zilcha-Mano, S., Mikulincer, M., Shaver, P.R., An attachment perspective on human-pet relationships: Conceptualization and assessment of pet attachment orientations (2011) J Res Pers, 45 (4), pp. 345-357","Rabb, N.; HRI Lab, 200 Boston Avenue, United States; email: nicholas.rabb@tufts.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Review,"Article in Press","",Scopus,2-s2.0-85109345996
"Hannibal G.","56512012200;","Social robotics and the nature of trust: A philosophical investigation into the conceptual challenge of trust ascription in human-robot interaction",2021,"AISB Convention 2021: Communication and Conversations",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109069202&partnerID=40&md5=a2e2ddbb3bc46c8ddfa770759f454197","Department of Philosophy, TU Wien, Austria","Hannibal, G., Department of Philosophy, TU Wien, Austria",[No abstract available],,,,,,,,,,,,,,,,,,"Breazeal, C., Toward Sociable Robots (2003) Robotics and Autonomous Systems, 42, pp. 167-175; Butler, J., (2004) Undoing Gender, , New York: Routledge; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with Trust for Human-Robot Collaboration (2018) Proceedings of the 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 307-315. , Chicago, USA: ACM; Coeckelbergh, M., (2013) Human being @ risk: Enhancement, technology, and the evaluation of vulnerability transformations, , Berlin: Springer (Science & Business Media); Coeckelbergh, M., Pop, C., Simut, R., Peca, A., Pintea, S., David, D., Vanderborght, B., A Survey of Expectations About the Role of Robots in Robot-Assisted Therapy for Children with ASD: Ethical Acceptability, Trust, Sociability, Appearance, and Attachment (2016) Science and Engineering Ethics, 22 (1), pp. 47-65; Dautenhahn, K., Getting to Know Each Other - Artificial Social Intelligence for Au tonomous Robots (1995) Robotics and Autonomous Systems, 16 (2–4), pp. 333-356; Dautenhahn, K., The Art of Designing Socially Intelligent Agents: Science, Fiction, and the Human in the Loop (1998) Applied Artificial Intelligence, 12 (7–8), pp. 573-617; Erebak, S., Turgut, T., Caregivers’ attitudes toward potential robot coworkers in elder care (2019) Cognition, Technology & Work, 21 (2), pp. 327-336; Fineman, M. A., The Vulnerable Subject: Anchoring Equality in the Human Condition (2008) Yale Journal of Law & Feminism, 20 (1), pp. 8-40; Ishak, D., Nathan-Roberts, D., Analysis of Elderly Human-Robot Team Trust Models (2015) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), pp. 65-69. , Los Angeles, California, USA: SAGE PublicationsSage CA: Los Angeles, CA; Iwamura, Y., Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets? 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L., Dautenhahn, K., A User Study on Visualization of Agent Migration between Two Companion Robots (2009) 13th International Conference on Human-Computer Interaction (CHII). Vol. 17th, Lecture Notes in Computer Science (5610-5624) and Lecture Notes in Artificial Intelligence (5638-5639), , J. A. Jacko, C. Stephani dis, D. Harris, D. D. Schmorrow, M. Grootjen, B.-T. Karsh, I. Estabrooke (Eds), San Diego, USA: Springer; Lewis, M., Sycara, K., Walker, P., The Role of Trust in Human-Robot Interaction (2018) Foundations of Trusted Autonomy (Studies in Systems, Decision and Control, 177, pp. 135-159. , H. 25 A. Abbass, D. J. Reid, & J. Scholz (Eds), 117) –). Cham, Switzerland: Springer Open; Melson, G. F., Kahn, P. H., Beck, A., Friedman, B., Toward Understanding Children’s and Adults’ Encounters with Social Robots (2006) Papers from the AAAI Work shop on Human Implications of Human-Robot Interaction (HRI), pp. 36-43. , T. Metzler (Ed), –). Boston, USA: AAAI Press; Nyholm, S., (2020) Humans and robots: Ethics, agency, and anthropomorphism, , London, UK: Rowman & Littlefield Publishers; ıas, C., Echevarr´ıa, C., Nunez, P., Manso, L., Bustos, P., Leal, S., Parra, C., Ursus: A Robotic Assistant for Training of Children with Motor Impairments (2013) Converging Clinical and Engineering Research on Neu rorehabilitation (Biosystems Biorobotics, 1, pp. 249-253. , Su´arez Mej J. Pons, D. Torricelli, & M. Pajaro (Eds), 1) –). Berlin and Heidelberg, Germany: Springer; Takayama, L., Nass, C., Ju, W., Beyond dirty, dangerous and dull: what everyday people think robots should do (2008) 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 25-32. , Amsterdam, The Netherlands: ACM; Turkle, S., (2011) Alone Together: Why We Expect More From Technology and Less From Each Other, , New York, USA: Basic Books; van Straten, C. L., Peter, J., K¨uhne, R., de Jong, C., Barco, A., Technological and 28 Interpersonal Trust in Child-Robot Interaction (2018) Proceedings of the 6th International Conference on Human-Agent Interaction (HAI), pp. 253-259. , Southampton, UK: ACM; Vinanzi, S., Patacchiola, M., Chella, A., Cangelosi, A., Would a robot trust you? Developmental robotics model of trust and theory of mind (2019) Philosophical Transactions of the Royal Society B, 374 (1771), pp. 1-9; Wagner, A. R., Robinette, P., Towards robots that trust: Human subject validation of the situational conditions for trust (2015) Interaction Studies, 16 (1), pp. 89-117; Weiss, A., Igelsb¨ock, J., Wurhofer, D., Tscheligi, M., Looking Forward to a “Robotic Society”? (2011) International Journal of Social Robotics, 3 (2), pp. 111-123; You, S., Robert, L. P., Human-Robot Similarity and Willingness to Work with a Robotic Coworker (2018) Proceedings of 13th ACM/IEEE International Conference on Human Robot Interaction (HRI), pp. 251-260. , Chicago, USA: ACM; Yuki, M., Maddux, W. W., Brewer, M. B., Takemura, K., Cross-Cultural Differences in Relationship- and Group-Based Trust (2005) Personality and Social Psychology Bulletin, 31 (1), pp. 48-62","Hannibal, G.; Department of Philosophy, Austria; email: glenda.hannibal@tuwien.ac.at",,,"The Society for the Study of Artificial Intelligence and Simulation of Behaviour","AISB Convention 2021: Communication and Conversations","7 April 2021 through 9 April 2021",,169573,,9781713829423,,,"English","AISB Conv.: Commun. Conversat.",Conference Paper,"Final","",Scopus,2-s2.0-85109069202
"Fang Z., Lou L., Tang K., Wang W., Chen B., Wang Y., Zheng Y.","57201897537;56540874400;7401589663;37006077200;55723067700;57201910511;7404837363;","A CMOS-integrated radar-assisted cognitive sensing platform for seamless human-robot interactions",2021,"Proceedings - IEEE International Symposium on Circuits and Systems","2021-May",,"9401535","","",,1,"10.1109/ISCAS51556.2021.9401535","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109040585&doi=10.1109%2fISCAS51556.2021.9401535&partnerID=40&md5=867773951e2b7695c41ad2c0a3b424fd","School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; School of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China; Singapore University of Technology and Design, Singapore, Singapore","Fang, Z., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Lou, L., School of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China; Tang, K., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Wang, W., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Chen, B., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Wang, Y., Singapore University of Technology and Design, Singapore, Singapore; Zheng, Y., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","With the rapid development of the internet of things (IoT), industry 4.0, smart manufacturing, intelligent building techniques, robots are becoming more and more demanding for emerging new applications. For robots used in complex environments, precise sensing of its surroundings is essential to enable safe and robust operation. Comprehensive and cognitive perception capability is needed to recognize human subjects in a complex and dynamic environment to avoid the possible collisions. Moreover, comprehensive sensing is required to enable accurate simultaneous localization and mapping (SLAM) under scenarios with clutters and moving human subjects. To achieve the goal, a CMOS-integrated radar-assisted robot sensing platform is proposed. By leveraging the phased-array radar techniques and time-phase processing techniques, high accuracy can be achieved for localization and recognition of human subjects. Fabricated in a 65-nm CMOS process, the chip-scale radar sensing platform is with compact size. A series of experiments have been carried out on verifying the capabilities of the radar platform for ranging and human recognition based on vital signs, exploring the potential for seamless human-robot interaction applications. © 2021 IEEE","CMOS; Cognitive sensing; Edge devices; Human/robot interaction; Internet of things (IoT); Mixed-signal IC; Radar; Robots; Sensor platform","CMOS integrated circuits; Industrial robots; Intelligent buildings; Internet of things; Man machine systems; Radar; Cognitive Sensing; Complex environments; Dynamic environments; Internet of thing (IOT); Perception capability; Phased array radars; Simultaneous localization and mapping; Smart manufacturing; Social robots",,,,,,"ACKNOWLEDGMENT The authors would like to thank the funding supported by Singapore MOE AcRF Tier 2: MOE2019-T2-2-179.",,,,,,,,,,"Zaffar, M., Sensors, slam and long-term autonomy: A review (2018) NASA/ESA Conference on Adaptive Hardware and Systems (AHS); Durrant-Whyte, H., Bailey, T., Simultaneous localization and mapping: Part I (2006) IEEE Robotics & Automation Magazine, 13 (2), pp. 99-110. , June; Cao, Y., Yang, C., Li, R., Knoll, A., Beltrame, G., Accurate position tracking with a single UWB anchor (2020) IEEE International Conference on Robotics and Automation (ICRA); Atanasov, N., Decentralized active information acquisition: Theory and application to multi-robot SLAM (2015) IEEE International Conference on Robotics and Automation (ICRA); Tang, K., A 4Tx/4RX pulsed chirping phased-array radar transceiver in 65-nm CMOS for X-band synthetic aperture radar application (2020) IEEE J. Solid-State Circuits, 55 (11), pp. 2970-2983. , Nov; Koo, V.C., A new unmanned aerial vehicle synthetic aperture RADAR for environmental monitoring (2012) Proc. In Electromagnetics Research, 122, pp. 245-268; Ansari, M.A.H., Law, C.L., Grating lobe suppression of multicycle IR-UWB collaborative radar sensor in wireless sensor network system (2020) IEEE Sens. Letters, 4 (1), pp. 1-4. , Jan; Park, P., Kim, S., Woo, S., Kim, C., A centimeter resolution, 10 m range CMOS impulse radio radar for human motion monitoring (2014) IEEE J. Solid-State Circuits, 49 (5), pp. 1125-1134. , May; Peng, Z., A portable FMCW interferometry radar with programmable low-IF architecture for localization, ISAR imaging, and vital sign tracking (2017) IEEE Trans. Microw. Theory Techn., 65 (4), pp. 1334-1344. , Apr; Nosrati, M., Tavassolian, N., High-accuracy heart rate variability monitoring using Doppler radar based on Gaussian pulse train modeling and FTPR algorithm (2018) IEEE Trans. Microw. Theory Techn., 66 (1), pp. 556-567. , Jan; Lou, L., A 253mW/channel 4TX/4RX pulsed chirping phased-array radar TRX in 65nm CMOS for X-band synthetic-aperture radar imaging (2018) IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers, pp. 160-162. , Feb; Lou, L., Radar Transceivers for Inverse Synthetic Aperture Radar (ISAR) Imaging of Human Activity in 65nm CMOS (2019) IEEE Int. System-on-Chip Conf., pp. 471-474. , Singapore,. Sep; Fang, Z., A Ku-band FMCW Radar on Chip for Wireless Micro Physiological Signal Monitoring by Interferometry Phase Analysis (2018) IEEE Int. Symp. Circuits Syst., 1, pp. 1-4. , May; Fang, Z., Wide field-of-view locating and multimodal vital sign monitoring based on X -band CMOS-integrated phased-array radar sensor (2020) IEEE Trans. Microw. Theory Techn., 68 (9), pp. 4054-4065. , Sept",,,,"Institute of Electrical and Electronics Engineers Inc.","53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021","22 May 2021 through 28 May 2021",,169837,02714310,9781728192017,PICSD,,"English","Proc IEEE Int Symp Circuits Syst",Conference Paper,"Final","",Scopus,2-s2.0-85109040585
"You S., Robert L.P., Jr.","37057989500;8247734500;","Introduction to the human-robot interactions minitrack at HICSS 54",2021,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"555","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108370447&partnerID=40&md5=9023cdd323edd3d98009d3f176f9d607","HEC Paris, France; University of Michigan, United States","You, S., HEC Paris, France; Robert, L.P., Jr., University of Michigan, United States",[No abstract available],,,,,,,,,,,,,,,,,,,,"Bui T.X.","NSA","IEEE Computer Society","54th Annual Hawaii International Conference on System Sciences, HICSS 2021","4 January 2021 through 8 January 2021",,169537,15301605,9780998133140,,,"English","Proc. Annu. Hawaii Int. Conf. Syst. Sci.",Editorial,"Final","",Scopus,2-s2.0-85108370447
"Vasylkovskyi V., Guerreiro S., Sequeira J.S.","56784404400;24536769600;7006523955;","Designing and Validating a Blockchain-based Architecture to Enforce Privacy in Human Robot Interaction",2021,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"566","575",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108353955&partnerID=40&md5=851dc69d311e5e2fcc21f5c614a1001e","INESC-ID, Instituto Superior Técnico, University of Lisbon, Portugal; Institute for Systems and Robotics, Instituto Superior Técnico, University of Lisbon, Portugal","Vasylkovskyi, V., INESC-ID, Instituto Superior Técnico, University of Lisbon, Portugal; Guerreiro, S., INESC-ID, Instituto Superior Técnico, University of Lisbon, Portugal; Sequeira, J.S., Institute for Systems and Robotics, Instituto Superior Técnico, University of Lisbon, Portugal","Social Robots (SR) can record large streams of raw data in the form of images, audio, Radio Frequency Identification (RFID), among other sensory sources, which could be instrumental in enforcing Human-robot interactions (HRI). However, the emotional bonds between humans and SR can raise an SR accessing/inferring profoundly private information, e.g., emotional states. Therefore, in what concerns privacy, SR may be a liability. Clarifying the ownership of data collected by robots is an issue of concern in the European Union (EU) General Data Protection Regulation (GDPR), which requires'privacy-by-design'. The paper presents BlockRobot - a Blockchain (BC)-based software design model for developing privacy-by-design application in Human-Robot Interactions (HRI). Based on this design model, users can access the private data generated in their HRI. This paper's contributions are the design and validation of a model to develop privacy-by-design applications for HRI. This model integrates BC technology with robots to audit robotic events such as Red, Green, Blue, Depth (RGB-D) images, and RFID tags. Smart contracts and crypto-wallets accomplish identity management. To demonstrate the utility of the initial implementation of a Decentralized Application (dApp), we implement a prototype, based on the private permissioned Evolution of Scalability (EOS) Blockchain integrated with the social robot moving in a non-laboratory environment. © 2021 IEEE Computer Society. All rights reserved.",,"Application programs; Blockchain; Data streams; Machine design; Man machine systems; Privacy by design; Radio frequency identification (RFID); Software design; Design applications; Emotional state; General data protection regulations; Human robot Interaction (HRI); Identity management; Laboratory environment; Private information; Software design model; Social robots",,,,,"822404; 2020-2023; Fundação para a Ciência e a Tecnologia, FCT: UIDB/50021/2020","The first and second authors state that this work was supported by the European Commission program H2020 under the grant agreement 822404 (project QualiChain) and by national funds through Fundac¸ão para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 (INESC-ID). The third author state that this work was supported by LARSyS – FCT Plurianual funding 2020-2023.","The first and second authors state that this work was supported by the European Commission program H2020 under the grant agreement 822404 (project QualiChain) and by national funds through Funda??o para a Ci?ncia e a Tecnologia (FCT) with reference UIDB/50021/2020 (INESC-ID). The third author state that this work was supported by LARSyS - FCT Plurianual funding 2020-2023.",,,,,,,,,"Cucco, E., Fisher, M., Dennis, L., Dixon, C., Webster, M., Broecker, B., Williams, R., Tuyls, K., (2017) Towards Robots for Social Engagement, , Department of Computer Science, University of Liverpool, UK; Lutz, C., Tamò, A., Privacy and healthcare robots-an ant analysis (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics; Dziergwa, M., Kaczmarek, P., Kedzierski, J., Rgb-d sensors in social robotics (2015) Journal of Automation Mobile Robotics and Intelligent Systems, 9; Hegel, F., Lohse, M., Swadzba, A., Wachsmuth, S., Rohlfing, K., Wrede, B., Classes of applications for social robots: A user study (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 938-943; Alonso-Martín, F., Salichs, M.A., Integration of a voice recognition system in a social robot (2011) Cybernetics and Systems: An International Journal, 42 (4), pp. 215-245; Sequeira, J., Gameiro, D., A probabilistic approach to RFID-based localization for human-robot interaction in social robotics (2017) Electronics, 6 (2), p. 32; Vitale, J., Tonkin, M., Herse, S., Ojha, S., Clark, J., Williams, M.-A., Wang, X., Judge, W., (2018) Be More Transparent and Users Will like You:A Robot Privacy and User Experience Design Experiment; Richert, A., Müller, S., Schröder, S., Jeschke, S., Anthropomorphism in social robotics: Empirical results on human-robot interaction in hybrid production workplaces (2018) AI & SOCIETY, 33 (3), pp. 413-424; Finck, M., Blockchains and data protection in the european union (2018) Eur. 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Annu. Hawaii Int. Conf. Syst. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85108353955
"Ilyas C.M.A., Rehm M., Nasrollahi K., Madadi Y., Moeslund T.B., Seydi V.","57202323833;10039896700;24476795100;57221284030;6507267791;23490316700;","Deep transfer learning in human–robot interaction for cognitive and physical rehabilitation purposes",2021,"Pattern Analysis and Applications",,,,"","",,,"10.1007/s10044-021-00988-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108306861&doi=10.1007%2fs10044-021-00988-8&partnerID=40&md5=2d85d3c3e4bd6e68c422ee0776194bf9","Visual Analysis and Perception (VAP) Lab, Aalborg University, Aalborg, Denmark; Human–Robot Interaction (HRI) Lab, Aalborg University, Aalborg, Denmark; Department of Computer Engineering, Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran; Research Department of Milestone Systems A/S, Copenhagen, Denmark","Ilyas, C.M.A., Visual Analysis and Perception (VAP) Lab, Aalborg University, Aalborg, Denmark, Human–Robot Interaction (HRI) Lab, Aalborg University, Aalborg, Denmark; Rehm, M., Human–Robot Interaction (HRI) Lab, Aalborg University, Aalborg, Denmark; Nasrollahi, K., Visual Analysis and Perception (VAP) Lab, Aalborg University, Aalborg, Denmark, Research Department of Milestone Systems A/S, Copenhagen, Denmark; Madadi, Y., Visual Analysis and Perception (VAP) Lab, Aalborg University, Aalborg, Denmark, Department of Computer Engineering, Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran; Moeslund, T.B., Visual Analysis and Perception (VAP) Lab, Aalborg University, Aalborg, Denmark; Seydi, V., Department of Computer Engineering, Faculty of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran","This paper presents the extraction of the emotional signals from traumatic brain-injured (TBI) patients through the analysis of facial features and implementation of the effective emotion-recognition model through the Pepper robot to assist in the rehabilitation process. The identification of emotional cues from TBI patients is very challenging due to unique and diverse psychological, physiological, and behavioral challenges such as non-cooperation, facial/body paralysis, upper or lower limb impairments, cognitive, motor, and hearing skills inhibition. It is essential to read subtle changes in the emotional cues of TBI patients for effective communication and the development of affect-based systems. To analyze the variations of the emotional signal in TBI patients, a new database is collected in a natural and unconstrained environment from eleven residents of a neurological center in three different modalities, RGB, thermal and depth in three specified scenarios performing physical, cognitive and social communication rehabilitation activities. Due to the lack of labeled data, a deep transfer learning method is applied to efficiently classify emotions. The emotion classification model is tested through closed-field study and installment of a Pepper robot equipped with the trained model. Our deep trained and fine-tuned emotional recognition model composed of CNN-LSTM has improved the performance by 1.47% on MMI, and 4.96% on FER2013 validation data set. In addition, use of temporal information and transfer learning techniques to overcome TBI-data limitations has increased the performance efficacy on challenging dataset of neurologically impaired people. Findings that emerged from the study illustrate the noticeable effectiveness of SoftBank Pepper robot equipped with deep trained emotion recognition model in developing rehabilitation strategies by monitoring the TBI patient’s emotions. This research article presents the technical solution for real therapeutic robot interaction to rehabilitate patients with standard monitoring, assessment, and feedback in the neuro centers. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","Assessment and monitoring; Assistive care; Augmentative and Assistive Technology (AAT); Cognitive, social, and physical therapy; Deep transfer learning; Emotion recognition; Human–robot interaction; Rehabilitation strategies; TBI patients database; Traumatic brain injury (TBI)",,,,,,,,,,,,,,,,,"Adolphs, R., Neural systems for recognizing emotion (2002) Curr Opin Neurobiol, 12 (2), pp. 169-177; Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A., Face recognition using hog-ebgm (2008) Pat Recognit Lett, 29 (10), pp. 1537-1543; Albuquerque, V.H.C., Damaševičius, R., Garcia, N.M., Pinheiro, P.R., (2017) Brain Computer Interface Systems for Neurorobotics: Methods and Applications; Balconi, M., Neuropsychology of facial expressions. the role of consciousness in processing emotional faces (2012) Neuropsychol Trends, 11, pp. 19-40; Barman, A., Chatterjee, A., Bhide, R., Cognitive impairment and rehabilitation strategies after traumatic brain injury (2016) Indian J Psychol Med, 38 (3), pp. 172-181; Bellantonio, M., Haque, M.A., Rodriguez, P., Nasrollahi, K., Telve, T., Escalera, S., Gonzalez, J., Anbarjafari, G., (2017) Spatio-temporal pain recognition in CNN-based super-resolved facial images, pp. 151-162. , Springer International Publishing, Cham; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: a systematic review into effects and effectiveness (2012) J Am Med Dir Assoc, 13 (2), pp. 114-120; Berretti, S., Ben Amor, B., Daoudi, M., del Bimbo, A., 3d facial expression recognition using sift descriptors of automatically detected keypoints (2011) Vis Comput, 27 (11), p. 1021; A deep learning perspective on the origin of facial expressions (2017) Arxiv Preprint Arxiv:170501842; Burgner-Kahrs, J., Rucker, D.C., Choset, H., Continuum robots for medical applications: a survey (2015) IEEE Trans Robot, 31 (6), pp. 1261-1280; Cabibihan, J.J., Javed, H., Ang, M., Aljunied, S.M., Why robots? a survey on the roles and benefits of social robots in the therapy of children with autism (2013) Int J Soc Robot, 5 (4), pp. 593-618; Calvaresi, D., Cesarini, D., Sernani, P., Marinoni, M., Dragoni, A.F., Sturm, A., Exploring the ambient assisted living domain: a systematic review (2017) J Ambient Intell Human Comput, 8 (2), pp. 239-257; Chen, L., Zhou, M., Su, W., Wu, M., She, J., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human–robot interaction (2018) Inform Sci, 428, pp. 49-61; Corneanu, C.A., Simón, M.O., Cohn, J.F., Guerrero, S.E., Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: history, trends, and affect-related applications (2016) IEEE Trans Pattern Anal Mach Intell, 38 (8), pp. 1548-1568; Dhall, A., Goecke, R., Lucey, S., Gedeon, T., Acted facial expressions in the wild database. 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Appl.",Article,"Article in Press","",Scopus,2-s2.0-85108306861
"Stock-Homburg R.","56364282100;","Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00778-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107511010&doi=10.1007%2fs12369-021-00778-6&partnerID=40&md5=a832415e614bf48c0259fade200a457f","Professor for Marketing and HR, Technical University Darmstadt, Hochschulstr. 1, Darmstadt, 64289, Germany","Stock-Homburg, R., Professor for Marketing and HR, Technical University Darmstadt, Hochschulstr. 1, Darmstadt, 64289, Germany","Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators. © 2021, The Author(s).","Artificial emotions; Emotions; Human–robot interaction (HRI); Review; Social robots; Survey","Agricultural robots; Robotics; Artificial emotions; Human recognition; Interdisciplinary fields; Knowledge production; Research domains; Robot interactions; Robotic psychology; Systematic Review; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG","This research was funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft), Zentrum für verantwortungsbewusste Digitalisierung (ZEVEDI), and the leap in time foundation.",,,,,,,,,,"Abd Latif, M.H., Yusof, H.M., Sidek, S., Rusli, N., Thermal imaging based affective state recognition (2015) 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). 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(2019) IEEE Robot Automa Lett, 5 (1), pp. 127-134; Zhu, C., Ahmad, W., Emotion recognition from speech to improve human–robot interaction (2019) 2019 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (Dasc/Picom/Cbdcom/Cyberscitech). IEEE, pp. 370-375; Zimmerman, D.W., Comparative power of student t test and Mann–Whitney u test for unequal sample sizes and variances (1987) J Exp Educ, 55 (3), pp. 171-174","Stock-Homburg, R.; Professor for Marketing and HR, Hochschulstr. 1, Germany; email: rsh@bwl.tu-darmstadt.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85107511010
"Otaran A., Tokatli O., Patoglu V.","57190182363;36667722400;19640643500;","Physical Human-Robot Interaction Using HandsOn-SEA: An Educational Robotic Platform with Series Elastic Actuation",2021,"IEEE Transactions on Haptics",,,,"","",,,"10.1109/TOH.2021.3081982","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107195500&doi=10.1109%2fTOH.2021.3081982&partnerID=40&md5=b22332c78f7d7052a9f7d69a401d6d19","Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: ataotaran@sabanciuniv.edu); Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: otokatli@sabanciuniv.edu); Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: vpatoglu@sabanciuniv.edu)","Otaran, A., Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: ataotaran@sabanciuniv.edu); Tokatli, O., Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: otokatli@sabanciuniv.edu); Patoglu, V., Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: vpatoglu@sabanciuniv.edu)","For gaining proficiency in physical human-robot interactions, it is crucial for engineering students to be provided with the opportunity to gain hands-on experience with robotic devices that feature kinesthetic feedback. We propose HandsOn-SEA, a low-cost, single degree-of-freedom, force-controlled educational robot with series elastic actuation and introduce educational modules for the use of the device to allow students to experience the fundamental performance trade-offs inherent in robotic systems. The novelty of the proposed robot is due to the deliberate introduction of a compliant joint between the actuator and the handle, whose deflections are measured to perform closed-loop force control. As an admittance-type robot, HandsOn-SEA relies on force feedback to achieve the desired level of safety and transparency and complements the existing impedance-type educational robots. We present the integration of HandsOn-SEA into the robotics curriculum, by providing guidelines for its use in a senior-level robotics course, to help students experience the challenges involved in the synergistic design and control of robotic devices. We systematically evaluate the efficacy of the device in a robotics course delivered for five semesters and provide evidence that HandsOn-SEA is effective in instilling fundamental concepts and trade-offs in the design and control of robotic devices. IEEE","Educational Robotics; Educational robots; Force; Haptic interfaces; Interaction Control; Performance evaluation; Physical Human-Robot Interaction (pHRI); Robot sensing systems; Robots; Robustness; Series Elastic Actuation (SEA)","Biofeedback; Commerce; Costs; Curricula; Degrees of freedom (mechanics); Economic and social effects; Feedback; Human robot interaction; Man machine systems; Robotics; Students; Universal joints; Educational robotics; Fundamental concepts; Kinesthetic feedback; Performance trade-off; Physical human-robot interactions; Robotics curriculums; Series elastic actuations; Single degree of freedoms; Educational robots",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,19391412,,,,"English","IEEE Trans. Haptic",Article,"Article in Press","",Scopus,2-s2.0-85107195500
"Rifinski D., Erel H., Feiner A., Hoffman G., Zuckerman O.","57223749451;36945086000;57223747092;35726707500;10042089800;","Human-human-robot interaction: robotic object’s responsive gestures improve interpersonal evaluation in human interaction",2021,"Human-Computer Interaction","36","4",,"333","359",,5,"10.1080/07370024.2020.1719839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106074012&doi=10.1080%2f07370024.2020.1719839&partnerID=40&md5=96c0e2d1d5f7eeac65fa588c5769b70d","Interdisciplinary Center (IDC) Herzliya, School of Communications, Herzliya, Israel; Cornell University, Sibley School of Mechanical and Aerospace Engineering, Ithaca, NY, United States","Rifinski, D., Interdisciplinary Center (IDC) Herzliya, School of Communications, Herzliya, Israel; Erel, H., Interdisciplinary Center (IDC) Herzliya, School of Communications, Herzliya, Israel; Feiner, A., Interdisciplinary Center (IDC) Herzliya, School of Communications, Herzliya, Israel; Hoffman, G., Cornell University, Sibley School of Mechanical and Aerospace Engineering, Ithaca, NY, United States; Zuckerman, O., Interdisciplinary Center (IDC) Herzliya, School of Communications, Herzliya, Israel",[No abstract available],"Collaboration Tools; Conversation Quality; Human-Human-Robot Interaction; Human-Robot Interaction; Interpersonal Evaluation; Social Robots; Ubicomp",,,,,,"Ministry of Science, Technology and Space: 54178","This research was supported by the Israel Ministry of Science, Technology and Space (Grant #54178). 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"Gao Q., Chen Y., Ju Z., Liang Y.","57219899863;36571325400;25655056800;57223431842;","Dynamic Hand Gesture Recognition Based on 3D Hand Pose Estimation for Human-Robot Interaction",2021,"IEEE Sensors Journal",,,,"","",,1,"10.1109/JSEN.2021.3059685","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105875311&doi=10.1109%2fJSEN.2021.3059685&partnerID=40&md5=e7ec80492b9a9c81ae2fc5e566301a9e","Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China. (e-mail: yqchen@cuhk.edu.cn); School of Computing, University of Portsmouth, Portsmouth, PO1 3HE U.K.; Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.","Gao, Q., Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.; Chen, Y., Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China. (e-mail: yqchen@cuhk.edu.cn); Ju, Z., School of Computing, University of Portsmouth, Portsmouth, PO1 3HE U.K.; Liang, Y., Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.","Dynamic hand gesture recognition is a challenging problem in the area of hand-based human-robot interaction (HRI), such as issues of a complex environment and dynamic perception. In the context of this problem, we learn from the principle of the data-glove-based hand gesture recognition method and propose a dynamic hand gesture recognition method based on 3D hand pose estimation. This method uses 3D hand pose estimation, data fusion and deep neural network to improve the recognition accuracy of dynamic hand gestures. First, a 2D hand pose estimation method based on OpenPose is improved to obtain a fast 3D hand pose estimation method. Second, the weighted sum fusion method is utilized to combine the RGB, depth and 3D skeleton data of hand gestures. Finally, a 3DCNN + ConvLSTM framework is used to identify and classify the combined dynamic hand gesture data. In the experiment, the proposed method is verified on a developed dynamic hand gesture database for HRI and gets 92.4% accuracy. Comparative experiment results verify the reliability and efficiency of the proposed method. IEEE","Data gloves; Databases; dynamic hand gestures; Feature extraction; Gesture recognition; hand pose estimation; neural network; Pose estimation; Skeleton; Three-dimensional displays","Data fusion; Deep neural networks; Man machine systems; Palmprint recognition; Social robots; 3D hand pose estimations; Comparative experiments; Complex environments; Fusion methods; Hand pose estimations; Hand-gesture recognition; Human robot Interaction (HRI); Recognition accuracy; Gesture recognition",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Article in Press","",Scopus,2-s2.0-85105875311
"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","Gazeemd: Detecting visual intention in gaze-based human-robot interaction",2021,"Robotics","10","2","68","","",,1,"10.3390/robotics10020068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105740422&doi=10.3390%2frobotics10020068&partnerID=40&md5=8219ff5e8e6576d302ee3a13cee677fb","Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium; G.Z. 324, Campus Groenenborger, Groenenborgerlaan 171, Antwerp, 2020, Belgium","Shi, L., Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium, G.Z. 324, Campus Groenenborger, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Copot, C., Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium; Vanlanduit, S., Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium","In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario. © 2021 by the authors. 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"Cousin C., Duenas V., Dixon W.","57193090588;57193098845;57202484614;","Fes cycling and closed-loop feedback control for rehabilitative human–robot interaction",2021,"Robotics","10","2","61","","",,,"10.3390/robotics10020061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105733512&doi=10.3390%2frobotics10020061&partnerID=40&md5=db311b84c101c2e2f7ec987853f20004","Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States; Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY  13244, United States; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  32611, United States","Cousin, C., Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States; Duenas, V., Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY  13244, United States; Dixon, W., Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  32611, United States","For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Tradeoffs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Adaptive control; Cycling; FES; Human–robot interaction; NMES; Nonlinear control; Rehabilitation; Robotics",,,,,,"National Science Foundation, NSF: 1762829","Funding: This research is supported in part by NSF award number 1762829. 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"Fronemann N., Pollmann K., Loh W.","56422460900;57128090300;57202956487;","Should my robot know what's best for me? Human–robot interaction between user experience and ethical design",2021,"AI and Society",,,,"","",,2,"10.1007/s00146-021-01210-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105397718&doi=10.1007%2fs00146-021-01210-3&partnerID=40&md5=367cd13a158ac0c800d247019dca9b68","Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Int. Center for Ethics in the Sciences and Humanities IZEW, University of Tuebingen, Wilhelmstr. 19, Tuebingen, 72074, Germany","Fronemann, N., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Pollmann, K., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Loh, W., Int. Center for Ethics in the Sciences and Humanities IZEW, University of Tuebingen, Wilhelmstr. 19, Tuebingen, 72074, Germany","To integrate social robots in real-life contexts, it is crucial that they are accepted by the users. Acceptance is not only related to the functionality of the robot but also strongly depends on how the user experiences the interaction. Established design principles from usability and user experience research can be applied to the realm of human–robot interaction, to design robot behavior for the comfort and well-being of the user. Focusing the design on these aspects alone, however, comes with certain ethical challenges, especially regarding the user’s privacy and autonomy. Based on an example scenario of human–robot interaction in elder care, this paper discusses how established design principles can be used in social robotic design. It then juxtaposes these with ethical considerations such as privacy and user autonomy. Combining user experience and ethical perspectives, we propose adjustments to the original design principles and canvass our own design recommendations for a positive and ethically acceptable social human–robot interaction design. 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"Burema D.","57198492127;","A critical analysis of the representations of older adults in the field of human–robot interaction",2021,"AI and Society",,,,"","",,2,"10.1007/s00146-021-01205-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105386171&doi=10.1007%2fs00146-021-01205-0&partnerID=40&md5=db97c3b2b2a7faf943dfbc4e069fa098","Bremen International Graduate School of Social Sciences (BIGSSS), University of Bremen, Jacobs University, Bremen, Germany","Burema, D., Bremen International Graduate School of Social Sciences (BIGSSS), University of Bremen, Jacobs University, Bremen, Germany","This paper argues that there is a need to critically assess bias in the representations of older adults in the field of Human–Robot Interaction. This need stems from the recognition that technology development is a socially constructed process that has the potential to reinforce problematic understandings of older adults. Based on a qualitative content analysis of 96 academic publications, this paper indicates that older adults are represented as; frail by default, independent by effort; silent and technologically illiterate; burdensome; and problematic for society. Within these documents, few counternarratives are present that do not take such essentialist representations. In these texts, the goal of social robots in elder care is to “enable” older adults to “better” themselves. The older body is seen as “fixable” with social robots, reinforcing an ageist and neoliberal narrative: older adults are reduced to potential care receivers in ways that shift care responsibilities away from the welfare state onto the individual. © 2021, The Author(s).","Ageism; Elder care; Human–robot interaction; Neoliberalism; Older adults; User representations","Economic and social effects; Academic publications; Content analysis; Critical analysis; Elder care; Older adults; Robot interactions; Technology development; Welfare state; Social robots",,,,,"713639; Horizon 2020","Open Access funding enabled and organized by Projekt DEAL. This work was supported by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 713639.",,,,,,,,,,"Abbey, R., Hyde, S., No country for older people? 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"Weiss A., Spiel K.","23089739100;56266706100;","Robots beyond Science Fiction: mutual learning in human–robot interaction on the way to participatory approaches",2021,"AI and Society",,,,"","",,1,"10.1007/s00146-021-01209-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105377875&doi=10.1007%2fs00146-021-01209-w&partnerID=40&md5=5455915adf17d271939ed25e7cd64f23","Human-Computer Interaction (HCI) Group, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien, Argentinierstr. 8, Wien, 1040, Austria","Weiss, A., Human-Computer Interaction (HCI) Group, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien, Argentinierstr. 8, Wien, 1040, Austria; Spiel, K., Human-Computer Interaction (HCI) Group, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien, Argentinierstr. 8, Wien, 1040, Austria","Putting laypeople in an active role as direct expert contributors in the design of service robots becomes more and more prominent in the research fields of human–robot interaction (HRI) and social robotics (SR). Currently, though, HRI is caught in a dilemma of how to create meaningful service robots for human social environments, combining expectations shaped by popular media with technology readiness. We recapitulate traditional stakeholder involvement, including two cases in which new intelligent robots were conceptualized and realized for close interaction with humans. Thereby, we show how the robot narrative (impacted by science fiction, the term robot itself, and assumptions on human-like intelligence) together with aspects of power balancing stakeholders, such as hardware constraints and missing perspectives beyond primary users, and the adaptivity of robots through machine learning that creates unpredictability, pose specific challenges for participatory design processes in HRI. We conclude with thoughts on a way forward for the HRI community in developing a culture of participation that considers humans when conceptualizing, building, and using robots. © 2021, The Author(s).","Human–robot interaction; Methodology; Participatory design; Service robots","Balancing; Intelligent robots; Machine design; Mobile robots; Hardware constraints; Human-like intelligence; Participatory approach; Participatory design; Robot interactions; Social environment; Stakeholder involvement; Technology readiness; Social robots",,,,,"Austrian Science Fund, FWF: V587-G29","We greatly acknowledge the financial support from the Austrian Science Foundation (FWF) under Grant Agreement No. V587-G29 (SharedSpace) and T 1146-G (Exceptional Norms).",,,,,,,,,,"Alissandrakis, A., Syrdal’r, D.S., Miyake, Y., Helping robots imitate acknowledgmentof, and adaptation to, the robot’s feedback to a human task demonstration (2011) New Front Hum Robot Interact, 2, pp. 9-34; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot Auton Syst, 57 (5), pp. 469-483; Azenkot, S., Feng, C., Cakmak, M., Enabling building service robots to guide blind people a participatory design approach (2016) In 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 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"Frijns H.A., Schürer O., Koeszegi S.T.","57192806605;53875402300;8705799600;","Communication Models in Human–Robot Interaction: An Asymmetric MODel of ALterity in Human–Robot Interaction (AMODAL-HRI)",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00785-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105371803&doi=10.1007%2fs12369-021-00785-7&partnerID=40&md5=9248bda8b57da68973de2acdfe43a316","TU Wien, Institute of Management Science, Theresianumgasse 27, Vienna, Austria; TU Wien, Department of Architecture Theory, Karlsplatz 13, Vienna, Austria","Frijns, H.A., TU Wien, Institute of Management Science, Theresianumgasse 27, Vienna, Austria; Schürer, O., TU Wien, Department of Architecture Theory, Karlsplatz 13, Vienna, Austria; Koeszegi, S.T., TU Wien, Institute of Management Science, Theresianumgasse 27, Vienna, Austria","We argue for an interdisciplinary approach that connects existing models and theories in Human–Robot Interaction (HRI) to traditions in communication theory. In this article, we review existing models of interpersonal communication and interaction models that have been applied and developed in the contexts of HRI and social robotics. We argue that often, symmetric models are proposed in which the human and robot agents are depicted as having similar ways of functioning (similar capabilities, components, processes). However, we argue that models of human–robot interaction or communication should be asymmetric instead. We propose an asymmetric interaction model called AMODAL-HRI (an Asymmetric MODel of ALterity in Human–Robot Interaction). This model is based on theory on joint action, common robot architectures and cognitive architectures, and Kincaid’s model of communication. On the basis of this model, we discuss key differences between humans and robots that influence human expectations regarding interacting with robots, and identify design implications. © 2021, The Author(s).","Communication models; Communication theory; Human–Machine communication; Human–Robot interaction; Interaction design","Agricultural robots; Information theory; Machine design; Asymmetric interaction; Asymmetric models; Cognitive architectures; Communication models; Design implications; Interpersonal communication and interaction; Robot architecture; Robot interactions; Social robots",,,,,"Technische Universität Wien","This work was supported by Doctoral College TrustRobots, Technische Universität Wien. We would like to thank all anonymous reviewers, Michael Koller, and Guglielmo Papagni for their feedback on previous versions of this paper.",,,,,,,,,,"Alač, M., Social robots: things or agents? 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"Wang X., Sun J., Gao Z., Zhao L., Liu J., He N.","55982882200;57223094231;8264349600;36916248900;57218452315;57223088917;","Cartesian Admittance Control Based on Maxwell Model for Human Robot Interaction",2021,"Advances in Intelligent Systems and Computing","1305 AISC",,,"451","458",,,"10.1007/978-981-33-4575-1_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104793955&doi=10.1007%2f978-981-33-4575-1_43&partnerID=40&md5=28e698631e13db613e87ac7d63f98444","Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; XCMG Construction Machinery, Xuzhou, China","Wang, X., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Sun, J., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Gao, Z., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Zhao, L., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Liu, J., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; He, N., XCMG Construction Machinery, Xuzhou, China","Classical impedance control methods use Voigt model to describe relationship between external force and displacement. It is a kind of viscoelastic model shows elastic mechanical properties. This feature always generates a repulsive force because spring plays a major role and make user uncomfortable. By contrast, Maxwell model shows plastic mechanical properties and it shows the plastic properties. This characteristic will improve the comfortableness of human robot interaction. Therefore, we present a Maxwell model-based Cartesian admittance control that uses input of position loop to the manipulator. We compared two methods by experiment and result shows that our method can provide a good comfortableness in the case of having same effect of traditional Cartesian admittance control. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Admittance control; Human robot interaction; Industrial robot","Man machine systems; Process engineering; Viscoelasticity; Admittance control; Elastic mechanical properties; Impedance control methods; Maxwell models; Plastic property; Position loops; Repulsive forces; Viscoelastic modeling; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 2020-MS-032, 61903357; China Postdoctoral Science Foundation: 2020M672600; Department of Education of Liaoning Province: LNJC201912","This work was supported by the National key R&D plan tasks 2018YFF0300304-04, National Natural Science Foundation of China under contact 61903357, Scientific research project of Liaoning Provincial Department of Education LNJC201912, Liaoning Provincial Natural Science Foundation of China2020-MS-032 and China Postdoctoral Science Foundation2020M672600.","Acknowledgements This work was supported by the National key R&D plan tasks 2018YFF0300304-04, National Natural Science Foundation of China under contact 61903357, Scientific research project of Liaoning Provincial Department of Education LNJC201912, Liaoning Provincial Natural Science Foundation of China 2020-MS-032 and China Postdoctoral Science Foundation 2020M672600.",,,,,,,,,"Dhillon, B.S., (2012) Robot Reliability and Safety, , Springer Science & Business Media, Berlin; Goodrich, M.A., Schultz, A.C., (2008) Human-Robot Interaction: A Survey, , Now Publishers Inc; Qiao, H., Dalay, B.S., Parkin, R.M., Precise robotic chamferless peg-hole insertion operation without force feedback and remote centre compliance (RCC) (1994) J. 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Rheol., 10 (1), pp. 191-213; Ehlers, W., Markert, B., On the viscoelastic behaviour of fluid saturated porous materials (2000) Granular Matter, 2 (3), pp. 153-161; Fu, L., Zhao, J., Design and implementation of plastic deformation behavior by Cartesian impedance control based on Maxwell model (2018) Complexity, 2018, pp. 1-9; Fu, L., Zhao, J., Maxwell model-based compliance control for human robot friendly interaction (2020) IEEE Trans. Cogn. Dev. Syst.; Liu, G., Han, B., Li, Q., DOREP: An educational experiment platform for robot control based on MATLAB and the real-time controller (2019) International Conference on Intelligent Robotics and Applications, pp. 555-565. , , pp. , Springer, Shenyang","Gao, Z.; Faculty of Information and Control Engineering, China; email: gzj@sjzu.edu.cn","Li Y.Zhu Q.Qiao F.Fan Z.Chen Y.",,"Springer Science and Business Media Deutschland GmbH","2nd International Symposium on Simulation and Process Modelling, ISSPM 2020","29 August 2020 through 30 August 2020",,257579,21945357,9789813345744,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85104793955
"Wang H., Li X., Zhang X.","57211739989;23028270800;35180693900;","Multimodal Human-robot Interaction on Service Robot",2021,"IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)",,,"9391068","2290","2295",,,"10.1109/IAEAC50856.2021.9391068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104640348&doi=10.1109%2fIAEAC50856.2021.9391068&partnerID=40&md5=a6f6f2c1a8f03a1e7d8dc19bbe15951c","Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China","Wang, H., Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China, Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Li, X., Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China, Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Zhang, X., Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China, Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China","In order to improve the efficiency of multimodal fusion in human-robot interaction (HRI), an improved technique is proposed to synthesize visual and audio data. The robotic auditory system uses a microphone array to obtain auditory information and uses the MUSIC algorithm to determine the azimuth of the sound source, and uses end-to-end gating CNN recognizes speech results; For the visual system, a two-layer neural network system is used to detect and recognize dynamic gestures. An improved D-S evidence theory algorithm based on the rule intention voter is designed to fuse the output results of the two modules for determining intention of the current interactive object. Experimental results validate the efficiency and accuracy of multimodal fusion system. © 2021 IEEE.","audio; human-robot interaction; multimodal fusion; service robot; visual","Efficiency; Man machine systems; Multilayer neural networks; Network layers; Auditory systems; Human robot Interaction (HRI); Improved techniques; Interactive objects; Microphone arrays; Multi-modal fusion; MUSIC algorithms; Neural network systems; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61703012; Natural Science Foundation of Beijing Municipality: 4182010","The research work is financially supported by National Natural Science Foundation of China (No. 61703012), Beijing Natural Science Foundation (No. 4182010).",,,,,,,,,,"Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review[J] (2017) International Journal of Industrial Ergonomics, p. 690; Narain, B.R., Priscilla, D.M.S., (2017) Gesture Command Recognition System for Human Machine Interaction[J]., , Social Science Electronic Publishing; Ahuja, M.K., Singh, A., Static vision based hand gesture recognition using principal component analysis[C] (2016) Ieee International Conference on Moocs.; Xu, D., Wu, X., Chen, Y.L., Online dynamic gesture recognition for human robot interaction[J] (2015) Journal of Intelligent & Robotic Systems; Gao, Q., Liu, J., Ju, Z., (2017) Static Hand Gesture Recognition with Parallel CNNs for Space Human-Robot Interaction[J].; Mi, J., Tang, S., Deng, Z., Object affordance based multimodal fusion for natural human-robot interaction[J] (2019) Cognitive Systems Research, 54, pp. 128-137; Liu, H., Fang, T., Zhou, T., Deep learning-based multimodal control interface for human-robot collaboration[J] (2018) Procedia Cirp on SciVerse ScienceDirect; Schmidt, A., Deleforge, A., Kellermann, W., Ego-noise reduction using a motor data-guided multichannel dictionary[C] (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).; Trick, S., Koert, D., Peters, J., Multimodal uncertainty reduction for intention recognition in human-robot interaction[C] (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).; Schiffer, S., Ferrein, A., Lakemeyer, G., Caesar: An intelligent domestic service robot[J] (2012) Intelligent Service Robotics, 5 (4), pp. 259-273; Rifinski, D., Erel, H., Feiner, A., Human-human-robot interaction: Robotic object's responsive gestures improve interpersonal evaluation in human interaction[J] (2020) Human-Computer Interaction, pp. 1-27; Meng, J., Feng, Z., Xu, T., A method of fusing gesture and speech for human-robot interaction[C] (2020) Proceedings of 2020 the 6th International Conference on Computing and Data Engineering., pp. 265-269; Liu, H., Fang, T., Zhou, T., Towards robust human-robot collaborative manufacturing: Multimodal fusion[J] (2018) Ieee Access, 6, pp. 74762-74771","Wang, H.; Beijing University of Technology, China; email: 19801355105@163.com",,"Chengdu Union Institute of Science and Technology;Chongqing Geeks Education Technology Co., Ltd;Chongqing Global Union Academy of Science and Technology;Global Union Academy of Science and Technology;IEEE Beijing Section",,"5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021","12 March 2021 through 14 March 2021",,168296,26896621,,,,"English","Adv. Inf. Tech. Electron. and Autom. Control Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85104640348
"Park K.-B., Choi S.H., Lee J.Y., Ghasemi Y., Mohammed M., Jeong H.","57199326425;57199324903;57194376603;57222736395;57223423906;57193237154;","Hands-free human-robot interaction using multimodal gestures and deep learning in wearable mixed reality",2021,"IEEE Access","9",,"9395580","55448","55464",,4,"10.1109/ACCESS.2021.3071364","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103894294&doi=10.1109%2fACCESS.2021.3071364&partnerID=40&md5=a3e04951985aca3b3386d50d59f5768e","Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States","Park, K.-B., Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Choi, S.H., Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Lee, J.Y., Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Ghasemi, Y., Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States; Mohammed, M., Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States; Jeong, H., Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States","This study proposes a novel hands-free interaction method using multimodal gestures such as eye gazing and head gestures and deep learning for human-robot interaction (HRI) in mixed reality (MR) environments. Since human operators hold some objects for conducting tasks, there are many constrained situations where they cannot use their hands for HRI interactions. To provide more effective and intuitive task assistance, the proposed hands-free method supports coarse-to-fine interactions. Eye gazing-based interaction is used for coarse interactions such as searching and previewing of target objects, and head gesture interactions are used for fine interactions such as selection and 3D manipulation. In addition, deep learning-based object detection is applied to estimate the initial positioning of physical objects to be manipulated by the robot. The result of object detection is then combined with 3D spatial mapping in the MR environment for supporting accurate initial object positioning. Furthermore, virtual object-based indirect manipulation is proposed to support more intuitive and efficient control of the robot, compared with traditional direct manipulation (e.g., joint-based and end effector-based manipulations). In particular, a digital twin, the synchronized virtual robot of the real robot, is used to provide a preview and simulation of the real robot to manipulate it more effectively and accurately. Two case studies were conducted to confirm the originality and advantages of the proposed hands-free HRI: (1) performance evaluation of initial object positioning and (2) comparative analysis with traditional direct robot manipulations. The deep learning-based initial positioning reduces much effort for robot manipulation using eye gazing and head gestures. The object-based indirect manipulation also supports more effective HRI than previous direct interaction methods. © 2013 IEEE.","Deep learning; Eye gazing; Hands-free interaction; Head gestures; Human-robot interaction; Mixed reality; Object detection","Deep learning; Digital twin; End effectors; Man machine systems; Mixed reality; Object detection; Object recognition; Wearable technology; Comparative analysis; Direct interactions; Direct manipulation; Efficient control; Hands-free interactions; Human robot Interaction (HRI); Physical objects; Robot manipulation; Human robot interaction",,,,,"Ministry of Science, ICT and Future Planning, MSIP; National Research Foundation of Korea, NRF; Institute for Information and Communications Technology Promotion, IITP: 2020-0-01532; Kementerian Pendidikan Malaysia, KPM: 2019R1I1A3A01059082","This work was supported in part by the Republic of Korea’s Ministry of Science and ICT (MSIT) through the High-Potential Individuals Global Training Program supervised by the Institute of Information and Communications Technology Planning and Evaluation (IITP) under Grant 2020-0-01532, and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Education through the Basic Science Research Program under Grant 2019R1I1A3A01059082.",,,,,,,,,,"Matthias, B., Kock, S., Jerregard, H., Kallman, M., Lundberg, I., Safety of collaborative industrial robots: Certification possibilities for a collaborative assembly robot concept (2011) Proc IEEE Int. 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ICCAR, pp. 719-726. , Apr; Chadalavada, R.T., Andreasson, H., Schindler, M., Palm, R., Lilienthal, A.J., Bi-directional navigation intent communication using spatial augmented reality and eye-Tracking glasses for improved safety in human-robot interaction (2020) Robot. Comput.-Integr. Manuf, 61. , Feb, Art 101830; Mohammed, M., Jeong, H., Lee, J.Y., Human-robot collision avoidance scheme for industrial settings based on injury classification Proc. ACM/IEEE HRI, 2021, pp. 549-551. , Mar; Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P., Focal loss for dense object detection (2017) Proc IEEE Int. Conf. Comput. Vis. (ICCV), pp. 2980-2988. , Oct; (2020) Unity3D, , https://unity.com/, [Online]; (2020) Universal Robots, , https://www.universal-robots.com/, [Online]; (2017) COCO Dataset, , https://cocodataset.org/#home/, [Online]; Jiang, G., Luo, M., Bai, K., Chen, S., A precise positioning method for a puncture robot based on a PSO-optimized BP neural network algorithm (2017) Appl. Sci, 7 (10). , Sep, Art. no 969; Kebria, P.M., Al-Wais, S., Abdi, H., Nahavandi, S., Kinematic and dynamic modelling of UR5 manipulator (2016) Proc. SMC, pp. 4229-4234. , Oct; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable undesirable emergent behavior in complex systems (2017) Transdisciplinary Perspectives on Complex Systems, pp. 85-113. , Cham Switzerland Springer Aug; Zhu, M., Recall, precision and average precision (2004) Dept. Statist. Actuarial Sci., Univ. Waterloo, , Waterloo, ON, Canada Working Paper 2004-09; Hart, S.G., Staveland, L.E., Development of nasa-Tlx (task load index): Results of empirical and theoretical research (1988) Adv. Psychol, 52, pp. 139-183. , Apr","Lee, J.Y.; Department of Industrial Engineering, South Korea; email: jaeyeol@chonnam.ac.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103894294
"Hamad Y.M., Aydin Y., Basdogan C.","57222734188;56084865200;6603706568;","Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction",2021,"IEEE Transactions on Haptics",,,,"","",,,"10.1109/TOH.2021.3071626","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103883896&doi=10.1109%2fTOH.2021.3071626&partnerID=40&md5=9beb6c6e18f29513fc8db6c169069b35","College of Engineering, Koc University, Istanbul, None, Turkey, (e-mail: yalqaysi13@ku.edu.tr); College of Engineering, Koc University, Istanbul, None, Turkey, (e-mail: yaydin@ku.edu.tr); College of Engineering, Koc University, Istanbul, None, Turkey, 34450 (e-mail: cbasdogan@ku.edu.tr)","Hamad, Y.M., College of Engineering, Koc University, Istanbul, None, Turkey, (e-mail: yalqaysi13@ku.edu.tr); Aydin, Y., College of Engineering, Koc University, Istanbul, None, Turkey, (e-mail: yaydin@ku.edu.tr); Basdogan, C., College of Engineering, Koc University, Istanbul, None, Turkey, 34450 (e-mail: cbasdogan@ku.edu.tr)","The goal of this study is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance. IEEE","adaptive force amplification; admittance control; collaborative manipulation; Fitts&#x0027; task; human intention; Physical human-robot interaction","Fuzzy logic; Machine design; Admittance control; Force amplification; Manipulated objects; Manipulation task; Movement intentions; Physical human-robot interactions; Quantitative values; Reaching movements; Human robot interaction",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,19391412,,,,"English","IEEE Trans. Haptic",Article,"Article in Press","",Scopus,2-s2.0-85103883896
"Kowalski C., Gliesche P., Fifelski-Von Böhlen C., Brinkmann A., Hein A.","57209347501;57209345908;57220090569;57215314169;56529792300;","Handling comparison between a human and a patient simulator for nursing care related physical human-robot interaction",2021,"HEALTHINF 2021 - 14th International Conference on Health Informatics; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021",,,,"605","612",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103837354&partnerID=40&md5=22f69b7d67b3cdeb645b31263e208bb3","OFFIS Institute for Information Technology, Oldenburg, 26121, Germany; Carl von Ossietzky University, Oldenburg, 26129, Germany","Kowalski, C., OFFIS Institute for Information Technology, Oldenburg, 26121, Germany; Gliesche, P., OFFIS Institute for Information Technology, Oldenburg, 26121, Germany; Fifelski-Von Böhlen, C., Carl von Ossietzky University, Oldenburg, 26129, Germany; Brinkmann, A., Carl von Ossietzky University, Oldenburg, 26129, Germany; Hein, A., OFFIS Institute for Information Technology, Oldenburg, 26121, Germany, Carl von Ossietzky University, Oldenburg, 26129, Germany","The occurrence of musculoskeletal diseases among nursing staff leads to an early withdrawal from the profession, which reinforces the already existing lack of caregivers. To counteract this problem, we would like to provide physical relief through robotic assistance at the bedside. However, the problem arises that for safety reasons robotic assistance concepts should not be tested on humans at first. In this case, patient simulators with an average person’s weight can function as a substitute. For the best results, both cases should behave very similarly when evaluating robot assistance concepts so that the transfer from patient simulator to human is small and therefore no major adjustments need to be made. To measure this potential difference, we have compared the handling properties of both cases in this paper. We examined force measuring platform data while a nurse mobilized an 80 kg human and a patient simulator from the back to the side. The experimental results show that moving a patient simulator is more physically demanding compared to moving a human with similar weight and that conventional collaborative lightweight robots are able to push and move a patient simulator that is weighing far higher than the robot’s actual payload suggests. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved","Human-robot Interaction; Nursing Care; Physical Relief","Biomedical engineering; Medical informatics; Nursing; Robotics; Simulators; Handling properties; Musculoskeletal disease; Nursing care; Nursing staff; Patient simulators; Physical human-robot interactions; Potential difference; Robot assistance; Human robot interaction",,,,,"16SV7819K; Bundesministerium für Bildung und Forschung, BMBF","This work was funded by the German Ministry for Education and Research (BMBF) within the research project Nursing Care Innovation Center (grant 16SV7819K).",,,,,,,,,,"Akhtar, R., Sherratt, M. J., Cruickshank, J. K., Derby, B., Characterizing the elastic properties of tissues (2011) Materials Today, 14 (3), pp. 96-105; Byravan, A., Fox, D., Se3-nets: Learning rigid body motion using deep neural networks (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 173-180. , IEEE; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Dogar, M., Srinivasa, S., A framework for push-grasping in clutter (2011) Robotics: Science and systems VII, 1; Ehrentraut, O., Hackmann, T., Krämer, L., Schmutz, S., (2015) Zukunft der Pflegepolitik: Perspektiven, Handlungsoptionen und Politikempfehlungen, , Friedrich-Ebert-Stiftung, Abteilung Wirtschafts-und Sozialpolitik; Ehrhardt, S., Monszpart, A., Mitra, N. J., Vedaldi, A., (2017) Learning a physical long-term predictor, , arXiv preprint arXiv:1703.00247; Erickson, Z., Clever, H. M., Gangaram, V., Turk, G., Liu, C. 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A., Perceived physical demands and reported musculoskeletal problems in registered nurses (2003) American journal of preventive medicine, 24 (3), pp. 270-275; Zhu, S., Kimmel, A., Boularias, A., (2017) Information-theoretic model identification and policy search using physics engines with application to robotic manipulation, , arXiv preprint arXiv:1703.07822",,"Pesquita C.Fred A.Gamboa H.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","14th International Conference on Health Informatics, HEALTHINF 2021 - Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021","11 February 2021 through 13 February 2021",,167533,,9789897584909,,,"English","HEALTHINF - Int. Conf. Health Informatics; Part Int. Jt. Conf. Biomed. Eng. Syst. Technol., BIOSTEC",Conference Paper,"Final","",Scopus,2-s2.0-85103837354
"Chen L., Liu Z., Wu M., Hirota K., Pedrycz W.","55774663200;56101506800;57218664297;7402829702;57210775415;","Multimodal Emotion Recognition and Intention Understanding in Human-Robot Interaction",2021,"Studies in Systems, Decision and Control","329",,,"255","288",,,"10.1007/978-3-030-62147-6_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103558783&doi=10.1007%2f978-3-030-62147-6_10&partnerID=40&md5=388a213cc8557172e5558f82ffac7747","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  TR6 2G7, Canada","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Liu, Z., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  TR6 2G7, Canada","Emotion recognition and intention understanding are important components of human-robot interaction. In multimodal emotion recognition and intent understanding, feature extraction and selection of recognition methods are related to the calculation of affective computing and the diversity of human-robot interaction. Therefore, by studying multimodal emotion recognition and intention understanding we to create an emotional and human-friendly human-robot interaction environment. This chapter introduces the characteristics of multimodal emotion recognition and intention understanding, presents different modalities emotion feature extraction methods and emotion recognition methods, proposes the intention understanding method, and finally applies them in practice to achieve human-robot interaction. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Human-robot interaction; Intention understanding; Multimodal emotion recognition",,,,,,,,,,,,,,,,,"Mehrabian, A., Communication without words (1968) Psychol. 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Syst., 45 (1), pp. 51-61; Wang, J.N., Xiong, R., Chu, J., Facial feature points detecting based on Gaussian mixture models (2015) Pattern Recognit. Lett., 53, pp. 62-68; Asteriadis, S., Nikolaidis, N., Pitas, I., Facial feature detection using distance vector fields (2009) Pattern Recognit, 42 (7), pp. 1388-1398; Chen, L.F., Liu, Z.T., Wu, M., Ding, M., Dong, F.Y., Hirota, K., Emotion-age-gender-nationality based intention understanding in human-robot interaction using two-layer fuzzy support vector regression (2015) Int. J. Soc. Robot., 7 (5), pp. 709-729; Lin, K.P., A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (2014) IEEE Trans. Fuzzy Syst., 22 (5), pp. 1074-1087; Nguyen, D.D., Ngo, L.T., Multiple kernel interval type-2 fuzzy c-means clustering (2013) IEEE International Conference on Fuzzy Systems, Hyderabad, India, pp. 1-8. , , pp; Dong, W.M., Wong, F.S., Fuzzy weighted averages and implementation of the extension principle (1987) Fuzzy Sets Syst, 21 (2), pp. 183-199","Chen, L.; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsChina; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,21984182,,,,"English","Stud. Syst. Decis. Control",Book Chapter,"Final","",Scopus,2-s2.0-85103558783
"Tao H., Rahman M.A., Jing W., Li Y., Li J., Al-Saffar A., Zhang R., Salih S.Q.","36350315600;55457938100;55546532300;55719081300;56769465100;57201725434;57208779192;57203978808;","Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems",2021,"Work","68","3",,"903","912",,,"10.3233/WOR-203424","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103516005&doi=10.3233%2fWOR-203424&partnerID=40&md5=998725ee816d2ae9c0e031899bcd5b85","School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Business School, Lanzhou City University, Lanzhou, China; School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam","Tao, H., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Rahman, M.A., Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Jing, W., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Li, Y., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Li, J., Business School, Lanzhou City University, Lanzhou, China; Al-Saffar, A., Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Zhang, R., School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Salih, S.Q., Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam","BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection. RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs. CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses. © 2021 - IOS Press. All rights reserved.","input mapping; interaction response; Linear processing; recognition; robotic systems","article; extraction; human; learning; robotics; Humans; Learning; Robotics",,,,,"YB111; 2019GY-131; 2018F-33; Baoji University of Arts And Sciences, BUAS: ZK2018062; Education Department of Shaanxi Province: 19JK0040","In this manuscript, interaction modeling and classification scheme is introduced for improving the response accuracy of HRI systems. The fetched This work is supported by Shaanxi Provincial Department of Education Scientific Research Project (19JK0040), the doctoral scientific research initial funding project of Baoji University of Arts and Sciences (ZK2018062), the Key Research and Development Program in Shaanxi Province (2019GY-131), Gansu social science planning program in 2017 (YB111) and Strategic Project of Colleges and Universities in Gansu Province (2018F-33).",,,,,,,,,,"Cao, H.L., Esteban, P.G., Simut, R., Van De Perre, G., Lefeber, D., Vanderborght, B., A collaborative homeostatic-based behavior controller for social robots in human-robot interaction experiments (2017) International Journal of Social Robotics., 9 (5), pp. 675-690; Johannsmeier, L., Haddadin, S., A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes (2016) Ieee Robotics and Automation Letters., 2 (1), pp. 41-48; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence., 247, pp. 45-69; 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Malik, A.A., Bilberg, A., Developing a reference model for human-robot interaction (2019) International Journal on Interactive Design and Manufacturing (IJIDeM)., pp. 1-7; Gao, X., Zhou, C., Chao, F., Yang, L., Lin, C.M., Shang, C., A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions (2019) Ieee Access., 7, pp. 144043-144053; Wang, L., Du, Z., Dong, W., Shen, Y., Zhao, G., Hierarchical human machine interaction learning for a lower extremity augmentation device (2019) International Journal of Social Robotics., 11 (1), pp. 123-139; Wang, Z., Lou, X., Yu, Z., Guo, B., Zhou, X., Enabling noninvasive and real-time human-machine interactions based on wireless sensing and fog computing (2019) Personal and Ubiquitous Computing., 23 (1), pp. 29-41; Dröder, K., Bobka, P., Germann, T., Gabriel, F., Dietrich, F., A machine learning-enhanced digital twin approach for human-robot-collaboration (2018) Procedia CIRP., 76, pp. 187-192; Segreto, T., Teti, R., Machine learning for in-process endpoint detection in robot-assisted polishing using multiple sensor monitoring (2019) The International Journal of Advanced Manufacturing Technology., pp. 1-5; Zarkowski, M., Multi-party turn-taking in repeated human-robot interactions: An interdisciplinary evaluation (2019) International Journal of Social Robotics., pp. 1-5; Mi, J., Tang, S., Deng, Z., Goerner, M., Zhang, J., Object affordance based multi-modal fusion for natural Human-Robot interaction (2019) Cognitive Systems Research., 54, pp. 128-137","Li, J.; Business School, China; email: jinglilzu@163.com",,,"IOS Press BV",,,,,10519815,,WORKF,"33720867","English","Work",Article,"Final","",Scopus,2-s2.0-85103516005
"Jørgensen J., Bojesen K.B., Jochum E.","57199227765;57200601656;57074708900;","Is a Soft Robot More “Natural”? Exploring the Perception of Soft Robotics in Human–Robot Interaction",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00761-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103100092&doi=10.1007%2fs12369-021-00761-1&partnerID=40&md5=e46191f6b05fb33e0617747332a77ba4","Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark; Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Nordstjernevej 29-67, Glostrup, 2600, Denmark; Research Laboratory for Art and Technology, Department of Communication and Psychology, Aalborg University, Rendsburggade 14, Aalborg, 9000, Denmark","Jørgensen, J., Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark; Bojesen, K.B., Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Nordstjernevej 29-67, Glostrup, 2600, Denmark; Jochum, E., Research Laboratory for Art and Technology, Department of Communication and Psychology, Aalborg University, Rendsburggade 14, Aalborg, 9000, Denmark","Soft robotics technology has been proposed for a number of applications that involve human–robot interaction. It is commonly presumed that soft robots are perceived as more natural, and thus more appealing, than rigid robots, an assumption that has not hitherto been tested or validated. This study investigates human perception of and physical interaction with soft robots as compared with rigid robots. Using a mixed-methods approach, we conducted an observational study to explore whether soft robots are perceived as more natural, and what types of interactions soft robots encourage. In a between-subjects study, participants interacted with a soft robotic tentacle or a rigid robot of a similar shape. The interactions were video recorded, and data was also obtained from questionnaires (Nvideo = 123, Nquest = 94). Despite their drastically different appearances and materials, we found no significant differences in how appealing or natural the robots were rated to be. Appeal was positively associated with perceived naturalness in all cases, however we observed a wide variation in how participants define “natural”. Although participants showed no clear preference, qualitative analysis of video data indicated that soft robots and rigid robots elicit different interaction patterns and behaviors. The findings highlight the key role of physical embodiment and materiality in human–robot interaction, and challenge existing assumptions about what makes robots appear natural. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Embodiment; Haptic interaction; Human factors; Human–robot interaction; Naturalness; Soft robotics","Agricultural robots; Social robots; Surveys; Human perception; Interaction pattern; Mixed method; Observational study; Physical interactions; Qualitative analysis; Robot interactions; Soft robotics; Robotics",,,,,,,,,,,,,,,,"Pfeifer, R., Lungarella, M., Iida, F., The challenges ahead for bio-inspired “soft” robotics (2012) Commun ACM, 55, pp. 76-87; Majidi, C., Soft robotics: a perspective—current trends and prospects for the future (2013) Soft Robot, 1, pp. 5-11; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521, pp. 467-475; Verl, A., Albu-Schäffer, A., Brock, O., Raatz, A., (2015) Soft robotics: transferring theory to application, , Springer, Berlin; Soft Robotics Inc. 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J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85103100092
"Zafar Z., Ashok A., Berns K.","56462691100;57222516272;7006119626;","Personality traits assessment using p.A.D. Emotional space in human-robot interaction",2021,"VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","2",,,"111","118",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102974518&partnerID=40&md5=579d9d515e2e418c556711a934135b57","Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany","Zafar, Z., Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany; Ashok, A., Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany; Berns, K., Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany","Cognitive social robotics is the field of research that is committed to building social robots that facilitate to draw parallels with human beings. Humans assess the behavior and personality of their counterparts to adapt their behavior and show empathy to flourish human-human interaction. Similarly, assessment of human personality is highly critical in realizing natural and intelligent human-robot interaction. Numerous personality traits assessment systems have been reported in the literature; however, most of them target the big five personality traits. From only visual information, this work proposes to use pleasure, arousal, and dominance emotional space for the assessment of personality traits based on the work of Mehrabian. To validate the system, three different scenarios have been developed to assess 12 different personality traits on a social humanoid robot. Experimental results show that the system can assess human personality traits with 84% accuracy in real-time and, hence, it can adapt its behavior according to the perceived personality of the interaction partner. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Human-robot interaction; PAD emotional space; Personality trait assessment; Robot adaptivity","Anthropomorphic robots; Computer graphics; Computer vision; Man machine systems; Assessment system; Human being; Human-human interactions; Humanoid robot; Personality traits; Real time; Social robotics; Visual information; Social robots",,,,,,,,,,,,,,,,"Al-Darraji, S., Berns, K., Rodić, A., Action unit based facial expression recognition using deep learning (2017) Advances in Robot Design and Intelligent Control, pp. 413-420. , Rodić, A. and Borangiu, T., editors, pages Cham. 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Stud, 4, p. 57; Mehrabian, A., Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament (1996) Current Psychology, 14 (4), pp. 261-292; Nass, C., Lee, K. M., Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency-attraction (2001) Journal of experimental psychology: applied, 7 (3), p. 171; Rodić, A., Urukalo, D., Vujović, M., Spasojević, S., Tomić, M., Berns, K., Al-Darraji, S., Zafar, Z., Embodiment of human personality with ei-robots by mapping behaviour traits from live-model (2016) International Conference on Robotics in Alpe-Adria Danube Region, pp. 438-448. , Springer; Russell, J. A., A circumplex model of affect (1980) Journal of personality and social psychology, 39 (6), p. 1161; Russell, J. A., Mehrabian, A., Evidence for a three-factor theory of emotions (1977) Journal of research in Personality, 11 (3), pp. 273-294; Salam, H., Celiktutan, O., Hupont, I., Gunes, H., Chetouani, M., Fully automatic analysis of engagement and its relationship to personality in human-robot interactions (2016) IEEE Access, 5, pp. 705-721; Saleh, S., Berns, K., Nonverbal communication with a humanoid robot via head gestures (2015) Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 15. , page ACM; Watson, D., Clark, L. A., Measurement and mismeasurement of mood: Recurrent and emergent issues (1997) Journal of personality assessment, 68 (2), pp. 267-296; Watson, D., Tellegen, A., Toward a consensual structure of mood (1985) Psychological bulletin, 98 (2), p. 219; Wundt, W. M., Judd, C. H., (1897) Outlines of psychology, 1. , Scholarly Press; Zafar, Z., Paplu, S. H., Berns, K., Automatic assessment of human personality traits: A step towards intelligent human-robot interaction (2018) 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1-9. , pages IEEE; Zafar, Z., Venugopal, R., Berns, K., Real-time recognition of human postures for human-robot interaction (2018) 11th International Conference on Advances in Computer-Human Interactions (ACHI), pp. 29-35. , (2018b) pages",,"Paljic A.Peck T.Braz J.Bouatouch K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021","8 February 2021 through 10 February 2021",,167535,,9789897584886,,,"English","VISIGRAPP - Proc. Int. Jt. Conf. Comput. Vis., Imaging Comput. Graph. Theory Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85102974518
"Sørensen A.R., Palinko O., Krüger N.","57222516289;35099082400;7004442773;","Classification of visual interest based on gaze and facial features for human-robot interaction",2021,"VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","2",,,"198","204",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102972571&partnerID=40&md5=6e7f4f3455e7633ac85f0a764aacb424","The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark","Sørensen, A.R., The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark; Palinko, O., The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark; Krüger, N., The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark","It is important for a social robot to know if a nearby human is showing interest in interacting with it. We approximate this interest with expressed visual interest. To find it, we train a number of classifiers with previously labeled data. The input features for these are facial features like head orientation, eye gaze and facial action units, which are provided by the OpenFace library. As training data, we use video footage collected during an in-the-wild human-robot interaction scenario, where a social robot was approaching people at a cafeteria to serve them water. The most successful classifier that we trained tested at a 94% accuracy for detecting interest on an unrelated testing dataset. This allows us to create an effective tool for our social robot, which enables it to start talking to people only when it is fairly certain that the addressed persons are interested in talking to it. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Classification; Gaze; Human-robot interaction; Visual interest","Classification (of information); Computer graphics; Computer vision; Educational robots; End effectors; Man machine systems; Statistical tests; Effective tool; Eye-gaze; Facial action; Facial feature; Input features; Training data; Video footage; Visual interest; Social robots",,,,,,"This work has been supported by InnovationsFonden Danmark in the context of the Project “Seamless huMan-robot interactiOn fOr THe support of elderly people” (SMOOTH).",,,,,,,,,,"Argyle, M., (1972) Non-verbal communication in human social interaction; Mavridis, N., A review of verbal and non-verbal human–robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35; Juel, W. K., Haarslev, F., Ramírez, E. R., Marchetti, E., Fischer, K., Shaikh, D., Krüger, N., SMOOTH Robot: Design for a novel modular welfare robot (2020) Journal of Intelligent & Robotic Systems, 98 (1), pp. 19-37; Palinko, O., Fischer, K., Ruiz Ramirez, E., Damsgaard Nissen, L., Langedijk, R. M., A Drink-Serving Mobile Social Robot Selects who to Interact with Using Gaze (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 384-385. , (March); Baltrušaitis, T., Robinson, P., Morency, L. P., Openface: an open source facial behavior analysis toolkit (2016) 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1-10. , (March) IEEE; Hjortsjö, C. H., Man's face and mimic language (1969), Studentlitteratur; Argyle, M., Cook, M., (1976) Gaze and mutual gaze; Palinko, O., Rea, F., Sandini, G., Sciutti, A., Eye gaze tracking for a humanoid robot (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 318-324. , (November) IEEE; Munoz-Salinas, R., Aguirre, E., García-Silvente, M., González, A., A fuzzy system for visual detection of interest in human-robot interaction (2005) 2nd International Conference on Machine Intelligence (ACIDCA-ICMI’2005), pp. 574-581; Palinko, O., Rea, F., Sandini, G., Sciutti, A., Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5048-5054. , (October) IEEE; Zhang, Y., (2010) New advances in machine learning, , (Ed). BoD–Books on Demand; Ekman, R., (2012) What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS)",,"Paljic A.Peck T.Braz J.Bouatouch K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021","8 February 2021 through 10 February 2021",,167535,,9789897584886,,,"English","VISIGRAPP - Proc. Int. Jt. Conf. Comput. Vis., Imaging Comput. Graph. Theory Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85102972571
"Benn C., Grastien A.","56191930100;8925681900;","Reducing moral ambiguity in partially observed human–robot interactions",2021,"Advanced Robotics","35","9",,"537","552",,,"10.1080/01691864.2021.1894233","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102709642&doi=10.1080%2f01691864.2021.1894233&partnerID=40&md5=239b0742b930ac43d927d770139feac4","Humanising Machine Intelligence Grand Challenge, The Australian National University, Canberra, Australia","Benn, C., Humanising Machine Intelligence Grand Challenge, The Australian National University, Canberra, Australia; Grastien, A., Humanising Machine Intelligence Grand Challenge, The Australian National University, Canberra, Australia","Human–robot interactions are increasingly taking place between a robot agent and a human observer who, unable to witness all aspects of the robot's behaviour, is uncertain as to how the robot will behave. This uncertainty has serious consequences when it concerns the normative aspects of machine behaviour: that is, whether the robot's actions are morally permissible. Bringing together distinct threads from robotic design and machine ethics, we demonstrate the importance of conveying ethical understanding and commitment in order to reduce moral ambiguity and how this can demand a behavioural demonstration. We provide a framework that structures these considerations in the form of a broad constraint on robot behaviour: roughly, to avoid behaviour, even if it is permissible, if that behaviour could appear impermissible. Thus, we formalise a model of communicative-behavioural ethics in human–robot interactions. We apply this constraint to a series of example cases demonstrating how it can be modified to incorporate different sources of information including preferences and probabilities. This reveals the complexity of less idealised cases and highlights how the constraint can be fine-tuned along a number of dimensions to take into account, amongst other thing, risk attitudes. © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","Communication; ethics; human–robot interactions; signalling; uncertainty","Agricultural robots; Behavioral research; Philosophical aspects; Human observers; Risk attitude; Robot interactions; Robotic design; Sources of informations; Human robot interaction",,,,,,,,,,,,,,,,"Floridi, L., Sanders, J.W., On the morality of artificial agents (2004) Minds Machines, 14 (3), pp. 349-379; Moor, J.H., The nature, importance, and difficulty of machine ethics (2006) IEEE Intell Syst, 21 (4), pp. 18-21; Anderson, M., Anderson, S.L., Machine ethics: creating an ethical intelligent agent (2007) Ai Magazine, 28 (4), p. 15; Wallach, W., Asaro, P., (2017) Machine ethics and robot ethics, , London: Routledge; Chen, M., Nikolaidis, S., Soh, H., et al. Planning with trust for human-robot collaboration. In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction; 2018. p. 307–315; Ramírez, M., Geffner, H., Plan recognition as planning. In: 21st International Joint Conference on Artificial Intelligence (ijcai-09); 2009. p. 1778–1783; Keren, S., Gal, A., Karpas, E., Goal recognition design. In: 24th International Conference on Automated Planning and Scheduling (icaps-14); 2014. p. 154–162; Jéron, T., Marchand, H., Pinchinat, S., et al. Supervision patterns discrete-event systems diagis. In: Seventeenth International Workshop on Principles of Diagis (dx-06); 2006. p. 117–124; Sampath, M., Sengupta, R., Lafortune, S., Diagnosability of discrete-event systems (1995) IEEE Trans Automat Control (TAC), 40 (9), pp. 1555-1575. , et al; Brandán Briones, L., Lazovik, A., Dague, P., Optimal observability for diagability. In: Nineteenth International Workshop on Principles of Diagis (dx-08); 2008. p. 31–38; Lin, F., Opacity of discrete event systems and its applications (2011) Automatica, 47 (3), pp. 496-503; Dragan, A., Srinivasa, S., Integrating human observer inferences into robot motion planning (2014) Auton Robots, 37 (4), pp. 351-368; Nikolaidis, S., Dragan, A., Srinivasa, S., Viewpoint-based legibility optimization. In: Proceedings of Human-Robot Interaction; 2016 March; Chakraborti, T., Kulkarni, A., Sreedharan, S., et al. Explicability? Legibility? Predictability? Transparency? Privacy? Security? The emerging landscape of interpretable agent behavior. In: 29th International Conference on Automated Planning and Scheduling (icaps-19); 2019. p. 86–95; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2014) Foundations and Trends in Robotics, 5 (4), pp. 261-349. , https://doi.org/DOI:10.1561/2300000052; Gopalan, N., Tellex, S., Modeling and solving human-robot collaborative tasks using pomdps. In: Rss Workshop on Model Learning for Human-Robot Communication; 2015; Park, J.S., Park, C., Manocha, D., Intention-aware motion planning using learning based human motion prediction (2017) In: Robot: Sci Syst; Allen, C., Varner, G., Zinser, J., Prolegomena to any future artificial moral agent (2000) J Exp Theor Artif Intell, 12 (3), pp. 251-261; Hooker, J.N., Kim, T.W.N., Toward non-intuition-based machine and artificial intelligence ethics: A deontological approach based on modal logic. 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In: Proceedings of the AAAI Conference on Artificial Intelligence; 33. 2019. p. 7635–7642; Powers, T.M., Prospects for a kantian machine (2006) IEEE Intell Syst, 21 (4), pp. 46-51; Talbot, B., Jenkins, R., Purves, D., When robots should do the wrong thing (2017) Rob Ethics, 2, pp. 258-273; Scharre, P., Horowitz, M., An introduction to autonomy in weapon systems (2015) Cent New Am Secur Ethical Autonomy Proj, pp. 1-23; Bentham, J., (1970) An introduction to the principles of morals and legislation, , Garden City: Doubleday,. originally published 1789; Mill, J.S., (1998) Utilitarianism, , New York: Oxford University Press,. edited with an introduction by Roger Crisp, originally published 1861; Sidgwick, H., (1907) The methods of ethics, , London: Macmillan,. seventh edition, first edition, 1874; Wallach, W., Allen, C., (2008) Moral machines: teaching robots right from wrong, , New York: Oxford University Press; Soh, H., Xie, Y., Chen, M., Multi-task trust transfer for human–robot interaction (2020) Int J Rob Res, 39 (2-3), pp. 233-249. , et al; Kelley, J.F., An iterative design methodology for user-friendly natural language office information applications (1984) ACM Trans Inf Syst (TOIS), 2 (1), pp. 26-41; Wagner, A.R., Robinette, P., Howard, A., Modeling the human-robot trust phenomenon: A conceptual framework based on risk (2018) ACM Trans Interact Intell Syst (TiiS), 8 (4), pp. 1-24; Sanneman, L., Shah, J.A., Trust considerations for explainable robots: A human factors perspective. arXiv preprint. 2020; Lee, J.D., See, K.A., Trust in automation: designing for appropriate reliance (2004) Hum Factors, 46 (1), pp. 50-80; Yang, X.J., Unhelkar, V.V., Li, K., et al. Evaluating effects of user experience and system transparency on trust automation. In: 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE. 2017. p. 408–416; Edmonds, M., Gao, F., Liu, H., A tale of two explanations: enhancing human trust by explaining robot behavior (2019) Sci Rob, 4 (37). , et al; Kauppinen, S., Brain, C., Moore, M., European medium-term conflict detection field trials [atc]. In: Proceedings. the 21st Digital Avionics Systems Conference. IEEE; 1. 2002. p. 2C1–2C1; Li, D., Rau, P.P., Li, Y., A cross-cultural study: effect of robot appearance and task (2010) Int J Soc Rob, 2 (2), pp. 175-186; O'Neill, O., Linking trust to trustworthiness (2018) Int J Philos Stud, 26 (2), pp. 293-300; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion. In: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE; 2013. p. 301–308","Benn, C.; Humanising Machine Intelligence Grand Challenge, Australia; email: cmabenn@gmail.com",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85102709642
"Sirithunge C., Jayasekara A.G.B.P., Chandima D.P.","57205389590;35090187000;55453866000;","An Evaluation of Human Conversational Preferences in Social Human-Robot Interaction",2021,"Applied Bionics and Biomechanics","2021",,"3648479","","",,,"10.1155/2021/3648479","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102254856&doi=10.1155%2f2021%2f3648479&partnerID=40&md5=a314800c755004167f975b5d021c41b1","Intelligent Service Robotics Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka","Sirithunge, C., Intelligent Service Robotics Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka; Jayasekara, A.G.B.P., Intelligent Service Robotics Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka; Chandima, D.P., Intelligent Service Robotics Group, Department of Electrical Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka","To generate context-aware behaviors in robots, robots are required to have a careful evaluation of its encounters with humans. Unwrapping emotional hints in observable cues in an encounter will improve a robot's etiquettes in a social encounter. This article presents an extended human study conducted to examine how several factors in an encounter influence a person's preferences upon an interaction at a particular moment. We analyzed the nature of conversation preferred by a user considering the type of conversation a robot could have with its user, having the interaction initiated by the robot itself. We took an effort to explore how such preferences differ as the factors present in the surrounding alter. A social robot equipped with the capability to initiate a conversation is deployed to conduct the study by means of a wizard-of-oz (WoZ) experiment. During this study, conversational preferences of users could vary from ""no interaction at all""to a ""long conversation.""We changed three factors in an encounter which can be different from each other in each circumstance: the audience or outsiders in the environment, user's task, and the domestic area in which the interaction takes place. Conversational preferences of users within the abovementioned conditions were analyzed in a later stage, and critical observations are highlighted. Finally, implications that could be helpful in shaping future social human-robot encounters were derived from the analysis of the results. © 2021 Chapa Sirithunge et al.",,"Biophysics; Medicine; Context-Aware; Human robots; Human study; IMPROVE-A; Social human-robot interactions; Wizard of Oz; Social robots; article; conversation; human; human experiment; robotics",,,,,,,,,,,,,,,,"Sakamoto, D., Sugiura, Y., Inami, M., Igarashi, T., Graphical instruction for home robots (2016) Computer, 49 (7), pp. 20-25. , 2-s2.0-84978149850; Doughty, B., (2016) Roller Brush for Surface Cleaning Robots, , uS Patent 9, 326, 654; Orejana, J.R., Macdonald, B.A., Ahn, H.S., Peri, K., Broadbent, E., Healthcare robots in homes of rural older adults (2015) International Conference on Social Robotics, pp. 512-521. , Springer; Rabbitt, S.M., Kazdin, A.E., Scassellati, B., Integrating socially assistive robotics into mental healthcare interventions: Applications and recommendations for expanded use (2015) Clinical Psychology Review, 35, pp. 35-46. , 2-s2.0-84911912070; Fridin, M., Belokopytov, M., Acceptance of socially assistive humanoid robot by preschool and elementary school teachers (2014) Computers in Human Behavior, 33, pp. 23-31. , 2-s2.0-84892764625; Yorita, A., Kubota, N., Cognitive development in partner robots for information support to elderly people (2011) IEEE Transactions on Autonomous Mental Development, 3 (1), pp. 64-73. , 2-s2.0-79952938203; Randell, R., Greenhalgh, J., Hindmarsh, J., Dowding, D., Jayne, D., Pearman, A., Gardner, P., Kotze, A., Integration of robotic surgery into routine practice and impacts on communication, collaboration, and decision making: A realist process evaluation protocol (2014) Implementation Science, 9 (1), p. 52. , 2-s2.0-84900030121; Ray, C., Mondada, F., Siegwart, R., What Do People Expect from Robots?, pp. 3816-3821. , 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems September 2008 Nice, France 2-s2.0-69549083461; Strohkorb, S., Scassellati, B., Promoting Collaboration with Social Robots, pp. 639-640. , 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) March 2016 Christchurch, New Zealand 2-s2.0-84964835077; Hoffman, G., Breazeal, C., Robotic Partners Bodies and Minds: An Embodied Approach to Fluid Human-Robot Collaboration, , AAAI Conference on Cognitive Robotics 2006; Heerink, M., Vanderborght, B., Broekens, J., Albó-Canals, J., New friends: Social robots in therapy and education (2016) International Journal of Social Robotics, 8 (4), pp. 443-444. , 2-s2.0-84985905492; Robins, B., Dautenhahn, K., Boekhorst, R.T., Billard, A., Robotic assistants in therapy and education of children with autism: Can a small humanoid robot help encourage social interaction skills? (2005) Universal Access in the Information Society, 4 (2), pp. 105-120. , 2-s2.0-31044439549; Nicolescu, M.N., Mataric, M.J., Learning and interacting in human-robot domains (2001) IEEE Transactions on Systems, Man, and Cybernetics-part A: Systems and Humans, 31 (5), pp. 419-430. , 2-s2.0-0035438933; Traeger, M.L., Strohkorb Sebo, S., Jung, M., Scassellati, B., Christakis, N.A., Vulnerable robots positively shape human conversational dynamics in a human-robot team (2020) Proceedings of the National Academy of Sciences of the United States of America, 117 (12), pp. 6370-6375. , 32152118; Short, E.S., Deng, E.C., Feil-Seifer, D., Matarić, M.J., Understanding agency in interactions between ChildrenWith autism and socially assistive robots (2017) Journal of Human-Robot Interaction, 6 (3), pp. 21-47; Ozeki, T., Mouri, T., Sugiura, H., Yano, Y., Miyosawa, K., Use of communication robots to converse with people suffering from schizophrenia (2020) ROBOMECH Journal, 7 (1); Tiberio, L., Cesta, A., Olivetti Belardinelli, M., Psychophysiological methods to evaluate users response in human robot interaction: A review and feasibility study (2013) Robotics, 2 (2), pp. 92-121. , 2-s2.0-85041957368; De Graaf, M.M.A., Allouch, S.B., Klamer, T., Sharing a life with Harvey: Exploring the acceptance of and relationship- building with a social robot (2015) Computers in Human Behavior, 43, pp. 1-14. , 2-s2.0-84910626313; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1480), pp. 679-704. , 2-s2.0-34447343105 17301026; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 143-166. , 2-s2.0-0037475135; Kidd, C.D., Breazeal, C., Robots at Home: Understanding Long-term Human-robot Interaction, pp. 3230-3235. , 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems September 2008 Nice, France 2-s2.0-69549106560; Arkin, R.C., Fujita, M., Takagi, T., Hasegawa, R., An ethological and emotional basis for human-robot interaction (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 191-201. , 2-s2.0-0037475126; Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H., Hagita, N., Footing in human-robot conversations: How robots might shape participant roles using gaze cues (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction - HRI '09, pp. 61-68. , La Jolla, CA, USA; Salichs, M.A., Encinar, I.P., Salichs, E., Castro-González, Á., Malfaz, M., Study of scenarios and technical requirements of a social assistive robot for alzheimers disease patients and their caregivers (2016) International Journal of Social Robotics, 8 (1), pp. 85-102. , 2-s2.0-84957599050; Law, E., Cai, V., Liu, Q.F., Sasy, S., Goh, J., Blidaru, A., Kulic, D., A Wizard-of-oz Study of Curiosity in Human-robot Interaction, pp. 607-614. , 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) August 2017 Lisbon, Portugal 2-s2.0-85045856541; Hebesberger, D., Koertner, T., Gisinger, C., Pripfl, J., A long-term autonomous robot at a care hospital: A mixed methods study on social acceptance and experiences of staff and older adults (2017) International Journal of Social Robotics, 9 (3), pp. 417-429. , 2-s2.0-85021651185; Miehle, J., Bagci, I., Minker, W., Ultes, S., (2019) A Social Companion and Conversational Partner for the Elderly, , Cham Springer International Publishing 2-s2.0-85051790159; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., An affective guide robot in a shopping mall (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction - HRI '09, pp. 173-180. , La Jolla, CA, USA 2-s2.0-67650671560; Iwamura, Y., Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets? (2011) Proceedings of the 6th International Conference on Human-robot Interaction - HRI '11, pp. 449-457. , EPFL, Lausanne, Switzerland 2-s2.0-79953152344; Sirithunge, H.P.C., Muthugala, M.A.V.J., Jayasekara, A.G.B.P., Chandima, D.P., Interpretation of Interaction Demanding of A User Based on Nonverbal Behavior in A Domestic Environment, pp. 1-8. , 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) July 2017 Naples, Italy 2-s2.0-85030149489; Riek, L.D., Wizard of oz studies in hri: A systematic review and new reporting guidelines (2012) Journal of Human-Robot Interaction, 1 (1); Klemmer, S.R., Sinha, A.K., Chen, J., Landay, J.A., Aboobaker, N., Wang, A., Suede: A wizard of oz prototyping tool for speech user interfaces (2000) Proceedings of the 13th Annual ACM Symposium on User Interface Software and Technology - UIST '00, pp. 1-10. , San Diego, CA, USA; Sidner, C.L., Lee, C., Lesh, N., The Role of Dialog in Human Robot Interaction, , International Workshop on Language Understanding and Agents for Real World Interaction 2003 Hokkaido University, Japan; Sirithunge, C., Jayasekara, A.B.P., Chandima, D.P., Proactive robots with the perception of nonverbal human behavior: A review (2019) IEEE Access, 7, pp. 77308-77327. , 2-s2.0-85068250359; Ajzen, I., From intentions to actions: A theory of planned behavior (1985) Action Control, pp. 11-39. , Springer; Sheppard, B.H., Hartwick, J., Warshaw, P.R., The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research (1988) Journal of Consumer Research, 15 (3), pp. 325-343; Ajzen, I., Perceived behavioral control, self-efficacy, locus of control, and the theory of planned Behavior1 (2002) Journal of Applied Social Psychology, 32 (4), pp. 665-683. , 2-s2.0-0035999319; Sirithunge, H.P.C., Muthugala, M.A.V.J., Jayasekara, A.G.B.P., Chandima, D.P., A Wizard of Oz Study of Human Interest towards Robot Initiated Human-robot Interaction, pp. 515-521. , 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) August 2018 Nanjing, China 2-s2.0-85058093379","Sirithunge, C.; Intelligent Service Robotics Group, Sri Lanka; email: hpcschapa@gmail.com",,,"Hindawi Limited",,,,,11762322,,,,"English","Appl. Bionics Biomech.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102254856
"Roda-Sanchez L., Olivares T., Garrido-Hidalgo C., De La Vara J.L., Fernandez-Caballero A.","57202112308;6602909884;57193328014;24400984100;6602230534;","Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system",2021,"Integrated Computer-Aided Engineering","28","2",,"159","175",,,"10.3233/ICA-200637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102170520&doi=10.3233%2fICA-200637&partnerID=40&md5=8d6200fa1484006a1b9cda830646c3fb","Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain; Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","Roda-Sanchez, L., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain; Olivares, T., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain, Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Garrido-Hidalgo, C., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain; De La Vara, J.L., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain, Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Fernandez-Caballero, A., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain, Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","In the context of fast-growing digitization of industrial environments, Industry 4.0 aims to improve key elements to achieve more efficient processes, flexibility in customizing products and reduction in energy consumption, among other objectives. This paper presents a system that exploits the Internet of Things (IoT), massive data computation, and human-robot collaboration to reach these goals. The described system combines technological and human-centered aspects to enhance human-robot interaction. In fact, the human factor cannot be left aside when technological advances affecting society are foreseen. The proposal has been tested on a gesture control system that enables a natural interaction with a robotic arm through the use of IoT-oriented inertial measurement unit devices. These devices capture the movements of both human's arms. Experiments of a technical nature have been run to measure accuracy and latency. In addition, human-centered tests have been conducted with real users to determine the level of intuitiveness and acceptance of the proposed gesture control. The results obtained demonstrate that the proposal meets the demands in terms of real-time, success rate, flexibility and scalability, which are fundamental requirements in Industry 4.0. The usability results have enabled drawing useful conclusions on the use of such human-robot interaction systems. © 2021 - IOS Press. All rights reserved.","gesture control; human-robot interaction; Industry 4.0; Internet of Things; wearables; wireless communications","Acceptance tests; Control systems; Energy efficiency; Energy utilization; Industrial robots; Industry 4.0; Internet of things; Man machine systems; Efficient process; Human-robot collaboration; Industrial environments; Inertial measurement unit; Internet of thing (IOT); Natural interactions; Reduction in energy consumption; Technological advances; Social robots",,,,,"RYC-2017-1053 22836; Universidad de Castilla-La Mancha, UCLM; Horizon 2020: DIN2018-010177, PCI2020-112001; European Regional Development Fund, FEDER; Agencia Estatal de Investigación, AEI; Electronic Components and Systems for European Leadership, ECSEL: 876659, 876852; University of the East, UE: PID2020-115220RBC21, RTI2018-098156-B-C52","This work was partially supported by Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal 1045 de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) under PID2020-115220RBC21 and RTI2018-098156-B-C52 projects. The work leading to this paper has also received funding from iRel40 and VALU3S projects. iRel40 and VALU3S are European co-funded innovation projects that have been granted by the ECSEL Joint Undertaking (JU) under grant agreements No 876659 and 876852, respectively. The funding of the projects comes from the Horizon 2020 research programme and participating countries. National funding is provided by Germany, including the Free States of Saxony and Thuringia, Austria, Belgium, Finland, France, Italy, the Netherlands, Slovakia, Spain, Sweden, and Turkey. Therefore, this work has received Spanish co-funding under PCI2020-112240 and PCI2020-112001 grants, respectively. Luis Roda-Sanchez is pursuing his Industrial PhD under DIN2018-010177 fellowship from Ministerio de Ciencia e Innovacion. Jose Luis de la Vara holds a ?Ramon y Cajal? fellowship (RYC-2017-1053 22836) from Ministerio de Ciencia e Innovacion. Celia Garrido-Hidalgo holds 2019-PREDUCLM-10703 fellowship from FEDER, UE at Universidad de Castilla-La Mancha.",,,,,,,,,,"Famaey, J., Flexible multimodal sub-gigahertz communication for heterogeneous internet of things applications (2018) IEEE Communications Magazine, 56 (7), pp. 146-153; Sabri, L., Bouznad, S., Rama Fiorini, S., Chibani, A., Prestes, E., Amirat, Y., An integrated semantic framework for designing context-aware internet of robotic things systems (2018) Integrated Computer-Aided Engineering, 25 (2), pp. 137-156; Garrido-Hidalgo, C., Olivares, T., Ramirez, F.J., Roda-Sanchez, L., An end-to-end internet of things solution for reverse supply chain management in Industry 4. 0 (2019) Computers in Industry, 112, pp. 103127-103140; Boyes, H., Hallaq, B., Cunningham, J., Watson, T., The industrial internet of things (IIoT): An analysis framework (2018) Computers in Industry, 101, pp. 1-12; Lei, Q., Zhang, H., Yang, Y., He, Y., Bai, Y., Liu, S., An investigation of applications of hand gestures recognition in industrial robots (2019) International Journal of Mechanical Engineering and Robotic, 8 (5); 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Prakash, P., Dhanasekaran, C., Surya, K., Varunny Pius, K., Vishal, A.S., Vignesh Kumar, S., Gesture controlled dual six axis robotic arms with rover using MPU (2020) Materials Today: Proceedings, 21, pp. 547-556; Dajun, Z., Minghui, S., Fei, C., Chih-Min, L., Longzhi, Y., Changjing, S., Changle, Z., Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller (2018) Neurocomputing, 282, pp. 218-231; Li, Y., Yang, N., Li, L., Liu, L., Yang, Y., Finger gesture recognition using a smartwatch with integrated motion sensors (2018) Integrated Computer-Aided Engineering, 16 (2), pp. 123-129; Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human behavior and hand gesture classification for smart human-robot interaction (2017) Procedia Manufacturing, 11, pp. 91-98; Wu, H., Yang, L., Fu, S., Zhang, X., Beyond remote control: Exploring natural gesture inputs for smart TV systems (2019) Integrated Computer-Aided Engineering, 11 (4), pp. 335-354; 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Almagro, M., Fresno, V., De La Paz, F., Speech gestural interpretation by applying word representations in robotics (2019) Integrated Computer-Aided Engineering, 26 (1), pp. 97-109; Daw-Tung, L., De-Cheng, P., Integrating a mixed-feature model and multiclass support vector machine for facial expression recognition (2009) Integrated Computer-Aided Engineering, 16 (1), pp. 61-74; Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., Ruiz, M.C., OperaBLE: An IoT-based wearable to improve efficiency and smart worker care services in Industry 4. 0 (2018) Journal of Sensors, 2018, p. 6272793; Garrido-Hidalgo, C., Hortelano, D., Roda-Sanchez, L., Olivares, T., Ruiz, M.C., López, V., IoT heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable Industry 4. 0 (2018) IEEE Access, 6, pp. 28417-28437; Martinez-Gomez, J., Fernández-Caballero, A., Garcia-Varea, I., Rodriguez, L., Romero-Gonzalez, C., A taxonomy of vision systems for ground mobile robots (2014) International Journal of Advanced Robotic Systems, 11, p. 111; Gascueña, J.M., Fernández-Caballero, A., Agent-oriented modeling and development of a person-following mobile robot (2011) Expert Systems with Applications, 38 (4), pp. 4280-4290; Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B., Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4. 0 (2019) Information Fusion, 50, pp. 92-111; Kiruba, K., Shiloah, E.D., Sunil, R.R.C., Hexagonal volume local binary pattern (H-VLBP) with deep stacked autoencoder for human action recognition (2019) Cognitive Systems Research, 58, pp. 71-93; Mehta, D., Rhodin, H., Casas, D., Fua, P., Sotnychenko, O., Xu, W., Theobalt, C., Monocular 3D human pose estimation in the wild using improved CNN supervision (2017) 2017 International Conference on 3D Vision, pp. 506-516; Tsarouchi, P., Athanasatos, A., Makris, S., Chatzigeorgiou, X., Chryssolouris, G., High level robot programming using body and hand gestures (2016) Procedia CIRP, 55, pp. 1-5; Mueller, F., Davis, M., Bernard, F., Sotnychenko, O., Verschoor, M., Otaduy, M.A., Casas, D., Theobalt, C., Real-time pose and shape reconstruction of two interacting hands with a single depth camera (2019) ACM Transactions on Graphics, 38 (4); Devine, S., Rafferty, K., Ferguson, S., Real time robotic arm control using hand gestures with multiple end effectors (2016) UKACC International Conference on Control; Erdogan, K., Durdu, A., Yilmaz, N., Intention recognition using leap motion controller and Artificial Neural Networks (2016) International Conference on Control, Decision and Information Technologies, pp. 689-693; Kruusamäe, K., Pryor, M., High-precision telerobot with human-centered variable perspective and scalable gestural interface (2016) 9th International Conference on Human System Interactions, pp. 190-196; Sokolova, M.V., Serrano-Cuerda, J., Castillo, J.C., Fernández-Caballero, A., A fuzzy model for human fall detection in infrared video (2013) Journal of Intelligent & Fuzzy Systems, 24 (2), pp. 215-228; Bachmann, D., Weichert, F., Rinkenauer, G., Review of threedimensional human-computer interaction with focus on the leap motion controller (2018) Sensors, 18, p. 2194; Naveenkumar, M., Domnic, S., Deep ensemble network using distance maps and body part features for skeleton based action recognition (2020) Pattern Recognition, 100, p. 107125; 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(eds); Hortelano, D., Olivares, T., Ruiz, M.C., Garrido-Hidalgo, C., López, V., From sensor networks to internet of things. Bluetooth low energy, a standard for this evolution (2017) Sensors, 17 (2), p. 372; Gascueña, J.M., Navarro, E., Fernández-Sotos, P., Fernández-Caballero, A., Pavón, J., IDK and ICARO to develop multiagent systems in support of ambient intelligence (2015) Journal of Intelligent and Fuzzy Systems, 28 (1), pp. 3-15; Rivas-Casado, A., Martinez-Tomás, R., Fernández-Caballero, A., Multi agent system for knowledge based event recognition and composition (2011) Expert Systems, 28 (5), pp. 488-501; Alavi, S., Arsenault, D., Whitehead, A., Quaternion-based gesture recognition using wireless wearable motion capture sensors (2016) Sensors, 16, p. 605; Georgi, M., Amma, C., Schultz, T., Recognizing hand and finger gestures with IMU based motion and EMG based muscle activity sensing (2015) International Joint Conference on Biomedical Engineering Systems and Technologies, 4, pp. 99-108; Rondón, R., Gidlund, M., Landernäs, K., Evaluating bluetooth low energy suitability for time-critical industrial IoT applications (2017) International Journal of Wireless Information Networks, 24, pp. 278-290; (2020) PubNub: How Fast Is Realtime? Human Perception and Technology, , https://www.pubnub.com/blog/how-fast-is-realtimehuman-perception-and-technology/, PubNub Realtime Applications. Last accessed 24 Jan; Felser, M., Real-time ethernet-industry prospective (2005) Proceedings of the IEEE, 93, pp. 1118-1129; (2020) BQ: 3D Printing BQ, , https://www.bq.com/en/witbox-2Witbox2, Last accessed 24 Jan; (2018) IBM: MQTT Vs HTTP by IBM, , https://www.ibm.com/developerworks/IBMdeveloper, Last accessed 15 Nov; Lund, A., Measuring usability with the USE questionnaire (2001) Usability and User Experience Newsletter of the STC Usability SIG, 8","Roda-Sanchez, L.; Instituto de Investigacion en Informatica de Albacete (I3A)Spain; email: Luis.Roda@uclm.es",,,"IOS Press BV",,,,,10692509,,ICAEE,,"English","Integr. Comput. Aided Eng.",Article,"Final","",Scopus,2-s2.0-85102170520
"Zhong H., Li X., Gao L., Li C.","57208718405;56021323400;56406738100;8583249500;","Toward Safe Human-Robot Interaction: A Fast-Response Admittance Control Method for Series Elastic Actuator",2021,"IEEE Transactions on Automation Science and Engineering",,,,"","",,1,"10.1109/TASE.2021.3057883","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101772343&doi=10.1109%2fTASE.2021.3057883&partnerID=40&md5=d2196e8af820c8c7daa61ee8788c5493","State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: gaoliang@mail.hust.edu.cn); State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China.","Zhong, H., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; Li, X., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; Gao, L., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: gaoliang@mail.hust.edu.cn); Li, C., State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China.","Series elastic actuator (SEA) is a promising compliance device due to its lower output mechanical impedance, and it is widely applied to ensure safe human-robot interaction. Although some efforts have been made to achieve accurate stiffness tracking, the time-delay issue in SEA control has still not been well investigated. However, the time delay can cause an inaccurate response and increase the risk of injury. To overcome this problem, this article proposes a fast-response admittance control method for SEAs. First, an admittance control scheme considering the external force estimation is developed for a hydraulic SEA. Then, a parallel adaptive time-series (ATS) (P-ATS) compensator is proposed and further adopted in the admittance control scheme to compensate for the time delay and tracking error. The P-ATS compensator is a modification of the ATS compensator, which is enhanced with a unique parallel mechanism. Such a mechanism can save more computational resources on locating better parameters the for P-ATS compensator, thus improving its performance. Moreover, the parameter setting is converted to an optimization task, which is solved by the whale swarm algorithm (WSA) to achieve higher accuracy. The newly located parameters are compared to the current parameters based on a proposed evaluation criterion, thus guaranteeing the quality of the updated parameters. All the above strategies are employed to improve the SEA admittance control performance. The results obtained from both simulation and real-world experiments validate that, compared to conventional methods, the proposed method achieves a better performance in SEA stiffness tracking with lower time delay and tracking error. IEEE","Admittance; Admittance control; delay compensation; Delay effects; Delays; Force; Hydraulic systems; Impedance; manufacturing automation; Robots; series elastic actuator (SEA).",,,,,,"U.S. Naval Research Laboratory, NRL","This research was supported by SDIO/IST and managed by the Naval Research Laboratory.",,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85101772343
"Buoncompagni L., Capitanelli A., Cristofanini M., Giuni A., Mastrogiovanni F., Motolese C., Nisticò A., Sperindé A., Zaccaria R.","57188881830;57194281062;57222072914;36995776300;56962745700;57204952810;57202382018;36775623700;7003339894;","Persuasive and polite sentences to drive human-robot interaction in smart homes for elderly care",2021,"CEUR Workshop Proceedings","2806",,,"33","38",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101272684&partnerID=40&md5=1405fd05e1ff9ac38a1392cda63490a1","Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy; Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy","Buoncompagni, L., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Capitanelli, A., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Cristofanini, M., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Giuni, A., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy; Mastrogiovanni, F., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Motolese, C., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy; Nisticò, A., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy, Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Sperindé, A., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy; Zaccaria, R., Teseo srl, Piazza Montano 2a/1, Genoa, 16126, Italy","This paper addresses a scenario where a smart environment drives a proactive robot to motivate the elderly in maintaining a healthy lifestyle. The robot is in charge to trigger a vocal interaction with the person by proposing a suggestion that is polite and persuasive. We focus on the representation of sentences based on some attributes and, through spatial and temporal reasoning, we classify them over time. In an ontology, attributes are used to classify sentences and decide if the robot should start an interaction, and with which sentence. We present a preliminary implementation of the system, and we show that it is suitable for undertaking an iterative development process guided by domain experts toward a polite and persuasive virtual coaching. © 2020 Copyright for this paper by its authors.","Elderly care; Human-Robot vocal interaction; Ontology; Pragmatics; Smart home","Intelligent buildings; Robotics; Domain experts; Elderly care; Healthy lifestyles; Iterative development; Smart environment; Smart homes; Spatial and temporal reasoning; Vocal interaction; Human robot interaction",,,,,,,,,,,,,,,,"Hammer, S., Lugrin, B., Bogomolov, S., Janowski, K., Andre, E., (2016) Investigating Politeness Strategies and Their Persuasiveness for a Robotic Elderly Assistant, 9638, pp. 315-326; Chidambaram, V., Chiang, Y.-H., Mutlu, B., Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues (2012) Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, pp. 293-300; Multidisciplinary Studies of Illness in Aged Persons: II. A New Classification of Functional Status in Activities of Daily Living (1959) Journal of Chronic Diseases, 9 (1), pp. 55-62. , The Staff of the Benjamin Rose Hospital, in; Sbisà, M., Speech acts in context (2002) Language & Communication, 22 (4), pp. 421-436; Jakobson, R., Halle, M., (2010) Fundamentals of language, 1. , Walter de Gruyter; Lakoff, R. T., (1973) The Logic of Politeness: Minding your P’s and Q’s, , Chicago Linguistics Society; Fraser, B., Perspectives on Politeness (1990) Journal of pragmatics, 14 (2), pp. 219-236; Grice, H. P., Cole, P., Morgan, J., (1975) Logic and Conversation, pp. 41-58. , 1975; Leech, G. N., (2014) The pragmatics of politeness, , Oxford University Press, USA; Coupland, J., Coupland, N., Robinson, J. D., How Are You?”: Negotiating Phatic Communion (1992) Language in Society, 21 (2), pp. 207-230; Jakobson, R., Linguistics and Poetics (1960) Style in language, pp. 350-377. , MA: MIT Press; Laver, J., (1975) Communicative Functions of Phatic Communion, Organization of behavior in face-to-face interaction, pp. 215-238; Scaglia, C., Deissi e Cortesia in Italiano Linguistica e filologia; Bailoni, T., Dragoni, M., Eccher, C., Guerini, M., Maimone, R., PerKApp: A context Aware Motivational System for Healthier Lifestyles (2016) 2016 IEEE International Smart Cities Conference (ISC2), pp. 1-4. , IEEE; Guerini, M., Stock, O., Zancanaro, M., Persuasion Models for Intelligent Interfaces (2003) Proceedings of the IJCAI Workshop on Computational Models of Natural Argument; Grau, B. C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P., Sattler, U., OWL 2: The next step for OWL (2008) Journal of Web Semantics, 6 (4), pp. 309-322; Bobillo, F., Straccia, U., The Fuzzy Ontology Reasoner fuzzyDL (2016) Knowledge-Based Systems, 95, pp. 12-34",,"Finzi A.Castellini A.Buoncompagni L.Anzalone S.",,"CEUR-WS","7th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2020","26 November 2020",,166814,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85101272684
"Arora A.S., Fleming M., Arora A., Taras V., Xu J.","35736838900;57221963295;55437932800;35077310500;55506799900;","Finding “H” in HRI: Examining human personality traits, robotic anthropomorphism, and robot likeability in human-robot interaction",2021,"International Journal of Intelligent Information Technologies","17","1",,"19","38",,,"10.4018/IJIIT.2021010102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100821953&doi=10.4018%2fIJIIT.2021010102&partnerID=40&md5=bb1f99a68cfe0255d02951c2e531081d","University of the District of Columbia, United States; University of North Carolina, Greensboro, United States","Arora, A.S., University of the District of Columbia, United States; Fleming, M., University of the District of Columbia, United States; Arora, A., University of the District of Columbia, United States; Taras, V., University of North Carolina, Greensboro, United States; Xu, J., University of the District of Columbia, United States","The study examines the relationship between the big five personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness) and robot likeability and successful HRI implementation in varying human-robot interaction (HRI) situations. Further, this research investigates the influence of human-like attributes in robots (a.k.a. robotic anthropomorphism) on the likeability of robots. The research found that robotic anthropomorphism positively influences the relationship between human personality variables (e.g., extraversion and agreeableness) and robot likeability in human interaction with social robots. Further, anthropomorphism positively influences extraversion and robot likeability during industrial robotic interactions with humans. Extraversion, agreeableness, and neuroticism were found to play a significant role. This research bridges the gap by providing an in-depth understanding of the big five human personality traits, robotic anthropomorphism, and robot likeability in social-collaborative robotics. Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.","Agreeableness; Big Five; Conscientiousness; Extroversion; HRI; HRI Implementation; Neuroticism; Openness; Robot Likeability; Robotic Anthropomorphism; Social-Collaborative Robotics; Uncanny Effect","Economic and social effects; Industrial robots; Man machine systems; Robotics; Human interactions; Human like; Human robot Interaction (HRI); In-depth understanding; Industrial robotics; Likeability; Personality traits; Personality variables; Social robots",,,,,"National Science Foundation, NSF; U.S. Department of Defense, DOD; U.S. Department of Energy, USDOE; National Aeronautics and Space Administration, NASA; U.S. Department of Agriculture, USDA","Jiajun Xu (PhD) is an Associate Professor in the Department of Mechanical Engineering, and Director of the Center for Advanced Manufacturing at the University of the District of Columbia (UDC). He received his Ph.D. in Mechanical Engineering from the University of Maryland-College Park. His current research interests include: in-situ characterization and multiscale modeling of energy transport inside nanostructured materials, thermal management and energy conversion using nano-enhanced phase change materials, in-situ monitoring and process optimization of direct metal laser sintering-based (DMLS) additive manufacturing, and environment-friendly wastewater treatment using nanoparticle-infused mesoporous materials. His research work is sponsored by the National Aeronautics and Space Administration, National Science Foundation, Department of Defense, Department of Energy, and the U.S. Department of Agriculture.","Anshu Saxena Arora, Ph.D., PMP is Associate Professor of Marketing and Director of Logistics and International Trade (LIT) Analytics Center in the School of Business and Public Administration at the University of the District of Columbia. Previously, she worked as the Associate Professor of Marketing at the Jay S. Sidhu School of Business and Leadership, Wilkes University, Wilkes-Barre, PA. Before joining Wilkes University, she was Associate Professor of Marketing and Chairperson of Department of Business (Management, Marketing and Supporting Areas) in the College of Business Administration, Savannah State University, Savannah, Georgia, USA. Dr. Arora was awarded Ph.D. in the area of Consumer Navigation Behavior in Hypermedia CMEs from the Indian Institute of Technology, Delhi, India. She is a Certified Project Management Professional (PMP) from Project Management Institute, USA and holds a Foundation Certificate in IT Service Management from Information Systems Examination Board, UK. In July 2019, Dr. Arora received the prestigious National Science Foundation (NSF) grant for establishing the Logistics & International Trade (LIT) Analytics Center of Excellence and developing STEM-Business focused LIT program at the University of the District of Columbia. She is the Senior Editor of Marketing for the International Journal of Emerging Markets (IJoEM) published by Emerald Publications. She is also the Series Editor for International Marketing and Management Research published by Palgrave Macmillan Publications, UK. Dr. Arora has been a Visiting Professor at ISC Paris Business School in France, University of California, Davis, and Thunderbird School of Global Management at Arizona State University. She has more than a decade of industrial and academic experience. She has worked in companies like Hyundai Motors India Limited, Lufthansa German Airlines, and Siemens India. She has published more than 45 research papers in national and international journals of repute, and has presented about 55 papers in national and international conferences. Her research interests and expertise include digital and social media marketing, social media (marketing + advertising) strategy, marketing analytics, consumer behavior, relational supply chain strategy relationships, and project management.",,,,,,,,,"Ab Aziz, A., Ahmad, F., Yusof, N., Ahmad, F. K., Yusof, S. A. M., Designing a Robot-assisted Therapy for Individuals with Anxiety Traits and States (2015) 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), pp. 98-103. , IEEE; Aly, A., Tapus, A., Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human-robot interaction (2015) Autonomous Robots, 40 (2), pp. 193-209; Arora, A. 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"Tashtoush T., Garcia L., Landa G., Amor F., Laborde A.N., Oliva D., Safar F.","45261616700;57225789277;57221847297;57221855437;57221843502;22835830800;57190664343;","Human-Robot Interaction and Collaboration (HRI-C) Utilizing Top-View RGB-D Camera System",2021,"International Journal of Advanced Computer Science and Applications","12","1",,"10","17",,2,"10.14569/IJACSA.2021.0120102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100389191&doi=10.14569%2fIJACSA.2021.0120102&partnerID=40&md5=358b0393fad6ba3daf95ce4aee845b75","School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Department of Science and Technology, National University of Quilmes, Quilmes, Argentina","Tashtoush, T., School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Garcia, L., School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Landa, G., School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Amor, F., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina; Laborde, A.N., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina; Oliva, D., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina; Safar, F., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina","In this study, a smart and affordable system that utilizes an RGB-D camera to measure the exact position ofan operator with respect to an adjacent robotic manipulator was developed. This developed technology was implemented in a simulated human operation in an automated manufacturing robot to achieve two goals; enhancing the safety measures around the robot by adding an affordable smart system for human detection and robot control and developing a system that will allow the between the human-robot collaboration to finish a predefined task. The system utilized an Xbox Kinect V2 sensor/camera and Scorbot ER-V Plus to model and mimics the selected applications. To achieve these goals, a geometric model for the Scorbot and Xbox Kinect V2 was developed, a robotics joint calibration was applied, an algorithm of background segmentation was utilized to detect the operator and a dynamic binary mask for the robot was implemented, and the efficiency of both systems based on the response time and localization error was analyzed. The first application of the Add-on Safety Device aims to monitor the working-space and control the robot to avoid any collisions when an operator enters or gets closer. This application will reduced and remove physical barriers around the robots, expand the physical work area, reduce the proximity limitations, and enhance the human-robots interaction (HRI) in an industrial environment while sustaining a low cost. The system was able to respond to human intrusion to prevent any collision within 500 ms on average, and it was found that the system’s bottleneck was PC and robot inter-communication speed. The second application was developing a successful collaborative scenario between a robot and a human operator, where a robot will deposit an object on the operator’s hand, mimicking a real-life human-robot collaboration (HRC) tasks. The system was able to detect the operator’s hand and it’s location then command the robot to place an object on the hand, the system was able to place the object within a mean error of 2:4 cm, and the limitation of this system was the internal variables and data transmitting speed between the robot controller and main computer. These results are encouraging and ongoing work aims to experiment with different operations and implement gesture detection in real-time collaboration tasks while keeping the human operator safe and predicting their behavior. © 2021, International Journal of Advanced Computer Science and Applications. All Rights Reserved.","computer vision; human-robot collaboration (HRC); human-robot interaction (HRI); industrial system modeling; Kinect; manufacturing systems design; RGB camera; robot end-effector; Robotics manipulator; robotics safety; scorbot","Accident prevention; Cameras; Computer vision; Costs; End effectors; Machine design; Man machine systems; Manipulators; Manufacture; Robotics; Human-robot collaboration; Human-robot interaction; Humans-robot interactions; Industrial system modeling; Industrial systems; Kinect; Manufacturing system design; RGB cameras; Robot's end effectors; Robotic manipulators; Robotic safety; Scorbot; System models; Human robot interaction",,,,,"P120A170102; Texas A and M International University, TAMIU; Universidad Nacional de Quilmes, UNQ","This project was funded and supported by “100,000 Strong in the Americas Innovation Fund – Exxon Mobil”, and the U.S. Department of Education - Minority Science and Engineering Improvement (MSEIP) Program (Grant # P120A170102), National University of Quilmes, and Texas A&M International University. With their support, this project was a successful collaboration between the two institutes.",,,,,,,,,,"Matheson, E., Minto, R., Zampieri, E. G., Faccio, M., Rosati, G., Human-Robot Collaboration in Manufacturing Applications: A Review (2019) Robotics, 8 (4), p. 100. , [1]; Ferraguti, F., Bertuletti, M., Landi, C. T., Bonfe, M., Fantuzzi, C., Secchi, C., A control barrier function approach for maximizing performance while fulfilling to iso/ts 15066 regulations (2020) IEEE Robotics and Automation Letters, 5 (4). , https://doi.org/10.1109/LRA.2020.3010494, [2]; Robla-Gomez, S., Becerra, V. M., Llata, J. 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Appl.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85100389191
"Coronado E., Deuff D., Carreno-Medrano P., Tian L., Kulic D., Sumartojo S., Mastrogiovanni F., Venture G.","57188864138;15061153300;56336138700;56151869000;10244652900;55548214000;56962745700;15623574000;","Towards a modular and distributed end-user development framework for human-robot interaction",2021,"IEEE Access","9",,"9323043","12675","12692",,4,"10.1109/ACCESS.2021.3051605","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099723475&doi=10.1109%2fACCESS.2021.3051605&partnerID=40&md5=2908c576aad060a257ca924f551ad8a4","Department of Mechanical System Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan; Orange Lab, Lannion, 22300, France; Faculty of Engineering, Monash University, Clayton, VIC  3800, Australia; Faculty of Art, Design and Architecture, Monash University, Caulfield East, VIC  3145, Australia; Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, 16145, Italy","Coronado, E., Department of Mechanical System Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan; Deuff, D., Orange Lab, Lannion, 22300, France; Carreno-Medrano, P., Faculty of Engineering, Monash University, Clayton, VIC  3800, Australia; Tian, L., Faculty of Engineering, Monash University, Clayton, VIC  3800, Australia; Kulic, D., Faculty of Engineering, Monash University, Clayton, VIC  3800, Australia; Sumartojo, S., Faculty of Art, Design and Architecture, Monash University, Caulfield East, VIC  3145, Australia; Mastrogiovanni, F., Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, 16145, Italy; Venture, G., Department of Mechanical System Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan","In an effort towards the democratization of Robotics, this article presents a novel End-User Development framework called Robot Interfaces From Zero Experience (RIZE). The framework provides a set of useful software tools for the creation of robot-oriented software architectures and programming interfaces, as well as the modeling and execution of robot behaviors, with a specific emphasis on social behaviors. Programming interfaces built on top of RIZE enable professionals with different backgrounds and interests to design, adapt, and scale-up robotics applications. As an example of a programming interface, we present Open RIZE, which exploits an End-User Programming paradigm combining blocks, tables, and forms-filling interfaces. Unlike previous approaches, robot behavioral code generated by Open RIZE is intrinsically modular, re-usable, scalable, neutral to the employed programming language, and platform-agnostic. In the article, we present the main design guidelines and features of Open RIZE. Additionally, we perform an initial usability evaluation of the Open RIZE interface in an online workshop. Preliminary results using the System Usability Scale with 10 novice end-users indicate that Open RIZE is easy-to-use and learn. © 2013 IEEE.","end-user development; human-robot interaction; robot programming; Robotics; service robots; social robots","Behavioral research; End effectors; Robot programming; Robotics; End user development; End user programming; Programming interface; Robot interface; Robotics applications; Social behavior; System usability; Usability evaluation; Human robot interaction",,,,,"Tokyo University of Agriculture and Technology, TUAT","This work was supported by the Institute for Global Innovation Research, Tokyo University of Agriculture and Technology.",,,,,,,,,,"Coronado, E., Venture, G., Towards IoT-aided human robot interaction using NEP and ROS: A platform-independent, accessible and distributed approach (2020) Sensors, 20 (5), p. 1500. , Mar; Fischer, G., End user development and meta-design: Foundations for cultures of participation (2012) End-User Computing, pp. 202-226. , Development, and Software Engineering: New Challenges. 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Accessed: Jun. 1; Carreno-Medrano, P., Tian, L., Allen, A., Sumartojo, S., Mintrom, M., Coronado, E., Venture, G., Kulic, D., (2021) Aligning Robot's Behaviours and Users' Perceptions through Participatory Prototyping, , http://arxiv.org/abs/2101.03660; Lewis, J.R., The system usability scale: Past, present, and future (2018) Int. J. Hum.-Comput. Interact., 34 (7), pp. 577-590. , Jul; Tullis, T.S., Stetson, J.N., A comparison of questionnaires for assessing website usability (2004) Proc. Usability Professionals Assoc. (UPA) Conf., pp. 1-14; (2017), https://www.usability.gov/what-and-why/visualdesign, Usability.gov. (2017). Visual Design Basics. Accessed: Jun. 1; Bangor, A., Kortum, P., Miller, J., Determining what individual SUS scores mean: Adding an adjective rating scale (2009) J. Usability Stud., 4 (3), pp. 114-123","Coronado, E.; Department of Mechanical System Engineering, Japan; email: enriquecoronadozu@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85099723475
"Sharifi M., Zakerimanesh A., Mehr J.K., Torabi A., Mushahwar V.K., Tavakoli M.","55224489900;57193490432;57221662636;57203129433;6603751651;8668966400;","Impedance Variation and Learning Strategies in Human-Robot Interaction",2021,"IEEE Transactions on Cybernetics",,,,"","",,3,"10.1109/TCYB.2020.3043798","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099722915&doi=10.1109%2fTCYB.2020.3043798&partnerID=40&md5=f1fa0e522a31ca7831fab1349e60cfc0","Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.","Sharifi, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Zakerimanesh, A., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Mehr, J.K., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Torabi, A., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Mushahwar, V.K., Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Tavakoli, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.","In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed. IEEE","Human-robot interaction (HRI); impedance and admittance models; impedance control; impedance learning; impedance variation; robot learning; robot stability",,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682267,,,,"English","IEEE Trans. Cybern.",Article,"Article in Press","",Scopus,2-s2.0-85099722915
"Ning Y., Liu Y., Xi F., Huang K., Li B.","57193811149;57211268565;7102360062;57221528142;55628528575;","Human-Robot Interaction Control for Robot Driven by Variable Stiffness Actuator with Force Self-Sensing",2021,"IEEE Access","9",,"9311618","6696","6705",,2,"10.1109/ACCESS.2020.3048418","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099513380&doi=10.1109%2fACCESS.2020.3048418&partnerID=40&md5=390e8da408a67f9d76cfe106a5199032","State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China; School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Shenzhen Cansinga Technology Company Ltd., Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, 518055, China","Ning, Y., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Liu, Y., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Xi, F., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Huang, K., Shenzhen Cansinga Technology Company Ltd., Shenzhen, 518055, China; Li, B., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China, Peng Cheng Laboratory, Shenzhen, 518055, China","Robots driven by variable stiffness actuators (VSAs) have been an important technology as they could provide intrinsic compliance for safe human-robot interaction. The internal compliance of VSA also makes it possible for the actuator to act as a torque sensor and estimate the external force. This paper presents a physical human-robot interaction control strategy for robots driven by VSA with force self-sensing. The VSA adopted in the robotic system improves the safe performance of physical human-robot interaction. The interaction force is directly estimated by measuring the internal deformation of VSA with a stiffness region control which ensures a better resolution of force estimation and avoids transgressing the limits of deformation simultaneously. Then an online estimation method for the human motion intention is developed to generate the desired trajectory based on the estimated force. The impedance control is designed to enable the robot to actively follow the desired trajectory with compliance. Under the proposed control strategy, the robot is capable of actively tracking the 'motion intention' of human with the estimated interaction force, which is demonstrated through experiment studies. © 2013 IEEE.","Force self-sensing; motion intention estimation; physical human-robot interaction; variable stiffness actuator (VSA)","Actuators; Compliance control; Deformation; Man machine systems; Motion tracking; Stiffness; Control strategies; Desired trajectories; Human motion intention; Human-robot interaction control; Internal deformation; On-line estimation; Physical human-robot interactions; Safe human-robot interaction; Social robots",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1302200; Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001; Shenzhen Research and Development Program: JCYJ20200109112818703, JSGG20180504170011639","This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B090915001, in part by the National Key Research and Development Program of China under Grant 2017YFB1302200, and in part by the Shenzhen Research and Development Program of China under Grant JSGG20180504170011639 and Grant JCYJ20200109112818703.",,,,,,,,,,"Vanderborght, B., Variable impedance actuators: A review (2013) Robot. Auton. Syst., 61 (12), pp. 1601-1614. , Aug; Wolf, S., Grioli, G., Eiberger, O., Friedl, W., Grebenstein, M., Höppner, H., Burdet, E., Albu-Schäffer, A., Variable stiffness actuators: Review on design and components (2016) IEEE/ Asme Trans. Mechatronics, 21 (5), pp. 2418-2430. , Oct; Li, X., Liu, Y.-H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7. , Apr; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Humanfirobot interaction control of rehabilitation robots with series elastic actuators (2015) Ieee Trans. Robot., 31 (5), pp. 1089-1100. , Oct; Mghames, S., Laghi, M., Santina, C.D., Garabini, M., Catalano, M., Grioli, G., Bicchi, A., Design, control and validation of the variable stiffness exoskeleton FLExo (2017) Proc. Int. Conf. Rehabil. Robot. (ICORR), pp. 539-546. , London, U.K., Jul; Liu, Y., Guo, S., Hirata, H., Ishihara, H., Tamiya, T., Development of a powered variable-stiffness exoskeleton device for elbow rehabilitation (2018) Biomed. Microdevices, 20 (3), p. 64. , Aug; Li, S., Li, J., Tian, G., Shang, H., Stiffness adjustment for a singlelink robot arm driven by series elastic actuator in muscle training (2019) Ieee Access, 7, pp. 65029-65039; Remy, C.D., Siegwart, R., Gehring, C., Bloesch, M., Hoepinger, M.A., Hutter, M., StarlETH: A compliant quadrupedal robot for fast, efficient, and versatile locomotion (2012) Proc. 15th Int. Conf. Climbing Walking Robot, pp. 1-8; Paine, N., Mehling, J.S., Holley, J., Radford, N.A., Johnson, G., Fok, C.-L., Sentis, L., Actuator control for the NASA-JSC valkyrie humanoid robot: A decoupled dynamics approach for torque control of series elastic robots (2015) J. Field Robot., 32 (3), pp. 378-396. , May; Ajoudani, A., Lee, J., Rocchi, A., Ferrati, M., Hoffman, E.M., Settimi, A., Caldwell, D.G., Tsagarakis, N.G., A manipulation framework for compliant humanoid COMAN: Application to a valve turning task (2014) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 664-670. , Nov; Basafa, E., Sheikholeslami, M., Mirbagheri, A., Farahmand, F., Vossoughi, G.R., Design and implementation of series elastic actuators for a haptic laparoscopic device (2009) Proc. Annu. Int. Conf. Ieee Eng. Med. Biol. Soc, pp. 6054-6057. , Sep; Oblak, J., Matjai, Z., Design of a series visco-elastic actuator for multi-purpose rehabilitation haptic device (2011) J. Neuroeng. Rehabil., 8 (1), pp. 1-13. , Dec; Cestari, M., Sanz-Merodio, D., Arevalo, J.C., Garcia, E., An adjustable compliant joint for lower-limb exoskeletons (2015) IEEE/ Asme Trans. Mecha-tronics, 20 (2), pp. 889-898. , Apr; Shepherd, M.K., Rouse, E.J., The VSPA foot: A quasi-passive anklefoot prosthesis with continuously variable stiffness (2017) Ieee Trans. Neural Syst. Rehabil. Eng., 25 (12), pp. 2375-2386. , Dec; Xu, J., Liu, Y., Chen, J., Li, Y., Xu, L., Peng, C., Chen, S., Xu, H., A multi-mode rehabilitation robot with magnetorheological actuators based on human motion intention estimation (2019) Ieee Trans. Neural Syst. Rehabil. Eng., 27 (10), pp. 2216-2228. , Oct; Ferrez, P.W., Del R Millan, J., Error-related EEG potentials generated during simulated brainficomputer interaction (2008) Ieee Trans. Biomed. Eng., 55 (3), pp. 923-929. , Mar; Wang, W., Hou, Z.-G., Cheng, L., Tong, L., Peng, L., Peng, L., Tan, M., Toward patients' motion intention recognition: Dynamics modeling and identification of iLegAn LLRR under motion constraints (2016) Ieee Trans. Syst., Man, Cybern. Syst., 46 (7), pp. 980-992. , Jul; Dinh, B.K., Xiloyannis, M., Antuvan, C.W., Cappello, L., Masia, L., Hierarchical cascade controller for assistance modulation in a soft wearable arm exoskeleton (2017) Ieee Robot. Autom. Lett., 2 (3), pp. 1786-1793. , Jul; Duchaine, V., St-Onge, B.M., Gao, D., Gosselin, C., Stable and intuitive control of an intelligent assist device (2012) Ieee Trans. Haptics, 5 (2), pp. 148-159. , Apr; Liang, C., Hsiao, T., Admittance control of powered exoskeletons based on joint torque estimation (2020) Ieee Access, 8, pp. 94404-94414; Li, Y., Ge, S.S., Humanfirobot collaboration based on motion intention estimation (2014) IEEE/ Asme Trans. Mechatronics, 19 (3), pp. 1007-1014. , Jun; Chen, C.-F., Du, Z.-J., He, L., Shi, Y.-J., Wang, J.-Q., Xu, G.-Q., Zhang, Y., Dong, W., Development and hybrid control of an electrically actuated lower limb exoskeleton for motion assistance (2019) Ieee Access, 7, pp. 169107-169122; Veneman, J.F., Ekkelenkamp, R., Kruidhof, R., Helm Der Van T., F.C., Kooij Der, H.Van, A series elastic-and bowden-cable-based actuation system for use as torque actuator in exoskeleton-type robots (2006) Int. J. Robot. Res., 25 (3), pp. 261-281. , Mar; Huard, B., Grossard, M., Moreau, S., Poinot, T., Sensorless force/position control of a single-acting actuator applied to compliant object interaction (2015) Ieee Trans. Ind. Electron., 62 (6), pp. 3651-3661. , Jun; Stienen, A.H.A., Hekman, E.E.G., Braak, H.T., Aalsma, A.M.M., Helm Der Van T., F.C., Kooij Der, H.Van, Design of a rotational hydroelastic actuator for a powered exoskeleton for upper limb rehabilitation (2010) Ieee Trans. Biomed. Eng., 57 (3), pp. 728-735. , Mar; Kim, B.-S., Song, J.-B., Object grasping using a 1 DOF variable stiffness gripper actuated by a hybrid variable stiffness actuator (2011) Proc. Ieee Int. Conf. Robot. Automat. (ICRA), pp. 4620-4625. , Shanghai, China, May; Ning, Y., Huang, H., Xu, W., Zhang, W., Li, B., Design and implementation of a novel variable stiffness actuator with cam-based relocation mechanism (2021) J. Mech. Robot., 13 (2). , Apr; Kim, B.-S., Song, J.-B., Design and control of a variable stiffness actuator based on adjustable moment arm (2012) Ieee Trans. Robot., 28 (5), pp. 1145-1151. , Oct; Tee, K.P., Ge, S.S., Tay, E.H., Barrier Lyapunov functions for the control of output-constrained nonlinear systems (2009) Automatica, 45 (4), pp. 918-927. , Apr; Rahman, M.M., Ikeura, R., Mizutani, K., Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans (2002) Int. J. C Mech. Syst., Mach. Elements Manuf., 45 (2), pp. 510-518","Xi, F.; School of Mechanical Engineering and Automation, China; email: fengxi@ryerson.ca
Huang, K.; Shenzhen Cansinga Technology Company Ltd.China; email: k.huang@cansinga.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85099513380
"Lim Y., Pongsakornsathien N., Gardi A., Sabatini R., Kistan T., Ezer N., Bursch D.J.","56767439900;57208256969;57198865986;56962744800;55763620100;24331325800;57221533692;","Adaptive human-robot interactions for multiple unmanned aerial vehicles",2021,"Robotics","10","1","12","1","30",,3,"10.3390/robotics10010012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099342707&doi=10.3390%2frobotics10010012&partnerID=40&md5=a44ef181b1dba251ee76bcab2128eb04","School of Engineering, RMIT University, Bundoora, VIC  3083, Australia; Saab-NTU Joint Lab, Nanyang Technological University, Singapore, 637460, Singapore; THALES Australia, WTC North Wharf, Melbourne, VIC  3000, Australia; Northrop Grumman Corporation, 1550 W. Nursery Rd, Linthicum HeightsMD  21090, United States","Lim, Y., School of Engineering, RMIT University, Bundoora, VIC  3083, Australia, Saab-NTU Joint Lab, Nanyang Technological University, Singapore, 637460, Singapore; Pongsakornsathien, N., School of Engineering, RMIT University, Bundoora, VIC  3083, Australia; Gardi, A., School of Engineering, RMIT University, Bundoora, VIC  3083, Australia; Sabatini, R., School of Engineering, RMIT University, Bundoora, VIC  3083, Australia; Kistan, T., School of Engineering, RMIT University, Bundoora, VIC  3083, Australia, THALES Australia, WTC North Wharf, Melbourne, VIC  3000, Australia; Ezer, N., Northrop Grumman Corporation, 1550 W. Nursery Rd, Linthicum HeightsMD  21090, United States; Bursch, D.J., Northrop Grumman Corporation, 1550 W. Nursery Rd, Linthicum HeightsMD  21090, United States","Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2 ) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2 ) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage. © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.","Aerial robotics; Autonomous systems; Avionics; Cyber-physical systems; Human-in-the-loop; Human-machine inter-face; Human-machine interaction; Human-machine system; Human-robot interactions; Mental workload; Neuroergonomics; Neurophysiological response; Neurophysiology; Remote pilot; Unmanned aerial vehicle; Unmanned aircraft system",,,,,,"Northrop Grumman: RE-03671, RE-03975; Australasian Menopause Society, AMS","Funding: This research was funded by THALES Airspace Mobility Solutions (AMS) Australia, and the Northrop Grumman Corporation (USA), with different aspects of this work being separately supported under the collaborative research projects RE-03975 and RE-03671 respectively.",,,,,,,,,,"Skorobogatov, G., Barrado, C., Salami, E., Multiple UAV Systems: A Survey (2020) Unmanned Syst, 8, pp. 149-169. , [CrossRef]; Boccalatte, M., Brogi, F., Catalfamo, F., Maddaluno, S., Martino, M., Mellano, V., Prin, P.R., Torta, G., A Multi-UAS Cooperative Mission Over Non-Segregated Civil Areas (2012) J. 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"Chatzimichali A., Harrison R., Chrysostomou D.","57357138500;57221417333;24477398900;","Toward privacy-sensitive human-robot interaction: Privacy terms and human-data interaction in the personal robot era",2021,"Paladyn","12","1",,"160","174",,2,"10.1515/pjbr-2021-0013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099095609&doi=10.1515%2fpjbr-2021-0013&partnerID=40&md5=daa6674c74d707bd144641e7ce3c39f1","Department of Architecture and the Built Environment, University of West of England, Frenchay Campus, Coldharbour Ln, Stoke Gifford, Bristol, BS16 1QY, United Kingdom; Department of Engineering Mathematics, University of West of England, Frenchay Campus, Coldharbour Ln, Stoke Gifford, Bristol, BS16 1QY, United Kingdom; Robotics and Automation Group, Department of Materials and Production, Aalborg University, Fibigerstraede 16, Aalborg East, DK 9220, Denmark","Chatzimichali, A., Department of Architecture and the Built Environment, University of West of England, Frenchay Campus, Coldharbour Ln, Stoke Gifford, Bristol, BS16 1QY, United Kingdom; Harrison, R., Department of Engineering Mathematics, University of West of England, Frenchay Campus, Coldharbour Ln, Stoke Gifford, Bristol, BS16 1QY, United Kingdom; Chrysostomou, D., Robotics and Automation Group, Department of Materials and Production, Aalborg University, Fibigerstraede 16, Aalborg East, DK 9220, Denmark","Can we have personal robots without giving away personal data? Besides, what is the role of a robots Privacy Policy in that question? This work explores for the first time privacy in the context of consumer robotics through the lens of information communicated to users through Privacy Policies and Terms and Conditions. Privacy, personal and non-personal data are discussed under the light of the human-robot relationship, while we attempt to draw connections to dimensions related to personalization, trust, and transparency. We introduce a novel methodology to assess how the ""Organization for Economic Cooperation and Development Guidelines Governing the Protection of Privacy and Trans-Border Flows of Personal Data""are reflected upon the publicly available Privacy Policies and Terms and Conditions in the consumer robotics field. We draw comparisons between the ways eight consumer robotic companies approach privacy principles. Current findings demonstrate significant deviations in the structure and context of privacy terms. Some practical dimensions in terms of improving the context and the format of privacy terms are discussed. The ultimate goal of this work is to raise awareness regarding the various privacy strategies used by robot companies while ultimately creating a usable way to make this information more relevant and accessible to users. © 2021 Anna Chatzimichali et al., published by De Gruyter 2021.","human-data interaction; human-robot interaction; personal data; privacy-sensitive robotics; user privacy",,,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R033838/1","The work reported in this paper is supported by a South West Creative Technology Network Fellowship in Automation and partially supported by EPSRC Grant EP/R033838/1.",,,,,,,,,,"Estolatan, E., Geuna, A., Guerzoni, M., Nuccio, M., (2018) Mapping the Evolution of the Robotics Industry: A Cross Country Comparison, , https://ideas.repec.org/p/uto/dipeco/201812.html, Department of Economics and Statistics Cognetti de Martiis, University of Turin, Jul; Yang, G.-Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Combating COVID-19' the role of robotics in managing public health and infectious diseases (2020) Sci. 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"González-González C.S., Gil-Iranzo R.M., Paderewski-Rodríguez P.","22234459200;57218375983;6506414966;","Human-robot interaction and sexbots: A systematic literature review",2021,"Sensors (Switzerland)","21","1","216","1","18",,7,"10.3390/s21010216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098971187&doi=10.3390%2fs21010216&partnerID=40&md5=da2d09b5d570e5ea681853c85b42dd2f","Departamento de Ingeniería Informática y de Sistemas, Escuela de Ingeniería y Tecnología, Universidad de La Laguna, La Laguna, 38204, Spain; Departamento de Informática e Ingeniería Industrial, Escuela Politécnica Superior, Universitat de Lleida, LLeida, 25001, Spain; Departmento de Lenguajes y Sistemas Informáticos, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, Granada, 18071, Spain","González-González, C.S., Departamento de Ingeniería Informática y de Sistemas, Escuela de Ingeniería y Tecnología, Universidad de La Laguna, La Laguna, 38204, Spain; Gil-Iranzo, R.M., Departamento de Informática e Ingeniería Industrial, Escuela Politécnica Superior, Universitat de Lleida, LLeida, 25001, Spain; Paderewski-Rodríguez, P., Departmento de Lenguajes y Sistemas Informáticos, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, Universidad de Granada, Granada, 18071, Spain","At present, sexual robots have become a new paradigm of social robots. In this paper, we developed a systematic literature review about sexual robots (sexbots). To do this, we used the Scopus and WoS databases to answer different research questions regarding the design, interaction, and gender and ethical approaches from 1980 until 2020. In our review, we found a male bias in this discipline, and in recent years, articles have shown that user opinion has become more relevant. Some insights and recommendations on gender and ethics in designing sexual robots were also made. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Ethics; Gender; Human-robot interaction; Sexbots; Sexual robots; Social robots","Philosophical aspects; Research questions; Systematic literature review; Social robots; attitude; human; robotics; sexual behavior; Attitude; Humans; Robotics; Sexual Behavior",,,,,,,,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2008) Found. Trends® Human Comput. 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"Chen L., Su W., Li M., Wu M., Pedrycz W., Hirota K.","55774663200;57196287282;57204930290;57218664297;57210775415;7402829702;","A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction",2021,"Science China Information Sciences","64","1","112208","","",,,"10.1007/s11432-019-2749-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098947115&doi=10.1007%2fs11432-019-2749-0&partnerID=40&md5=aeae1791cc147deab3a303c15132930c","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, T6R 2G7, Canada; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Li, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, T6R 2G7, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and challenging. In view of these problems, we propose a population randomization-based multi-objective genetic algorithm. The gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time. © 2020, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.","gesture adaptation; human-robot interaction; multi-objective genetic algorithm","Biology; Energy utilization; Errors; Man machine systems; Particle swarm optimization (PSO); Random processes; Robotic arms; Robotics; Social robots; Biological evolution; Multi-objective genetic algorithm; Objective functions; Original signal; Population evolution; Retention strategies; Single-objective genetic algorithms; Tracking effects; Genetic algorithms",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61773353, 61973286; Natural Science Foundation of Hubei Province: 2015CFA010; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040; China University of Geosciences, CUG: 2018039; Wuhan Science and Technology Project: 2017010201010133","This work was supported by National Natural Science Foundation of China (Grant Nos. 61973286, 61603356, 61733016, 61773353), 111 Project (Grant No. B17040), Hubei Provincial Natural Science Foundation (Grant No. 2015CFA010), Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. 2018039), and Wuhan Science and Technology Project (Grant No. 2017010201010133).",,,,,,,,,,"Zhang, J.W., Wang, L., Xing, L.N., Large-scale medical examination scheduling technology based on intelligent optimization (2019) J Comb Optim, 37, pp. 385-404; 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China Inf. Sci.",Article,"Final","",Scopus,2-s2.0-85098947115
"Liu X., Ge S.S., Zhao F., Mei X.","56278821100;25924925100;57195553684;7103346480;","A Dynamic Behavior Control Framework for Physical Human-Robot Interaction",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","101","1","14","","",,,"10.1007/s10846-020-01286-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097498306&doi=10.1007%2fs10846-020-01286-x&partnerID=40&md5=9836a9717d1275391d8fff02b4a65c86","Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China; Social Robotics Lab, National University of Singapore, Singapore, Singapore; Institute for Future, Qingdao University, Qingdao, China","Liu, X., Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China; Ge, S.S., Social Robotics Lab, National University of Singapore, Singapore, Singapore, Institute for Future, Qingdao University, Qingdao, China; Zhao, F., Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China; Mei, X., Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China","The problem of dynamic behavior control for human-robot collaborative system is studied in this paper. A general dynamic behavior control framework is presented, based on which the interaction control strategies for single-point human-robot collaboration and multi-point human-robot interaction are studied. Through typical human-robot collaborative operation scenarios, the causes of role conflicts under different human-robot collaboration control strategies are analyzed, and the collaborative control algorithm integrating the active motion imitation and passive motion following is presented to reduce the conflicts during the human-robot collaboration process. For multi-point human-robot interaction, the interaction problems are classified and the interaction control strategies for single-task and multi-task scenarios are designed, both in the Cartesian space and the joint space. For multi-task interaction, a smooth task transition strategy from the collaboration control to the safety control is presented to improve the security during the task switching process. Experimental studies verify the effectiveness of the presented method. © 2020, Springer Nature B.V.","Active collaboration control; Dynamic behavior control framework; Human-robot interaction; Multi-point interaction control","Agricultural robots; Behavioral research; Man machine systems; Collaborative control; Collaborative operations; Collaborative systems; Control strategies; Human-robot collaboration; Interaction controls; Interaction problems; Physical human-robot interactions; Social robots",,,,,"National Natural Science Foundation of China, NSFC;NNSF;NNSFC: 91748208; China Scholarship Council, CSC: 201706280378; Shaanxi Provincial Science and Technology Department: 2017ZDL-G-3-1",,,,,,,,,,,"Albu-Schäffer, A., Ott, C., Hirzinger, G., A unified passivity-based control framework for position, torque and impedance control of flexible joint robots (2007) The International Journal of Robotics Research, 26 (1), pp. 23-39; Ang, M.H., Wei, L., Yong, L.S., An industrial application of control of dynamic behavior of robots-a walk-through programmed welding robot (2000) IEEE International Conference on Robotics & Automation, 3, pp. 2352-2357. , . 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"Hinz N.-A., Ciardo F., Wykowska A.","57205877645;57164931800;15924284200;","ERP markers of action planning and outcome monitoring in human – robot interaction",2021,"Acta Psychologica","212",,"103216","","",,4,"10.1016/j.actpsy.2020.103216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097430846&doi=10.1016%2fj.actpsy.2020.103216&partnerID=40&md5=a7a51f21e20d82a2368f377e610fe928","Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy; Department of Psychology and Educational Sciences, Ludwig-Maximilians-University, Geschwister-Scholl-Platz 1, München, 80539, Germany","Hinz, N.-A., Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy, Department of Psychology and Educational Sciences, Ludwig-Maximilians-University, Geschwister-Scholl-Platz 1, München, 80539, Germany; Ciardo, F., Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy; Wykowska, A., Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy","The present study aimed to examine event-related potentials (ERPs) of action planning and outcome monitoring in human-robot interaction. To this end, participants were instructed to perform costly actions (i.e. losing points) to stop a balloon from inflating and to prevent its explosion. They performed the task alone (individual condition) or with a robot (joint condition). Similar to findings from human-human interactions, results showed that action planning was affected by the presence of another agent, robot in this case. Specifically, the early readiness potential (eRP) amplitude was larger in the joint, than in the individual, condition. The presence of the robot affected also outcome perception and monitoring. Our results showed that the P1/N1 complex was suppressed in the joint, compared to the individual condition when the worst outcome was expected, suggesting that the presence of the robot affects attention allocation to negative outcomes of one's own actions. Similarly, results also showed that larger losses elicited smaller feedback-related negativity (FRN) in the joint than in the individual condition. Taken together, our results indicate that the social presence of a robot may influence the way we plan our actions and also the way we monitor their consequences. Implications of the study for the human-robot interaction field are discussed. © 2020 The Author(s)","Feedback-related negativity (FRN); Human-robot interaction; Joint action; P1/N1 complex; Readiness potential (RP)","attention; electroencephalography; evoked response; human; robotics; Attention; Electroencephalography; Evoked Potentials; Humans; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 715058; European Commission, EC; European Research Council, ERC; Horizon 2020","This work received support by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant awarded to AW, titled ?InStance: Intentional Stance for Social Attunement.? G.A. No: ERC-2016-StG-715058). The content of this paper is the sole responsibility of the authors. The European Commission or its services cannot be held responsible for any use that may be made of the information it contains.","This work received support by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant awarded to AW, titled “InStance: Intentional Stance for Social Attunement.” G.A. No: ERC-2016-StG-715058 ). The content of this paper is the sole responsibility of the authors. 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The stability and importance of individual differences in anthropomorphism (2010) Perspectives on Psychological Science, 5 (3), pp. 219-232; Wickham, H., (2016), ggplot2: Elegant Graphics for Data Analysis: Springer-Verlag New York; Zajonc, R.B., Wolosin, R.J., Wolosin, M.A., Loh, W.D., Social facilitation and imitation in group risk-taking (1970) Journal of Experimental Social Psychology, 6 (1), pp. 26-46","Wykowska, A.; Istituto Italiano di Tecnologia, Via Enrico Melen 83, Italy; email: agnieszka.wykowska@iit.it",,,"Elsevier B.V.",,,,,00016918,,APSOA,"33285360","English","Acta Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097430846
"González-Docasal A., Aceta C., Arzelus H., Álvarez A., Fernández I., Kildal J.","57219985509;57219988202;56303519100;56598789500;42761280800;36695073600;","Towards a Natural Human-Robot Interaction in an Industrial Environment",2021,"Lecture Notes in Electrical Engineering","704",,,"243","255",,,"10.1007/978-981-15-8395-7_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096421785&doi=10.1007%2f978-981-15-8395-7_18&partnerID=40&md5=696fbfc0ea92a592899a29761d4de9fd","Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain","González-Docasal, A., Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Aceta, C., Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain; Arzelus, H., Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Álvarez, A., Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Fernández, I., Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain; Kildal, J., Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain","Nowadays, modern industry has adopted robots as part of their processes. In many scenarios, such machines collaborate with humans to perform specific tasks in their same environment or simply guide them in a natural, safe and efficient way. Our approach improves a previously conducted work on a multi-modal human-robot interaction system with different audio acquisition and speech recognition modules for a more natural communication. The semantic interpreter, with the aid of a knowledge manager, parses the resulting transcription and, using contextual information, selects the order that the operator has uttered and sends it to the robot to be executed. This setup is evaluated in a real manufacture scenario in a laboratory environment with a large set of end users both quantitatively and qualitatively. The gathered results reveal that the system behaves robustly and that the assignment was also considered by the end users as manageable, whilst the system in overall was received with a high level of trust and usability. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",,"Industrial robots; Man machine systems; Semantics; Speech communication; Speech processing; Speech recognition; Contextual information; Industrial environments; Knowledge managers; Laboratory environment; Multi-modal; Natural communication; Specific tasks; Speech recognition modules; Social robots",,,,,"Eusko Jaurlaritza","Acknowledgements This work was supported by the Department of Economic Development and Competitiveness of the Basque Government via the LangileOK project.",,,,,,,,,,"Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Zhu, Z., (2015) Deep Speech 2: End-To-End Speech Recognition in English and Mandarin; Anastasakos, T., McDonough, J., Schwartz, R., Makhoul, J., A compact model for speaker-adaptive training (1996) Proceeding of Fourth International Conference on Spoken Language Processing. 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"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Experiments and Applications of Emotional Human-Robot Interaction Systems",2021,"Studies in Computational Intelligence","926",,,"223","244",,,"10.1007/978-3-030-61577-2_13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096230408&doi=10.1007%2f978-3-030-61577-2_13&partnerID=40&md5=3ae960e15df2b3f7332ab950c6e28c95","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","The ability of robots to correctly recognize emotional states, understand emotional intentions, and give emotional feedback is an important aspect and challenge in the study of emotional robot systems. In order to realize a human-robot interaction system with certain emotion recognition and intention understanding, and to establish a natural and harmonious human-robot interaction process, this section proposes a human-robot interaction system scheme based on affective computing. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Yamazaki, Y., Dong, F., Masuda, Y., Uehara, Y., Kormushev, P., Intent expression using eye robot for mascot robot system (2009) Comput. Sci, pp. 576-580; Hu, J., Wellman, M.P., Nash q-learning for general-sum stochastic games (2003) J. Mach. Learn. Res., 4 (4), pp. 1039-1069; Erol, B.A., Majumdar, A., Benavidez, P., Rad, P., Choo, K.R., Jamshidi, M., Toward artificial emotional intelligence for cooperative social human-machine interaction (2020) IEEE Trans. Comput. Soc. Syst., 7 (1), pp. 234-246; Ficocelli, M., Terao, J., Nejat, G., Promoting interactions between humans and robots using robotic emotional behavior (2016) IEEE Trans. Cybern., 46 (12), pp. 2911-2923; Liu, Z.T., Dong, F.Y., Hirota, K., Wu, M., Li, D.Y., Yamazaki, Y., Emotional states based 3-D fuzzy atmosfield for casual communication between humans and robots (2011) Proceedings of International Conference on Fuzzy Systems, Taipei, pp. 777-782; Xu, F., Zhang, J., Wang, J., Microexpression identification and categorization using a facial dynamics map (2017) IEEE Trans. Affect. Comput., 8 (2), pp. 254-267; Yang, Z., Narayanan, S., Modeling dynamics of expressive body gestures in dyadic interactions (2017) IEEE Trans. Affect. Comput., 8 (3), pp. 369-381; Sheikhan, M., Bejani, M., Gharavian, D., Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method (2013) Neural Comput. Appl., 23 (1), pp. 215-227; Ding, Y., Hu, X., Xia, Z.Y., Liu, Y.J., Zhang, D., Inter-brain EEG feature extraction and analysis for continuous implicit emotion tagging during video watching IEEE Trans. Affect. Comput., , https://doi.org/10.1109/TAFFC.2018.2849758; Doheny, E.P., Goulding, C., Flood, M.W., McManus, L., Lowery, M.M., Feature-based evaluation of a wearable surface EMG sensor against laboratory standard EMG during force-varying and fatiguing contractions (2020) IEEE Sens. J., 20 (5), pp. 2757-2765; Peng, J.C., Lina, Z., Research of wave filter method of human sphygmus signal (2016) Laser Technol, 40 (1), pp. 42-46; Kolling, A., Walker, P., Chakraborty, N., Sycara, K., Lewis, M., Human interaction with robot swarms: A survey (2015) IEEE Trans. Hum.-Mach. Syst., 46 (1), pp. 9-26; Bicho, E., Erlhagen, W., Sousa, E., Louro, L., The power of prediction: Robots that read intentions (2012) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5458-5459. , Vilamoura, Portugal; Salichs, M.A., Barber, R., Khamis, A.M., Malfaz, M., Gorostiza, J.F., Maggie: A robotic platform for human-robot social interaction (2006) Proceedings of the IEEE Conference on Robotics, Automation and Mechatronics, pp. 1-7. , Bangkok; Hirota, K., Dong, F., Development of mascot robot system in NEDO project (2008) In Proceedings of International IEEE Conference, Varna, pp. 38-44; Itoh, K., Miwa, H., Matsumoto, M., Various emotional expressions with emotion expression humanoid robot WE-4RII (2004) Proceedings of IEEE Technical Exhibition Based Conference on Robotics and Automation, pp. 35-36. , Minato-ku, Tokyo; Chen, L.F., Liu, Z.T., Dong, F.Y., Yamazaki, Y., Wu, M., Adapting multi-robot behavior to communication atmosphere in humans-robots interaction using fuzzy production rule based friend-Q learning (2013) J. Adv. Comput. Intell. Intell. Inform., 17 (2), pp. 291-301; (2018) Blue Frog Robotics, Buddy: The First Emotional Companion Robot, , http://www.Bluefrogrobotics.com/en/buddy_the-emotionnal-robot/; Robotics, H., (2016) Sophia, , http://www.hansonrobotics.com/robot/sophia/; (2018) Live Science, Meet Erica, Japan’s Next Robot News Anchor, , https://www.livescience.com/61575-erica-robot-replace-japanese-news-anchor.html; Lui, J.H., Samani, H., Tien, K.Y., An affective mood booster robot based on emotional processing unit (2018) Proceedings of 2017 International Automatic Control Conference, pp. 1-6; Somchanok, T., Michiko, O., Emotion recognition using ECG signals with local pattern description methods (2016) Int. J. Affect. Eng, 15 (2), pp. 51-61; Chen, L.F., Liu, Z.T., Wu, M., Dong, F.Y., Yamazaki, Y., Hirota, K., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction (2014) J. Multimodal User Interfaces, 8 (3), pp. 289-303; Klug, M., Zell, A., Emotion-based human-robot interaction (2013) Proceedings of IEEE International Conference on Computational Cybernetics, pp. 365-368; Boccanfuso, L., Barney, E., Foster, C., Emotional robot to examine differences in play patterns and affective response of children with and without ASD (2016) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction, pp. 19-26; Liu, Z.T., Wu, M., Cao, W.H., Chen, L.F., Xu, J.P., Zhang, R., A facial expression emotion recognition based humans-robots interaction system (2017) IEEE/CAA J. Autom. Sin, 4 (4), pp. 668-676; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z.T., She, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2020) IEEE Trans. Syst. Man Cybern.: Syst., 50 (2), pp. 490-501; Liu, Z.T., Pan, F.F., Wu, M., Cao, W.H., Chen, L.F., A multimodal emotional communication based humans-robots interaction system (2016) Proceedings of the 35Th Chinese Control Conference, 4 (4), pp. 668-676. , Chengdu","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096230408
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Emotional Human-Robot Interaction Systems",2021,"Studies in Computational Intelligence","926",,,"215","222",,,"10.1007/978-3-030-61577-2_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096211489&doi=10.1007%2f978-3-030-61577-2_12&partnerID=40&md5=334269ff8b0ba7a2001a79ab60e5b745","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","This chapter introduces the basic structure of robot emotion system, analyzes the development function of the system according to the needs of users, and constructs a set of robot emotion interaction system based on the actual equipment. The interaction system of emotion robot constructed in this chapter provides an experimental platform for the verification of emotion recognition algorithm and emotion intention understanding model. At the same time, in order to verify the emotional intention understanding model proposed in this chapter, two reasonable scenarios are set up to realize the understanding of emotional intention in specific situations. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Schuller, D., Schuller, B.W., The Age of Artificial Emotional Intelligence (2018) Computer, 51 (9), pp. 38-46; Li, F., Feng, J., Fu, M., A Natural Human-robot interaction Method in Virtual Roaming Proceedings of the 2019 15Th International Conference on Computational Intelligence and Security, p. 2019. , CIS; Vinciarelli, A., Pantic, M., Boulard, H., Social Signal Processing: Survey of an Emerging Domain (2009) Image and Vision Computing, 27 (12), pp. 1743-1759; Gómez, J.C., Serrano, A.G., Martl Nez, P., Intentional processing as a key for rational behaviour through Natural Interaction (2006) Interacting with Computers, 18 (6), pp. 1419-1446; Fan, J., Campbell, M., Kingsbury, B., Artificial intelligence research at IBM (2011) IBM Journal of Research and Development, 55 (5), pp. 1-16. , 16, 4; Zheng, X.Q., Shiomi, M., Minato, T., Ishiguro, H., What Kinds of Robot’s Touch Will Match Expressed Emotions? (2019) IEEE Robotics and Automation Letters, 5 (1), pp. 127-134; Christopher, P., Johnson, L., Carnegie, D.A., Mobile Robot Navigation Modulated by Artificial Emotions (2009) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40 (2), pp. 469-480; (2018) Blue Frog Robotics. Buddy: The First Emotional Companion Robot, , http://www.Bluefrogrobotics.com/en/buddy_the-emotionnal-robot/; (2016) Hanson Robotics, , http://www.hansonrobotics.com/robot/sophia/, Sophia; (2018) Live Science, , https://www.livescience.com/61575-erica-robot-replace-japanese-news-anchor.html, Meet Erica, Japan’s Next Robot News Anchor; Lee, S.B., Yoo, S.H., Design of the companion robot interaction for supporting major tasks of the elderly Proceedings of the 14Th International Conference on Ubiquitous Robots and Ambient Intelligence, p. 2017. , URAI; Lui, J.H., Samani, H., Tien, K.Y., An affective mood booster robot based on emotional processing unit (2018) Proceedings of 2017 International Automatic Control Conference, pp. 1-6; Somchanok, T., Michiko, O., Emotion recognition using ECG signals with local pattern description methods (2016) International Journal of Affective Engineering, 15 (2), pp. 51-61; Saunderson, S., Nejat, G., It Would Make Me Happy if You Used My Guess: Comparing Robot Persuasive Strategies in Social HumanÂ¨CRobot Interaction (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 1707-1714; Klug, M., Zell, A., Emotion-based human-robot interaction (2013) Proceedings of IEEE International Conference on Computational Cybernetics, pp. 365-368; Boccanfuso, L., Barney, E., Foster, C., Emotional robot to examine differences in play patterns and affective response of children with and without ASD (2016) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction, pp. 19-26; Ficocelli, M., Terao, J., Nejat, G., Promoting interactions between humans and robots using robotic emotional behavior (2016) IEEE Transactions on Cybernetics, 46 (12), pp. 2911-2923; Xu, F., Zhang, J., Wang, J., Microexpression identification and categorization using a facial dynamics map (2017) IEEE Transactions on Affective Computing, 8 (2), pp. 254-267; Sheikhan, M., Bejani, M., Gharavian, D., Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method (2013) Neural Computing and Applications, 23 (1), pp. 215-227; Yang, Z., Narayanan, S., Modeling dynamics of expressive body gestures in dyadic interactions (2017) IEEE Transactions on Affective Computing, 8 (3), pp. 369-381; Ding, Y., Hu, X., Xia, Z.Y., Liu, Y.J., Zhang, D., Inter-brain EEG feature extraction and analysis for continuous implicit emotion tagging during video watching IEEE Transactions on Affective Computing, , https://doi.org/10.1109/TAFFC.2018.2849758; Peng, J.C., Lina, Z., Research of wave filter method of human sphygmus signal (2016) Laser Technology, 40 (1), pp. 42-46; Chen, C.L.P., Liu, Z., Broad learning system: An effective and efficient incremental learning system without the need for deep architecture (2018) IEEE Transactions on Neural Networks and Learning Systems, 29 (1), pp. 10-24; Liu, Z.T., Dong, F.Y., Hirota, K., Wu, M., Li, D.Y., Yamazaki, Y., Emotional states based 3-D fuzzy atmosfield for casual communication between humans and robots (2011) In Proceedings of International Conference on Fuzzy Systems, Taipei, pp. 777-782; Jiang, S.Y., Lin, C.Y., Huang, K.T., Shared control design of a walking-assistant robot (2017) IEEE Transactions on Control Systems Technology, 25 (6), pp. 2143-2150; Lu, X., Bao, W., Wang, S., Three-dimensional interfacial stress decoupling method for rehabilitation therapy robot (2017) IEEE Transactions on Industrial Electronics, 64 (5), pp. 3970-3977","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096211489
"Zhao J., Gong S., Xie B., Duan Y., Zhang Z.","57129187500;57207468351;36462894100;57216927327;56725003500;","Human arm motion prediction in human-robot interaction based on a modified minimum jerk model",2021,"Advanced Robotics","35","3-4",,"205","218",,,"10.1080/01691864.2020.1840432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095734096&doi=10.1080%2f01691864.2020.1840432&partnerID=40&md5=13ff995712b835cd5e226c11ab3cd3a2","College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Department of Electrical and Computer Engineering, University of Kentucky, United States","Zhao, J., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Gong, S., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Xie, B., Department of Electrical and Computer Engineering, University of Kentucky, United States; Duan, Y., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Zhang, Z., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China","Accurate prediction of human motion is essential to ensure the efficiency and safety of human-robot interaction (HRI), especially when humans and robots interact closely in a shared environment. A novel method is developed in this work to address three fundamental problems in human arm motion prediction, i.e. given the early-stage fingertip trajectory of a human arm reaching motion, how to predict the motion duration, the motion destination, and the remaining fingertip trajectory. First, a modified minimum jerk model (MMJM), containing three input parameters—the motion duration, the motion destination, and the early-stage fingertip trajectory, is developed to express and predict the remaining fingertip trajectory. Next, these unknown parameters are determined by determining the optimal starting time of motion prediction and employing Gaussian process regression models (GPRs). Finally, the proposed human arm motion prediction method is validated by simulations and HRI experiments. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","Gaussian process regression; Human-robot interaction; minimum jerk model; motion prediction","Agricultural robots; Forecasting; Man machine systems; Motion estimation; Regression analysis; Trajectories; Accurate prediction; Fingertip trajectory; Gaussian process regression model; Human arm motion; Human robot Interaction (HRI); Input parameter; Minimum jerk model; Motion prediction; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51975008; Beijing Municipal Natural Science Foundation: 3192002","This work was supported by the National Natural Science Foundation of China (No. 51975008) and the Beijing Municipal Natural Science Foundation (No. 3192002).",,,,,,,,,,"Hentout, A., Aouache, M., Maoudj, A., Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (2019) Adv Robot, 33 (15-16), pp. 764-799; Unhelkar, V.V., Lasota, P.A., Tyroller, Q., Human-Aware robotic Assistant for Collaborative Assembly: Integrating human motion prediction With planning in time (2018) IEEE Robot Autom Lett, 3 (3), pp. 2394-2401; Gui, L.Y., Zhang, K., Wang, Y.X., Teaching Robots to Predict Human Motion. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, October 1, 2018 - October 5, 2018. 2018. Madrid, Spain: Institute of Electrical and Electronics Engineers Inc; Reily, B., Han, F., Parker, L.E., Skeleton-based bio-inspired human activity prediction for real-time human–robot interaction (2018) Auton Robots, 42 (6), pp. 1281-1298; Li, Y., Wang, Z., Yang, X., Efficient convolutional hierarchical autoencoder for human motion prediction (2019) Vis Comput, 35 (6-8), pp. 1143-1156; Long, Y., Du, Z.-J., Wang, W.-D., Human motion intent learning based motion assistance control for a wearable exoskeleton (2018) Robot Comput Integr Manuf, 49, pp. 317-327; Torricelli, D., Cortés, C., Lete, N., A Subject-Specific kinematic model to predict human motion in Exoskeleton-Assisted Gait (2018) Front Neurorobot, 12, p. 18; Hua, Y., Fan, J., Liu, G., A novel Weight-Bearing lower Limb Exoskeleton based on motion intention prediction and Locomotion state Identification (2019) IEEE Access, 7, pp. 37620-37638; Bi, L., Feleke, A.G., Guan, C., A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomed Signal Process Control, 51, pp. 113-127; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion. 2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013, November 3, 2013 - November 8, 2013. 2013. Tokyo, Japan: Institute of Electrical and Electronics Engineers Inc; Perez-D'Arpino, C., Shah, J.A., Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification. 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, May 26, 2015 - May 30, 2015. 2015. Seattle, WA, United states: Institute of Electrical and Electronics Engineers Inc; Luo, R., Berenson, D., A framework for unsupervised online human reaching motion recognition and early prediction. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, September 28, 2015 - October 2, 2015. 2015. Messeplatz 1, Hamburg, Germany: Institute of Electrical and Electronics Engineers Inc; Luo, R., Hayne, R., Berenson, D., Unsupervised early prediction of human reaching for human–robot collaboration in shared workspaces (2018) Auton Robots, 42 (3), pp. 631-648; Park, J.S., Park, C., Manocha, D., I-Planner: intention-aware motion planning using learning-based human motion prediction (2019) Int J Rob Res, 38 (1), pp. 23-39; Butepage, J., Kjellstrom, H., Kragic, D., Anticipating Many Futures: Online Human Motion Prediction and Generation for Human-Robot Interaction. 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, May 21, 2018 - May 25, 2018. 2018. Brisbane, QLD, Australia: Institute of Electrical and Electronics Engineers Inc; Liu, H., Wang, L., Human motion prediction for human-robot collaboration (2017) J Manuf Syst, 44, pp. 287-294; Zhou, T., Wachs, J.P., Early prediction for physical human robot collaboration in the operating room (2018) Auton Robots, 42 (5), pp. 977-995; Wang, Y., Sheng, Y., Wang, J., Optimal collision-free robot trajectory generation based on time series prediction of human motion (2018) IEEE Robotics and Automation Letters, 3 (1), pp. 226-233; Wang, W., Qin, L., Yuan, X., Bionic control of exoskeleton robot based on motion intention for rehabilitation training (2019) Adv Robot, 33 (12), pp. 590-601; Pereira, A., Althoff, M., Overapproximative human Arm occupancy prediction for collision avoidance (2018) IEEE Trans Autom Sci Eng, 15 (2), pp. 818-831; Mainprice, J., Hayne, R., Berenson, D., Predicting human reaching motion collaborative tasks using Inverse Optimal Control and iterative re-planning. 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, May 26, 2015 - May 30, 2015. 2015. Seattle, WA, United states: Institute of Electrical and Electronics Engineers Inc; Mainprice, J., Hayne, R., Berenson, D., Goal Set inverse optimal control and iterative replanning for predicting human reaching motions in shared workspaces (2016) IEEE Trans Robot, 32 (4), pp. 897-908; Lasota, P.A., Shah, J.A., A multiple-predictor approach to human motion prediction. 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, May 29, 2017 - June 3, 2017. 2017. Singapore, Singapore: Institute of Electrical and Electronics Engineers Inc; Amor, H.B., Neumann, G., Kamthe, S., Interaction primitives for human-robot cooperation tasks. 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, May 31, 2014 - June 7, 2014. 2014. Hong Kong, China: Institute of Electrical and Electronics Engineers Inc; Maeda, Y., Hara, T., Arai, T., Human-robot cooperative manipulation with motion estimation. 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29, 2001 - November 3, 2001. 2001. Maui, HI, United states: Institute of Electrical and Electronics Engineers Inc; Flash, T., Hogan, N., The coordination of arm movements - an experimentally confirmed mathematical-model (1985) J Neurosci, 5 (7), pp. 1688-1703; Seeger, M., Gaussian processes for machine learning (2004) Int J Neural Syst, 14 (2), pp. 69-106; Kong, D., Chen, Y., Li, N., Gaussian process regression for tool wear prediction (2018) Mech Syst Signal Process, 104, pp. 556-574; Dubey, R.V., Euler, J.A., Babcock, S.M., Real-time implementation of an optimization scheme for seven-degree-of-freedom redundant manipulators (1991) IEEE Trans Robot Autom, 7 (5), pp. 579-588","Xie, B.; Department of Electrical and Computer Engineering, United States; email: Biyun.Xie@uky.edu",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85095734096
"Karim B., Tan Q., Alvarez J.C.","57191078082;24529398000;57209074060;","Towards Fog-Based HiTLCPS for Human Robot Interactions in Smart Lab: Use Cases and Architecture Overview",2021,"Advances in Intelligent Systems and Computing","1268 AISC",,,"590","599",,,"10.1007/978-3-030-57802-2_57","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091312884&doi=10.1007%2f978-3-030-57802-2_57&partnerID=40&md5=3967c2dedd01f0ae5707b96f3fa77da2","University of Oviedo, Oviedo, Spain; Athabasca University, Athabasca, Canada","Karim, B., University of Oviedo, Oviedo, Spain; Tan, Q., Athabasca University, Athabasca, Canada; Alvarez, J.C., University of Oviedo, Oviedo, Spain","This paper provides use case definitions and a high-level system architecture overview for human robot interaction in a fog computing-based Human in The Loop Cyber Physical System. Our focus is to develop a practical, natural, meaningful human robot interaction framework for single and multiple avatar (CPS) robots, and this paper outlines the research road ahead of us. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Cloud computing; Fog computing; Human-in-the-Loop CPS; Human-Machine interaction; Robotics; Social robotics","Embedded systems; Fog computing; Man machine systems; Soft computing; High-level systems; Human-in-the-loop; Smart labs; Social robots",,,,,,,,,,,,,,,,"Tan, Q., Denojean-Mairet, M., Toward a telepresence robot empowered smart lab (2019) Smart Learn. Environ, 6, p. 5. , https://doi.org/10.1186/s40561-019-0084-3; Habib, L., Pacaux-Lemoine, M.-P., Millot, P., Adaptation of the level of automation according to the type of cooperative partner (2017) IEEE International Conference on Systems, Man, and Cybernetics, Banff, Canada, pp. 864-869. , October; Hoc, J.-M., Lemoine, M.-P., Cognitive evaluation of human-human and human-machine cooperation modes in air traffic control (1998) Int. J. Aviat. Psychol, 8 (1), pp. 1-32; Navarro, J., Mars, F., Hoc, J-M., Lateral control support for car drivers: a human-machine cooperation approach (2007) Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, ECCE 2007, 250, pp. 249-252. , ACM; Feng, S., Quivira, F., Schirner, G., Framework for rapid development of embedded human-in-the-loop cyber-physical systems (2016) 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 208-215. , October; Truong, X.-T., Ngo, T.D., Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion (2017) IEEE Trans. Autom. Sci. Eng, 14 (4), pp. 1743-1760; Bowen, C., Alterovitz, R., Closed-loop global motion planning for reactive, collision-free execution of learned tasks (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems; Yan, W., Weber, C., Wermter, S., A neural approach for robot navigation based on cognitive map learning (2012) The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE; Tapus, A., Siegwart, R., A cognitive modeling of space using fingerprints of places for mobile robot navigation (2006) Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: a survey (2013) Int. J. Soc. Rob, 5 (2), pp. 291-308; Bonaccorsi, M., Fiorini, L., Cavallo, F., Saffiotti, A., A cloud robotics solution to improve social assistive robots for active and healthy aging (2016) Int. J. Soc. Rob, 8 (3), pp. 393-408; Park, H., Choi, Y., Jung, Y., Myaeng, S., Supporting mixed initiative human-robot interaction: a script-based cognitive architecture approach (2008) 2008 IEEE International Joint Conference on Neural Networks, pp. 4107-4113; Qu, W., Chen, Z., A new cognitive approach based on dynamic evolution of the grey hazy set (2014) 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, pp. 572-577; Sarker, V.K., Queralta, J.P., Gia, T.N., Tenhunen, H., Westerlund, T., Offloading SLAM for indoor mobile robots with edge-fog-cloud computing (2019) 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, pp. 1-6; Cockburn, A., (2000) Writing Effective Use Cases, , Addison-Wesley Professional, Boston ISBN 0-201-70225-8","Karim, B.; University of OviedoSpain; email: behzadk0@gmail.com","Herrero A.Cambra C.Urda D.Sedano J.Quintian H.Corchado E.",,"Springer Science and Business Media Deutschland GmbH","15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020","16 September 2020 through 18 September 2020",,244579,21945357,9783030578015,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85091312884
"Ajmera Y., Javed A.","57214155033;57198262278;","Shared autonomy in web-based human robot interaction",2021,"Advances in Intelligent Systems and Computing","1252 AISC",,,"696","702",,1,"10.1007/978-3-030-55190-2_55","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090098530&doi=10.1007%2f978-3-030-55190-2_55&partnerID=40&md5=3955b47bc10b979827261d299e6e1288","Mechanical Department, Birla Institute of Technology and Science, Pilani, Hyderabad, India","Ajmera, Y., Mechanical Department, Birla Institute of Technology and Science, Pilani, Hyderabad, India; Javed, A., Mechanical Department, Birla Institute of Technology and Science, Pilani, Hyderabad, India","In this paper, we aim to achieve a human-robot work balance by implementing shared autonomy through a web interface. Shared autonomy integrates user input with the autonomous capabilities of the robot and therefore increases the overall performance of the robot. Presenting only the relevant information to the user on the web page lowers the cognitive load of the operator. Through our web interface, we provide a mechanism for the operator to directly interact using the displayed information by applying a point-and-click paradigm. Further, we present our idea to employ a human-robot mutual adaptation in a shared autonomy setting through our web interface for effective team collaboration. © Springer Nature Switzerland AG 2021.","Human-robot interaction; Shared autonomy; Telerobotics","Intelligent systems; Websites; Autonomous capability; Cognitive loads; Human robots; Mutual adaptation; Shared autonomy; Team collaboration; User input; Web interface; Social robots",,,,,,,,,,,,,,,,"Kot, T., Novák, P., Application of virtual reality in teleoperation of the military mobile robotic system taros (2018) Int. J. Adv. Robot. Syst, 15 (1), p. 1729881417751545; Korpela, C., Chaney, K., Brahmbhatt, P., Applied robotics for installation and base operations for industrial hygiene (2015) 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), pp. 1-6. , IEEE; Lopez, J., Perez, D., Paz, E., Santana, A., Watchbot: a building maintenance and surveillance system based on autonomous robots (2013) Robot. Auton. Syst, 61 (12), pp. 1559-1571; Mishra, R., Ajmera, Y., Mishra, N., Javed, A., Ego-centric framework for a three-wheel omni-drive telepresence robot (2019) 2019 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO), pp. 281-286. , October; Pitzer, B., Osentoski, S., Jay, G., Crick, C., Jenkins, O.C., Pr2 remote lab: an environment for remote development and experimentation (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3200-3205. , IEEE; Musić, S., Hirche, S., Control sharing in human-robot team interaction (2017) Ann. Rev. Control, 44, pp. 342-354; Gómez, C., Hernández, A.C., Crespo, J., Barber, R., Learning robotics through a friendly graphical user interface (2016) ICERI 2016 Proceedings, pp. 483-492; Lankenau, P., (2016) Virtour: Telepresence system for remotely operated building tours; Birkenkampf, P., Leidner, D., Borst, C., A knowledge-driven shared autonomy human-robot interface for tablet computers (2014) 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 152-159. , IEEE; Nikolaidis, S., Zhu, Y.X., Hsu, D., Srinivasa, S., Human-robot mutual adaptation in shared autonomy (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI, pp. 294-302. , IEEE; Toris, R., Kammerl, J., Lu, D.V., Lee, J., Jenkins, O.C., Osentoski, S., Wills, M., Chernova, S., Robot web tools: efficient messaging for cloud robotics (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4530-4537. , IEEE; Crick, C., Jay, G., Osentoski, S., Pitzer, B., Jenkins, O.C., Rosbridge: ROS for non-ROS users (2017) Robotics Research, pp. 493-504. , Springer, Heidelberg; Osentoski, S., Jay, G., Crick, C., Pitzer, B., DuHadway, C., Jenkins, O.C., (2011) Robots as web services: reproducible experimentation and application development using rosjs; Nikolaidis, S., Kuznetsov, A., Hsu, D., Srinivasa, S., Formalizing human-robot mutual adaptation: a bounded memory model (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 75-82. , IEEE Press; Ong, S.C.W., Png, S.W., Hsu, D., Lee, W.S., Planning under uncertainty for robotic tasks with mixed observability (2010) Int. J. Robot. Res, 29 (8), pp. 1053-1068","Ajmera, Y.; Mechanical Department, India; email: f20170644@hyderabad.bits-pilani.ac.in","Arai K.Kapoor S.Bhatia R.",,"Springer","Intelligent Systems Conference, IntelliSys 2020","3 September 2020 through 4 September 2020",,244279,21945357,9783030551896,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85090098530
"Singh K.J., Kapoor D.S., Sohi B.S.","56701439200;55844199200;16242568300;","Selecting social robot by understanding human–robot interaction",2021,"Advances in Intelligent Systems and Computing","1166",,,"203","213",,,"10.1007/978-981-15-5148-2_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089626781&doi=10.1007%2f978-981-15-5148-2_18&partnerID=40&md5=2cf6d0dd44d8addd54f77fc42b6bc74f","Embedded Systems and Robotics Research Group, Chandigarh University, Mohali, Punjab  140413, India","Singh, K.J., Embedded Systems and Robotics Research Group, Chandigarh University, Mohali, Punjab  140413, India; Kapoor, D.S., Embedded Systems and Robotics Research Group, Chandigarh University, Mohali, Punjab  140413, India; Sohi, B.S., Embedded Systems and Robotics Research Group, Chandigarh University, Mohali, Punjab  140413, India","The recent advancements in science and technology have led to the development and deployment of robots in numerous applications spanning all spheres of human life. Investment in robotics domain is bringing diversity of robots in consumer market, which will lead to increase in interactions between humans and robots. Consumers with diverse expectations and limited domain knowledge often find difficult to select a robot. In this article, a generalized block diagram for HRI in social robotics has been discussed, which provides a design overview of HRI to consumers. It also enables consumers to develop general vocabulary about technical terms and informs about key factors for consideration while selecting a robot, to make the selection process easier and efficient for them. In order to illustrate the utility of factors deduced from block diagram, a Multi-Criteria Decision-Making (MCDM) is performed with the help of a fuzzy inference engine to relatively rank different companion robots as an outcome. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.","Fuzzy; HRI; MCDM; Social robots","Decision making; Fuzzy inference; Robotics; Companion robot; Consumer market; Limited domains; Multi-criteria decision making; Robot interactions; Science and Technology; Social robotics; Technical terms; Social robots",,,,,,,,,,,,,,,,"(2018) Robots and the workplace of the future, , https://ifr.org/downloads/papers/IFR_Robots_and_the_Workplace:of_the_Future_Positioning_Paper.pdf, IFR; Cruz-Maya, A., Garcia, F., Pandey, A.K., (2019) Enabling socially competent navigation through incorporating HRI, , http://arxiv.org/abs/1904.09116, arXiv preprint; Beer, J.M., Fisk, A.D., Rogers, W.A., Toward a framework for levels of robot autonomy in human-robot interaction (2014) J. Hum. Robot Interact, pp. 74-99; Druin, A., Cooperative inquiry: developing new technologies for children with children (1999) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 592-599; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot. Auton. Syst, pp. 143-166; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics, pp. 1935-1972; Deisenroth, M.P., Neumann, G., Peters, J., A survey on policy search for robotics (2013) Found. Trends® Robot, pp. 1-142; Adla, R., Yousef, B., Al-Holou, N., Multi sensor data fusion, methods and problems (2013) Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, pp. 22-23. , Las Vegas, NV, USA; Demir, K.A., Döven, K.A., Sezen, B., Industry 5.0 and human-robot co-working (2019) Procedia Computer Science, pp. 688-695; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, pp. 517-527; Murphy, A., (2017) Social and entertainment: robotics outlook 2025, , https://loupventures.com/social-and-entertainment-robotics-outlook-2025/; Das, M.C., Sarkar, B., Ray, S., A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology (2012) Soc. Econ. Plan. Sci, 2012, pp. 230-241; Petrović, G., Mihajlović, J., Ćojbašić, Ž., Madić, M., Marinković, D., Comparison of three fuzzy MCDM methods for solving the supplier selection problem (2019) Facta Universitatis, Series: Mechanical Engineering","Singh, K.J.; Embedded Systems and Robotics Research Group, India; email: kiranjot.693@gmail.com","Gupta D.Khanna A.Bhattacharyya S.Hassanien A.E.Anand S.Jaiswal A.",,"Springer","3rd International Conference on Innovative Computing and Communication, ICICC 2020","21 February 2020 through 23 February 2020",,243279,21945357,9789811551475,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85089626781
"Díaz A., Mahu R., Novoa J., Wuth J., Datta J., Yoma N.B.","57189037645;56225650900;57140069500;26039642700;57220466147;6603542511;","Assessing the effect of visual servoing on the performance of linear microphone arrays in moving human-robot interaction scenarios",2021,"Computer Speech and Language","65",,"101136","","",,1,"10.1016/j.csl.2020.101136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089286171&doi=10.1016%2fj.csl.2020.101136&partnerID=40&md5=21d77753322d4ca70ee66ec998de6ffe","Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile","Díaz, A., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Mahu, R., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Novoa, J., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Wuth, J., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Datta, J., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Yoma, N.B., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile","Social robotics is becoming a reality and voice-based human-robot interaction is essential for a successful human-robot collaborative symbiosis. The main objective of this paper is to assess the effect of visual servoing in the performance of a linear microphone array regarding distant ASR in a mobile, dynamic and non-stationary robotic testbed that can be representative of real HRI scenarios. Visual servoing and image target tracking are different tasks, and this paper focuses on an effect that is rarely addressed in the literature: the dependence of the beamforming directivity on look direction. The datasets required to carry out the study reported here do not exist and had to be generated. A state-of-the-art mobile robotic testbed had to be set up with target speech and noise sources. A linear microphone array was chosen as a case of study and its response was measured. Standard beamforming methods were evaluated with respect to visual servoing: delay-and-sum combined with image tracking; weighted delay-and-sum; and, MVDR also combined with image tracking. The results presented here show that the performance of beamforming methods is dramatically degraded in moving and non-stationary conditions. In this context, visual servoing in HRI can significantly improve the performance of a linear microphone array regarding ASR accuracy. The average reduction in WER achieved when the robot head was steered toward the target speech source was as high as 28.2%. Finally, it is worth highlighting that the methodology adopted here is applicable to any microphone array, linear or not. © 2020 Elsevier Ltd","Automatic speech recognition; Beamforming; Human-robot interaction; Source tracking; Visual servoing","Beamforming; Man machine systems; Microphones; Robotics; Speech communication; Target tracking; Testbeds; Visual servoing; Beamforming methods; Linear microphonearray; Microphone arrays; Non-stationary condition; Robotic testbed; Social robotics; Standard beamforming; State of the art; Social robots",,,,,,,,,,,,,,,,"Kumatani, K., Arakawa, T., Yamamoto, K., McDonough, J., Raj, B., Singh, R., Tashev, I., Microphone array processing for distant speech recognition: towards real-world deployment (2012) Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, Hollywood, California; Araki, S., Ito, N., Delcroix, M., Ogawa, A., Kinoshita, K., Higuchi, T., Yoshioka, T., Nakatani, T., Online meeting recognition in noisy environments with time-frequency mask based MVDR beamforming (2017) 2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA), , IEEE; Araki, S., Ono, N., Kinoshita, K., Delcroix, M., Meeting recognition with asynchronous distributed microphone array using block-wise refinement of mask-based MVDR beamformer (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Araki, S., Okada, M., Higuchi, T., Ogawa, A., Nakatani, T., Spatial correlation model based observation vector clustering and MVDR beamforming for meeting recognition (2016) 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Higuchi, T., Ito, N., Araki, S., Yoshioka, T., Delcroix, M., Nakatani, T., Online MVDR beamformer based on complex Gaussian mixture model with spatial prior for noise robust ASR (2017) IEEE/ACM Trans. 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Am., 130 (6), pp. 3827-3837; Huang, G., Benesty, J., Chen, J., On the design of frequency-invariant beampatterns with uniform circular microphone arrays (2017) IEEE/ACM Trans Audio Speech Lang Process, 25 (5), pp. 1140-1153; Tiana-Roig, E., Jacobsen, F., Deconvolution for the localization of sound sources using a circular microphone array (2013) J. Acoust. Soc. Am., 134 (3), pp. 2078-2089; Huang, G., Chen, J., Benesty, J., On the design of robust steerable frequency-invariant beampatterns with concentric circular microphone arrays (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Meyer, J., Beamforming for a circular microphone array mounted on spherically shaped objects (2001) J. Acoust. Soc. Am., 109 (1), pp. 185-193; Król, D., Lorenc, A., Świcęiński, R., Detecting laterality and nasality in speech with the use of a multi-channel recorder (2015) IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Lorenc, A., Król, D., Klessa, K., An acoustic camera approach to studying nasality in speech: the case of Polish nasalized vowels (2018) J. Acoust. Soc. Am., 144 (6), pp. 3603-3617; Haddad, K., Hald, J.R., 3D localization of acoustic sources with a spherical array (2008) J. Acoust. Soc. Am., 123 (5), p. 3311; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: an open-source Robot Operating System (2009) ICRA Workshop on Open Source Software","Yoma, N.B.; Speech Process. and Transm. Lab., Chile; email: nbecerra@ing.uchile.cl",,,"Academic Press",,,,,08852308,,CSPLE,,"English","Comput Speech Lang",Article,"Final","",Scopus,2-s2.0-85089286171
"Zieringer C., Bauer B., Stache N.C., Wittenberg C.","57218203069;57209602002;15924610900;7006561833;","Human-Robot Interaction via a Virtual Twin and OPC UA",2021,"Advances in Intelligent Systems and Computing","1210 AISC",,,"101","107",,2,"10.1007/978-3-030-51758-8_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088243717&doi=10.1007%2f978-3-030-51758-8_14&partnerID=40&md5=2faf9dc929c8b73ee37d589726d6ac93","Department Mechanics and Electronics, Heilbronn University of Applied Sciences, Heilbronn, 74081, Germany","Zieringer, C., Department Mechanics and Electronics, Heilbronn University of Applied Sciences, Heilbronn, 74081, Germany; Bauer, B., Department Mechanics and Electronics, Heilbronn University of Applied Sciences, Heilbronn, 74081, Germany; Stache, N.C., Department Mechanics and Electronics, Heilbronn University of Applied Sciences, Heilbronn, 74081, Germany; Wittenberg, C., Department Mechanics and Electronics, Heilbronn University of Applied Sciences, Heilbronn, 74081, Germany","The idea of a digital factory is closely combined with the concept of virtual or digital twins. The development and implementation of virtual twins is facilitated by the so-called game engines which offer huge possibilities in the virtual space. These game engines provide programming interfaces to several development environments. This paper describes early steps of the development of a virtual twin for a 6-axle industrial robot including a user interface. This user interface was evaluated. The results of the evaluation are the basis for a bold redesign, which is described in this paper shortly. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Human-robot interaction; IIoT; Robotics; Virtual engineering; Virtual reality; Virtual twin","Digital twin; Drones; Human engineering; Security of data; User interfaces; Development environment; Digital factories; Game Engine; Programming interface; Virtual spaces; Social robots",,,,,,,,,,,,,,,,"Felix, H., (1998) Unternehmens-und Fabrikplanung: Planungsprozesse, Leistungen und Beziehungen, , REFA Fachbuchreihe, Hanser Verlag, Munich; During, A., Komischke, T., Wittenberg, C., Berger, U., A vision for an information management tool for plant engineering – functionality and user interface (2004) Proceedings of the TMCE 2004, pp. 12-16. , Horváth, X. (eds) Rotterdam Millpress, Lausanne; Wittenberg, C., Cause the trend Industry 4.0 in the automated industry to new requirements on user interfaces? (2015) Proceedings of Human-Computer Interaction-Users and Contexts, pp. 238-245. , Kurosu, M. (ed) Springer, Heidelberg; Wittenberg, C., Human-CPS interaction – requirements and mobile human-machine interaction methods for the Industry 4.0 (2016) Proceedings of the 13th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Kyoto, 30 August–3 September, IFAC-PapersOnLine, 49 (19), pp. 420-425; Armendia, M., Alzaga, A., Peysson, F., Fuertjes, T., Cugnon, F., Ozturk, E., Flum, D., Machine tool: from the digital twin to the cyber-physical systems (2019) Twin-Control, pp. 3-21. , Armendia, M., Ghassempouri, M., Ozturk, E., Peysson, F. (eds) Springer, Berlin; Lange, J., Iwanitz, F., Burke, T., (2010) OPC – From Data Access to Unified Architecture, , VDE VERLAG GMBH, Berlin; Wittenberg, C., Bauer, B., Stache, N., A smart factory in a laboratory size for developing and testing innovative human-machine interaction concepts (2020) Advances in Usability and User Experience, AHFE 2019. Advances in Intelligent Systems and Computing, 972, pp. 160-166. , Ahram, T., Falcão, C. (eds) Springer, Cham; Zieringer, C., Bauer, B., Wittenberg, C., Erstellung eines virtuellen Zwillings mit OPCUA und der Unreal Engine 4 4 (Implementation of a virtual twin with OPC UA and the unreal engine) (2019) Tagungsband AALE, , Bauer, B., Wittenberg, C. (eds) VDE-Verlag, Berlin; Ehmann, D., Wittenberg, C., The idea of virtual teach-in in the field of industrial robotics (2018) Proceedings of 2018 IEEE 14th International Conference on Control and Automation (ICCA), Anchorage, USA, pp. 680-685","Wittenberg, C.; Department Mechanics and Electronics, Germany; email: carsten.wittenberg@hs-heilbronn.de","Zallio M.",,"Springer","AHFE Virtual Conference on Human Factors in Robots, Drones and Unmanned Systems, and the International Conference on Human Factors in Cybersecurity, 2020","16 July 2020 through 20 July 2020",,241779,21945357,9783030517571,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85088243717
"Maniscalco U., Messina A., Storniolo P.","22734994000;57193569361;6506554923;","The human–robot interaction in robot-aided medical care",2021,"Smart Innovation, Systems and Technologies","189",,,"233","242",,3,"10.1007/978-981-15-5784-2_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086304170&doi=10.1007%2f978-981-15-5784-2_19&partnerID=40&md5=75095e9ce0385a0e509965ebb1c09fb5","Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy","Maniscalco, U., Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy; Messina, A., Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy; Storniolo, P., Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy","This article deals with the problem of the interaction and engagement between humans and humanoid Robots in circumstances where it is essential to be sure that engagement has occurred and it persists for the right time. In particular, in Robot-aided medical care (but also in other critical situations), it is essential to be sure that the flow of information between the human and the humanoid Robot effectively occurs between the actual patient and the Robot. Many sensory data of the Robot will be involved in a data fusion algorithm to provide robustness and stability in the Human–Robot interaction. The methodology described in this article has been realized on real Robots (Nao and Pepper) and implemented through Python scripts in ROS (Robot Operating System). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.","Human–Robot interaction; Robot; Robotics","Anthropomorphic robots; Data fusion; Intelligent systems; Medical robotics; Data fusion algorithm; Humanoid robot; Real robot; Robot interactions; Robot operating system; Sensory data; Human robot interaction",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01_00900","Acknowledgements This research was partially supported by the project AMICO—Assistenza Medicale In COntextual Awareness, with funding from the National Programs of the Italian Ministry of Education, Universities and Research (code: ARS01_00900).",,,,,,,,,,"Sidner, C.L., Lee, C., Kidd, C.D., Lesh, N., Rich, C., Explorations in engagement for humans and Robots (2005) Artif. Intell., 166 (1-2), pp. 140-164. , ISSN 0004-3702; Ehrlich, S., Wykowska, A., Ramirez-Amaro, K., Cheng, G., When to engage in interaction and how? EEG-based enhancement of robot’s ability to sense social signals in HRI (2014) 14Th IEEE-RAS International Conference on Humanoid Robots (Humanoids). IEEE, pp. 1104-1109. , pp., 2014; Bartl, C., Dorner, D., Psi: A theory of the integration of cognition, emotion and motivation (1998) Proceedings of the 2Nd European Conference on Cognitive Modelling. DTIC Document, pp. 66-73. , pp; Patterson, M.L., (1983) Nonverbal Behavior. a Functional Perspective, , Springer, New York; Cassell, J., Nudge nudge wink wink: Elements of face-to-face conversation for embodied conversational agents (2000) Embodied Conversational Agents, pp. 1-18. , Cassell, J., Sullivan, J., Prevost, S., Churchill, E. (eds.), pp., MIT Press, Cambridge, MA; Rauterberg, M., Dtwyler, M., Sperisen, M., From competition to collaboration through a shared social space (1995) Proceedings of the East-West International Conference on Human-Computer Interaction (EWHCI95), 2, pp. 94-101. , Blumental, B., Gornostaev, J., Unger, C. (eds.), vol., pp; Hall, E.T., (1968) Proxemics. Curr. Anthropol., 9, pp. 83-108; Argyle, M., Cook, M., Gaze and Mutual Gaze (1976) Cambridge University Press, New York; Miyauchi, D., Sakurai, A., Makamura, A., Kuno, Y., Active eye contact for human–robot communication (2004) Proceedings of CHI 2004–Late Breaking Results, Vol. CD Disc 2, pp. 1099-1104. , pp., ACM Press, New York; Minutolo, A., Esposito, M., de Pietro, G., A conversational chatbot based on kowledge-graphs for factoid medical questions (2017) Somet, pp. 139-152. , pp., September; Caggianese, G., de Pietro, G., Esposito, M., Gallo, L., Minutolo, A., Neroni, P., Discovering Leonardo with artificial intelligence and holograms: A user study (2020) Pattern Recognit. Lett.","Maniscalco, U.; Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Italy; email: umberto.maniscalco@icar.cnr.it","Zimmermann A.Howlett R.J.Jain L.C.",,"Springer","International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020","17 June 2020 through 19 June 2020",,240529,21903018,9789811557835,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85086304170
"Aguiar Noury G., Tsekeni M., Morales V., Burke R., Palomino M., Masala G.L.","57208167376;57217115788;57217113911;57217113396;24824764400;8581904600;","Experiment protocol for human–robot interaction studies with seniors with mild cognitive impairments",2021,"Smart Innovation, Systems and Technologies","189",,,"243","253",,,"10.1007/978-981-15-5784-2_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086303109&doi=10.1007%2f978-981-15-5784-2_20&partnerID=40&md5=3c02f96e6a417f1e5d4d64c6f367a576","University of Plymouth, Plymouth, United Kingdom; Manchester Metropolitan University, Manchester, United Kingdom","Aguiar Noury, G., University of Plymouth, Plymouth, United Kingdom; Tsekeni, M., University of Plymouth, Plymouth, United Kingdom; Morales, V., University of Plymouth, Plymouth, United Kingdom; Burke, R., University of Plymouth, Plymouth, United Kingdom; Palomino, M., University of Plymouth, Plymouth, United Kingdom; Masala, G.L., Manchester Metropolitan University, Manchester, United Kingdom","While assistive robotics (AR) have shown promise in supporting seniors with daily life activities and psycho-social development, evaluation of AR systems presents novel challenges. From a technical point of view, reproducing HRI experiments has been problematic due to the lack of protocols, standardization and benchmarking tools, which ultimately impairs the evaluation of previous experiments. On the other hand, working with seniors with cognitive decline presents a major design challenge for researchers, since communication skills, state of mind and attention of participants are compromised. To address these challenges, this paper presents practical recommendations and a protocol for conducting HRI experiments with seniors with mild cognitive decline (MCI). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.","Assistive technology; Experimental design; Human–robot interaction; Measurement and evaluation","Intelligent systems; Social robots; Assistive robotics; Benchmarking tools; Communication skills; Daily life activities; Mild cognitive impairments; Practical recommendation; Robot interactions; Social development; Economic and social effects",,,,,"Interreg: 2S05-014","Acknowledgements Authors of this paper acknowledge the funding provided by the Interreg 2 Seas Mers Zeeën AGE’In project (2S05-014) to support the work in the research described in this publication.",,,,,,,,,,"Lee, Y., Hwang, J., Lim, S., Kim, J.T., Identifying characteristics of design guidelines for elderly care environments from the holistic health perspective (2013) Indoor Built Environ, 22, pp. 242-259; (2017) Robotics in Social Care: A Connected Care Ecosystem for Independent Living; Kidd, C., Breazeal, C., Human-robot interaction experiments: Lessons learned (2005) Proceeding of AISB’05 Convention; Bethel, C.L., Burke, J.L., Murphy, R.R., Salomon, K., Psychophysiological experimental design for use in human-robot interaction studies (2007) Proceedings of the 2007 International Symposium on Collaborative Technologies and Systems, CTS, pp. 99-105. , pp; EURobotics: Strategic Research Agenda For Robotics in Europe 2014-2020 (2014) IEEE Robot. Autom. Mag., 24, p. 171; Desideri, L., Ottaviani, C., Malavasi, M., Di Marzio, R., Bonifacci, P., (2019) Emotional Processes in Human-Robot Interaction during Brief Cognitive Testing, , Elsevier; Di Nuovo, A., Varrasi, S., Lucas, A., Conti, D., McNamara, J., Soranzo, A., Assessment of cognitive skills via human-robot interaction and cloud computing (2019) J. Bionic Eng., 16, pp. 526-539; Tricco, A.C., Soobiah, C., Lillie, E., Perrier, L., Chen, M.H., Hemmelgarn, B., Majumdar, S.R., Straus, S.E., Use of cognitive enhancers for mild cognitive impairment: Protocol for a systematic review and network meta-analysis (2012) Syst. Rev., 1, p. 25; Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., Park, T., Methodological issues in scenario-based evaluation of human-robot interaction (2015) Int. J. Soc. Robot., 7, pp. 279-291; Jones, R., Asthana, S., Walmsley, A., Sheaff, R., Milligan, J., Paisey, M., Aguiar Noury, G., (2019) Developing the Ehealth Sector in Cornwall, Plymouth; Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E., Mild cognitive impairment (1999) Arch. Neurol., 56, p. 303; Eshkoor, S.A., Hamid, T.A., Mun, C.Y., Ng, C.K., Mild cognitive impairment and its management in older people (2015) Clin. Interv. Aging, 10, pp. 687-693; Berghmans, R.L.P., Meulen, R.H.J.T., Ethical issues in research with dementia patients (1995) Int. J. Geriatr. Psychiatry, 10, pp. 647-651; Comito, C., Caniot, M., Lagrue, E., Coignard, P., Fattal, C., Psychological and symbolic determinants relating to the first meeting with a humanoid robot (2016) Ann. Phys. Rehabil. Med., 59; Amy A1 Robot–Collaborative Robotics, , https://www.robotcenter.co.uk/products/amy-a1-robot, Accessed 11 Feb 2020; An Interactive Open Source Robot for Kids, Developers and Eldercare, , http://thecorpora.com/, Accessed 11 Feb 2020; (2016) Alzheimer’s Society: Assessing Cognition in Older People: A Practical Toolkit for Health Professionals, pp. 1-2. , pp; Tests for Diagnosing Dementia-Dementia Guide-Nhs Choices, , https://www.nhs.uk/conditions/dementia/diagnosis-tests/, Accessed 24 Jan 2020; (2020) Dementia Collaborative Research Centre-Assessment and Better Care: Gpcog|Home, , http://gpcog.com.au/, Accessed 24 Jan; Hartford Institute for Geriatric Nursing: Mental status assessment of older adults: The mini-cog (2017) Alzheimer’s Dement, 13, pp. 325-373; (2004) MOCA Montreal Cognitive Assessment; Servidoni, A.B., Conterno, L.O., Hearing loss in the elderly: Is the hearing handicap inventory for the elderly-screening version effective in diagnosis when compared to the audiometric test? (2018) Int. Arch. Otorhinolaryngol, 22, pp. 1-8; Schuchard, R.A., Validity and interpretation of amsler grid reports (1993) Arch. Ophthalmol., 111, pp. 776-780; Makai, P., Brouwer, W.B.F., Koopmanschap, M.A., Nieboer, A.P., Capabilities and quality of life in Dutch psycho-geriatric nursing homes: An exploratory study using a proxy version of the ICECAP-O (2012) Qual. Life Res., 21, pp. 801-812; Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., Parkinson, J., Stewart-Brown, S., The warwick-edinburgh mental well-being scale (WEMWBS): Development and UK validation (2007) Health Qual. Life Outcomes, 5, p. 63; Radloff, L.S., The use of the center for epidemiologic studies depression scale in adolescents and young adults (1991) J. Youth Adolesc., 20, pp. 149-166; de Jong Gierveld, J., van Tilburg, T.G., Social isolation and loneliness (2016) Encyclopedia of Mental Health, pp. 175-178. , 2nd edn., pp; Ware, J.E., Sherbourne, C.D., The MOS 36-item short-form health survey (Sf-36): I. Conceptual framework and item selection (1992) Med. Care., 30, pp. 473-483; Sidner, C.L., Kidd, C.D., Lee, C., Lesh, N., Where to look: A study of human-robot engagement (2004) Proceedings of the 9Th International Conference on Intelligent User Interface-IUI’04, pp. 78-84. , pp; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 383-388; Kapoor, A., Burleson, W., Picard, R.W., Automatic prediction of frustration (2007) Int. J. Hum. Comput. Stud., 65, pp. 724-736; Posner, J., Russell, J.A., Peterson, B.S., The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology (2005) Dev. Psychopathol., 17, pp. 715-734; Stern, R.M., Ray, W.J., Quigley, K.S., (2012) Psychophysiological Recording","Aguiar Noury, G.; University of PlymouthUnited Kingdom; email: gabriel.aguiar@plymouth.ac.uk","Zimmermann A.Howlett R.J.Jain L.C.",,"Springer","International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020","17 June 2020 through 19 June 2020",,240529,21903018,9789811557835,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85086303109
"Spezialetti M., Placidi G., Rossi S.","55421242300;7006022089;55619320887;","Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives",2020,"Frontiers in Robotics and AI","7",,"532279","","",,7,"10.3389/frobt.2020.532279","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098994284&doi=10.3389%2ffrobt.2020.532279&partnerID=40&md5=7ee74d5455ae3564abf5b4adedce43b4","PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy; Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy","Spezialetti, M., PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy; Placidi, G.; Rossi, S., PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy","A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities. © Copyright © 2020 Spezialetti, Placidi and Rossi.","affective computing; emotion recognition (ER); human-robot interaction; machine learning; multimodal data",,,,,,"F/190066/01-02/X44","This work has been supported by PON I&C 2014-2020 within the BRILLO research project Bartending Robot for Interactive Long Lasting Operations Prog. n. F/190066/01-02/X44.",,,,,,,,,,"Akin, M., Comparison of wavelet transform and FFT methods in the analysis of EEG signals (2002) J. Med. Syst, 26, pp. 241-247. , 12018610; Alarcao, S.M., Fonseca, M.J., Emotions recognition using EEG signals: a survey (2017) IEEE Trans. Affect. 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"Jarske S., Raudaskoski S., Kaipainen K.","57221334151;40462296800;35117849100;","The ""social"" of the Socially Interactive Robot: Rethinking Human-Robot Interaction through Ethnomethodology",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"194","203",,,"10.3233/FAIA200915","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098893255&doi=10.3233%2fFAIA200915&partnerID=40&md5=7b3e6728092d873358d9b574a51936dc","Unit of Computing Sciences, Tampere University, Finland; Unit of Social Sciences, Tampere University, Finland","Jarske, S., Unit of Computing Sciences, Tampere University, Finland; Raudaskoski, S., Unit of Social Sciences, Tampere University, Finland; Kaipainen, K., Unit of Computing Sciences, Tampere University, Finland","As social robots project socially interactive skills including speech and gestures, they are in a position to project normative practices that humans ordinarily rely upon in their everyday interactions with each other. Social robots enable experiences that are reducible to interaction as a normative practice, such as a sense of moral obligation to respond to a robot's greeting. This may have consequences both for the user experience and the design of social robots that are currently overlooked. We propose that theoretical-methodological tools from ethnomethodology should be applied to evaluate and investigate the experiences related to social interaction with social robots. © 2020 The authors and IOS Press. All rights reserved.","cooperation; ethnomethodology; moral order; normative practices; sequences in interaction; shared intentionality; social interaction; Socially interactive robots","End effectors; Machine design; Robotics; Social aspects; User experience; Ethnomethodology; Interactive robot; Methodological tools; Moral obligations; Social interactions; Social robots",,,,,,,,,,,,,,,,"Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Rob Auton Syst, 42, pp. 143-166; Chen, Y., Wu, F., Shuai, W., Chen, X., Robots serve humans in public places-KeJia robot as a shopping assistant (2017) Int J Adv Robot Syst, 14. , 172988141770356; Aaltonen, I., Arvola, A., Heikkila, P., Lammi, H., Hello pepper, may i tickle you? (2017) Proc. Companion 2017 ACM/IEEE Int. Conf. Human-Robot Interact, pp. 53-54. , New York, NY, USA: ACM; Kaipainen, K., Ahtinen, A., Hiltunen, A., Nice surprise, more present than a machine""-Experiences evoked by a social robot for guidance and edutainment at a city service point (2018) Proc. 22nd Int. Acad. Mindtrek Conf. (Mindtrek '18), pp. 163-171. , New York, NY, USA: ACM; Iio, T., Satake, S., Kanda, T., Hayashi, K., Ferreri, F., Hagita, N., Human-Like guide robot that proactively explains exhibits (2020) Int J Soc Robot, 12, pp. 549-566; Mubin, O., Ahmad, M.I., Kaur, S., Shi, W., Khan, A., Social robots in public spaces: A meta-review (2018) Proc. Int. Conf. Soc. Robot. (ICSR 2018), pp. 213-220. , vol. 11357 LNAI, Springer Verlag; Deng, E., Mutlu, B., Mataric, M.J., Embodiment in socially interactive robots (2019) Found Trends Robot, 7, pp. 251-356; Li, J., The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) Int J Hum Comput Stud, 77, pp. 23-37; Sabanovic, S., Bennett, C.C., Wan-Ling, C., Huber, L., PARO robot affects diverse interaction modalities in group sensory therapy for older adults with dementia (2013) 2013 Ieee 13th Int. Conf. Rehabil. Robot., pp. 1-6; Ahtinen, A., Kaipainen, K., Learning and teaching experiences with a persuasive social robot in primary school-findings and implications from a 4-month field study (2020) Persuas. Technol. Des. Futur. Chang. Persuas, pp. 73-84. , In: S. G-H, T. J, C. M, editors. Lect. Notes Comput. Sci. vol. 12064 LNCS, Springer, Cham; 2020; Alac, M., Social robots: Things or agents? (2016) Ai Soc, 31, pp. 519-535; Alac, M., Movellan, J., Tanaka, F., When a robot is social: Spatial arrangements and multimodal semiotic engagement in the practice of social robotics (2011) Soc Stud Sci, 41, pp. 893-926; Pelikan, H.R.M., Broth, M., Why that Nao? How humans adapt to a conventional humanoid robot in taking turns-at-talk (2016) Proc. 2016 Chi Conf. Hum. Factors Comput. Syst, pp. 4921-4932. , New York, NY, USA: ACM; Ghosh, M., Kuzuoka, H., An ethnomethodological study of a museum guide robot's attempt at engagement and disengagement (2014) J Robot, 2014, pp. 1-20; Garfinkel, H., (1967) Studies in ethnomethodology, , Englewood Cliffs, NJ, USA: Prentice-Hall; Garfinkel, H., A conception of, and experiments with, ""trust"" as a condition of stable concerted actions (1963) Motiv. Soc. Interact. Cogn. Approaches, pp. 187-238. , In: Harvey OJ, editor., New York, NY, USA: Ronald Press; Tomasello, M., (2008) Origins of Human Communication, , Cambridge, MA, US: MIT Press; Heritage, J., (1984) Garfinkel and Ethnomethodology, , Cambridge, UK: Polity Press; Tomasello, M., Carpenter, M., Shared intentionality (2007) Dev Sci, 10, pp. 121-125; Levinson, S.C., On the human ""interaction engine (2006) Roots Hum. Soc. Cult. Cogn. Interact, pp. 39-69. , Oxford, UK: Berg; Suchman, L.A., (1987) Plans and Situated Actions: The Problem of Human-machine Communication, , New York, NY, USA: Cambridge University Press; Schegloff, E.A., (2007) Sequence Organization in Interaction: A Primer in Conversation Analysis I, , Cambridge University Press; Merleau-Ponty, M., (2003) Nature: Course Notes from the College de France, , Evanston: Northwestern University Press; Katila, J., Raudaskoski, S., Interaction analysis as an embodied and interactive process: Multimodal, cooperative, and intercorporeal ways of seeing video data as complementary professional visions (2020) Hum Stud, pp. 1-26; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1, pp. 71-81; Kahn, P.H., Friedman, B., Perez-Granados, D.R., Freier, N.G., Robotic pets in the lives of preschool children (2006) Interact Stud, 7, pp. 405-436; Jones, R.A., What makes a robot 'social'? (2017) Soc Stud Sci, 47, pp. 556-579; Bickhard, M.H., Robot sociality: Genuine or simulation? (2014) Front Artif Intell Appl, 273, pp. 81-85; Kaipainen, K., Jarske, S., Varsaluoma, J., Vaananen, K., Persuading youth in civic participation with social robots: What is appropriate? (2020) Front. Artif. Intell. Appl., , IOS Press; Jorgensen, S.M.H., Tafdrup, O.A., Technological fantasies of Nao-remarks about alterity relations (2017) Transformations, 29, pp. 88-103; Parviainen, J., Van Aerschot, L., Sarkikoski, T., Pekkarinen, S., Melkas, H., Hennala, L., Motions with emotions? (2019) Techne Res Philos Technol, 23, pp. 318-341; Hasse, C., Artefacts that talk: Mediating technologies as multistable signs and tools (2013) Subjectivity, 6, pp. 79-100; Hasse, C., How robots challenge institutional practices (2020) Learn Cult Soc Interact, 26, p. 100223; Blond, L., Studying robots outside the lab: HRI as ethnography (2019) Paladyn, J Behav Robot, 10, pp. 117-127; Turkle, S., (2011) Alone Together: Why We Expect More from Technology and Less from Each Other, , Basic Books; Blond, L., (2019) Dances with Robots: Understanding Social Robots in Practice, , Aarhus University; Sorenson, J., Zawieska, K., Vermeulen, B., Madsen, S., Trentemoller, S., Pyka, A., (2019) Perspectives on Robots; Bodenhagen, L., Fischer, K., Winther, T.S., Langedijk, R.M., Skjoth, M.M., Robot use cases for real needs: A large-scale ethnographic case study (2019) Paladyn, J Behav Robot, 10, pp. 193-206; Fischer, K., Seibt, J., Rodogno, R., Rasmussen, M.K., Weiss, A., Bodenhagen, L., Integrative social robotics hands-on (2020) Interact Stud Soc Behav Commun Biol Artif Syst, 21, pp. 145-185; Yamazaki, K., Heath, C., Yamazaki, A., Okada, M., Kuno, Y., Kobayashi, Y., Revealing Gauguin (2009) Proc. 27th Int. Conf. Hum. Factors Comput. Syst.-CHI 09, p. 1437. , New York, New York, USA: ACM Press; Pitsch, K., Kuzuoka, H., Suzuki, Y., Sussenbach, L., Luff, P., Heath, C., The first five seconds"": Contingent stepwise entry into an interaction as a means to secure sustained engagement in HRI (2009) RO-MAN 2009-18th Ieee Int. Symp. Robot Hum. Interact. Commun, pp. 985-991; Raudaskoski, S., (2009) Tool and Machine: The Affordances of the Mobile Phone, , Tampere University","Jarske, S.; Corresponding Author: Salla Jarske, Korkeakoulunkatu, 1, Finland; email: salla.jarske@Tuni.fi","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098893255
"Remmers P.","57221325856;","The Artificial Nature of Social Robots: A Phenomenological Interpretation of Two Conflicting Tendencies in Human-Robot Interaction",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"78","85",,,"10.3233/FAIA200902","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098889175&doi=10.3233%2fFAIA200902&partnerID=40&md5=db1e1c844196e7903332b0da7a18f94f","Institute of Philosophy, History of Literature, Science, and Technology, Technische Universität Berlin, Germany","Remmers, P., Institute of Philosophy, History of Literature, Science, and Technology, Technische Universität Berlin, Germany","Effects of anthropomorphism or zoomorphism in social robotics motivate two opposing tendencies in the philosophy and ethics of robots: A ârational' tendency that discourages excessive anthropomorphism because it is based on an illusion and a âvisionary' tendency that promotes the relational reality of human-robot interaction. I argue for two claims: First, the opposition between these tendencies cannot be resolved and leads to a kind of technological antinomy. Second, we can deal with this antinomy by way of an analogy between our treatment of robots as social interactors and the perception of objects in pictures according to a phenomenological theory of image perception. Following this analogy, human-or animal-likeness in social robots is interpreted neither as a psychological illusion, nor as a relational reality. Instead, robots belong to a special ontological category shaped by perception and interaction, similar to objects in images. © 2020 The authors and IOS Press. All rights reserved.","anthropomorphism; Human-robot interaction; illusion; image perception; phenomenology","Man machine systems; Robotics; Image perception; Interactors; Phenomenological interpretation; Phenomenological theory; Social robotics; Social robots",,,,,,,,,,,,,,,,"Mayer, M.P., Kuz, S., Schlick, C.M., Using anthropomorphism to improve the human-machine interaction in industrial environments (part II) (2013) In: International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, pp. 93-100. , http://link.springer.com/chapter/10.1007/978-3-642-39182-8_11, Internet]. Springer, [cited 2017 Feb 21]; Goetz, J., Kiesler, S., Powers, A., Matching robot appearance and behavior to tasks to improve human-robot cooperation In: The 12th Ieee International Workshop on Robot and Human Interactive Communication, 2003 Proceedings Roman 2003, pp. 55-60. , http://ieeexplore.ieee.org/abstract/document/1251796, Internet]. IEEE; 2003 [cited 2017 Feb 21]; Bryson, J.J., Robots should be slaves (2010) Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues. Amsterdam, pp. 63-74. , In: Wilks Y, editor. Philadelphia: John Benjamins; Johnson, D.G., Verdicchio, M., Why robots should not be treated like animals (2018) Ethics Inf Technol., 20 (4), pp. 291-301. , Sep 24; Gunkel, D.J., A vindication of the rights of machines (2014) Philos Technol., 27 (1), pp. 113-132. , Mar; Coeckelbergh, M., The moral standing of machines: Towards a relational and non-cartesian moral hermeneutics (2014) Philos Technol, 27 (1), pp. 61-77. , Mar; Remmers, P., The ethical significance of human likeness in robotics and ai (2019) Ethics Prog, 10 (2), pp. 52-67. , Oct 10; Dennett, D.C., (1987) The Intentional Stance, , Cambridge (Massachusetts): MIT Press; Remmers, P., Mensch-roboter-interaktion (2018) Philosophische und Ethische Perspektiven, , Berlin: Logos Verlag; Gombrich, E.H., (1984) Art and Illusion: A Study in the Psychology of Pictorial Representation, , 7th ed. London: Phaidon; Husserl, E., Phantasie, Bildbewusstsein, Erinnerung (1980) Zur Phänomenologie der Anschaulichen Vergegenwärtigungen. Texte Aus Dem Nachlass (1898-1925), , Marbach E, editor. Den Haag: Martinus Nijhoff; Wiesing, L., (2010) Artificial Presence: Philosophical Studies in Image Theory, , Stanford, CA: Stanford University Press; Beiboer, J., Sandoval, E.B., Validating the accuracy of imaged-based research into the uncanny valley: An experimental proposal (2019) In: Proceedings of the 14th ACM/IEEE International Conference on Human-robot Interaction, pp. 608-609. , Daegu, Republic of Korea: IEEE Press","Remmers, P.; Technische Universität Berlin, Sekr. H 72, Straße des 17. Juni 135, Germany; email: peter.remmers@tu-Berlin.de","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098889175
"Rueben M., Rothberg E., Mataric M.J.","36814315700;57216354415;7005611984;","Applying the Theory of Make-Believe to Human-Robot Interaction",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"40","50",,,"10.3233/FAIA200899","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098883299&doi=10.3233%2fFAIA200899&partnerID=40&md5=7c82ac8151c667254aab3d5c6dbe7cc7","Dept. of Computer Science, University of Southern California, United States; Department of Computer Science and Engineering, Ohio State University, United States","Rueben, M., Dept. of Computer Science, University of Southern California, United States; Rothberg, E., Department of Computer Science and Engineering, Ohio State University, United States; Mataric, M.J., Dept. of Computer Science, University of Southern California, United States","People often make ascriptions that they believe to be literally false. A robot, for example, may be treated as if it were a dog, or as if it had certain intentions, emotions, or personality traits. How can one do this while also believing that robots cannot really have such traits? In this paper we explore how Kendall Walton's theory of make-believe might account for this apparent paradox. We propose several extensions to Walton's theory, some implications for how we make attributions and use mental models, and an informal account of human-robot interaction from the human's perspective. © 2020 The authors and IOS Press. All rights reserved.","anthropomorphism; fictionalism; Human-robot interaction; intentional stance; make-believe; mental state attribution; pretence theory; prop theory; theory of mind","Man machine systems; Robotics; Mental model; Personality traits; Human robot interaction",,,,,"National Science Foundation, NSF: IIS-1528121","This work was supported by an NSF NRI grant for “Socially Aware, Expressive, and Pers onalized Mobile Remote Pres ence: Co-Robots as Gateways to Acces s to K-12 In-School Education”, NSF IIS-1528121. The authors als o gratefully acknowledge Tim Buchanan for the early dis cus s ions that led to this paper, Sam Thellman and Joel Oes ch for their advice and encouragement, and Kathryn Nels on for ins piring the idea of faking or imitating participation in a make-believe. Finally, the authors would like to thank the two anonymous reviewers of this manus cript for their thoughtful and detailed comments .",,,,,,,,,,"Blake, R., Sekuler, R., (2005) Perception, , 5th ed. McGraw-Hill; Heider, F., (2013) The Psychology of Interpersonal Relations, , Psychology Press; Dennett, D.C., Précis of the intentional stance (1988) Behavioral and Brain Sciences, 11 (3), pp. 495-505; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) Journal of Social Issues, 56 (1), pp. 81-103; Zlotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: Opportunities and challenges in human-robot interaction (2015) International Journal of Social Robotics, 7 (3), pp. 347-360; Fussell, S.R., Kiesler, S., Setlock, L.D., Yew, V., How people anthropomorphize robots (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, pp. 145-152. , In, New York, NY, USA: ACM; Haikonen, P.O., Consciousness and robot sentience (2012) World Scientific, 2. , Series on Machine Consciousness; Walton, K.L., Metaphor and prop oriented make-believe (2005) Fictionalism in Metaphysics, pp. 65-87; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) The American Journal of Psychology, 57 (2), pp. 243-259; Malle, B.F., Korman, J., (2013) Attribution Theory, , Oxford University Press; Nass, C., Steuer, J., Tauber, E.R., Computers are social actors (1994) In: Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 72-78. , New York, NY, USA: ACM, CHI '94; Thellman, S., Ziemke, T., The Intentional Stance Toward Robots: Conceptual and Methodological Considerations (2019) In: The 41st Annual Conference of the Cognitive Science Society, pp. 1097-1103. , July 24-26, Montreal, Canada; Krämer, N.C., Von, D.P.A., Eimler, S., (2012) Human-agent and Human-robot Interaction Theory: Similarities to and Differences from Human-human Interaction. In: Human-computer Interaction: The Agency Perspective, pp. 215-240. , Springer; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychological Review, 114 (4), p. 864; Zlotowski, J., Sumioka, H., Eyssel, F., Nishio, S., Bartneck, C., Ishiguro, H., Model of dual anthropomorphism: The relationship between the media equation effect and implicit anthropomorphism (2018) International Journal of Social Robotics, 10 (5), pp. 701-714; Severson, R.L., Woodard, S.R., Imagining others' minds: The positive relation between children's role play and anthropomorphism (2018) Frontiers in Psychology, 9, p. 2140; Clark, H.H., Van Der Wege, M.M., Imagination in narratives (2015) In: The Handbook of Discourse Analysis, pp. 406-421. , 2nd ed. John Wiley and Sons, Inc; Walton, K.L., (1990) Mimesis As Make-believe: On the Foundations of the Representational Arts, , Harvard University Press; Novitz, D., Fiction, Imagination and Emotion (1980) The Journal of Aesthetics and Art Criticism, 38 (3), pp. 279-288; Gerrig, R., (2018) Experiencing Narrative Worlds, , Routledge; Byrne, R.M., Towards a model theory of imaginary thinking (1996) In: Mental Models in Cognitive Science: Essays in Honour of Phil Johnson-Laird, pp. 155-174. , Psychology Press; Walton, K.L., Appreciating fiction: Suspending disbelief or pretending belief? (1980) Dispositio, 5 (13-14), pp. 1-18; Tannen, D., What's in a frame? Surface evidence for underlying expectations (1993) Framing in Discourse, 14, p. 56; Seibt, J., Towards an ontology of simulated social interaction (2017) In: Sociality and Normativity for Robots: Philosophical Inquiries into Human-Robot Interactions, pp. 11-39. , Springer; Seibt, J., Damholdt, M.F., Vestergaard, C., Five Principles of Integrative Social Robotics (2018) Proceedings of Robophilosophy/TRANSOR 2018, pp. 28-42. , In: Coeckelbergh M, Loh J, Funk M, Seibt J, Norskov M, editors. 2018 Feb 14-17; Vienna, AT. IOS Press; Seibt, J., Integrative social robotics""-a new method paradigm to solve the description problem and the regulation problem? (2020) Proceedings of Robophilosophy/TRANSOR 2016, pp. 104-115. , In: J Seibt, M Norskov, SS Andersen, editors. 2016 Aug 19-22; Aarhus DK. IOS Press; Damholdt, M.F., Vestergaard, C., Seibt, J., Testing for 'Anthropomorphization': A Case for Mixed Methods in Human-Robot Interaction (2020) Human-Robot Interaction, pp. 203-227. , In: Jost C, Le Pévédic B, Belpaeme T, Bethel C, Chrysostomou D, Crook N, et al. editors. Springer, Cham; Clark, H.H., (1996) Using Language, , Cambridge university press; Clark, H.H., Depicting as a method of communication (2016) Psychological Review, 123 (3), pp. 324-347; Remmers, P., The artificial nature of social robots: A phenomenological interpretation of two conflicting tendencies in human-robot interaction (2020) Proceedings of the Robophilosophy 2020 Conference, , In: Norskov M, Seibt J, Quick O, editors. 2020 Aug 18-21; Aarhus, DK. IOS Press; Musial, M., Magical thinking and empathy towards robots (2016) In: What Social Robots Can and Should Do Proceedings of Robophilosophy, pp. 347-355; Johnson-Laird, P.N., (1983) Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness, , Harvard University Press; Airenti, G., The development of anthropomorphism in interaction: Intersubjectivity, imagination, and theory of mind (2018) Frontiers in Psychology, 9, p. 2136","Rueben, M.; Ronald Tutor Hall (RTH), 3710 McClintock Ave, Room 423, United States; email: mrueben@usc.edu","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098883299
"Schwaninger I.","57215293552;","Practice-Based Trust Research: Towards Situated Human-Robot Interaction in Older People's Living Spaces",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"663","665",,,"10.3233/FAIA200975","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098865856&doi=10.3233%2fFAIA200975&partnerID=40&md5=dde74c6408950b7c53ec4e68051703a4","Human-Computer Interaction Group, Tu Wien, Austria","Schwaninger, I., Human-Computer Interaction Group, Tu Wien, Austria",[No abstract available],"older adults; relatedness; situated human-robot interaction; Trust",,,,,,,,,,,,,,,,,"Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI '12), pp. 109-110. , DOI10.1145/2157689.2157709, Mar 5-8; Boston, Massachusetts, USA. New York, NY: ACM; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) The Academy of Management Review, 20 (3), pp. 709-734; Schwaninger, I., Fitzpatrick, G., Weiss, A., Exploring trust in human-agent collaboration (2019) Proceedings of the 17th European Conference on Computer-Supported Cooperative Work (ECSCW '19)-Exploratory Papers, , Jun 8-12; Salzburg, Austria. EUSSET; Kuutti, K., Bannon, L.J., The turn to practice in HCI: Towards a research agenda (2014) Proceedings of the Sigchi Conference on Human Factors in Computing Systems (CHI '14), pp. 3543-3552. , Apr 26-May 01; Toronto, ON, Canada. New York, NY: ACM; Wulf, V., Rohde, M., Pipek, V., Stevens, G., Engaging with practices: Design case studies as a research framework in CSCW (2011) Proceedings of the Acm 2011 Conference on Computer Supported CooperativeWork (CSCW '11)., pp. 505-512. , Sept 24-28; Aarhus, Denmark. EUSSET; Ryan, R.M., Deci, E.L., A Self-Determination Theory Perspective on Social, Institutional, Cultural, and Economic Supports for Autonomy and Their Importance for Well-Being (2011) Human Autonomy in Cross-Cultural Context. Cross-Cultural Advancements in Positive Psychology, 1, pp. 45-64. , Chirkov V. Ryan R. Sheldon K. editors., Dordrecht (NL): Springer; Ryan, R.M., Deci, E.L., (2012) Motivation, Personality, and Development within Embedded Social Contexts: An Overview of Self-Determination Theory, , New York: Oxford University Press; Richard, M.R., Deci, E.L., (2017) Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness, , New York, London: Guilford Publications; Gompei, T., Umemuro, H., Factors and development of cognitive and affective trust on social robots (2018) Lecture Notes in Computer Science, pp. 45-54. , Ge S. et al. editors. Social Robotics. ICSR 2018., Springer, Cham; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3 (2), pp. 77-101; Schwaninger, I., Frauenberger, C., Fitzpatrick, G., Unpacking forms of relatedness around older people and telecare (2020) Companion Publication of the 2020 Acm Designing Interactive Systems Conference (DIS' 20 Companion), pp. 163-169. , Jul 6-10; Eindhoven, Netherlands. New York, NY: ACM","Schwaninger, I.; Human-Computer Interaction Group, Argentinierstraße 8, Austria; email: isabel.schwaninger@tuwien.ac.at","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098865856
"Schmidbauer C., Schlund S., Ionescu T.B., Hader B.","57209321116;9436044000;26662293600;57221675331;","Adaptive task sharing in human-robot interaction in assembly",2020,"IEEE International Conference on Industrial Engineering and Engineering Management","2020-December",,"9309971","546","550",,1,"10.1109/IEEM45057.2020.9309971","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099772475&doi=10.1109%2fIEEM45057.2020.9309971&partnerID=40&md5=5c190427119c187b727821a8cfa7030d","Institute of Management Science, TU Wien, Vienna, Austria","Schmidbauer, C., Institute of Management Science, TU Wien, Vienna, Austria; Schlund, S., Institute of Management Science, TU Wien, Vienna, Austria; Ionescu, T.B., Institute of Management Science, TU Wien, Vienna, Austria; Hader, B., Institute of Management Science, TU Wien, Vienna, Austria","The ongoing development of collaborative robots (cobots) bears great potential for assembly processes by enabling new possibilities of task allocation between humans and machines. A detailed, empirically based evaluation of how cobots can live up to these possibilities in industrial practice is still missing in the manufacturing literature. In this paper some of the existing shortcomings -referred to as ""the productivity gap""in current assembly automation processes are discussed. To cope with this gap, adaptive human-cobot task sharing is proposed and implemented as a complementary task allocation model. Its potential to increase productivity and flexibility in new and existing assembly applications is evaluated. © 2020 IEEE.","Adaptive manufacturing; Human-robot interaction; Hybrid assembly; Task allocation","Productivity; Assembly automation; Assembly process; Collaborative robots; Industrial practices; Still missing; Task allocation; Task sharing; Human robot interaction",,,,,"FFG-852789, FFG-871459; Siemens; Österreichische Forschungsförderungsgesellschaft, FFG","This research was supported by Siemens AG Osterreich, the Austrian Association for the Promotion of Management Science Research and Training (VBW), the Austrian Research Promotion Agency (FFG), the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK) through the endowed professorship ""HCCPPAS"" (FFG-852789), the ""Trust Robots"" doctoral college, and the ""CoMeMak"" project (FFG-871459).",,,,,,,,,,"Ionescu, T.B., Schlund, S., A participatory programming model for democratizing cobot technology in public and industrial fablabs (2019) 52nd Cirp Manufacturing Systems Conference, Procedia Cirp; Mayrhofer, W., Kames, D., Schlund, S., (2019) Made in Austria-Produktionsarbeit in Österreich; Ranz, F., Komenda, T., Reisinger, G., Hold, P., Hummel, V., Sihn, W., A morphology of human robot collaboration systems for industrial assembly (2018) Procedia Cirp, 72 (1), pp. 99-104; Brandstötter, M., Komenda, T., Ranz, F., Wedenig, P., Gattringer, H., Kaiser, L., Breitenhuber, G., Hofbaur, M., Versatile collaborative robot applications through safety-rated modification limits (2019) 28th International Conference on Robotics in Alpe-Adria-Danube Region; Schmidbauer, C., Komenda, T., Schlund, S., Teaching cobots in learning factories-user and usability-driven implications (2020) Procedia Manufacturing, 45, pp. 398-404; El Zaatari, S., Marei, M., Li, W.D., Usman, Z., Cobot programming for collaborative industrial tasks: An overview (2019) Robotics and Autonomous Systems, 116, pp. 162-180; Froschauer, R., Lindorfer, R., Workflow-based programming of human-robot interaction for collaborative assembly stations (2019) Proceedings of the Arg &Oagm Workshop, pp. 85-90; Bainbridge, L., (1982) Ironies of Automation Ifac Analysis, Design and Evaluation of Man-Machine Systems, 15 (6), pp. 129-135; Challenger, R., Clegg, C., Shepherd, C., Function allocation in complex systems: Reframing an old problem (2013) Ergonomics, 56 (7), pp. 1051-1069; Fitts, P., Human engineering for an effective air navigation and traffic control system (1951) National Research Council, , Washington D.C; Ranz, F., Hummel, V., Sihn, W., Capability-based task allocation in human-robot collaboration (2017) 7th CLF, Procedia Manufacturing 9, pp. 182-189; Michalos, G., Spiliotopoulos, J., Makris, S., Chryssolouris, G., A method for planning human robot shared tasks (2018) Cirp Journal of Manufacturing Science and Technology, 22, pp. 76-90; Müller, R., Vette, M., Geenen, A., Skill-based dynamic task allocation in human-robot-cooperation with the example of welding application (2017) 27th FAIM, Procedia Manufacturing, 11, pp. 13-21; Dekker, S.W.A., Woods, D.D., Maba-maba or abracadabra? Progress on human-automation coordination (2002) Cognition, Technology and Work, 4 (4), pp. 240-244; Bradshaw, J.M., Dignum, V., Jonker, C.M., Sierhuis, M., Human-agent-robot teamwork (2012) Procedia 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 487-488; Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) Cirp Annals, 68 (2), pp. 701-726; Nikolakis, N., Kousi, N., Michalos, G., Makris, S., Dynamic scheduling of shared human-robot manufacturing operations (2018) 51st Cirp Cms, 72, pp. 9-14; Ansari, F., Hold, P., Mayrhofer, W., Schlund, S., Sihn, W., Autodidact: Introducing the concept of mutual learning into a smart factory industry 4.0 (2018) Proceedings of 15th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA); Ender, J., Wagner, J.C., Kunert, G., Guo, F.B., Larek, R., Pawletta, T., Concept of a self-learning workplace cell for wokrer assistance while collaboration with a robot within the self-adapting-production-planning-system Iapgos, pp. 4-9. , 4/2019; Ebrahimzadeh, A., Chowdhury, M., Maier, M., Human-agent-robot task coordination in fiwi-base tactile internet infrastructures using context-and self-awareness (2019) Ieee Transactions on Network and Service Management, 16 (3), pp. 1127-1142; https://www.omg.org/spec/BPMN/2.0, Business Process Model And Notation(accessed on the 31th of May 2020); Assembly and handling; Handling functions, handling units; Terminology, definitions and symbols (1990) S.l.:S.n, , VDI 2860; Bokranz, R., Landau, K., Produktivitätsmanagement von arbeitssystemen: Mtm-handbuch (2006) Schäffer-Poeschel Stuttgart; https://camunda.com/solutions/, CAMUNDA(accessed on the 19th of May 2020); https://bpmn.io/, BPMN.IO(accessed on the 31th of May 2020); Schlotzhauer, A., Kaiser, L., Wachter, J., Brandstötter, M., Hofbaur, M., On the trustability of the safety measures of collaborative robots: 2d collision-force-map of a sensitive manipulator for safe hrc (2019) 15th International Conference on Automation Science and Engineering",,,,"IEEE Computer Society","2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020","14 December 2020 through 17 December 2020",,166486,21573611,9781538672204,,,"English","IEEE Int. Conf. Ind. Eng. Eng. Manage.",Conference Paper,"Final","",Scopus,2-s2.0-85099772475
"Liu Y., Yang C., Wang M.","57211088566;57204881979;55553729118;","Human-Robot interaction Based on Biosignals",2020,"2020 International Symposium on Autonomous Systems, ISAS 2020",,,"9378844","58","63",,,"10.1109/ISAS49493.2020.9378844","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103792810&doi=10.1109%2fISAS49493.2020.9378844&partnerID=40&md5=95b7a62fc118212c91365efe3a540c8c","South China University of Technology, Guangzhou, China","Liu, Y., South China University of Technology, Guangzhou, China; Yang, C., South China University of Technology, Guangzhou, China; Wang, M., South China University of Technology, Guangzhou, China","This paper introduces a novel manner for human-robot interaction (HRI) based on electroencephalogram (EEG) and surface electromyography (sEMG) signals. the P300-based BCI system aims to provide a target point for the mobile robot and indicates whether or not the robot will perform the grab task. Considering the relative position of the robot and the target cube will lead to the failure of the grasping task, we use different gestures which evoke corresponding sEMG signals to fine-tune the position of the robot. To get the target point closely related to the target character from the BCI system, we use linear discriminant analysis (LDA) to do the P300 detection task. and the average charater recognition for all subjects offline can reach 92%. Similarly, a simple convolutional neural network (CNN) is constructed for gestures classification and obtained 98% accuracy offline. in addition, the validation of the proposed method is verfied by the experimental results on mobile robot and Gaussian mechanical arm. © 2020 IEEE.","CNN; EEG; HRI; LDA; P300; sEMG","Biomedical signal processing; Convolutional neural networks; Discriminant analysis; Electroencephalography; Man machine systems; Mobile robots; Navigation; Detection tasks; Electro-encephalogram (EEG); Human robot Interaction (HRI); Linear discriminant analysis; Mechanical arm; Relative positions; Surface electromyography; Target character; Human robot interaction",,,,,"2019B1515120076; National Natural Science Foundation of China, NSFC: 61811530281, 61861136009, U20A20200; Fundamental Research Funds for the Central Universities","This work was supported in part by National Nature Science Foundation of China (NSFC) under Grant U20A20200, Grant 61811530281, and Grant 61861136009, in part by Guangdong Regional Joint Foundation under Grand 2019B1515120076, and in part by the Fundamental Research for the Central Universities.",,,,,,,,,,"Yang, C., Wu, H., Li, Z., et, al.., Mind control of a robotic arm with visual fusion technology[J] (2017) Ieee Transactions on Industrial Informatics, 14 (9), pp. 3822-3830; Mao, Y., Jin, J., Xu, R., et, al.., The Influence of Visual Attention on the Performance of a Novel Tactile P300 Brain-Computer interface with Cheeks-Stim Paradigm[J] (2020) International Journal of Neural Systems; Wei, M., Lin, F., A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain s sensorimotor region activated tasks[J] (2020) International Journal of Intelligent Computing and Cybernetics; Damper, R.I., Burnett, J.W., Gray, P.W., Straus, L.P., Symes, R.A., Hand-held text-to-speech device for the non-vocal disabled (1987) J Biomed Eng, 9 (4), pp. 332-340. , Oct; Cecotti, H., Graser, A., Convolutional neural networks for P300 detection with application to brain-computer interfaces[J] (2010) Ieee Transactions on Pattern Analysis and Machine Intelligence, 33 (3), pp. 433-445; Finke, A., Lenhardt, A., Ritter, H., The MindGame: A P300-based braincomputer interface game[J] (2009) Neural Networks, 22 (9), pp. 1329-1333; Achanccaray, D., Flores, C., Fonseca, C., et, al.., A P300-based brain computer interface for smart home interaction through an ANFIS ensemble[C] (2017) 2017 Ieee International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-5; Loureiro, R., Amirabdollahian, F., Topping, M., et, al.., Upper limb robot mediated stroke therapyGENTLE/s approach[J] (2003) Autonomous Robots, 15 (1), pp. 35-51; Oskoei, M.A., Hu, H., Myoelectric control systemsA survey[J] (2007) Biomedical Signal Processing and Control, 2 (4), pp. 275-294; Xiaoling, L., Motor imagery-based EEG signals classification by combining temporal and spatial deep characteristics[J] (2020) International Journal of Intelligent Computing and Cybernetics; Zong, C., Ji, Z., Yu, J., et, al.., An angle-changeable tracked robot with humanrobot interaction in unstructured environments[J] (2020) Assembly Automation; Luo, Z., Yang, G., Study of myoelectric prostheses based on fuzzy control and touch feedback[C] (2005) 2005 International Conference on Neural Networks and Brain, 3, pp. 1815-1819; Sun, Q., Liu, X., Mu, X., et, al.., Estimation for normal contact stiffness of joint surfaces by considering the variation of critical deformation[J] (2020) Assembly Automation; Segalowitz, S.J., Barnes, K.L., The reliability of ERP components in the auditory oddball paradigm[J] (1993) Psychophysiology, 30 (5), pp. 451-459; Townsend, G., LaPallo, B.K., Boulay, C.B., et, al.., A novel P300-based braincomputer interface stimulus presentation paradigm: Moving beyond rows and columns[J] (2010) Clinical Neurophysiology, 121 (7), pp. 1109-1120; Kuang, Y., Cheng, H., Zheng, Y., et, al.., One-shot gesture recognition with attention-based DTW for human-robot collaboration[J] (2019) Assembly Automation; Manyakov, N.V., Chumerin, N., Combaz, A., et, al.., Comparison of classification methods for P300 brain-computer interface on disabled subjects[J] (2011) Computational Intelligence and Neuroscience, 2011; Valakos, D., DAvossa, G., Mylonas, D., P300 response modulation reflects breaches of non-probabilistic expectations[J] (2020) Scientific Reports, 10 (1), pp. 1-11; Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N., Face recognition using LDA-based algorithms[J] (2003) Ieee Transactions on Neural Networks, 14 (1), pp. 195-200; Townsend, J.T., Theoretical analysis of an alphabetic confusion matrix[J] (1971) Perception & Psychophysics, 9 (1), pp. 40-50; Li, X.A., Sun, K., Guo, C., et, al.., Enhanced static modeling of commercial pneumatic artificial muscles[J] (2020) Assembly Automation; Yang, C., Luo, J., Liu, C., et, al.., Haptics electromyography perception and learning enhanced intelligence for teleoperated robot[J] (2018) Ieee Transactions on Automation Science and Engineering, 16 (4), pp. 1512-1521; Bouteraa, Y., Abdallah, I.B., Elmogy, A., Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation[J] (2020) Industrial Robot: The International Journal of Robotics Research and Application; Zeng, C., Yang, C., Cheng, H., et, al.., Simultaneously Encoding Movement and sEMG-based Stiffness for Robotic Skill Learning[J] (2020) Ieee Transactions on Industrial Informatics",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 International Symposium on Autonomous Systems, ISAS 2020","6 December 2020 through 8 December 2020",,168040,,9781665418829,,,"English","Int. Symp. Auton. Syst., ISAS",Conference Paper,"Final","",Scopus,2-s2.0-85103792810
"Maeda Y., Sakai T., Kamei K., Cooper E.W.","7402845246;57221854572;7102550445;7402217092;","Human-robot interaction based on facial expression recognition using deep learning",2020,"2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020",,,"9322719","","",,1,"10.1109/SCISISIS50064.2020.9322719","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100431730&doi=10.1109%2fSCISISIS50064.2020.9322719&partnerID=40&md5=7ec038af201022ec15b0a3d80432359b","College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan; Takano Co., Ltd., 137 Miyada-mura, Kamiina-gun, Nagano, 399-4301, Japan","Maeda, Y., College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan; Sakai, T., Takano Co., Ltd., 137 Miyada-mura, Kamiina-gun, Nagano, 399-4301, Japan; Kamei, K., College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan; Cooper, E.W., College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577, Japan","In recent years, many robots for the purpose of communicating with people have been developed. Such a robot is required to have human interaction and communication ability. In order to perform the interaction naturally, the nonverbal communication such as human facial expression and body movement is important. In this research, we propose a method to classify emotions from human face images by deep learning and generate a robot emotional reaction by Markovian emotional model. Here, we perform to learn human facial images with various emotions using CNN (Convolutional Neural Network) which is a kind of deep learning, and recognize human emotions from facial images in the human interaction. Based on the human emotion obtained by deep learning, the robot returns its emotional behavior to the human. In this research, we executed the interaction experiment using an real communication robot and this result is also reported in this paper. © 2020 IEEE.","Communication Robot; Deep Learning; Emotion; Facial Expression; Markovian Emotional Model","Behavioral research; Convolutional neural networks; Deep learning; Face recognition; Intelligent computing; Intelligent systems; Soft computing; Emotional behavior; Emotional models; Emotional reactions; Facial expression recognition; Human facial expressions; Human interactions; Non-verbal communications; Real communication; Social robots",,,,,,,,,,,,,,,,"Kanda, T., Ishiguro, H., Imai, M., Ono, T., Development and evaluation of interactive humanoid robots (2004) Proceedings of the IEEE, 92 (11), pp. 1839-1850; Endo, N., Momoki, S., Zecca, M., Saito, M., Mizoguchi, Y., Itoh, K., Takanishi, A., Gdevelopment of whole-body emotion expression humanoid robot (2008) Proc. Of the 2008 IEEE International Conference on Robotics and Automation, pp. 2140-2145; Maeda, Y., Taki, R., Interactive emotion communication between human and robot (2011) International Journal of Innovative Computing, Information and Control (IJICIC), 7 (5 B), pp. 2961-2970; Banik, S.C., Watanabe, K., Izumi, K., Improvement of group performance of job distributed mobile robots by an emotionally biased control system (2008) Articial Life Robotics, (12), pp. 245-249; Maeda, Y., Geshi, S., Human-robot interaction using markovian emotional model based on facial recognition (2018) Proc. Of the 10th International Conference on Soft Computing and Intelligent Systems, and the 19th International Symposium on Advanced Intelligent Systems (SCIS-ISIS 2018), pp. 209-214; Keras Team, , https://github.com/keras-team/keras; Takara Tomy, , https://www.takaratomy.co.jp/products/cozmo/, in Japanese; Russell, J.A., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), pp. 1161-1178; Witvliet, C., Vrana, S., Psychophysiological responses as indices of affective dimensions (1995) Psychophysiology, 32, pp. 436-443",,,,"Institute of Electrical and Electronics Engineers Inc.","Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020","5 December 2020 through 8 December 2020",,166710,,9781728197326,,,"English","Jt. Int. Conf. Soft Comput. Intell. Syst. Int. Symp. Adv. Intell. Syst., SCIS-ISIS",Conference Paper,"Final","",Scopus,2-s2.0-85100431730
"Amor F., García L., Laborde A.N., Landa G., Tashtoush T., Oliva D., Safar F.","57221855437;57225789277;57221843502;57221847297;45261616700;22835830800;57190664343;","Using a top-view RGB-D camera for two applications involving human-robot interaction",2020,"2020 IEEE Congreso Bienal de Argentina, ARGENCON 2020 - 2020 IEEE Biennial Congress of Argentina, ARGENCON 2020",,,"9505396","","",,,"10.1109/ARGENCON49523.2020.9505396","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114016660&doi=10.1109%2fARGENCON49523.2020.9505396&partnerID=40&md5=55e51bba0fbe791f888d10a9df8b5be9","Departamento CyT, Universidad Nacional de Quilmes, Quilmes, Argentina; School of Engineering, Texas A&M International University, Laredo, TX, United States","Amor, F., Departamento CyT, Universidad Nacional de Quilmes, Quilmes, Argentina; García, L., School of Engineering, Texas A&M International University, Laredo, TX, United States; Laborde, A.N., Departamento CyT, Universidad Nacional de Quilmes, Quilmes, Argentina; Landa, G., School of Engineering, Texas A&M International University, Laredo, TX, United States; Tashtoush, T., School of Engineering, Texas A&M International University, Laredo, TX, United States; Oliva, D., Departamento CyT, Universidad Nacional de Quilmes, Quilmes, Argentina; Safar, F., Departamento CyT, Universidad Nacional de Quilmes, Quilmes, Argentina",[No abstract available],"Computer vision; Human-robot interaction; Kinect; Manipulator; Scorbot",,,,,,,,,,,,,,,,,"Matheson, E., Human–robot collaboration in manufacturing applications: A review (2019) Robotics, 8 (4), p. 100; Paolanti, M., Person re-identification with RGB-D camera in top-view configuration through multiple nearest neighbor classifiers and neighborhood component features selection (2018) Sensors, 18 (10), p. 3471; Siciliano, B., Khatib, O., (2016) Springer Handbook of Robotics, , Eds Springer; (1994) ACL Lenguaje De Control Avanzado Versión 1.43, , Eshed Robotec; Villena Martínez, V., (2015) Análisis Comparativo De Métodos De Calibrado Para Sensores RGB-D Y Su Influencia En El Registro De Múltiples Vistas, pp. 33-34. , Universidad de Alicante; Craig, J.J., (2009) Introduction to Robotics: Mechanics and Control, , 3rd ed... Pearson Education India; Del Vigo, L., Computer-aided design and simulation of a robotic manipulator for educational purposes (2018) 2018 Argentine Conference on Automatic Control (AADECA); Pessacg, F., de Cristoforis, P., Perri, V., Oliva, D., Implementación y validación de un modelo cinemático para el manipulador robotico SCORBOT ER-Vplus (2014) Argentina, CABA. VIII Jornadas Argentinas De Robotica, , ISBN-13. 978-987-1087-19-9; Russ, J.C., Neal, F.B., (2016) The Image Processing Handbook, pp. 381-391. , 7th ed.. CRC Press; Otsu, N., A threshold selection method from gray-level histograms (1979) IEEE Transactions on Systems, Man, and Cybernetics, 9, pp. 62-66; Russ, J.C., Neal, F.B., (2016) The Image Processing Handbook, pp. 490-492. , 7th ed.. CRC Press",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE Biennial Congress of Argentina, ARGENCON 2020","1 December 2020 through 4 December 2020",,171261,,9781728159577,,,"Spanish","IEEE Congr. Bien. Argentina, ARGENCON - IEEE Bienn. Congr. Argentina, ARGENCON",Conference Paper,"Final","",Scopus,2-s2.0-85114016660
"Zhang B., Yao J., Zhao X.-G., Tan X.-W.","57218441703;57221419488;16551339800;57201974714;","An adaptive human-robot interaction control method based on electromyography signals [一种基于肌电信号的自适应人机交互控制方法]",2020,"Kongzhi Lilun Yu Yingyong/Control Theory and Applications","37","12",,"2560","2570",,,"10.7641/CTA.2020.90316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099087185&doi=10.7641%2fCTA.2020.90316&partnerID=40&md5=81bfdfaed9f76993afc5d77aadc0b85f","Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China","Zhang, B., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Yao, J., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Zhao, X.-G., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Tan, X.-W., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China","Aiming at the issues in human-robot interaction of ankle rehabilitation robot's movement, this paper proposes a new surface electromyography (sEMG) signals based robust adaptive control strategy. Considering that sEMG signals of stroke patients are weak and it is difficult for them to maintain some certain actions, a new joint angle estimation method is proposed. The antagonistic relationship between the tibialis anterior muscle and gastrocnemius muscle during ankle joint movement is fully used in this method, and the motion type of the ankle joint is correlated with the contraction of a single muscle group. After the recognition of the motion intention and the estimation of the movement angle are completed by using the normalized characteristic value, the continuous and smooth angle estimation curve is obtained. To ensure human's safety when the interaction torque suddenly increases, this paper designs an adaptive control law, in which the stiffness parameter and the damping parameter are adaptively tuned. By using the interactive torque, the moving angle and speed of the robot end are adjusted in real time, so that the external flexibilities have been achieved. The experimental results demonstrate that the proposed human-robot interaction control method is effective and has potentiol for practical application. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.","Adaptive control; Ankle rehabilitation robot; EMG signals; Impedance control; Stability","Control theory; Man machine systems; Muscle; Adaptive control law; Ankle rehabilitation robots; Electromyography signals; Gastrocnemius muscle; Human-robot interaction control; Robust-adaptive control; Stiffness parameters; Surface electromyography; Human robot interaction",,,,,"20180540131; National Natural Science Foundation of China, NSFC: U1813214","本文责任编委: 詹宜巨. 国家自然科学基金深圳联合基金项目(U1813214), 辽宁省博士启动基金项目(20180540131)资助. Supported by the National Natural Science Foundation of China (U1813214) and the Liaoning Province PHD Foundation of China (20180540131).",,,,,,,,,,"ZHANG, Tong, Guidance for stroke rehabilitation in China (2012) Chinese Journal of the Frontiers of Medical Science (Electronic Version), 4 (6), pp. 55-56. , (张通. 中国脑卒中康复治疗指南(2011年完全版). 中国医学前沿杂志(电子版), 2012, 4(6): 55-56); BERNHARDT, M, FREY, M, COLOMBO, G, Hybrid forceposition control yields cooperative behaviour of the rehabilitation robot LOKOMAT (2005) Proceedings of IEEE International Conference on Rehabilitation Robotics, pp. 536-539. , Chicago: IEEE; SUN, Huabao, (2017) Research and design on training system of ankle joint rehabilitation robot, , Shenyang: Northeastern University, (孙华宝. 踝关节康复机器人训练系统的设计与研究. 沈阳: 东北大学, 2017); REN, Y, XU, T, WANG, L, Develop a wearable ankle robot for in-bed acute stroke rehabilitation (2011) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7483-7486. , Boston: IEEE; JAMWAL, P K, HUSSAIN, S, GHAYESHMH, Impedance control of an intrinsically compliant parallel ankle rehabilitation robot (2016) IEEE Transactions on Industrial Electronics, 63 (6), pp. 3638-3647; ZHANG, Yu, (2010) Development and research of ankle-foot rehabilitation exoskeleton orthosis, , Hangzhou: Zhejiang University, (张煜. 踝关节康复医疗外骨骼系统开发研究. 杭州: 浙江大学, 2010); ZHANG, M M, XIE, S, LI, X L, Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety (2018) IEEE Transactions on Industrial Electronics, 65 (2), pp. 1398-1407; YU, H Y, HUANG, S N, CHEN, G, Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100; FARJADIAN, A B, NABIAN, M, MAVROIDIS, C, Implementation of a task-dependent anisotropic impedance controller into a 2-DOF platform-based ankle rehabilitation robot (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 5590-5595. , Seattle: IEEE; MENG, W, XIE, S Q, LIU, Q, Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 173-184; AYAS, M S, ALTAS, I H., Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot (2017) Control Engineering Practice, 59 (1), pp. 44-54; DING, Qichuan, ZHAO, Xingang, HAN, Jianda, EMG-based estimation for multi-joint continuous movement of human upper limb (2014) Robot, 36 (4), pp. 469-476. , (丁其川, 赵新刚, 韩建达. 基于肌电信号的上肢多关节连续运动估计. 机器人, 2014, 36(4): 469-476); DUAN, F, DAI, L, CHANG, W, sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform (2016) IEEE Transactions on Industrial Electronics, 63 (3), pp. 1923-1934; XING, K, YANG, P, HUANG, J, A real-time EMG pattern recognition method for virtual myoelectric hand control (2014) Neurocomputing, 136 (1), pp. 345-355; TSAI, A C, HSIEH, T H, LUH, J J, A comparison of upperlimb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions (2014) Biomedical Signal Processing and Control, 11 (1), pp. 17-26; KIGUCHI, K, HAYASHI, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42 (4), pp. 1064-1071; YIN, Y H, FAN, Y J, XU, L D., EMG and EPP-integrated humanmachine interface between the paralyzed and rehabilitation exoskeleton (2012) IEEE Transactions on Information Technology in Biomedicine, 16 (4), pp. 542-549; AL-QURAISHI, M, ISHAK, A, AHMAD, S, Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements (2015) Proceedings of Asian Control Conference(ASCC), pp. 1-5. , Kota Kinabalu: IEEE; AO, D, SONG, R, GAO, J., Movement performance of human-robot cooperation control based on EMG-driven hill-type and proportional models for an ankle power-assist exoskeleton robot (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (8), pp. 1125-1134; YAO, S W, ZHUANG, Y, LI, Z J., Adaptive admittance control for an ankle exoskeleton using an EMG-driven musculoskeletal model (2018) Frontiers in Neurorobotics, 12 (16), pp. 1-12; DU, Yihao, QIU, Shi, XIE, Ping, Adaptive interaction control for lower limb rehabilitation robots (2018) Acta Automatica Sinica, 44 (4), pp. 743-750. , (杜义浩, 邱石, 谢平, 等. 下肢康复机器人的自适应人机交互控制策略. 自动化学报, 2018, 44(4): 743-750); ZHOU, Z, ZHOU, Y, WANG, N, On the design of a robot-assisted rehabilitation system for ankle joint with contracture and/or spasticity based on proprioceptive neuromuscular facilitation (2014) IEEE International Conference on Robotics and Automation (ICRA), pp. 736-741. , Hong Kong: IEEE; HAN, J, DING, Q, XIONG, A, A state-space EMG model for the estimation of continuous joint movements (2015) IEEE Transactions on Industrial Electronics, 62 (7), pp. 4267-4275; ZHANG, B, ZHAO, X G, LI, X G, Robust indirect adaptive control for a class of nonlinear systems and its application to shape memory alloy actuators (2018) IEEE Access, 16, pp. 35809-35823; DING, Qichuan, XIONG, Anbin, ZHAO, Xingang, A review on researches and applications of sEMG-based motion intent recognition methods (2016) Acta Automatica Sinica, 42 (1), pp. 13-25. , (丁其川, 熊安斌, 赵新刚, 等. 基于表面肌电的运动意图识别方法研究及应用综述. 自动化学报, 2016, 42(1): 13-25)","Zhao, X.-G.; Shenyang Institute of Automation, China; email: zhaoxingang@sia.cn",,,"South China University of Technology",,,,,10008152,,KLYYE,,"Chinese","Kong Zhi Li Lun Yu Ying Yong",Article,"Final","",Scopus,2-s2.0-85099087185
"Aydin Y., Tokatli O., Patoglu V., Basdogan C.","56084865200;36667722400;19640643500;6603706568;","A computational multicriteria optimization approach to controller design for physical human-robot interaction",2020,"IEEE Transactions on Robotics","36","6","9162045","1791","1804",,1,"10.1109/TRO.2020.2998606","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097751364&doi=10.1109%2fTRO.2020.2998606&partnerID=40&md5=c82c21b01c7853ee64de8da66e50de04","College of Engineering, Koc University, Istanbul, 34450, Turkey; Race Ukaea, Abingdon, OX14 3DB, United Kingdom; Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, 34956, Turkey","Aydin, Y., College of Engineering, Koc University, Istanbul, 34450, Turkey; Tokatli, O., Race Ukaea, Abingdon, OX14 3DB, United Kingdom; Patoglu, V., Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, 34956, Turkey; Basdogan, C., College of Engineering, Koc University, Istanbul, 34450, Turkey","Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness. © 2004-2012 IEEE.","Fractional order control; interaction controllers; multicriteria optimization; physical human-robot interaction (pHRI); transparency-stability robustness tradeoff","Agricultural robots; Controllers; Machine design; Man machine systems; Multiobjective optimization; Pareto principle; System stability; Transparency; Collaborative robots; Controller parameter; Multicriteria optimization; Pareto optimization; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical interactions; Stability robustness; Human robot interaction",,,,,"EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK","Manuscript received October 31, 2019; revised March 23, 2020; accepted May 16, 2020. Date of publication August 7, 2020; date of current version December 3, 2020. This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Contract EEEAG-117E645. This article was recommended for publication by Associate Editor G. Venture and Editor A. Billard upon evaluation of the reviewers’ comments. (Corresponding author: Cagatay Basdogan.) Yusuf Aydin and Cagatay Basdogan are with the College of Engineering, Koc University, Istanbul 34450, Turkey (e-mail: yaydin@ku.edu.tr; cbasdogan@ku.edu.tr).","The authors would like to thank to Prof. A. Kucukyilmaz for her valuable comments. Moreover, the author Y. Aydin thanks to A. Aytekin, O. Caldiran, E.S. Emgin, G. Serhat, and C. Yesil for fruitful discussions during the study. The Scientific and Technological Research Council of Turkey (TUBITAK) supported this work under contract EEEAG-117E645.",,,,,,,,,"Ikeura, R., Monden, H., Inooka, H., Cooperative motion control of a robot and a human (1994) Proc. Ieee Int.Workshop Robot Human Commun., pp. 112-117. , Jul; Mörtl, A., The role of roles: Physical cooperation between humans and robots (2012) Int. J. Robot. Res., 31 (13), pp. 1656-1674; Wojtara, T., Human-robot collaboration in precise positioning of a three-dimensional object (2009) Automatica, 45 (2), pp. 333-342; Tavakoli, M., Patel, R.V., Moallem, M., A haptic interface for computer-integrated endoscopic surgery and training (2006) Virtual Reality, 9 (2), pp. 160-176. , Mar; Pehlivan, A.U., Losey, D.P., O'Malley, M.K., Minimal assist-asneeded controller for upper limb robotic rehabilitation (2016) Ieee Trans. 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"Doğan F.I., Gillet S., Carter E.J., Leite I.","57212089057;57210358678;35975697000;25031658100;","The impact of adding perspective-taking to spatial referencing during human–robot interaction",2020,"Robotics and Autonomous Systems","134",,"103654","","",,,"10.1016/j.robot.2020.103654","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095450417&doi=10.1016%2fj.robot.2020.103654&partnerID=40&md5=49e09e1f769dcc5cbcee736d45a4cefc","KTH Royal Institute of Technology, Stockholm, Sweden","Doğan, F.I., KTH Royal Institute of Technology, Stockholm, Sweden; Gillet, S., KTH Royal Institute of Technology, Stockholm, Sweden; Carter, E.J., KTH Royal Institute of Technology, Stockholm, Sweden; Leite, I., KTH Royal Institute of Technology, Stockholm, Sweden","For effective verbal communication in collaborative tasks, robots need to account for the different perspectives of their human partners when referring to objects in a shared space. For example, when a robot helps its partner find correct pieces while assembling furniture, it needs to understand how its collaborator perceives the world and refer to objects accordingly. In this work, we propose a method to endow robots with perspective-taking abilities while spatially referring to objects. To examine the impact of our proposed method, we report the results of a user study showing that when the objects are spatially described from the users’ perspectives, participants take less time to find the referred objects, find the correct objects more often and consider the task easier. © 2020 The Author(s)","Perspective-taking; Spatial referring expressions","Agricultural robots; Collaborative tasks; Perspective taking; Robot interactions; Shared spaces; Spatial referencing; User study; Verbal communications; Social robots",,,,,"Vetenskapsrådet, VR: 2017-05189","This work was partially funded by a grant from the Swedish Research Council (reg. number 2017-05189 ). 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"Su D., Feng R.","57193014913;57211230117;","EISRP: Efficient infrared signal restoration processing for object tracking in human-robot interaction",2020,"Infrared Physics and Technology","111",,"103544","","",,1,"10.1016/j.infrared.2020.103544","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093964945&doi=10.1016%2fj.infrared.2020.103544&partnerID=40&md5=096881b169c28b8f0e4ba1c7bc1f1580","Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong  518055, China; Orbbec 3D Technology International Inc., Shenzhen, Guangdong  518055, China; Research Institute for Frontier Science, Beihang University, Haidian District, Beijing, 100191, China","Su, D., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong  518055, China, Orbbec 3D Technology International Inc., Shenzhen, Guangdong  518055, China; Feng, R., Research Institute for Frontier Science, Beihang University, Haidian District, Beijing, 100191, China","Infrared (IR) imaging spectral signal can accurately distinguish different industrial metal materials in the robot vision sensing. The novel IR imaging system can assist the robot limb to gasp the object in the human-robot interaction processing. However, the low-resolution problem is often existed in the IR spectral signals. To solve this issue, we present a novel IR spectral deconvolution method via total variation regularization for robust spectral signal recognition. To reveal the properties of high-resolution IR spectrum, the histogram of spectral gradients is computed and fitted by the logarithmic function. Then, the low-resolution and high-resolution IR spectra can be distinguished by total variation-regularized prior property. To optimize the non-convex deconvolution model, alternating minimization algorithm is introduced to ensure that the instrument function and high-resolution spectral signals converge quickly. The performance of the proposed method is evaluated on the simulated IR spectrum and real spectrum. A large number of quantitative and qualitative experimental results verify that the proposed method can obtain robust results and significantly outperform the existing state-of-the-art spectral deconvolution method. The high-resolution IR spectrum promotes the development of IR spectral recognition in human-robot interaction. © 2020 Elsevier B.V.","Gaze estimation; Human-robot interaction; Infrared imaging; IR signal processing; Robot vision","Industrial robots; Infrared imaging; Man machine systems; Object tracking; Social robots; Alternating minimization algorithms; Deconvolution models; High resolution ir spectrums; Instrument functions; Logarithmic functions; Spectral deconvolution; Spectral gradients; Total variation regularization; Signal reconstruction",,,,,,,,,,,,,,,,"Liu, H., Zhou, M., Multi-order blind deconvolution algorithm with adaptive Tikhonov regularization for infrared spectroscopic data (2015) Infrared Phys. 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"Al-Makhadmeh Z., Tolba A.","57196260251;56442428300;","Dependable information processing method for reliable human-robot interactions in smart city applications",2020,"Image and Vision Computing","104",,"104045","","",,1,"10.1016/j.imavis.2020.104045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092662310&doi=10.1016%2fj.imavis.2020.104045&partnerID=40&md5=50cba219a706cd56d0b86d0000a7fef1","Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia; Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-Kom, 32511, Egypt","Al-Makhadmeh, Z., Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia; Tolba, A., Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia, Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-Kom, 32511, Egypt","Human-robot interaction (HRI) is a multidisciplinary area that consists of several technologies that are used to create various smart city applications. The knowledge gain and analysis of the smart city environment improves response time. This paper introduces the dependable information processing (DIP) method for handling multi-attribute environmental information in a smart city application. Information sensed from the environment is categorized in the initial stage regarding how it meets application requirements. It helps to identify the need and response of the application through different interacting spans and previous trials. For attribute categorization and span validation, learning via K-means clustering is exploited to identify similar and dissimilar information attributes. This identification speeds up the process of successive responses with improved interaction sessions. The accuracy of user responses and latency-free assessment improves the reliability of DIP in smart city applications. The efficiency of the system is then evaluated using detection accuracy (97.34%), the response time (4.3 s), and interaction time (12.3 s), which is compared with other methods. © 2020 Elsevier B.V.","Attribute categorization; Human-robot interaction; Information processing; K-means clustering; Smart city","K-means clustering; Man machine systems; Processing; Smart city; Application requirements; Detection accuracy; Environmental information; Human robot Interaction (HRI); Interaction time; Multi-attributes; Human robot interaction",,,,,"Deanship of Scientific Research, King Saud University: RG-1439-088","The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1439-088 .",,,,,,,,,,"Onnasch, L., Roesler, E., A Taxonomy to Structure and Analyze Human–Robot Interaction (2020), International Journal of Social Robotics; Tiddi, I., Bastianelli, E., Daga, E., D'Aquin, M., Motta, E., Robot–City interaction: mapping the research landscape—a survey of the interactions between robots and modern cities (2019) Int. 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Res., 54, pp. 128-137","Al-Makhadmeh, Z.; Computer Science Department, Saudi Arabia; email: zalmakhadmee@ksu.edu.sa",,,"Elsevier Ltd",,,,,02628856,,IVCOD,,"English","Image Vision Comput",Article,"Final","",Scopus,2-s2.0-85092662310
"Kawasaki Y., Yorozu A., Takahashi M., Pagello E.","57205406502;51261534300;7406846479;7003342218;","A Multimodal Path Planning Approach to Human Robot Interaction Based on Integrating Action Modeling",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","3-4",,"955","972",,1,"10.1007/s10846-020-01244-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090143317&doi=10.1007%2fs10846-020-01244-7&partnerID=40&md5=9ad3ac3d592943f1cbc831b396b8b14b","The Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; The Department of System Design Engineering, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Intelligent Autonomous System Lab, Department of Information Engineering, University of Padova, Padua, Italy, and IT+Robotics Srl, Vicenza, Italy","Kawasaki, Y., The Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Yorozu, A., The Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Takahashi, M., The Department of System Design Engineering, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Pagello, E., Intelligent Autonomous System Lab, Department of Information Engineering, University of Padova, Padua, Italy, and IT+Robotics Srl, Vicenza, Italy","To complete a task consisting of a series of actions that involve human-robot interaction, it is necessary to plan a motion that considers each action individually as well as in relation to the following action. We then focus on the specific action of “approaching a group of people” in order to accurately obtain human data that is used to make the performance of tasks involving interactions with multiple people more smooth. The movement depends on the characteristics of the important sensors used for the task and on the placement of people at and around the destination. Considering the multiple tasks and placement of people, the pre-calculation of the destinations and paths is difficult. This paper thus presents a system of navigation that can accurately obtain human data based on sensor characteristics, task content, and real-time sensor data for processes involving human-robot interaction (HRI); this method does not navigate specifically toward a previously determined static point. Our goal was achieved by using a multimodal path planning based on integration of action modeling by considering both voice and image sensing of interacting people as well as obstacle avoidance. We experimentally verified our method by using a robot in a coffee shop environment. © 2020, Springer Nature B.V.","Action modeling; Human-robot interaction; Multimodal path planning; Robot navigation","Agricultural robots; Man machine systems; Social robots; Action modeling; Coffee shops; Human robot Interaction (HRI); Multiple people; Multiple tasks; Real time sensors; Sensor characteristics; Voice and images; Robot programming",,,,,"Japan Science and Technology Agency, JST: JPMJCR14E3; Core Research for Evolutional Science and Technology, CREST","This study was supported by “A Framework PRINTEPS to Develop Practical Artificial Intelligence” of the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) under Grant Number JPMJCR14E3.",,,,,,,,,,"McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI (2016) J. Intell. Robot. Syst.; Dantam, N.T., Chaudhuri, S., Kavraki, L.E., The Task-Motion Kit: An Open Source, General-Purpose Task and Motion-Planning Framework (2018) IEEE Robot. Autom. Mag.; Park, Y.S., Cho, H.S., Task Oriented Optimum Positioning of a Mobile Manipulator Base in a Cluttered Environment (1997) J. Intell. Robot. Syst; Cognetti, M., Mohammadi, P., Oriolo, G., Vendittelli, M., (2014) Task-Oriented Whole-Body Planning for Humanoids Based on Hybrid Motion Generation, pp. 4071-4076; Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A., Karlsson, L., Efficiently combining task and motion planning using geometric constraints (2014) Int. J. Robot. Res; Long, X., Wonsick, M., Dimitrov, V., Padir, T., (2017) Anytime Multi-Task Motion Planning for Humanoid Robots; Sisbot, E.A., Sisbot, L.F., Alami, T., A Human Aware Mobile Robot Motion Planner (2007) IEEE T. Robot; Rios-Martinez, J., Spalanzani, A., Laugier, C., Understanding human interaction for probabilistic autonomous navigation using risk-RRT approach (2011) IEEE Int. C. Int. Robot; Batista, M.R., Socially Acceptable Navigation of People with Multi-robot Teams (2019) J. Intell. Robot. Syst; Kruse, T., Pandey, A.K., Alami, R., Kirsch, A., Human-aware robot navigation: A survey (2013) Robot. Auton. Syst.; Lu, D.V., Hershberger, D., Smart, W.D., Layered costmaps for context-sensitive navigation (2014) IEEE Int. C. Int. Robot; Kollmitz, M., Hsiao, K., Gaa, J., Burgard, W., Time dependent planning on a layered social cost map for human-aware robot navigation (2015) European Conference on Mobile Robots; Charalampous, K., Kostavelis, I., Gasteratos, A., Recent trends in social aware robot navigation: A survey (2017) Robot. Auton. Syst; Rios-Martinez, J., Escobedo, A., Intention Driven Human Aware Navigation for Assisted Mobility (2012) Workshop on Assistance and Service Robotics in a Human Environment at International Conference on Intelligent Robots and Systems; Hirakawa, T., Yamashita, T., Fujiyoshi, H., Scene Context-aware Rapidly-exploring Random Trees for Global Path Planning (2019) IEEE International Conference on Pervasive Computing and Communications Workshops; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, E.A., Alami, R., Siméon, T., How may I serve you? A robot companion approaching a seated person in a helping context (2006) ACMIEEE Int. Conf. Hum, pp. 172-179; Koay, K.L., Sisbot, E.A., Syrdal, D.S., Walters, M.L., Dautenhahn, K., Alami, R., Exploratory study of a robot approaching a person in the context of handing over an object (2007) AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics, pp. 18-24; Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N., How to approach humans?-Strategies for social robots to initiate interaction (2008) ACMIEEE Int. Conf. Hum.; Kato, Y., Kanda, T., Ishiguro, H., May i help you?: Design of Human-like Polite Approaching Behavior (2015) ACMIEEE Int. Conf. Hum; Mizumaru, K., Satake, S., Kanda, T., Ono, T., Stop Doing it! Approaching Strategy for a Robot to Admonish Pedestrians (2019) ACMIEEE Int. Conf. Hum.; Mead, R., Matarić, M.J., Autonomous human robot proxemics: Socially aware navigation based on interaction potential (2017) Auton. Robot; Cao, Z., Simon, T., Wei, S., Sheikh, Y., (2016) Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields, pp. 7291-7299; (2017) Cmu-Perceptual-Computing-Lab: Openpose, , https://github.com/CMU-Perceptual-Computing-Lab/openpose, Accessed 11 December 2019; Khoshelham, K., Elberink, S.O., Accuracy and resolution of kinect depth data for indoor mapping applications (2012) SENSORS, 12 (2), pp. 1437-1454; Ishiguro, H., Miyashita, T., Kanda, T., (2005) Science of knowledge Communication robot, , Ohmsha; Lewis, J., (2011) Microphone specifications explained, , Application Note 1112, Analog Devices; Tsuzaki, R., Yoshida, K., Motion Control Based on Fuzzy Potential Method for Autonomous Mobile Robot with Omnidirectional Vision (2003) Journal of the Robotics Society of Japan, 6, pp. 676-682; Suzuki, T., Takahashi, M., Obstacle Avoidance for Autonomous Mobile Robots based on Position Prediction using Fuzzy Inference (2009) ICINCO-RA, pp. 299-304; Takahashi, M., Suzuki, T., Cinquegrani, F., Sorbello, R., Pagello, E., A mobile robot for transport applications in hospital domain with safe human detection algorithm (2009) IEEE Robio, pp. 1543-1548; (2019), http://wiki.ros.org/amcl, amcl, Accessed 11 December 2019; (2019), http://wiki.ros.org/gmapping, ROS.org: gmapping, Accessed 11 December 2019 (,)","Kawasaki, Y.; The Graduate School of Science and Technology, 3-14-1 Hiyoshi, Kohoku-ku, Japan; email: y-kawasaki@keio.jp",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85090143317
"Abrams A.M.H., der Pütten A.M.R.-V.","57201548735;56976422300;","I–C–E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction: Revisiting Theory from Social Psychology on Ingroup Identification (I), Cohesion (C) and Entitativity (E)",2020,"International Journal of Social Robotics","12","6",,"1213","1229",,1,"10.1007/s12369-020-00642-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082944167&doi=10.1007%2fs12369-020-00642-z&partnerID=40&md5=aa3fa7aee3e33d1820e3994d2ec22786","Chair Individual and Technology, RWTH Aachen University, Aachen, Germany","Abrams, A.M.H., Chair Individual and Technology, RWTH Aachen University, Aachen, Germany; der Pütten, A.M.R.-V., Chair Individual and Technology, RWTH Aachen University, Aachen, Germany","The research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: ingroup identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I–C–E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research. © 2020, The Author(s).","Cohesion; Entitativity; Group dynamics; HRI; Human-robot interaction; ICE framework; Ingroup identification","Agricultural robots; Behavioral research; Dynamics; Group theory; Human robot interaction; Man machine systems; Cohesion; Conceptual frameworks; Entitativity; Group dynamics; Research communities; Social psychology; Theoretical foundations; Ubiquitous robots; Social robots",,,,,,,,,,,,,,,,"Abrams, D., Hogg, M.A., Comments on the motivational status of self-esteem in social identity and intergroup discrimination (1988) Eur J Soc Psychol, 18 (4), pp. 317-334; Abrams, D., Hogg, M.A., Hinkle, S., Otten, S., The social identity perspective on small groups (2005) Theories of Small Groups. SAGE, pp. 99-138. , Poole MS, Hollingshead AB, London; Admoni, H., Hayes, B., Feil-Seifer, D., Ullman, D., Scassellati, B., ) Are you looking at me? 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In: Yanco H (ed), ACM, New York; Yzerbyt, V., Castano, E., Leyens, J.P., Paladino, M.P., The primacy of the ingroup: The interplay of entitativity and identification (2000) Eur Rev Soc Psychol, 11 (1), pp. 257-295; Zaraki, A., Mazzei, D., Giuliani, M., de Rossi, D., Designing and evaluating a social gaze-control system for a humanoid robot (2014) IEEE Trans Hum Mach Syst, 44 (2), pp. 157-168","Abrams, A.M.H.; Chair Individual and Technology, Germany; email: anna.abrams@humtec.rwth-aachen.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85082944167
"Kobylarz J., Bird J.J., Faria D.R., Ribeiro E.P., Ekárt A.","57215684622;57203547990;26031469100;8339159800;6603036791;","Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning",2020,"Journal of Ambient Intelligence and Humanized Computing","11","12",,"6021","6031",,11,"10.1007/s12652-020-01852-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081655281&doi=10.1007%2fs12652-020-01852-z&partnerID=40&md5=a717a41420357545111662c960d408b5","Department of Electrical Engineering, Federal University of Parana, Curitiba, Brazil; School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom","Kobylarz, J., Department of Electrical Engineering, Federal University of Parana, Curitiba, Brazil; Bird, J.J., School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom; Faria, D.R., School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom; Ribeiro, E.P., Department of Electrical Engineering, Federal University of Parana, Curitiba, Brazil; Ekárt, A., School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom","In this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of ’thumbs up’, ’thumbs down’, and ’relax’ in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a ‘20 Questions’ game. © 2020, The Author(s).","Electromyography; Gesture classification; Human-robot interaction; Inductive transfer learning; Machine learning; Myo armband; Pepper robot; Supervised transductive transfer Learning; Transfer learning","Calibration; Classification (of information); Decision trees; Electromyography; Human robot interaction; Learning systems; Man machine systems; Random forests; Social robots; Support vector machines; 10-fold cross-validation; Feature selection methods; Gesture classifications; Inductive transfer; Machine learning approaches; Myo armband; Statistical learning; Transfer learning methods; Transfer learning",,,,,,,,,,,,,,,,"Abduo, M., Galster, M., (2015) Myo Gesture Control Armband for Medical Applications, , https://www.semanticscholar.org/paper/Myo-Gesture-Control-Armband-for-Medical-Abduo-Galster/3b5ed355b09beecb7b2b6bbd23fead44b50374c6; 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Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q., (2019) A comprehensive survey on transfer learning; Zwillinger, D., Kokoska, S., (2000) CRC standard probability and statistics tables and formulae, , Chapman and Hall, Boca Raton","Bird, J.J.; School of Engineering and Applied Science, United Kingdom; email: birdj1@aston.ac.uk",,,"Springer Science and Business Media Deutschland GmbH",,,,,18685137,,,,"English","J. Ambient Intell. Humanized Comput.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85081655281
"Hossain M.P., Atikuzzaman Md., Rahim M.M., Hossain M.T., Mousome M.S., Rahaman M.A.","57219988333;37048672500;57222025273;57222011347;57222017563;56605926600;","Human robot interaction system for behavioral improvement of autistic children",2020,"2020 2nd International Conference on Advanced Information and Communication Technology, ICAICT 2020",,,"9333508","117","122",,,"10.1109/ICAICT51780.2020.9333508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101030232&doi=10.1109%2fICAICT51780.2020.9333508&partnerID=40&md5=3ccce9f7f92cb164e65b2424b81edc58","Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh","Hossain, M.P., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Atikuzzaman, Md., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Rahim, M.M., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Hossain, M.T., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Mousome, M.S., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Rahaman, M.A., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh","An estimated one-third of people with autism are nonverbal. Many Research has been done in this field and confirms that people with autism experience a lack of communication skills, anxiety, dealing with change, bullying, etc. To assist autistic children and to improve their behavior, we have developed an artificial intelligence robot that can react to their movement via an ultrasonic sensor and can talk to them via voice commands. Using this robot, children with ASD can develop both their behavior and communication skill. The robot works in two methods. The first one is the ultrasonic sensor, implemented with an AI algorithm that can detect a person within seventy centimeters. This ultrasonic sensor sends the data to the controller and the controller receives the signal and sends an activation signal to the robot. Finally, the robot moves his head to the direction of the person is located or to the moving direction. The second method is voice commands. The robot can perform each physical action it is given via voice commands like a salute, handshaking, body movement, and answering questions achieving 97.75% average accuracy with the response time of 328.75ms. This accuracy is achieved by testing the system for 10 iterations. As autistic children hesitate to communicate freely with normal people, this human-robot interaction will improve their behavior and communication skills as well. This humanoid robot will improve the quality of living of the autistic or disabled people. This humanoid robot can be used in any public sector like school, colleges, an autism center, and industry. © 2020 IEEE.","AI; Autism spectrum disorder; Human-robot interaction; Humanoid robot; Robotics","Anthropomorphic robots; Artificial intelligence; Behavioral research; Diseases; Industrial robots; Man machine systems; Ultrasonic applications; Ultrasonic sensors; Autistic children; Body movements; Communication skills; Disabled people; Humanoid robot; Moving direction; Physical action; Public sector; Social robots",,,,,,"ACKNOWLEDGMENT This work is partially supported by the Green University of Bangladesh.",,,,,,,,,,"(2019) Autistic Spectrum Disorder (Asd), , https://www.nhsinform.scot/illnesses-and-conditions/brain-nerves-and-spinal-cord/autistic-spectrum-disorder-asd, N. inform, accessed on 10/20/2019; Speaks, A., (2019) Autism Statistics and Facts, , https://www.autismspeaks.org/autism-facts-and-figures, online;Accessed on 10/20/2019; Shamsuddin, S., Yussof, H., Ismail, L.I., Mohamed, S., Hanapiah, F.A., Zahari, N.I., Humanoid robot nao interacting with autistic children of moderately impaired intelligence to augment communication skills (2012) Procedia Engineering, 41, pp. 1533-1538; Dickstein-Fischer, L., Alexander, E., Yan, X., Su, H., Harrington, K., Fischer, G.S., An affordable compact humanoid robot for autism spectrum disorder interventions in children (2011) 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5319-5322; Shamsuddin, S., Yussof, H., Hanapiah, F.A., Mohamed, S., Jamil, N.F.F., Yunus, F.W., Robot-assisted learning for communicationcare in autism intervention (2015) 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 822-827; Yang, X., Shyu, M.-L., Yu, H.-Q., Sun, S.-M., Yin, N.-S., Chen, W., Integrating image and textual information in human-robot interactions for children with autism spectrum disorder (2018) IEEE Transactions on Multimedia, 21 (3), pp. 746-759; Othman, A., Mohsin, M., How could robots improve social skills in children with autism? (2017) 2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA), pp. 1-5; Yang, J.-Y., Kwon, D.-S., The effect of multiple robot interaction on human-robot interaction (2012) 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 30-33; Dautenhahn, K., Methodology & Themes of human-robot interaction: A growing research field (2007) Ternational Journal of Advanced Robotic Systems, 4 (1), p. 15; Xue, Z., Ruehl, S., Hermann, A., Kerscher, T., Dillmann, R., An autonomous ice-cream serving robot (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 3451-3452; Shieh, M.-Y., Lu, C.-M., Chen, C.-C., Chuang, C.-Y., Lai, Y.-S., Design and implementation of an interactive nurse robot (2007) SICE Annual Conference 2007, pp. 2121-2125; Ban, K.-D., Kwak, K.-C., Yoon, H.-S., Chung, Y.-K., Fusion technique for user identification using camera and microphone in the intelligent service robots (2007) 2007 IEEE International Symposium on Consumer Electronics, pp. 1-6","Hossain, M.P.; Green University of Bangladesh, Bangladesh; email: parvezhossain92@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.","2nd International Conference on Advanced Information and Communication Technology, ICAICT 2020","28 November 2020 through 29 November 2020",,166883,,9780738123233,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85101030232
"Adiga S., Vaishnavi D.V., Saxena S., Tripathi S.","57221833422;57221604290;57215559197;14008732700;","Multimodal Emotion Recognition for Human Robot Interaction",2020,"2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020",,,"9311566","197","203",,1,"10.1109/ISCMI51676.2020.9311566","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100333848&doi=10.1109%2fISCMI51676.2020.9311566&partnerID=40&md5=df0cea81b32abc39f5aa0b28f3d6bb2c","Pes University, Bengaluru, India","Adiga, S., Pes University, Bengaluru, India; Vaishnavi, D.V., Pes University, Bengaluru, India; Saxena, S., Pes University, Bengaluru, India; Tripathi, S., Pes University, Bengaluru, India","Emotion recognition is one of the most important assets that humans possess, which helps them to communicate with each other in the most efficient manner. Humans recognize each other's emotions through facial features, audio features and body gestures. Limited work is reported in literature, wherein multiple modalities are considered for emotion recognition. In this work, models are trained on facial and speech samples using 1-Dimensional (1D) and 2-Dimensional (2D) Convolutional Neural Networks (CNN) and also trained using pretrained networks such as Visual Geometry Group-16 (VGG-16) and Inception Version 3 (V3). Suitable features are extracted from face and speech to classify the emotions. Analysis of speech features is performed to highlight the difference between the expressiveness based on genders. For the final combined model both facial and speech models are trained separately and combined based on a decision tree, which depends on the individual model's test case accuracy. © 2020 IEEE.","convolutional neural network; emotion recognition; facial expression; inception version-3 (V3); log-mel spectrograms; mel-frequency cepstral coefficients (MFCC); multimodal; speech; visual geometry group-16 (VGG-16)","Convolutional neural networks; Decision trees; Soft computing; Speech recognition; Audio features; Combined model; Emotion recognition; Facial feature; Individual modeling; Multimodal emotion recognition; Multiple modalities; Speech features; Social robots",,,,,,,,,,,,,,,,"Cornejo, J.Y.R., Pedrini, H., Audio-visual emotion recognition using a hybrid deep convolutional neural network based on census transform (2019) Ieee International Conference on Systems, Man and Cybernetics (Smc, pp. 3396-3402. , Oct; Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database (2009) Computer Vision and Pattern Recognition, 2009. 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Available:""Getting to know the mel-spectrogram; Xu, M., Hmm-based audio keyword generation"" (pdf) (2004) Advances in Multimedia Information Processing-pcm 2004: 5th Pacific Rim Conference on Multimedia. Springer, , In KiyoharuAizawa; Yuichi Nakamura; Shin'ichi Satoh (eds.), Archived from the original (pdf) on 2007-05-10; Sahidullah, Md., Goutam, S., Design, analysis and experimental evaluation of block based transformation in mfcc computation for speaker recognition (2012) Speech Communication., 54 (4), pp. 543-565. , May; Szegedy, C., Liu, W., Pierre Sermanet, Y., Reed, S., DumitruErhan, D., Vanhoucke, V., Rabinovich, A., (2014) Going Deeper with Convolutions, , Sep; Rothmann, D., Model Comparison, , towardsdatascience.com."" What's wrong with CNN and spectrograms for audio processing",,,,"Institute of Electrical and Electronics Engineers Inc.","7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020","14 November 2020 through 15 November 2020",,166446,,9781728175591,,,"English","Int. Conf. Soft Comput. Mach. Intell., ISCMI",Conference Paper,"Final","",Scopus,2-s2.0-85100333848
"Esterwood C., Robert L.P.","57216331965;8247734500;","Personality in Healthcare Human Robot Interaction (H-HRI): A Literature Review and Brief Critique",2020,"HAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction",,,,"87","95",,9,"10.1145/3406499.3415075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096539343&doi=10.1145%2f3406499.3415075&partnerID=40&md5=9d4221ac0969acc2e6d5fd5bbca7d58a","School of Information, Robotics Institute, University of Michigan, Ann Arbor, MI, United States","Esterwood, C., School of Information, Robotics Institute, University of Michigan, Ann Arbor, MI, United States; Robert, L.P., School of Information, Robotics Institute, University of Michigan, Ann Arbor, MI, United States","Robots are becoming an important way to deliver health care, and personality is vital to understanding their effectiveness. Despite this, there is a lack of a systematic overarching understanding of personality in health care human robot interaction (H-HRI). To address this, the authors conducted a review that identified 18 studies on personality in H-HRI. This paper presents the results of that systematic literature review. Insights are derived from this review regarding the methodologies, outcomes, and samples utilized. The authors of this review discuss findings across this literature while identifying several gaps worthy of attention. Overall, this paper is an important starting point in understanding personality in H-HRI. © 2020 ACM.","healthcare; healthcare human-robot interaction; hri; human robot interaction; personality; robots; systematic review","Health care; Man machine systems; Literature reviews; Systematic literature review; Social robots",,,,,,,,,,,,,,,,"Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Conference on Human Factors in Computing Systems - Proceedings, pp. 3603-3612. , 2015-April (2015); Blaikie, N., (2004) Encyclopedia of Social Science Research Methods; Bogue, R., Robots in healthcare (2011) Industrial Robot: An International Journal, , 2011; Broadbent, E., Kumar, V., Li, X., Sollers, J., Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: A robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PLoS ONE, 8, p. 8. , 2013; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: Review and future directions (2009) International Journal of Social Robotics, 1 (4), p. 319. , 2009; Caprara, G.V., Barbaranelli, C., Borgogni, L., Secchione, M., (2007) Big Five Questionnaire-2; Conti, D., Commodari, E., Buono, S., Personality factors and acceptability of socially assistive robotics in teachers with and without specialized training for children with disability (2017) Life Span and Disability, 20 (2), pp. 251-272. , 2017; Costa, P.T., McCrae, R.R., Normal personality assessment in clinical practice: The NEO personality inventory (1992) Psychological Assessment, 4 (1), p. 5. , 1992; Cruz-Maya, A., Tapus, A., Teaching nutrition and healthy eating by using multimedia with a kompai robot: Effects of stress and user's personality (2016) IEEE-RAS International Conference on Humanoid Robots, pp. 644-649. , 2016; Damholdt, M.F., Nørskov, M., Yamazaki, R., Hakli, R., Hansen, C.V., Vestergaard, C., Seibt, J., Attitudinal change in elderly citizens toward social robots: The role of personality traits and beliefs about robot functionality (2015) Frontiers in Psychology, p. 6. , NOV (2015); Dang, T.-H.-H., Tapus, A., Stress game: The role of motivational robotic assistance in reducing user's task stress (2015) International Journal of Social Robotics, 7 (2), pp. 227-240. , 2015; Deutsch, I., Erel, H., Paz, M., Hoffman, G., Zuckerman, O., Home robotic devices for older adults: Opportunities and concerns (2019) Computers in Human Behavior, 98, pp. 122-133. , 2019; Eysenck, H.J., (1968) Eysenck Personality Inventory, , Educational and Industrial Testing Service San Diego; Gockley, R., Matarić, M.J., Encouraging physical therapy compliance with a hands-off mobile robot (2006) HRI 2006: Proceedings of the 2006 ACM Conference on Human-Robot Interaction, 2006, pp. 150-155. , 2006; Goetz, J., Kiesler, S., Cooperation with a robotic assistant (2002) Conference on Human Factors in Computing Systems - Proceedings, pp. 578-579. , 2002; Goldberg, L.R., An alternative"" description of personality"": The big-five factor structure (1990) Journal of Personality and Social Psychology, 59 (6), p. 1216. , 1990; Gosling, S.D., Rentfrow, P.J., Swann, W.B., Jr., A very brief measure of the big-five personality domains (2003) Journal of Research in Personality, 37 (6), pp. 504-528. , 2003; Harzing, A.W., (2007) Publish or Perish App, , https://harzing.com/resources/publish-or-perish; Hoffman, G., Birnbaum, G.E., Vanunu, K., Sass, O., Reis, H.T., Robot responsiveness to human disclosure affects social impression and appeal (2014) ACM/IEEE International Conference on Human-Robot Interaction, pp. 1-7. , 2014; John, O.P., Donahue, E.M., Kentle, R.L., (1991) The Big Five Inventory-versions 4a and 54; John, O.P., Srivastava, S., The big five trait taxonomy: History, measurement, and theoretical perspectives (1999) Handbook of Personality: Theory and Research, 2 (1999), pp. 102-138. , 1999; Robert, L.P., Jr., Alahmad, R., Esterwood, C., Kim, S., You, S., Zhang, Q., A review of personality in human-robot interactions (2020) Foundations and TrendsR in Information Systems, 4 (2), pp. 107-212. , 2020; Kiesler, S., Powers, A., Fussell, S.R., Torrey, C., Anthropomorphic interactions with a robot and robot-like agent (2008) Social Cognition, 26 (2), pp. 169-181. , 2008; 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Rossi, S., Santangelo, G., Staffa, M., Varrasi, S., Conti, D., Di Nuovo, A., Psychometric evaluation supported by a social robot: Personality factors and technology acceptance (2018) RO-MAN 2018-27th IEEE International Symposium on Robot and Human Interactive Communication, pp. 802-807. , 2018; Sehili, M., Yang, F., Leynaert, V., Devillers, L., A corpus of social interaction between nao and elderly people (2014) 5th International Workshop on Emotion, Social Signals, Sentiment & Linked Open Data (ES3LOD2014). 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Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens (2017) International Journal of Human Computer Studies, 97, pp. 88-97. , 2017; Tan, L.F., Seetharaman, S., Preventing the spread of COVID- 19 to nursing homes: Experience from a Singapore geriatric centre (2020) Journal of the American Geriatrics Society, , 2020; Tapus, A., Matarić, M.J., User personality matching with a hands-off robot for post-stroke rehabilitation therapy (2008) Springer Tracts in Advanced Robotics, 39, pp. 165-175. , 2008; Tapus, A., Tapus, C., Mataric, M.J., User-robot personality matching and assistive robot behavior adaptation for (2006) Journal of the Robotics Society of Japan (JRSJ), 24 (5), pp. 14-16. , 2006; Tapus, A., Ţǎpuş, C., Matarić, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics, 1 (2), p. 169. , 2008; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human-robot interaction (2014) Computers in Human Behavior, 38, pp. 75-84. , 2014; Van Vliet, A.J., Effecten van teamsamenstelling op het moreel van uitgezonden militairen (Effects of teamcomposition on the morale of dispatched soldiers)(Rep. No. TM-01-A0404) (2001) Soesterberg: TNO Technische Menskunde, , 2001; Weiss, A., Van Dijk, B., Evers, V., Knowing me knowing you: Exploring effects of culture and context on perception of robot personality (2012) ICIC 2012 - Proceedings of the 4th International Conference on Intercultural Collaboration, pp. 133-136. , 2012; Westgate, M.J., Revtools: An R package to support article screening for evidence synthesis (2019) Research Synthesis Methods, , 2019; Wiggins, J.S., A psychological taxonomy of trait-descriptive terms: The interpersonal domain (1979) Journal of Personality and Social Psychology, 37 (3), p. 395. , 1979; Wiggins, J.S., Broughton, R., A geometric taxonomy of personality scales (1991) European Journal of Personality, 5 (5), pp. 343-365. , 1991; Yang, G.-Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Dario, P., Jacobstein, N., (2020) Combating COVID-19-The Role of Robotics in Managing Public Health and Infectious Diseases; You, S., Robert, L., Emotional attachment, performance, and viability in teams collaborating with embodied physical action (EPA) robots (2017) Journal of the Association for Information Systems, 19 (5), pp. 377-407. , 2017; You, S., Robert, L., Teaming up with robots: An IMOI (inputsmediators- outputs-inputs) framework of human-robot teamwork (2018) Int.J Robotic Eng(IJRE), 2 (3). , 2018; You, S., Robert, L., Trusting robots in teams: Examining the impacts of trusting robots on team performance and satisfaction (2018) Proceedings of the 52th Hawaii International Conference on System Sciences, pp. 8-11; You, S., Peter Robert, L., Subgroup formation in human- robot teams (2019) International Conference on Information Systems, , 2019; You, S., Robert, L.P., Jr., Human-robot similarity and willingness to work with a robotic co-worker (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 251-260; Zeng, Z., Chen, P.-J., Lew, A.A., From high-touch to high-tech: COVID-19 drives robotics adoption (2020) Tourism Geographies, pp. 1-11. , 2020; Zhang, Q., Esterwood, C., Jessie Yang, X., Robert, L., An automated vehicle (AV) like Me? The impact of personality similarities and differences between humans and AVs (2019) 2019 AAAI Fall Symposium, , 2019",,,"Cyber Agent;frontiers in Robotics and AI","Association for Computing Machinery, Inc","8th International Conference on Human-Agent Interaction, HAI 2020","10 November 2020 through 13 November 2020",,164494,,9781450380546,,,"English","HAI - Proc. Int. Conf. Hum.-Agent Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85096539343
"Dos Santos Rosa N., Francelin Romero R.A., Grassi V.","57194021223;57221819735;12795251100;","A Survey on the Aspects of Human-Robot Interaction in Autonomous Vehicles",2020,"2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020",,,"9307046","","",,,"10.1109/LARS/SBR/WRE51543.2020.9307046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100306717&doi=10.1109%2fLARS%2fSBR%2fWRE51543.2020.9307046&partnerID=40&md5=92a6fa6552412fe21565757ab0c443fd","São Carlos School of Engineering, University of São Paulo, Dept. of Electrical and Computer Engineering, São Carlos, Brazil; Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil","Dos Santos Rosa, N., São Carlos School of Engineering, University of São Paulo, Dept. of Electrical and Computer Engineering, São Carlos, Brazil; Francelin Romero, R.A., Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil; Grassi, V., São Carlos School of Engineering, University of São Paulo, Dept. of Electrical and Computer Engineering, São Carlos, Brazil","As the technical challenges on intelligent vehicles have been fulfilled, researchers have begun to discuss the social and ethical aspects regarding the development of platforms that allow more interaction between passengers and autonomously driven vehicles. In this context, this paper focuses on the main topics that are currently being studied to improve aspects of this interaction and to design potential new approaches. The main contributions, discussions, concerns, and prospects in the Human-Robot Interaction field for autonomous vehicles are presented, pointing at new directions of research in this area and challenges to be overcome. © 2020 IEEE.",,"Agricultural robots; Autonomous vehicles; Educational robots; Man machine systems; Professional aspects; Robotics; New approaches; Technical challenges; Human robot interaction",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES","This study was financed in part by the Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.",,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2008) Foundations and Trends ® in Human-Computer Interaction, 1 (3), pp. 203-275; Waldrop, M.M., Autonomous vehicles: No drivers required (2015) Nature News, 518 (7537), p. 20; Litman, T., (2017) Autonomous Vehicle Implementation Predictions, , Victoria Transport Policy Institute Victoria, Canada; Kun, A.L., Boll, S., Schmidt, A., Shifting gears: User interfaces in the age of autonomous driving (2016) IEEE Pervasive Computing, 15 (1), pp. 32-38; Dabral, S., Kamath, S., Appia, V., Mody, M., Zhang, B., Batur, U., Trends in camera based automotive driver assistance systems (adas) (2014) 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, pp. 1110-1115; Broy, M., Schmidt, A., Challenges in engineering cyber-physical systems (2014) Computer, 47 (2), pp. 70-72; Meschtscherjakov, A., Tscheligi, M., Szostak, D., Ratan, R., McCall, R., Politis, I., Krome, S., Experiencing autonomous vehicles: Crossing the boundaries between a drive and a ride (2015) Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2413-2416; Meschtscherjakov, A., Tscheligi, M., Szostak, D., Krome, S., Pfleging, B., Ratan, R., Politis, I., Ju, W., Hci and autonomous vehicles: Contextual experience informs design (2016) Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 3542-3549; Meschtscherjakov, A., Tscheligi, M., Fröhlich, P., McCall, R., Riener, A., Palanque, P., Mobile interaction with and in autonomous vehicles (2017) Proceedings of the 19th Intl. Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1-6; Meschtscherjakov, A., Tscheligi, M., Pfleging, B., Sadeghian Borojeni, S., Ju, W., Palanque, P., Riener, A., Kun, A.L., Interacting with autonomous vehicles: Learning from other domains (2018) Extended Abstracts of the 2018 CHI Conf. On Human Factors in Computing Systems, pp. 1-8; Lynberg, M., (2017) Automated Vehicles for Safety, , https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety, Sep; Rödel, C., Stadler, S., Meschtscherjakov, A., Tscheligi, M., Towards autonomous cars: The effect of autonomy levels on acceptance and user experience (2014) Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, pp. 1-8; Pfleging, B., Rang, M., Broy, N., Investigating user needs for nondriving-related activities during automated driving (2016) Proceedings of the 15th Intl. Conf. On Mobile and Ubiquitous Multimedia, pp. 91-99; Pettersson, I., Rydström, A., Strömberg, H., Hylving, L., Andersson, J., Klingegård, M., Karlsson, M., Living room on the move: Autonomous vehicles and social experiences (2016) Proceedings of the 9th Nordic Conference on Human-Computer Interaction, pp. 1-3; (2020) Volvo Cars Global Newsroom Media Library, , https://www.media.volvocars.com/, Volvo Car Corp; (2020) Daimler Global Media Site, , https://media.daimler.com/marsMediaSite/en/instance/ko/Start.xhtmloid=4836258, Daimler AG; Tscheligi, M., Bueker, U., Ju, W., Sendhoff, B., Panel toward 2020: Human interaction with autonomous vehicles (2014) 9th ACM/IEEE International Conference on Human-Robot Interaction; Wintersberger, P., Riener, A., Frison, A.-K., Automated driving system, male, or female driver: Who'd you prefer. comparative analysis of passengers' mental conditions, emotional states & qualitative feedback (2016) Proceedings of the 8th Intl. Conf. On Automotive User Interfaces and Interactive Vehicular Applications, pp. 51-58; Debernard, S., Chauvin, C., Pokam, R., Langlois, S., Designing human-machine interface for autonomous vehicles (2016) IFACPapersOnLine, 49 (19), pp. 609-614; Strömberg, H., Pettersson, I., Andersson, J., Rydström, A., Dey, D., Klingegård, M., Forlizzi, J., Designing for social experiences with and within autonomous vehicles-exploring methodological directions (2018) Design Science, 4; Jorge, C.C., Rossetti, R.J., On social interactions and the emergence of autonomous vehicles (2018) VEHITS, pp. 423-430; Lazányi, K., Hajdu, B., Trust in human-robot interactions (2017) 2017 IEEE 14th International Scientific Conference on Informatics. IEEE, pp. 216-220; Pedersen, B.K.M.K., Andersen, K.E., Köslich, S., Weigelin, B.C., Kuusinen, K., Simulations and self-driving cars: A study of trust and consequences (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 205-206; Ruijten, P.A., Terken, J., Chandramouli, S.N., Enhancing trust in autonomous vehicles through intelligent user interfaces that mimic human behavior (2018) Multimodal Technologies and Interaction, 2 (4), p. 62; Pieters, N., (2019) Morality in the Preference for Advanced Driver Assistance Systems: Identifying the Importance of Safety for Other Road Users in Consumers Preference for Extra Car Features; Schieben, A., Wilbrink, M., Kettwich, C., Madigan, R., Louw, T., Merat, N., Designing the interaction of automated vehicles with other traffic participants: Design considerations based on human needs and expectations (2019) Cognition, Technology & Work, 21 (1), pp. 69-85; Rasmussen, J., Information processing and human-machine interaction (1986) An Approach to Cognitive Engineering; Vicente, K.J., (1999) Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-based Work, , CRC Press; Hoc, J.-M., From human-machine interaction to human-machine cooperation (2000) Ergonomics, 43 (7), pp. 833-843; Lyons, J.B., Being transparent about transparency: A model for human-robot interaction (2013) 2013 AAAI Spring Symposium Series; Stevens, T., How Waymo Is Defining User Interface for Selfdriving Cars, , https://www.cnet.com/roadshow/news/how-waymo-is-defining-the-ui-of-autonomy/; Waytz, A., Heafner, J., Epley, N., The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle (2014) Journal of Experimental Social Psychology, 52, pp. 113-117; Yoder, J., (2016) Vehicle-to-Vehicle Communication, , https://www.nhtsa.gov/technology-innovation/vehicle-vehicle-communication, Oct; Jafarnejad, S., Codeca, L., Bronzi, W., Frank, R., Engel, T., A car hacking experiment: When connectivity meets vulnerability (2015) 2015 IEEE Globecom Workshops (GC Wkshps). IEEE, pp. 1-6",,"Goncalves L.M.G.Drews Junior P.L.J.da Silva B.M.F.Fernandes Curvelo C.da.C.Fabro J.A.dos Santos D.H.de Melo J.C.P.","Brazilian Computer Society (SBC);IEEE Northeast Brazil RAS-CS Joint Chapter","Institute of Electrical and Electronics Engineers Inc.","17th Latin American Robotics Symposium, 12th Brazilian Symposium on Robotics and 11th Workshop on Robotics in Education, LARS-SBR-WRE 2020","9 November 2020 through 12 November 2020",,166442,,9780738111537,,,"English","Lat. American Robot. Symp., Braz. Symp. Robot. Workshop Robot. Educ., LARS-SBR-WRE",Conference Paper,"Final","",Scopus,2-s2.0-85100306717
"Xu Y., Hu B., Yan J., Chen L., Zhang D.-X., Guan Z.-H.","57222004598;57207150639;57211535238;57209903288;8852967100;7202542255;","Robots Learn to Write via Human-Robot Interaction",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9326574","3653","3658",,,"10.1109/CAC51589.2020.9326574","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100934412&doi=10.1109%2fCAC51589.2020.9326574&partnerID=40&md5=9aaa1c61f1b8e0bfc0d63a3eaba67ef0","Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China","Xu, Y., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Hu, B., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Yan, J., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Chen, L., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Zhang, D.-X., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Guan, Z.-H., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China","Human-Robot Interaction (HRI) has been extensively investigated in academia, industries, and technology companies. How to teach a robot to write is recognized as a difficult task, involving perception, learning and control of the overall robotic system. This paper develops a robotic handwriting system on a NAO robot by using HRI and Q-learning. In the simulator setup, the human demonstrator first writes to form a desired writing content, with the coordinates of each stroke writing saved; then, by applying the Q-learning algorithm, the virtual NAO automatically learns to find the correct writing order and to optimize the strokes. With the learned knowledge, the real NAO uses its inverse kinematics for generating the joint values from the stroke trajectories, and thus can quickly replicate the letters or words written by the human demonstrator. It is shown that by using the proposed HRI-based handwriting system, the human can naturally and conveniently teach NAO to write many characters and NAO can also have an acceptable writing quality close to the human. © 2020 IEEE.","handwriting; Human-Robot Interaction (HRI); NAO robot; Q-learning","Industrial robots; Inverse kinematics; Learning algorithms; Learning systems; Man machine systems; Reinforcement learning; Robotics; Human robot Interaction (HRI); Q-learning; Q-learning algorithms; Robotic handwriting; Robotic systems; Technology companies; Writing quality; Human robot interaction",,,,,,,,,,,,,,,,"Stiefelhagen, R., Fugen, C., Gieselmann, R., Holzapfel, H., Nickel, K., Waibel, A., Natural human-robot interaction using speech, head pose and gestures 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3, pp. 2422-2427; Du, G.L., Chen, M.X., Liu, C.B., Zhang, B., Zhang, P., Online robot teaching with natural human-robot interaction (2018) IEEE Transactions on Industrial Electronics, 65 (12), pp. 9571-9581; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954; Han, J., Kim, D., R-learning services for elementary school students with a teaching assistant robot[c] 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 255-256; Liu, T.C., Chang, M., Human-robot interaction research issues of educational robots 2008 Second IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, pp. 209-210; Medwell, J., Wray, D., HandwritingCA forgotten language skill[J] (2008) Language and Education, 1 (22), pp. 34-47; Hood, D., Lemaignan, S., Dillenbourg, P., When children teach a robot to write: An autonomous teachable humanoid which uses simulated handwriting[c] (2015) Proceedings of the Tenth Annual IEEE International Conference on Human-Robot Interaction. ACM, pp. 83-90; Christensen, C.A., The role of orthographic motor integration in the production of creative and well-structured written text for students in secondary school (2005) Educ. Psychol., p. 441C453; Potkonjak, V., Popovic, M., Lazarevic, M., Sinanovic, J., Redundancy problem in writing: From human to anthropomorphic robot arm (1998) IEEE Trans. Syst. Man Cybern. Part B Cybern., p. 790C805; Huebel, N., Mueggler, E., Waibel, M., D'Andrea, R., Towards robotic calligraphy (2012) Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, p. 5165C5166. , Portugal, 7C12 October; Chao, F., Huang, Y., Lin, C., Yang, L., Hu, H., Zhou, C., Use of automatic Chinese character decomposition and human gestures for Chinese calligraphy robots (2019) IEEE Transactions on Human-Machine Systems, 49 (1), pp. 47-58. , Feb; Chang, C.W., Lee, J.H., Chao, P.Y., Wang, C.Y., Exploring the possibility of using humanoid robots as instructional tools for teaching a second language in primary school (2010) Educ. Technol. Soc., p. 13C24; Hood, D., Lemaignan, S., Dillenbourg, P., The cowriter project: Teaching a robot how to write (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI'15 Extended Abstracts, p. 269. , 2C5 March; Johal, W., Jacq, A., Paiva, A., Dillenbourg, P., Child-robot spatial arrangement in a learning by teaching activity (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 533-538. , New York, USA, 26-31 August; Jacq, A., Lemaignan, S., Garcia, F., Dillenbourg, P., Building successful long child-robot interaction in a learning context (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction, pp. 239-246; Chandra, S., Dillenbourg, P., Paiva, A., Classification of children's handwriting errors for the design of an educational co-writer robotic peer (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 215-225; Chandra, S., Paradeda, R., Yin, H., Dillenbourg, P., Prada, R., Paiva, A., Do children perceive whether a robotic peer is learning or not (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 41-49; Chandra, S., Dillenbourg, P., Paiva, A., Children teach handwriting to a social robot with different learning competencies (2019) International Journal of Social Robotics., pp. 1-28; Kucuk, S., Bingul, Z., Robot kinematics: Forward and inverse kinematics (2006) Industrial Robotics: Theory, Modelling and Control. IntechOpen; Watkins, C.J., Dayan, P., Michalski, R.S., Tecuci, G., Carbonell, J.G., Mitchell, T.M., Q-learning (1992) Machine Learning, 8, pp. 279-292",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Chinese Automation Congress, CAC 2020","6 November 2020 through 8 November 2020",,166835,,9781728176871,,,"English","Proc. - Chin. Autom. Congr., CAC",Conference Paper,"Final","",Scopus,2-s2.0-85100934412
"Domonkos M., Dombi Z., Botzheim J.","57215435061;57221835511;22133853500;","LED Strip Based Robot Movement Intention Signs for Human-Robot Interactions",2020,"20th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2020 - Proceedings",,,"9305854","121","126",,,"10.1109/CINTI51262.2020.9305854","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100331357&doi=10.1109%2fCINTI51262.2020.9305854&partnerID=40&md5=ffbae0f5feed625a46745e1eaf5e8a1e","Optics and Mechanical Engineering Informatics Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Department of Mechatronics, Budapest, 1111, Hungary","Domonkos, M., Optics and Mechanical Engineering Informatics Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Department of Mechatronics, Budapest, 1111, Hungary; Dombi, Z., Optics and Mechanical Engineering Informatics Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Department of Mechatronics, Budapest, 1111, Hungary; Botzheim, J., Optics and Mechanical Engineering Informatics Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Department of Mechatronics, Budapest, 1111, Hungary","As a new kind of robots, called cooperative robots, are more commonly used in industry, a new way of communication is becoming more important due to the increasing number of closer cooperation between human and robots. This paper proposes the idea behind a novel method of using visual communication between cobots and humans focusing mainly on the field of industrial robotics. This device can decrease the mental stress experienced by the coworker and can increase the trust resulting in a closer to ergonomic workspace from the coworker viewpoint. Other possible usage is also discussed. © 2020 IEEE.","cobot; cooperative robotics; human-robot interaction; robot feedback; visual feedback","Cooperative communication; Industrial robots; Intelligent computing; Light emitting diodes; Visual communication; Cooperative robots; Industrial robotics; Mental stress; Robot movements; Social robots",,,,,"BME IE-MI-FM TKP2020","The research reported in this paper and carried out at the Budapest University of Technology and Economics was supported by the “TKP2020, Institutional Excellence Program” of the National Research Development and Innovation Office in the field of Artificial Intelligence (BME IE-MI-FM TKP2020).",,,,,,,,,,"Ballard, L., Robotics' founding father george c. Devol-serial entrepreneur and inventor (2011) Robot-Congers, (31), p. 58; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-Analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53; Dániel, B., Thomessen, T., Korondi, P., Simplified human-robot interaction: Modeling and evaluation (2013) Modeling Identification and Control, 34 (4), pp. 199-211. , http://www.mic-journal.no/PDF/2013/MIC-2013-4-4.pdf, [online]; Dániel, B., Korondi, P., Sziebig, G., Thomessen, T., Evaluation of flexible graphical user interface for intuitive human robot interactions (2014) Acta Polytechnica Hungarica, 11 (1), pp. 135-151; Robots and robotic devices-safety requirements for industrial robots-part 1: Robots (2011) Iso/tc 299 Robotics, Standard; Robots and robotic devices-safety requirements for industrial robots-part 2: Robot systems and integration (2011) Iso/tc 299 Robotics, Standard; Robots and robotic devices-collaborative robots (2016) Iso/tc 299 Robotics, Standard; Brooks, D.J., (2017) Human-Centric Approach to Autonomous Robot Failures, , Ph.D. dissertation, University of Massachusetts, Lowell; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interacion: Literature review and model development (2018) Frontiers of Psychology, 9. , https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00861/full, [online]; Darvish, K., Wanderlingh, F., Bruno, B., Simetti, E., Mastrogiovanni, F., Casalino, G., Flexible human-robot cooperation models for assisted shop-floor tasks (2018) Mechatronics, 51, pp. 97-114; Bdiwi, M., Pfeifer, M., Sterzing, A., A new strategy for ensuring human safety during various levels of interaction with industrial robots (2017) Cirp Annals, 66",,,,"Institute of Electrical and Electronics Engineers Inc.","20th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2020","5 November 2020 through 7 November 2020",,166420,,9781728183398,,,"English","IEEE Int. Symp. Comput. Intell. Informatics, CINTI - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85100331357
"Wu S.-H., Hong X.-S.","9270533800;57221679404;","Integrating Computer Vision and Natural Language Instruction for Collaborative Robot Human-Robot Interaction",2020,"2020 International Automatic Control Conference, CACS 2020",,,"9289768","","",,,"10.1109/CACS50047.2020.9289768","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099759975&doi=10.1109%2fCACS50047.2020.9289768&partnerID=40&md5=849698567d91dfe710625bd3c84b3b12","Chaoyang University of Technology, Department of Computer Science and Information Engineering, Taichung, Taiwan","Wu, S.-H., Chaoyang University of Technology, Department of Computer Science and Information Engineering, Taichung, Taiwan; Hong, X.-S., Chaoyang University of Technology, Department of Computer Science and Information Engineering, Taichung, Taiwan","In this paper, we report how we design a human-robot co-working scheme. We believe that natural language is the best way that human and machine can exchange messages. However, understand voice command is not enough, what object is referred in what location is also very import. To better understand a natural language instruction, a robot also require computer vision to resolve the real-world object referencing problem. In this paper we show how to integrate the Google voice recognition result with the YOLO computer vision software with the KINOVA robot arm and achieve a collaborative robot interaction. © 2020 IEEE.","collaborative robot; Human-robot interaction; image recognition; natural language instruction","Automation; Human computer interaction; Human robot interaction; Machine design; Process control; Collaborative robots; Human robots; Natural languages; Real-world objects; Robot arms; Voice command; Computer vision",,,,,"Ministry of Education, MOE; Chaoyang University of Technology, CYUT","ACKNOWLEDGMENT This research is partially sponsored by Chaoyang University of Technology (CYUT) and Higher Education Sprout Project, Ministry of Education, Taiwan, under the project name: ""The R&D and the cultivation of talent for Health-Enhancement Products"".",,,,,,,,,,"Litzenberger, G., (2019) IFR Publishes Collaborative Industrial Robot Definition and Estimates Supply; (2020) Top Trends Robotics 2020, , https://ifr.org/post/international-federation-of-robotics-publishes-collaborative-industrial-robecon; (2019) World Robotics 2019 Preview, , https://ifr.org/ifr-press-releases, IFR, IFR, 04; Peshkin, M., Edward Colgate, J., ""Cobots"" work with people (1996) IEEE Robotics & Automation Magazine, 3 (4), p. 12. , Northwestern University; Olivares-Alarcos, A., Foix, S., Alenyà, G., On inferring intentions in shared tasks for industrial collaborative robots (2019) Electronics, 8 (11), p. 1306. , https://www.mdpi.com/2079-9292/8/11/1306/htm#B8-electronics-08-01306, 2019; Matúšová, M., Bučányová, M., Hrušková, E., (2019) The Future of Industry with Collaborative Robots, , https://www.matec-conferences.org/articles/matecconf/abs/2019/48/matecconf_mtem2019_02008/matecconf_mtem2019_02008.html, Slovak University of Technology, Faculty of Material Science and Technology, Institute of production systems; Bauer, W., (2016) Lightweight Robots in Manual Assembly - Best to Start Simply!; Rouse, M., (2018) Collaborative Robot (Cobot), , https://whatis.techtarget.com/definition/collaborative-robot-cobot, 06; Akella, P., Peshkin, M., Colgate, E., Cobots: A new generation of assembly tools for the line worker (1997) SPIE Robotics and Machine Perception International Technical Working Group Newletter, , Northwestern University; Augustin, S., SAFE CO-OPERATION between HUMAN BEINGS and ROBOTS - COBOTS, , https://perosh.eu/safe-co-operation-between-human-beings-and-robots-cobots/; (2018) Demystifying Collaborative Industrial Robots; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., (2015) You only Look Once: Unified, Real-Time Object Detection, , https://arxiv.org/pdf/1506.02640.pdf; (2015) The Manufacturer, I, Cobot: Future Collaboration of Man and Machine, , https://www.themanufacturer.com/articles/i-cobot-future-collaboration-of-man-and-machine/, The Manufacturer. 11; Quigley, M., Gerkey, B., Conley, K., Faus, J., Foote, T., Leibs, J., Berger, E., Ng, A., ROS: An Open-source Robot Operating System, , http://www.willowgarage.com/sites/default/files/icraoss09-ROS.pdf; KINOVA JACO 2 Documents, , https://www.kinovarobotics.com/sites/default/files/ULWS-RA-JAC-UG-INT-EN%20201804-1.0%20%28KINOVA%E2%84%A2%20Ultra%20lightweight%20robotic%20arm%20user%20guide%29_0.pdf","Wu, S.-H.; Chaoyang University of Technology, Taiwan; email: shwu@cyut.edu.tw",,,"Institute of Electrical and Electronics Engineers Inc.","2020 International Automatic Control Conference, CACS 2020","4 November 2020 through 7 November 2020",,165977,,9781728171982,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85099759975
"Mainampati M., Chandrasekaran B.","57216314540;56740438200;","Manual and Autonomous Mode based Navigation using Human Robot Interaction on a Robotic Vehicle",2020,"11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020",,,"9284945","559","564",,1,"10.1109/IEMCON51383.2020.9284945","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099289014&doi=10.1109%2fIEMCON51383.2020.9284945&partnerID=40&md5=27a4ba244cf0fdfe0c383e4fa9170369","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Mainampati, M., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","This paper describes how different algorithms are implemented on autonomous robots like GoPiGo robot and Turtlebot. A∗ and behavioral-based algorithms are programmed and used in this work. The GoPiGo robot is a vehicle control device; it can work in either manual or autonomous mode. Based on human instruction, the robot can switch back and forth between manual and autonomous control. A switching-based module is used to select the signals in manual mode and automated control signals in the automated mode. This research is a work in progress, and the next stage is using the Turtlebot to achieve the implementation of this work. © 2020 IEEE.","Autonomous robot; Human-Robot Interaction; Object detection; Path planning; Raspberry pi; Remote control","Control system synthesis; Mobile telecommunication systems; Robotics; Automated control; Automated mode; Autonomous control; Autonomous mode; Manual modes; Robotic vehicles; Vehicle Control; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"US5469356A-System for controlling a vehicle to selectively allow operation in either an autonomous mode or a manual mode (2019) Google Patents, , https://patents.google.com/patent/US5469356A/en, Accessed 7 Dec; Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J., Practical search techniques in path planning for autonomous driving (2008) Ann Arbor, 1001 (48105), pp. 18-80; Chang-An, L., Jin-Gang, C., Guo-Dong, L., Chun-Yang, L., Mobile robot path planning based on an improved rapidlyexploring random tree in unknown environment (2008) 2008 IEEE International Conference on Automation and Logistics (Pp. 2375-2379). IEEE, , September; Vanitha, M., Selvalakshmi, M., Selvarasu, R., Monitoring and controlling of mobile robot via internet through raspberry Pi board (2016) 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM), pp. 462-466. , March IEEE; (2019) Dexter Industries Wikipedia, Wikimedia Foundation, , https://en.wikipedia.org/wiki/Dexter_Industries, 25 Nov. Accessed 7 Dec. 2019; (2019) Raspbian for Robots by Dexter Industries, , https://sourceforge.net/projects/dexterindustriesraspbianflavor/, SourceForge Accessed 7 Dec; (2019), http://umaine.edu/computingcoursematerials/wpcontent/uploads/sites/511/2017/01/robotexercises.pdf, Accessed 7 Dec; (2019), http://www.am.chalmers.se/~wolff/AA/Chapter3.pdf, Accessed 7 Dec; Mataric, M.J., Behaviour-based control: Examples from navigation, learning, and group behaviour (1997) Journal of Experimental & Theoretical Artificial Intelligence, 9 (2-3), pp. 323-336; (2019) Wikipedia, Wikimedia Foundation, , https://en.m.wikipedia.org/wiki/IEEE_802.11n-2009, IEEE 802.11n-2009, 6 Dec; Alexander, B., Combining adjustable autonomy and shared control as a new platform for controlling robotic systems with ROS on TurtleBot (2018); Pietrzik, S., Chandrasekaran, B., Testing autonomous path planning algorithms and setup for robotic vehicle navigation (2018) 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 485-488. , November. IEEE",,"Paul R.","IEEE Vancouver Section;Institute of Engineering and Management (IEM);Smart;University of Engineering and Management (UEM)","Institute of Electrical and Electronics Engineers Inc.","11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020","4 November 2020 through 7 November 2020",,166022,,9781728184166,,,"English","Annu. IEEE Inf. Technol., Electron. Mob. Commun. Conf., IEMCON",Conference Paper,"Final","",Scopus,2-s2.0-85099289014
"Iwasaki M., Ikeda M., Kawamura T., Nakanishi H.","57204952957;57204955201;57204949710;35492560200;","State-transition modeling of human–robot interaction for easy crowdsourced robot control",2020,"Sensors (Switzerland)","20","22","6529","1","31",,,"10.3390/s20226529","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096038221&doi=10.3390%2fs20226529&partnerID=40&md5=a0d0d5d949316475e058844dabd2bef1","Department of Adaptive Machine Systems, Osaka University, Suita, 565-0871, Japan; Kyoto Innovation, Inc., Kyoto, 604-8151, Japan","Iwasaki, M., Department of Adaptive Machine Systems, Osaka University, Suita, 565-0871, Japan; Ikeda, M., Department of Adaptive Machine Systems, Osaka University, Suita, 565-0871, Japan; Kawamura, T., Kyoto Innovation, Inc., Kyoto, 604-8151, Japan; Nakanishi, H., Department of Adaptive Machine Systems, Osaka University, Suita, 565-0871, Japan","Robotic salespeople are often ignored by people due to their weak social presence, and thus have difficulty facilitating sales autonomously. However, for robots that are remotely controlled by humans, there is a need for experienced and trained operators. In this paper, we suggest crowdsourcing to allow general users on the internet to operate a robot remotely and facilitate customers’ purchasing activities while flexibly responding to various situations through a user interface. To implement this system, we examined how our developed remote interface can improve a robot’s social presence while being controlled by a human operator, including first-time users. Therefore, we investigated the typical flow of a customer–robot interaction that was effective for sales promotion, and modeled it as a state transition with automatic functions by accessing the robot’s sensor information. Furthermore, we created a user interface based on the model and examined whether it was effective in a real environment. Finally, we conducted experiments to examine whether the user interface could be operated by an amateur user and enhance the robot’s social presence. The results revealed that our model was able to improve the robot’s social presence and facilitate customers’ purchasing activity even when the operator was a first-time user. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Field trial; Multimodal conversation analysis; Robotic salesperson; Situation awareness","Crowdsourcing; Sales; User interfaces; Real environments; Remote interfaces; Robot interactions; Sales promotions; Sensor informations; Social presence; State transition models; State transitions; Social robots; commercial phenomena; computer interface; consumer attitude; crowdsourcing; human; robotics; Commerce; Consumer Behavior; Crowdsourcing; Humans; Robotics; User-Computer Interface",,,,,"Japan Society for the Promotion of Science, KAKEN: 19FC01, JP18KK0053, JP19H00605, JP19K21718, JP20H01585","Funding: This research was funded by JSPS KAKENHI, grant number JP19H00605, JP19K21718, JP18KK0053, JP20H01585, and ROIS NII Open Collaborative Research, grant number 19FC01.",,,,,,,,,,"Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Interactive Humanoid Robots for a Science Museum Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction—HRI ’06, pp. 305-312. , Salt Lake City, UT, USA, 2–4 March 2006; Gehle, R., Pitsch, K., Wrede, S., Signaling Trouble in Robot-to-Group Interaction. Emerging Visitor Dynamics with a Museum Guide Robot Proceedings of the Second International Conference on Human-Agent Interaction, pp. 361-368. , Tsukuba, Japan, 29–31 October 2014; Tanaka, F., Isshiki, K., Takahashi, F., Uekusa, M., Sei, R., Hayashi, K., Pepper learns together with children: Development of an educational application Proceedings of the 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 270-275. , Seoul, Korea, 3–5 November 2015; Nakanishi, J., Kuramoto, I., Baba, J., Kohei, O., Yoshikawa, Y., Ishiguro, H., Can a Humanoid Robot Engage in Heartwarming Interaction Service at a Hotel? Proceedings of the 6th International Conference on Human-Agent Interaction, pp. 45-53. , Southampton, UK, 15–18 December 2018; Nakanishi, J., Kuramoto, I., Baba, J., Ogawa, K., Yoshikawa, Y., Ishiguro, H., Continuous Hospitality with Social Robots at a hotel (2020) SN Appl. Sci, 2, pp. 1-13; Shiomi, M., Shinozawa, K., Nakagawa, Y., Miyashita, T., Sakamoto, T., Terakubo, T., Ishiguro, H., Hagita, N., Recommendation Effects of a Social Robot for Advertisement-Use Context in a Shopping Mall (2013) Int. J. Soc. Robot, 5, pp. 251-262; Lee, M.K., Kiesler, S., Forlizzi, J., Receptionist or Information Kiosk Proceedings of the 2010 ACM Conference on Information Technology Education—SIGITE ’10, pp. 31-40. , Midland, MI, USA, 7–9 October 2010; Glas, D.F., Wada, K., Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Personal Greetings: Personalizing Robot Utterances Based on Novelty of Observed Behavior (2016) Int. J. Soc. Robot, 9, pp. 181-198; Iwasaki, M., Zhou, J., Ikeda, M., Kawamura, T., Nakanishi, H., A Customer’s Attitude to a Robotic Salesperson Depends on Their Initial Interaction Proceedings of the 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 300-305. , Nanjing, China, 27–31 August 2018; Iwasaki, M., Zhou, J., Ikeda, M., Koike, Y., Onishi, Y., Kawamura, T., Nakanishi, H., “That Robot Stared Back at Me!”: Demonstrating Perceptual Ability Is Key to Successful Human–Robot Interactions (2019) Front. Robot. AI, 6; Cosgun, A., Florêncio, D.A.F., Christensen, H.I., Autonomous person following for telepresence robots Proceedings of the 2013 IEEE International Conference on Robotics and Automation, pp. 4335-4342. , Karlsruhe, Germany, 6–10 May 2013; Tsui, K.M., Norton, A., Brooks, D.J., McCann, E., Medvedev, M.S., Yanco, H.A., Design and development of two generations of semi-autonomous social telepresence robots Proceedings of the 2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA), pp. 1-6. , Woburn, MA, USA, 22–23 April 2013; Kelley, J.F., An iterative design methodology for user-friendly natural language office information applications (1984) ACM Trans. Inf. Syst, 2, pp. 26-41; Redondo, M.E.L., Comfortability Detection for Adaptive Human-Robot Interactions Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 35-39. , Cambridge, UK, 3–6 September 2019; Coyne, A.K., Murtagh, A., McGinn, C., Using the Geneva Emotion Wheel to Measure Perceived Affect in Human-Robot Interaction Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 491-498. , Cambridge, UK, 23–26 March 2020; Podpora, M., Gardecki, A., Beniak, R., Klin, B., Vicario, J.L., Kawala-Sterniuk, A., Human Interaction Smart Subsystem—Extending Speech-Based Human-Robot Interaction Systems with an Implementation of External Smart Sensors (2020) Sensors, 20, p. 2376; Poggi, L., (2007) Mind, Hands, Face and Body: A Goal and Belief View of Multimodal Communication, , Weidler: Berlin, Germany; Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., McOwan, P.W., Context-Sensitive Affect Recognition for a Robotic Game Companion (2014) ACM Trans. Interact. Intell. Syst, 4, pp. 1-25; Knapp, M.L., Hall, J.A., Horgan, T.G., (2003) Nonverbal Communication in Human Interaction, , Cengage Learning: Boston, MA, USA; Yanco, H., Personalization in HRI: A Longitudinal Field Experiment Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 319-326. , Boston, MA, USA, 5–8 March 2012; Michalowski, M., Sabanovic, S., Simmons, R., A spatial model of engagement for a social robot Proceedings of the 9th IEEE International Workshop on Advanced Motion Control, pp. 762-767. , Istanbul, Turkey, 27 29 March 2006; Iwasaki, M., Zhou, J., Ikeda, M., Onishi, Y., Kawamura, T., Nakanishi, H., Acting as if Being Aware of Visitors’ Attention Strengthens a Robotic Salesperson's Social Presence (2019) 7th International Conference on Human-Agent Interaction (HAI2019), pp. 19-27. , Kyoto, Japan, 6-10 October; Mura, D., Knoop, E., Catalano, M.G., Grioli, G., Bächer, M., Bicchi, A., On the role of stiffness and synchronization in human–robot handshaking (2020) Int. J. Robot. Res; Heilman, C., Lakishyk, K., Radas, S., An empirical investigation of in-store sampling promotions (2011) Br. Food J, 113, pp. 1252-1266; Tonkin, M., Vitale, J., Ojha, S., Williams, M.-A., Fuller, P., Judge, W., Wang, X., Would you like to sample? Robot Engagement in a Shopping Centre Proceedings of the 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 42-49. , Lisbon, Portugal, 28 August–1 September 2017; Hitchcock, D.B., Yates and Contingency Tables: 75 Years Later (2009) Electron. J. Hist. Probab. Stat, 5, pp. 1-14; Meissner, A., Trübswetter, A., Conti-Kufner, A.S., Schmidtler, J., Friend or Foe? Understanding Assembly Workers’ Acceptance of Human-robot Collaboration (2020) ACM Trans. Hum. Robot Interact, 10, pp. 1-30","Nakanishi, H.; Department of Adaptive Machine Systems, Japan; email: nakanishi@ams.eng.osaka-u.ac.jp",,,"MDPI AG",,,,,14248220,,,"33203133","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85096038221
"Woo J., Ohyama Y., Kubota N.","36603728900;8358930000;7202158579;","Robot partner development platform for human-robot interaction based on a user-centered design approach",2020,"Applied Sciences (Switzerland)","10","22","7992","1","20",,4,"10.3390/app10227992","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095945700&doi=10.3390%2fapp10227992&partnerID=40&md5=e4edb1e5bdcdfd30b13a109db1652ddc","Department of Mechanical Engineering, School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan; Graduate School of Systems Design, Tokyo Metropolitan University, Hino, 191-0065, Japan","Woo, J., Department of Mechanical Engineering, School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan; Ohyama, Y., Department of Mechanical Engineering, School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan; Kubota, N., Graduate School of Systems Design, Tokyo Metropolitan University, Hino, 191-0065, Japan","This paper presents a robot partner development platform based on smart devices. Humans communicate with others based on the basic motivations of human cooperation and have communicative motives based on social attributes. Understanding and applying these communicative motives become important in the development of socially-embedded robot partners. Therefore, it is becoming more important to develop robots that can be applied according to needs while taking these human communication elements into consideration. The role of a robot partner is more important in not only on the industrial sector but also in households. However, it seems that it will take time to disseminate robots. In the field of service robots, the development of robots according to various needs is important and the system integration of hardware and software becomes crucial. Therefore, in this paper, we propose a robot partner development platform for human-robot interaction. Firstly, we propose a modularized architecture of robot partners using a smart device to realize a flexible update based on the re-usability of hardware and software modules. In addition, we show examples of implementing a robot system using the proposed architecture. Next, we focus on the development of various robots using the modular robot partner system. Finally, we discuss the effectiveness of the proposed robot partner system through social implementation and experiments. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human-robot interaction; Information support; Robot partner; User-centered design",,,,,,,,,,,,,,,,,"Kim, S.H., Kim, D.H., Kim, W.S., Long-term care needs of the elderly in Korea and elderly long-term care insurance (2010) Soc.Work Public Health, 25, pp. 176-184. , [CrossRef] [PubMed]; Gardiner, C., Geldenhuys, G., Gott, M., Interventions to reduce social isolation and loneliness among older people: An integrative review (2018) Health Soc. Care Community, 26, pp. 147-157. , [CrossRef] [PubMed]; (2019) White Paper on an Aging Society, , https://www8.cao.go.jp/kourei/whitepaper/index-w.html, Cabinet Office, Government of Japan. (accessed on 3 October 2020); Fowler, J.H., Christakis, N.A., Dynamic spread of happiness in a large social net-work: Longitudinal analysis over 20 years in the framingham heart study (2008) BMJ, 337, p. a2338. , [CrossRef] [PubMed]; Earthy, J., Jones, B.S., Bevan, N., ISO standards for user-centered design and the specification of usability (2012) Usability in Government Systems, pp. 267-283. , Morgan Kaufmann: Burlington, MA, USA; (2019) Ergonomics of Human-System Interaction–Part 210: Human-Centred Design for Interactive Systems, , ISO 9241-210: International Standardization Organization (ISO): Geneva, Switzerland; Tomasello, M., (2010) Origins of Human Communication, , MIT Press: Cambridge, MA, USA; The 23rd Ota Industry Fair, , https://www.pio-ota.jp/k-fair/23/index.html, (accessed on 3 October 2020); (2019) Executive Summary World Robotics 2019 Service Robots, , International Federation of Robotics. International Federation of Robotics: Frankfurt, Germany; Osawa, H., Ema, A., Hattori, H., Akiya, N., Kanzaki, N., Kubo, A., Koyama, T., Ichise, R., What is real risk and benefit on work with robots?: From the analysis of a robot hotel (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 241-242. , Viennam, Austria, 6 March; Iwamoto, T., Nishi, K., Unokuchi, T., Simultaneous dialog robot system (2019) Inter-National Conference on Human-Computer Interaction, pp. 107-111. , Springer: Berlin/Heidelberg, Germany; Savery, R., Rose, R., Weinberg, G., Finding Shimi’s voice: Fostering human-robot communication with music and a NVIDIA Jetson TX2 Proceedings of the 17th Linux Audio Conference, p. 5. , Stanford, CA, USA, 23–26 March 2019; Milliez, G., Buddy: A companion robot for the whole family (2018) Proceedings of the Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, p. 40. , Chicago, IL, USA, 1 March; Potnuru, A., Jafarzadeh, M., Tadesse, Y., 3D printed dancing humanoid robot “Buddy” for homecare Proceedings of the 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 733-738. , Fort Worth, TX, USA, 21–25 August 2016; Ono, S., Woo, J., Matsuo, Y., Kusaka, J., Wada, K., Kubota, N., A Health Promotion Support System for Increasing Motivation Using a Robot Partner (2015) Trans. Inst. Syst. Control Inf. Eng, 284, pp. 161-171; Pandey, A.K., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) IEEE Robot. Autom. Mag, 25, pp. 40-48. , [CrossRef]; Kobayashi, T., Kuriyama, K., Arai, K., SNS Agency Robot for Elderly People Realizing Rich Media Communication Proceedings of the 2018 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), pp. 109-112. , Bamberg, Germany, 26–29 March 2018; Kobayashi, T., Kuriyama, K., Arai, K., Robot partner system for elderly people care by using sensor network Proceedings of the 2012 4th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics (BioRob), pp. 1329-1334. , Bamberg, Germany, 26–29 March 2018; Woo, J., Kubota, N., A Modular Structured Architecture Using Smart Devices for Socially-Embedded Robot Partners (2020) Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics, pp. 288-309. , IGI Global: Hershey, PA, USA; Woo, J., Botzheim, J., Kubota, N., System integration for cognitive model of a robot partner (2017) Intell. Autom. Soft Comput, pp. 1-14. , [CrossRef]; Woo, J., Botzheim, J., Kubota, N., A modular cognitive model of socially embedded robot partners for information support (2017) ROBOMECH J, 4, p. 10. , [CrossRef]; Torres, J., Cole, M., Owji, A., DeMastry, Z., Gordon, A.P., An approach for mechanical property optimization of fused deposition modeling with polylactic acid via design of experiments (2016) Rapid Prototyp. J, 22, pp. 387-404. , [CrossRef]; Woo, J., Botzheim, J., Kubota, N., Emotional empathy model for robot partners using recurrent spiking neural network model with Hebbian-LMS learning (2017) Malays. J. Comput. Sci, 30, pp. 258-285. , [CrossRef]; Morita, J., Nagai, Y., Moritsu, T., Relations between body motion and emotion: Analysis based on Laban Movement Analysis (2013) Proc. Annu. Meet. Cogn. Sci. Soc, 35, pp. 1026-1031; Woo, J., Botzheim, J., Kubota, N., Facial and gestural expression generation for robot partners Proceedings of the 2014 International Symposium on Micro-Nanomechatronics and Human Science (MHS), pp. 1-6. , Nagoya, Japan, 10–12 November 2014; Woo, J., Botzheim, J., Kubota, N., Verbal conversation system for a socially embedded robot partner using emotional model Proceedings of the 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 37-42. , Kobe, Japan, 31 August–4 September 2015; Woo, J., Botzheim, J., Kubota, N., A socially interactive robot partner using content-based conversation system for information support (2018) J. Adv. Comput. Intell. Intell. Inform, 22, pp. 989-997. , [CrossRef]; Jacquemont, M., Woo, J., Botzheim, J., Kubota, N., Sartori, N., Benoit, E., Human-centric point of view for a robot partner: A cooperative project between France and Japan Proceedings of the 2016 11th France-Japan & 9th Europe-Asia Congress on Mechatronics (MECATRONICS)/17th International Conference on Research and Education in Mechatronics (REM), pp. 164-169. , Compiegne, France, 15–17 June 2016; Yamamoto, S., Woo, J., Chin, W.H., Matsumura, K., Kubota, N., Interactive Information Support by Robot Partners Based on Informationally Structured Space (2020) J. Robot. Mechatron, 32, pp. 236-243. , [CrossRef]; Kahn, P.H., Ishiguro, H., Friedman, B., Kanda, T., Freier, N.G., Severson, R.L., Miller, J., What is a human?: Toward psychological benchmarks in the field of human–robot interaction (2007) Interact. Stud, 8, pp. 363-390. , [CrossRef]; Yamasaki, N., Anzai, Y., Active interface for human-robot interaction (1995) Proceedings of the 1995 IEEE International Conference on Robotics and Automation, 3, pp. 3103-3109. , Nagoya, Japan, 21–27 May; Nielsen, L., Personas, M.S., Dam, R.F., (2013) The Encyclopedia of Human-Computer Interaction, , http://www.interaction-design.org/encyclopedia/personas.html, 2nd ed.; TheInteraction Design Foundationn: Aarhus, Denmark, (accessed on 3 October 2020); Okamoto, S., Smith, J.S.S., (2004) Japanese Language, Gender, and Ideology: Cultural Models and Real People, , Oxford University Press: Oxford, UK; Preston, D.R., (1999) Handbook of Perceptual Dialectology, 1. , John Benjamins Publishing: Amsterdam, The Netherlands; Stern, D.N., (2018) The Interpersonal World of the Infant: A View from Psychoanalysis and Developmental Psychology, , Routledge: Abingdon, UK","Woo, J.; Department of Mechanical Engineering, Japan; email: woojsk@stf.teu.ac.jp",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85095945700
"Al-Sheekh S.D., Younus M.D.","57223280374;57223300358;","Real-Time Pose Estimation for Human-Robot Interaction",2020,"Proceedings - 2020 2nd Annual International Conference on Information and Sciences, AiCIS 2020",,,"9408252","86","90",,,"10.1109/AiCIS51645.2020.00023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105520836&doi=10.1109%2fAiCIS51645.2020.00023&partnerID=40&md5=8146d6e8cc4e2fe4fa09dfad7f8acc71","College of Electronics Engineering, Ninevah University, Computer and Information Engineering Department, Mosul, Iraq","Al-Sheekh, S.D., College of Electronics Engineering, Ninevah University, Computer and Information Engineering Department, Mosul, Iraq; Younus, M.D., College of Electronics Engineering, Ninevah University, Computer and Information Engineering Department, Mosul, Iraq","Text-based communication is one of the most popular categories used. Emails, SMS messages while the voice call is better than text. it connects several remote people at once and it is more personal and easier than text. currently, the video call is the best method for communication between people. After transmitting of text, voice, and video successfully the next step is motion transmission. Motion-based communication requires detection of human motion in the sending site and motivation of robot's servo motors in the receiving site. The most important step in motion-based communication is the detection of human pose in real-time and good accuracy because this detection will be driven to a servo motor. If there is some wrong in detection, it will be dangerous because the robot may be damage itself or the motion may be harmful. In this paper, we propose a realtime method for motion transmission by using deep neural networks (DNN) for the detection of human body joints, Then convert the obtained results into commands suitable to work in robot environment. And implement the results on robot so that the human motion corresponds to that of the robot in real-time. © 2020 IEEE.","Deep Neural Networks (DNN); human pose estimation; human-robot interaction; stacked hourglass network","Damage detection; Deep neural networks; Human bodies; Human motions; Motion transmission; Pose estimation; Robot environment; SMS messages; Text-based communication; Voice calls; Social robots",,,,,,,,,,,,,,,,"Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems; Long, J., Shelhamer, E., Darrell, T., Fully convolutional networks for semantic segmentation (2015) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Mehta, D., Monocular 3D human pose estimation in the wild using improved CNN supervision (2017) Proc. - 2017 Int. Conf. 3D Vision, 3DV, pp. 506-516. , 2018; Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation (2016) Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 9912, pp. 483-499; Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B., 2D human pose estimation: New benchmark and state of the art analysis (2014) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Toshev, A., Szegedy, C., Deeppose: Human pose estimation via deep neural networks (2014) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks (2017) Commun. ACM; Tompson, J., Jain, A., LeCun, Y., Bregler, C., Joint training of a convolutional network and a graphical model for human pose estimation (2014) Adv. Neural Inf. Process. Syst., 2, pp. 1799-1807. , January; Jain, A., Tompson, J., Andriluka, M., Taylor, G.W., Bregler, C., Learning human pose estimation features with convolutional networks (2014) 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings; Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C., Efficient object localization using Convolutional Networks (2015) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y., Convolutional pose machines (2016) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Michelucci, U., (2019) Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection; Xiao, B., Wu, H., Wei, Y., Simple baselines for human pose estimation and tracking (2018) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L., (2020) HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation; Zhang, Z., Tang, J., Wu, G., (2020) Simple and Lightweight Human Pose Estimation, , http://arxiv.org/abs/1911.10346, Online; Young-Jin, P., Tae-Woong, C., Jong-Hoon, P., (2009) With HBE-ROBONOVA-AI 2 Controlling Intelligent Biped Robot, pp. 487-491. , Gung-dong, Yuseong, Daejeon: Han Back Electronics Co.,Ltd",,,,"Institute of Electrical and Electronics Engineers Inc.","2nd Annual International Conference on Information and Sciences, AiCIS 2020","24 November 2020 through 25 November 2020",,168662,,9781728191690,,,"English","Proc. - Annu. Int. Conf. Inf. Sci., AiCIS",Conference Paper,"Final","",Scopus,2-s2.0-85105520836
"Muhammad U., Sipra K.A., Waqas M., Tu S., Koubaa A.","57224873171;57221500060;57215416666;57221496402;15923354900;","POSTER: Human-Robot Interaction: A Myo Armband Using EMG and IMU Signals",2020,"Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020",,,"9283779","251","252",,,"10.1109/SMART-TECH49988.2020.00064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099294096&doi=10.1109%2fSMART-TECH49988.2020.00064&partnerID=40&md5=7873de815a013faa81dcb87f05334d6b","Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan","Muhammad, U., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Sipra, K.A., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Waqas, M., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Tu, S., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Koubaa, A., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan","Human-Robot Interaction (HRI) plays an important role in a wide range of applications in healthcare, entertainment, and industries. Many ways are being devised to improve the interaction between humans and machines in the field of robotics. Therefore, our goal is to mimic the human arm movements on the 6-degree of freedom (DOF) robotic arm in real-time. We practically show the interfacing of Myo armband with robots that provide tremendously low calibration time without compromising efficiency. We allow a 6-DOF using electro-Myographic (EM G) and inertial measurement unit (IMU) signals for Myo armband and extract the values of EMG and IMU sensors. © 2020 IEEE.",,"Entertainment industry; Industrial robots; Man machine systems; Robotics; Calibration time; Human arm movements; Human robot Interaction (HRI); Inertial measurement unit; Real time; Human robot interaction",,,,,,,,,,,,,,,,"De Souza, G.A.M., Da Silva, M.G.N.M., Da Gama, A.E.F., Upper limb muscle activation: An emg analysis using myo R-Armband (2019) XXVI Brazilian Congress on Biomedical Engineering. Springer, pp. 397-404; Fan, Y., Yang, C., Wu, X., Improved teleoperation of an industrial robot arm system using leap motion and myo armband (2019) 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO. IEEE, pp. 1670-1675; Said, S., Sheikh, M., Al-Rashidi, F., Lakys, Y., Beyrouthy, T., Nait-Ali, A., A customizable wearable robust 3d printed bionic arm: Muscle controlled (2019) 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART. IEEE, pp. 1-6; Liu, M., Zhou, H., Pang, A., Research on motion control system of 6Dof robotic arm (2020) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019, pp. 53-61. , Springer; Patel, A., Ramsay, J., Imtiaz, M., Lu, Y., EmgBased human machine interface control (2019) 2019 12th International Conference on Human System Interaction (HSI. IEEE, pp. 127-131; Kim, D., Jorgensen, S.J., Stone, P., Sentis, L., Dynamic behaviors on the nao robot with closedLoop whole body operational space control (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids. IEEE, pp. 1121-1128; Prasek, M., Kotyrba, M., How to program nao robot using. net (2019) AIP Conference Proceedings, 2116 (1), p. 060010. , AIP Publishing LLC; Batayneh, W., Bataineh, A., Abandeh, S., Aljarrah, M., Banisaeed, M., Alzo'Ubei, B., Using emg signals to remotely control a 3d industrial robotic arm ASME International Mechanical Engineering Congress and Exposition, 59414. American Society of Mechanical Engineers, 2019, pp. V004T05A009",,"Koubaa A.Azar A.T.Qureshi B.","College of Computer and Information Sciences (CCIS);IEEE Student, Prince Sultan University Branch;Prince Sultan University;Research and Initiatives Center (RIC);Robotics and Internet-of-Things","Institute of Electrical and Electronics Engineers Inc.","1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020","3 November 2020 through 5 November 2020",,165860,,9781728174075,,,"English","Proc. - Int. Conf. Smart Syst. Emerg. Technol., SMART-TECH",Conference Paper,"Final","",Scopus,2-s2.0-85099294096
"Vasylkovskyi V., Guerreiro S., Sequeira J.S.","56784404400;24536769600;7006523955;","BlockRobot: Increasing Privacy in Human Robot Interaction by Using Blockchain",2020,"Proceedings - 2020 IEEE International Conference on Blockchain, Blockchain 2020",,,"9284787","106","115",,1,"10.1109/Blockchain50366.2020.00021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099235907&doi=10.1109%2fBlockchain50366.2020.00021&partnerID=40&md5=240bfbb50a7c72ef4e71bc77fab0cb56","University of Lisbon, INESC-ID Instituto Superior Técnico, Lisbon, Portugal; University of Lisbon, Institute for Systems and Robotics Instituto Superior Técnico, Lisbon, Portugal","Vasylkovskyi, V., University of Lisbon, INESC-ID Instituto Superior Técnico, Lisbon, Portugal; Guerreiro, S., University of Lisbon, INESC-ID Instituto Superior Técnico, Lisbon, Portugal; Sequeira, J.S., University of Lisbon, Institute for Systems and Robotics Instituto Superior Técnico, Lisbon, Portugal","Social robots can cause privacy concerns for humans, both because they collect personal data while moving around globally, and because humans can perceive them as social actors. Nowadays, robots can record and transmit (private) data in a human-readable format, which is identified as a problem in human-robot interaction (HRI). Privacy is an individual's right to have control over its data. With this problem in mind, we present BlockRobot- A new privacy-by-design conceptual model for access control of personal data based on Blockchain (BC) technology. With this solution, we ensure data privacy rights by giving each user control over its data in a decentralized manner without the need for an intermediate data controller. This BC solution grants confidentiality, integrity, and non-repudiation of data transparently and fairly to every user. As proof of concept, we demonstrate the initial implementation of a Decentralized Application (DAP) based on EOS Blockchain integrated with robotic events that contain private data. This paper details the experiments conducted with a Social Robot (SR) in a non-lab environment, and the collected data is analyzed using process mining techniques. © 2020 IEEE.","Blockchain; Confidentiality; Evolution of Scalability; Healthcare; Human Robot Interaction; Integrity; Privacy; Social robot","Access control; Blockchain; Man machine systems; Privacy by design; Conceptual model; Data controllers; Human robot Interaction (HRI); Human-readable; Non repudiation; Privacy concerns; Privacy rights; Proof of concept; Social robots",,,,,"822404; 2020-2023; Fundação para a Ciência e a Tecnologia, FCT: UIDB/50021/2020","The first and second authors state that this work was supported by the European Commission program H2020 under the grant agreement 822404 (project QualiChain) and by national funds through Fundac¸ão para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 (INESC-ID). The third author state that this work was supported by LARSyS – FCT Plurianual funding 2020-2023.",,,,,,,,,,"Cucco, E., Fisher, M., Dennis, L., Dixon, C., Webster, M., Broecker, B., Williams, R., Tuyls, K., (2017) Towards Robots for Social Engagement, , [Department of Computer Science, University of Liverpool, UK; Lutz, C., Tamò, A., Privacy and healthcare robots-an ant analysis (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics; Finck, M., Blockchains and data protection in the european union (2018) Eur. Data Prot. L. Rev., 4, p. 17; Ball, J., Nsa's prism surveillance program: How it works and what it can do (2013) The Guardian, 8; Goel, V., Facebook tinkers with users' emotions in news feed experiment, stirring outcry (2014) The New York Times, 29; Jay, R., Malcolm, W., Parry, E., Townsend, L., Bapat, A., (2017) Guide to the General Data Protection Regulation (GDPR), , Sweet & Maxwell; Finck, M., (2019) Blockchain and the General Data Protection Regulation: Can Distributed Ledgers Be Squared with European Data Protection Law?: Study, , European Parliament; Kruijff, J.D., Weigand, H., Understanding the blockchain using enterprise ontology (2017) International Conference on Advanced Information Systems Engineering, pp. 29-43. , Springer; Weber, I., Xu, X., Riveret, R., Governatori, G., Ponomarev, A., Mendling, J., Untrusted business process monitoring and execution using blockchain (2016) International Conference on Business Process Management, pp. 329-347. , Springer; López-Pintado, O., García-Bañuelos, L., Dumas, M., Weber, I., Caterpillar: A blockchain-based business process management system (2017) Proceedings of the Bpm Demo Track and Bpm Dissertation Award Co-located with 15th International Conference on Business Process Modeling (BPM 2017), , Barcelona, Spain; Mendling, J., Weber, I., Aalst, W., Brocke, J., Cabanillas, C., Daniel, F., Debois, S., Dustdar, S., Blockchains for business process management-challenges and opportunities (2018) Acm Transactions on Management Information Systems (TMIS), 9 (1), p. 4; Zyskind, G., Nathan, O., Decentralizing privacy: Using blockchain to protect personal data (2015) 2015 Ieee Security and Privacy Workshops, pp. 180-184; Truong, N.B., Sun, K., Lee, G.M., Guo, Y., Gdpr-compliant personal data management: A blockchain-based solution (2019) Ieee Transactions on Information Forensics and Security, 15, pp. 1746-1761; Faber, B., Michelet, G.C., Weidmann, N., Mukkamala, R.R., Vatrapu, R., Bpdims: A blockchain-based personal data and identity management system (2019) Proceedings of the 52nd Hawaii International Conference on System Sciences; Ferrer, E.C., Rudovic, O., Hardjono, T., Pentland, A., (2018) Robochain: A Secure Data-sharing Framework for Human-robot Interaction, , arXiv preprint arXiv:1802.04480; Degardin, B., (2018) Blockchain for Robotic Event Recognition, , Covilhã: Universidade da Beira Interior; Fernandes, M., Alexandre, L.A., Robotchain: Using tezos technology for robot event management (2019) Ledger; Brown, A., Rise of the machines? Amazon's army of more than 100, 000 warehouse robots still can't replace humans because they lack 'common sense' (2018) Daily Mail, , https://www.dailymail.co.uk/sciencetech/article-5808319/Amazon-100-000-warehouserobots-company-insists-replace-humans.html, accessed 9 March 2019; Carter, J.L., Wegman, M.N., Universal classes of hash functions (1979) Journal of Computer and System Sciences, 18 (2), pp. 143-154; Rivest, R.L., Shamir, A., Adleman, L., A method for obtaining digital signatures and public-key cryptosystems (1978) Communications of the Acm, 21 (2), pp. 120-126; Ajami, S., Rajabzadeh, A., Radio frequency identification (RFID) technology and patient safety (2013) Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 18 (9), p. 809; Benet, J., Ipfs-content addressed (2014) Versioned, P2P File System, 2; Izaguirre, J., Furgeson, J., Ma, Q., (2000) Proceedings of cse 331, data structures, department of computer science and engineering, university of notre dame, notre dame, Indiana, pp. 63-67; Merkle, R.C., Protocols for public key cryptosystems (1980) 1980 Ieee Symposium on Security and Privacy, pp. 122-122; Grigg, I., (2017) Eos-an Introduction, , https://whitepaperdatabase.com/eos-whitepaper, White paper; Kanda, T., Hirano, T., Eaton, D., Ishiguro, H., Person identification and interaction of social robots by using wireless tags (2003) Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), 2, pp. 1657-1664. , (Cat. No. 03CH37453), IEEE; Sequeira, J., Lima, P., Saffiotti, A., Gonzalez-Pacheco, V., Salichs, M.A., Monarch: Multi-robot cognitive systems operating in hospitals (2013) Icra 2013 Workshop on Many Robot Systems; Vitale, J., Tonkin, M., Herse, S., Ojha, S., Clark, J., Williams, M.-A., Wang, X., Judge, W., (2018) Be More Transparent and Users Will like You:a Robot Privacy and User Experience Design Experiment; Garriga, M., Arias, M., De Renzis, A., (2018) Blockchain and Cryptocurrency: A Comparative Framework of the Main Architectural Drivers, , arXiv preprint arXiv:1812.08806; (2018) Aitheon Whitepaper, , https://www.allcryptowhitepapers.com/aitheon-whitepaper/, Aitheon, "", "", [Online]; Belchior, R., Vasconcelos, A., Guerreiro, S., Correia, M., (2020) A Survey on Blockchain Interoperability: Past, Present, and Future Trends, , arXiv preprint arXiv:2005.14282",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE International Conference on Blockchain, Blockchain 2020","2 November 2020 through 6 November 2020",,165802,,9780738104959,,,"English","Proc. - IEEE Int. Conf. Blockchain, Blockchain",Conference Paper,"Final","",Scopus,2-s2.0-85099235907
"Moretti C.B., Delbem A.C.B., Krebs H.I.","56448259200;8575424400;57202890480;","Human-Robot Interaction: Kinematic and Kinetic Data Analysis Framework",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224415","235","239",,,"10.1109/BioRob49111.2020.9224415","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095593301&doi=10.1109%2fBioRob49111.2020.9224415&partnerID=40&md5=ed9097cfce5f181eb4c83be288e75d58","Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Sao Paulo, Brazil; Massachusetts Institute of Technology, Mechanical Engineering Department, Cambridge, MA  02139, United States","Moretti, C.B., Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Sao Paulo, Brazil, Massachusetts Institute of Technology, Mechanical Engineering Department, Cambridge, MA  02139, United States; Delbem, A.C.B., Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, Sao Paulo, Brazil; Krebs, H.I., Massachusetts Institute of Technology, Mechanical Engineering Department, Cambridge, MA  02139, United States","This paper summarizes our efforts on organizing a data structure for the analysis of human generated kinematic and kinetic data. We introduce a framework to perform data analysis in a straightforward manner, from the raw data to the refined results of a particular study. We applied this framework for data collected with the MIT MANUS gym. The proposed data structure carries a summary of the raw data, in terms of mean-aggregated robotic assay, also preserving the metrics of each movement separately for further study of movements at different granularity levels. Our framework allows us to export the structure of preprocessed data in a portable format, so that data can be accessed or edited with other statistical packages. Because the framework structure is modular and scalable, requiring no prior installation or setup, it simplifies the expansion to novel metrics and novel robotic devices. Hence, it is a convenient tool to be used in studies involving the analysis of human kinematic and kinetic data collected by any robotic device. © 2020 IEEE.",,"Agricultural robots; Data handling; Data structures; End effectors; Information analysis; Kinematics; Kinetics; Robotics; Analysis frameworks; Different granularities; Framework structures; Human kinematics; Kinetic data; Pre-processed data; Robotic devices; Statistical packages; Human robot interaction",,,,,"Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2019/06551-5, 2160632","*This work was supported by FAPESP, grant number 2019/06551-5 and MIT-MISTI Brazil, grant number 2160632.","The authors acknowledge CEPID-CeMEAI, FAPESP grant number 2019/06551-5 and MIT-MISTI Brazil grant number 2160632 for financial support.",,,,,,,,,"(2015) World Health Statistics, , http://www.who.int/gho/publications/worldhealthstatistics/ENWHS2012Full.pdf, WHO 2012. 9 feb; Horak, F.B., Assumptions underlying motor control for neurologic rehabilitation (1990) Contemporary Management of Motor Problems, pp. 11-27. , Chapter 4; Igo Krebs, H., Hogan, N., Aisen, M.L., Volpe, B.T., Robot-aided neurorehabilitation (1998) IEEE Transactions on Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society, 6 (1), pp. 75-87. , March; Sanchez, R.J., Liu, J., Rao, S., Shah, P., Smith, R., Rahman, T., Cramer, S.C., Reinkensmeyer, D.J., Automating arm movement training following severe stroke: Functional exercises with quantitative feedback in a gravity-reduced environment (2006) IEEE Trans Neural Syst Rehabil Eng, 14 (3), pp. 378-389. , Sep; Casadio, M., Sanguineti, V., Morasso, P.G., Arrichiello, V., Braccio di ferro: A new haptic workstation for neuromotor rehabilitation (2006) Technology and Health Care: Official Journal of the European Society for Engineering and Medicine, 14 (3), pp. 123-142; Dipietro, L., Krebs, H.I., Volpe, B.T., Stein, J., Bever, C., Mernoff, S.T., Fasoli, S.E., Hogan, N., Learning, not adaptation, characterizes stroke motor recovery: Evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace (2012) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20 (1), pp. 48-57. , Jan; Bosecker, C., Dipietro, L., Volpe, B., Krebs, H.I., Kinematic robotbased evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke (2010) Neurorehabil Neural Repair, 24 (1), pp. 62-69. , Jan; Colombo, R., Sterpi, I., Mazzone, A., Delconte, C., Pisano, F., Taking a lesson from patients' recovery strategies to optimize training during robot-aided rehabilitation (2012) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20 (3), pp. 276-285. , May; Lincoln, N.B., Crow, J.L., Jackson, J.M., Waters, G.R., Adams, S.A., Hodgson, P., The unreliability of sensory assessments (1991) Clinical Rehabilitation, 5 (4), pp. 273-282; Krebs, H.I., Volpe, B.T., Ferraro, M., Fasoli, S., Palazzolo, J., Rohrer, B., Edelstein, L., Hogan, N., Robot-aided neurorehabilitation: From evidence-based to science-based rehabilitation (2002) Top Stroke Rehabil, 8 (4), pp. 54-70; Semrau, J.A., Herter, T.M., Scott, S.H., Dukelow, S.P., Robotic identification of kinesthetic deficits after stroke (2013) Stroke, 44 (12), pp. 3414-3421. , Dec; Kurgan, L.A., Musilek, P., A survey of knowledge discovery and data mining process models (2006) Knowl. Eng. Rev., 21 (1), pp. 1-24. , March; Yao, Q., Tian, Y., Li, P., Tian, L., Qian, Y., Li, J., Design and development of a medical big data processing system based on hadoop (2015) Journal of Medical Systems, 39 (3), p. 23. , Feb; Greene, C.S., Tan, J., Ung, M., Moore, J.H., Cheng, C., Big data bioinformatics (2014) Journal of Cellular Physiology, 229 (12), pp. 1896-1900; Manogaran, G., Lopez, D., A survey of big data architectures and machine learning algorithms in healthcare (2017) International Journal of Biomedical Engineering and Technology, 25, p. 182. , 01; Moretti, C.B., Andrade, K.O., Caurin, G.A.P., Physiotherapy support web-based system for rehabilitation robotics: An initial architecture (2013) 22nd. International Congress of Mechanical Engineering (COBEM 2013), pp. 1171-1180; Tsuji, T., Kurita, N., Yamada, M., Sakaino, S., Kaneko, Y., Rehabilitation database based on haptic signal processing (2013) 2013 IEEE Region 10 Humanitarian Technology Conference, pp. 13-18. , Aug; Mandery, C., Terlemez, Ö., Do, M., Vahrenkamp, N., Asfour, T., Unifying representations and large-scale whole-body motion databases for studying human motion (2016) IEEE Transactions on Robotics, 32 (4), pp. 796-809. , Aug; Hwang, B., Kim, S., Lee, S., A full-body gesture database for automatic gesture recognition (2006) 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 243-248. , April; Gross, R., Shi, J., The cmu motion of body (mobo) database (2001) Technical Report CMU-RI-TR-01-18, , Carnegie Mellon University, Pittsburgh, PA, June; (1899) The Accuracy of Voluntary Movement, , Robert Sessions Woodworth . PhD thesis, New York; Krebs, H.I., Aisen, M.L., Volpe, B.T., Hogan, N., Quantization of continuous arm movements in humans with brain injury (1999) Proc. Natl. Acad. Sci. U. S. A., 96 (8), pp. 4645-4649. , Apr; Edwards, D.J., Cortes, M., Rykman-Peltz, A., Chang, J., Elder, J., Thickbroom, G., Mariman, J.J., Pascual-Leone, A., Clinical improvement with intensive robot-assisted arm training in chronic stroke is unchanged by supplementary tdcs (2019) Restor. Neurol. Neurosci., 37 (2), pp. 167-180",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095593301
"Nishimura S., Chaichaowarat R., Krebs H.I.","56027601400;55813237300;57202890480;","Human-Robot Interaction: Controller Design and Stability",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224464","1096","1101",,,"10.1109/BioRob49111.2020.9224464","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095582019&doi=10.1109%2fBioRob49111.2020.9224464&partnerID=40&md5=a3576be6f86db85b0d6bcc85a68d6a37","Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States; Faculty of Engineering, International School of Engineering, Chulalongkorn University, Bangkok, Thailand; Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States; Department of Physical Medicine and Rehabilitation, Fujita Health University, Nagoya, Japan; Institute of Neuroscience, University of Newcastle, Newcastle-upon-Tyne, United Kingdom; Sogang University, Seoul, South Korea; Department of Mechanical Sciences and Bioengineering, Osaka University, Osaka, Japan; Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom","Nishimura, S., Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States; Chaichaowarat, R., Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States, Faculty of Engineering, International School of Engineering, Chulalongkorn University, Bangkok, Thailand; Krebs, H.I., Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States, Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States, Department of Physical Medicine and Rehabilitation, Fujita Health University, Nagoya, Japan, Institute of Neuroscience, University of Newcastle, Newcastle-upon-Tyne, United Kingdom, Sogang University, Seoul, South Korea, Department of Mechanical Sciences and Bioengineering, Osaka University, Osaka, Japan, Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom","In this paper we derive the control parameter gains required to guarantee a stable human-robot interaction (HRI). One goal in HRI research field is to reduce the robot mechanical impedance and enable humans to easily manipulate the robot. Force feedback is an effective way to reduce robot inertia and friction, but stability is paramount especially when interacting with humans. This paper discusses gains and stability boundaries. The phase response of the open loop transfer function that represents the human robot interaction is used to derive the gains. The environment is modeled as a second-order spring-mass-damper system. The stability boundaries correspond to the region when the phase response of the transfer function becomes greater than -180° at all frequencies. This enables the system to interact stably regardless of the environment second-order system parameters. One of the interesting results is that the virtual spring used to generate the impedance field has nothing to do with the stability condition. The experimental results show the validity of our method. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Machine design; Man machine systems; Robotics; Springs (components); Stability; Transfer functions; Control parameters; Human robot Interaction (HRI); Mechanical impedances; Open-loop transfer functions; Second-order systemss; Spring-mass-damper system; Stability boundaries; Stability condition; Human robot interaction",,,,,"6941127","*This work was supported by Shanghai Electric Group Co. Ltd., Shanghai, China grant #6941127.","This work was supported by Shanghai Electric Group Co. Ltd., Shanghai, China grant 6941127.",,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975. , https://doi.org/10.1007/s10514-017-9677-2, Jun; McBean, J., Breazeal, C., Voice coil actuators for human-robot interaction (2004) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 1, pp. 852-858. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1389459&isnumber=30275, Sep; John Whitney P, J.M., Chen, T., Hodgins, J.K., A hybrid hydrostatic transmission and human-safe haptic telepresence robot (2016) Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 690-695. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487195&isnumber=7487087, May; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Humanrobot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7177120&isnumber=7274528, Oct; Oh, S., Kong, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 71-80. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7579567&isnumber=7855861, Feb; Morrell, J.B., (1996) Parallel Coupled Micro-macro Actuators, , http://hdl.handle.net/1721.1/11222, Ph. D. dissertation, Massachusetts Institute of Technology; Morrell, J.B., Salisbury, J.K., Parallel-coupled micromacro actuators (1998) The International Journal of Robotics Research, 17 (7), pp. 773-791. , https://doi.org/10.1177/027836499801700707, Jul; Hogan, N., Impedance control: An approach to manipulation: Part i-theory (1985) J. Dyn. Sys., Meas., Control, 107 (1), pp. 1-7. , https://doi.org/10.1115/1.3140702, Mar; Newman, W.S., Stability and performance limits of interaction controllers (1992) Journal of Dynamic Systems, Measurement, and Control, 114 (4), pp. 563-570. , https://doi.org/10.1115/1.2897725, Dec; Katsura, S., Matsumoto, Y., Ohnishi, K., Modeling of force sensing and validation of disturbance observer for force control (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 530-538. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4084673&isnumber=4084635, Feb; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved humanrobot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4154828&isnumber=4154817, Apr; Buerger, S.P., (2005) Stable, High-force, Low-impedance Robotic Actuators for Human-interactive Machines, , http://hdl.handle.net/1721.1/34130, Ph. D. dissertation, Massachusetts Institute of Technology; Astrom, K.J., Murray, R.M., (2008) Feedback Systems: An Introduction for Scientists and Engineers, , USA: Princeton University Press; Colgate, J.E., (1988) The Control of Dynamically Interacting Systems, , http://hdl.handle.net/1721.1/14380, Ph. D. dissertation, Massachusetts Institute of Technology",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095582019
"Shi K., Song A., Li Y., Chen D., Li H.","57208207669;57205479536;57208216048;57188708546;36468482700;","Cable-Driven 3-DOF Wrist Rehabilitation Robot with optimized Human-Robot Interaction Performance",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224316","112","117",,,"10.1109/BioRob49111.2020.9224316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095569323&doi=10.1109%2fBioRob49111.2020.9224316&partnerID=40&md5=0b3be36087d904caf36588daf33fdab5","Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China","Shi, K., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Song, A., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Li, Y., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Chen, D., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Li, H., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China","This paper developed a cable-driven 3-DOF (degree of freedom) wrist rehabilitation robot with optimized human-robot interaction performance. The workspace and cable tension efficiency of the robot are increased by adding the dynamic adaptive structure of cable attachment points, which improves the safety and comfort of human-robot interaction. In addition, the DASA (distributed active semi-active) system based on MRs(magnetorheological) is adopted, which reduces the effects of elastic elements (e.g., cables or Bowden cables) in the system, improves the force-feedback performance. The robot is compact and can be used independently or transplanted to any upper limb rehabilitation robot without wrist rehabilitation function. The performance of the system and control algorithm is verified by several experiments on healthy subjects. The results show that the system can meet the needs of rehabilitation training for workspace and force-feedback. This system also has the potential to be a force-feedback device for the healthy person. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Cables; Degrees of freedom (mechanics); Feedback; Man machine systems; Patient rehabilitation; Robotics; Attachment points; Degree of freedom; Dynamic-adaptive; Force feedback devices; Magneto-rheological; Rehabilitation training; Upper-limb rehabilitation; Wrist Rehabilitation; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 91648206; Southeast University, SEU; National Key Research and Development Program of China, NKRDPC: 2017YFB1303201","*Research supported by the National Key Research and Development Program of China(No.2017YFB1303201), the National Natural Science Foundation of China (No.91648206), and the Key Research and Development Program of Jiangsu Province (No.BE2018004-4) Ke Shi, Aiguo Song*, Ye Li, Dapeng Chen and Huijun Li are with the School of Instrument Science and Engineering, Southeast University, Nanjing 210096 China (e-mail: a.g.song@seu.edu.cn)",,,,,,,,,,"Stroke Center, , http://www.strokecenter.org/patients/about-stroke/stroke-statistics/reference; Kwakkel, G., Kollen, B.J., Krebs, H.I., Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review (2008) Neurorehabilitation and Neural Repair, 22 (2), pp. 111-121; Su, Y.-Y., Yu, Y.-L., Lin, C.-H., Lan, C.-C., A compact wrist rehabilitation robot with accurate force/stiffness control and misalignment adaptation (2019) Int J Intell Robot Appl, 12 (1), p. 83; Gupta, A., O'Malley, M.K., Patoglu, V., Burgar, C., Design, control and performance of ricewrist: A force feedback wrist exoskeleton for rehabilitation and training (2008) The International Journal of Robotics Research, 27 (2), pp. 233-251; Bian, H., Chen, Z., Wang, H., Zhao, T., Mechanical design of efw exo II: A hybrid exoskeleton for elbow-forearm-wrist rehabilitation (2017) IEEE International Conference on Rehabilitation Robotics, pp. 689-694; Buongiorno, D., WRES: A novel 3 dof wrist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation (2018) IEEE Robot. Autom. Lett., 3 (3), pp. 2152-2159; Alamdari, A., Krovi, V., Modeling and control of a novel home-based cable-driven parallel platform robot: Pacer (2015) IEEE International Conference on Intelligent Robots and Systems, pp. 6330-6335; Veneman, J.F., Ekkelenkamp, R., Kruidhof, R., Der Van Helm, T.F.C., Der Van Kooij, H., Design of a series elastic and bowdencable-based actuation system for use as torque-actuator in exoskeleton-type training robots (2005) International Conference on Rehabilitation Robotics, pp. 496-499. , Chicago, IL, USA; Mustafa, S.K., Yang, G., Yeo, S.H., Lin, W., Optimal design of a bio-inspired anthropocentric shoulder rehabilitator (2006) Applied Bionics and Biomechanics, 3 (3), pp. 199-208; Mao, Y., Agrawal, S.K., Design of a cable-driven arm exoskeleton (carex) for neural rehabilitation (2012) IEEE Transactions on Robotics, 28 (4), pp. 922-931; Cui, X., Chen, W., Agrawa, S.K., Wang, J., A novel customized cable-driven robot for 3-dof wrist and forearm motion training (2014) IEEE International Conference on Intelligent Robots and Systems, pp. 3579-3584; Cui, X., Chen, W., Jin, X., Agrawal, S.K., Design of a 7-dof cable-driven arm exoskeleton (carex-7) and a controller for dexterous motion training or assistance (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 161-172; Mustafa, S.K., Agrawal, S.K., On the force-closure analysis of n-dof cable-driven open chains based on reciprocal screw theory (2012) IEEE Transactions on Robotics, 28 (1), pp. 22-31; Viau, J., Projected pid controller for tendon-driven manipulators actuated by magneto-rheological clutches (2015) IEEE International Conference on Intelligent Robots and Systems, pp. 5954-5959; Bryson, J.T., Jin, X., Agrawal, S.K., Optimal design of cable-driven manipulators using particle swarm optimization (2016) Journal of Mechanisms and Robotics, 8 (4); Letier, P., (2006) Bowden Cable Actuator for Torque-Feedback in Haptic Applications; Palli, M., Model and control of tendon-sheath transmission systems (2006) IEEE International Conference on Robotics & Automation IEEE; Shimoga, K.B., Finger force and touch feedback issues in dexterous telemanipulation (1992) NASA-CIRSSE International Conference of Intelligent Robotic Systems for Space Exploration",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095569323
"Szabóová M., Sarnovský M., Krešňáková V.M., Machová K.","57208300075;57015679800;57217175622;17435396000;","Emotion analysis in human-robot interaction",2020,"Electronics (Switzerland)","9","11","1761","1","31",,3,"10.3390/electronics9111761","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094617369&doi=10.3390%2felectronics9111761&partnerID=40&md5=26d82129b9137f47fb0db7ef54a98ada","Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia","Szabóová, M., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia; Sarnovský, M., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia; Krešňáková, V.M., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia; Machová, K., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia","This paper connects two large research areas, namely sentiment analysis and human-robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human-robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to apply a combined method of dictionary approach with machine learning algorithms. As a result of the ambiguity and subjectivity of labeling emotions, it was possible to assign more than one emotion to a sentence; thus, we were dealing with a multi-label problem. Based on the overview of the problem, we performed experiments with the Naive Bayes, Support Vector Machine and Neural Network classifiers. Results obtained from classification were subsequently used in human-robot experiments. Despise the lower accuracy of emotion classification, we proved the importance of expressing emotion gestures based on the words we speak. © 2020 by the authors.","Dictionary approach; Human-robot interaction; Machine learning approach; Sentiment analysis; Social robotics",,,,,,"Agentúra na Podporu Výskumu a Vývoja, APVV: APVV-16-0213, APVV-17-0267","Funding: The work was supported by the Slovak Research and Development Agency under the contract No. APVV-16-0213 Knowledge-based approaches for intelligent analysis of big data and No. APVV-17-0267 Automated Recognition of Antisocial Behaviour in Online Communities.",,,,,,,,,,"Chivarov, N., Chikurtev, D., Chivarov, S., Pleva, M., Ondas, S., Juhar, J., Yovchev, K., Case Study on Human-Robot Interaction of the Remote-Controlled Service Robot for Elderly and Disabled Care (2020) Comput. Inform, 38, pp. 1210-1236; Wada, K., Shibata, T., Saito, T., Tanie, K., Analysis of factors that bring mental effects to elderly people in robot assisted activity (2002) IEEE Int. Conf. Intell. Robot. Syst, 2, pp. 1152-1157; Šabanovic, S., Bennett, C.C., Chang, W.L., Huber, L., PARO robot affects diverse interaction modalities in group sensory therapy for older adults with dementia (2013) IEEE Int. Conf. Rehabil. Robot; Huang, T., Huang, C., Elderly’s acceptance of companion robots from the perspective of user factors (2019) Univers. Access Inf. Soc; De Albornoz, J.C., Plaza, L., Gervás, P., SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis (2012) Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), pp. 23-25. , European Language Resources Association (ELRA): Istanbul, Turkey; Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M., Lexicon-Based Methods for Sentiment Analysis (2011) Comput. Linguist, 37, pp. 1-41; Szabo, P., Machova, K., Various approaches to the opinion classification problems solving (2012) Proceedings of the 2012 IEEE 10th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 59-62. , Herl’any, Slovakia, 26-28 January; Mohammad, S., Bravo-Marquez, F., (2018) WASSA-2017 Shared Task on Emotion Intensity, , arXiv arXiv:1708.03700; Gui, L., Xu, R., Lu, Q., Wu, D., Zhou, Y., Emotion cause extraction, a challenging task with corpus construction (2016) Communications in Computer and Information Science, 669, pp. 98-109. , Springer: Singapore; Ekman, P., Expression and the nature of emotion (1984) Approaches Emot, 3, p. 344; Amado-Boccara, I., Donnet, D., Olié, J.P., The concept of mood in psychology (1993) L’Encephale, 19, pp. 117-122; Grandjean, D., Sander, D., Scherer, K.R., Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization (2008) Conscious. Cogn, 17, pp. 484-495; Ortony, A., Clore, G.L., Collins, A., (1988) The Cognitive Structure of Emotions, , Cambridge University Press:Cambridge, MA, USA; Russell, J.A., A circumplex model of affect (1980) J. Personal. Soc. Psychol, 39, pp. 1161-1178; Lövheim, H., A new three-dimensional model for emotions and monoamine neurotransmitters (2012) Med. Hypotheses, 78, pp. 341-348; Shuman, V., Schlegel, K., Scherer, K., (2016) Geneva Emotion Wheel Rating Study PROPEREMO View Project A Developmental Perspective of Emotion Regulation View Project, , Technical Report; University of Geneva, Swiss Center for Affective Sciences: Geneva, Switzerland; Ekman, P., What Scientists Who Study Emotion Agree About (2016) Perspect. Psychol. Sci, 11, pp. 31-34; Darwin, C., The Expression of the Emotions in Man and Animals (1956) J. Nerv. Ment. 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"Villani V., Righi M., Sabattini L., Secchi C.","42862575700;57219372006;55210391800;7006311975;","Wearable devices for the assessment of cognitive effort for human-robot interaction",2020,"IEEE Sensors Journal","20","21","9115047","13047","13056",,5,"10.1109/JSEN.2020.3001635","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092512277&doi=10.1109%2fJSEN.2020.3001635&partnerID=40&md5=62eb20fa960bcb089ad9a37ab4c6d517","Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, 42122, Italy","Villani, V., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, 42122, Italy; Righi, M., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, 42122, Italy; Sabattini, L., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, 42122, Italy; Secchi, C., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, 42122, Italy","This paper is motivated by the need of assessing cognitive effort in affective robotics. In this context, the ultimate goal is that of assessing the mental state while the subject is interacting with a robotic system, by gathering implicit and objective information unobtrusively. To this end, we focus on wearable devices that do not affect the interaction of a human with a robot. In particular, we consider some commercial multi-purpose wearable devices, such as an armband, a smartwatch and a chest strap, and compare them in terms of accuracy in detecting cognitive effort. In an experiment setting, thirty participants were exposed to an increase in their cognitive effort by means of standard cognitive tests. Mental fatigue was estimated by measuring cardiac activity, in terms of heart rate and heart rate variability. The results have shown that the analysis of heart rate variability measured by the chest strap provides the most accurate detection of cognitive effort. Nevertheless, also measurements by the armband are sensitive to cognitive effort. © 2001-2012 IEEE.","affective design; human factors; human-robot interaction; Wearable systems","Heart; Robotics; Wearable technology; Cardiac activity; Cognitive efforts; Cognitive tests; Heart rate variability; Mental fatigue; Objective information; Robotic systems; Wearable devices; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 680734","Manuscript received May 15, 2020; accepted June 8, 2020. Date of publication June 11, 2020; date of current version October 2, 2020. This work was supported by the Experiment COllaborative robot aMPLifying and Extending huMAN capabiliTies (COMPLEMANT), which is part of the Smart Integrated Robotics System for Small-to-Medium Enterprises (SMEs) Controlled by Internet of Things based on Dynamic Manufacturing Processes (HORSE) Project funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 680734. The associate editor coordinating the review of this article and approving it for publication was Dr. Shanhong Xia. (Corresponding author: Valeria Villani.) The authors are with the Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy (e-mail: valeria.villani@unimore.it; massimiliano.righi@unimore.it; lorenzo.sabattini@unimore.it; cristian.secchi@unimore.it).",,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , Nov; (2020), http://www.ifr.org/industrial-robots/statistics, Executive Summary World Robotics 2016 Industrial Robots. Accessed: Jun. 9; Bethel, C.L., Salomon, K., Murphy, R.R., Burke, J.L., Survey of psychophysiology measurements applied to human-robot interaction (2007) Proc. 16th Ieee Int. Symp. Robot Human Interact. 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Methods Programs Biomed., 113 (1), pp. 210-220","Villani, V.; Department of Sciences and Methods for Engineering, Italy; email: valeria.villani@unimore.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","",Scopus,2-s2.0-85092512277
"Khoramshahi M., Billard A.","55841144900;7006389948;","A dynamical system approach for detection and reaction to human guidance in physical human–robot interaction",2020,"Autonomous Robots","44","8",,"1411","1429",,4,"10.1007/s10514-020-09934-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088639295&doi=10.1007%2fs10514-020-09934-9&partnerID=40&md5=532d6102e8e8c4b7a75af88b5d0a7d9c","Learning Algorithms and Systems Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland","Khoramshahi, M., Learning Algorithms and Systems Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland; Billard, A., Learning Algorithms and Systems Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland","A seamless interaction requires two robotic behaviors: the leader role where the robot rejects the external perturbations and focuses on the autonomous execution of the task, and the follower role where the robot ignores the task and complies with human intentional forces. The goal of this work is to provide (1) a unified robotic architecture to produce these two roles, and (2) a human-guidance detection algorithm to switch across the two roles. In the absence of human-guidance, the robot performs its task autonomously and upon detection of such guidances the robot passively follows the human motions. We employ dynamical systems to generate task-specific motion and admittance control to generate reactive motions toward the human-guidance. This structure enables the robot to reject undesirable perturbations, track the motions precisely, react to human-guidance by providing proper compliant behavior, and re-plan the motion reactively. We provide analytical investigation of our method in terms of tracking and compliant behavior. Finally, we evaluate our method experimentally using a 6-DoF manipulator. © 2020, The Author(s).",,"Agricultural robots; Air navigation; Dynamical systems; Robot programming; Robotics; Admittance control; Analytical investigations; Compliant behavior; Detection algorithm; Dynamical system approaches; External perturbations; Robot interactions; Robotic architectures; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 643950; Horizon 2020: ICT-23-2014","Open access funding provided by EPFL Lausanne. We are grateful for the support from the European Community?s Horizon 2020 Research and Innovation programme ICT-23-2014, Grant agreement 643950-SecondHands. We would like to thank Jos? R. Medina for his help with the controller implementations on Clearpath ridgeback and Laura Cohen for the illustrations.","Open access funding provided by EPFL Lausanne. We are grateful for the support from the European Community’s Horizon 2020 Research and Innovation programme ICT-23-2014, Grant agreement 643950-SecondHands. We would like to thank José R. Medina for his help with the controller implementations on Clearpath ridgeback and Laura Cohen for the illustrations.",,,,,,,,,"Bussy, A., Gergondet, P., Kheddar, A., Keith, F., Crosnier, A., (2012) Proactive behavior of a humanoid robot in a haptic transportation task with a human partner. 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"Shen Z., Elibol A., Chong N.Y.","57200151005;24733903500;7007012034;","Understanding nonverbal communication cues of human personality traits in human-robot interaction",2020,"IEEE/CAA Journal of Automatica Sinica","7","6","9106874","1465","1477",,4,"10.1109/JAS.2020.1003201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086240737&doi=10.1109%2fJAS.2020.1003201&partnerID=40&md5=011c28c35b445551513f1af9507cc555","Japan Advanced Institute of Science and Technology, Ishikawa, 923-1211, Japan","Shen, Z., Japan Advanced Institute of Science and Technology, Ishikawa, 923-1211, Japan; Elibol, A., Japan Advanced Institute of Science and Technology, Ishikawa, 923-1211, Japan; Chong, N.Y., Japan Advanced Institute of Science and Technology, Ishikawa, 923-1211, Japan","With the increasing presence of robots in our daily life, there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand users'mood, intention, and other aspects. During human-human interaction, personality traits have an important influence on human behavior, decision, mood, and many others. Therefore, we propose an efficient computational framework to endow the robot with the capability of understanding the user's personality traits based on the user's nonverbal communication cues represented by three visual features including the head motion, gaze, and body motion energy, and three vocal features including voice pitch, voice energy, and mel-frequency cepstral coefficient MFCC. We used the Pepper robot in this study as a communication robot to interact with each participant by asking questions, and meanwhile, the robot extracts the nonverbal features from each participant's habitual behavior using its on-board sensors. On the other hand, each participant's personality traits are evaluated with a questionnaire. We then train the ridge regression and linear support vector machine SVM classifiers using the nonverbal features and personality trait labels from a questionnaire and evaluate the performance of the classifiers. We have verified the validity of the proposed models that showed promising binary classification performance on recognizing each of the Big Five personality traits of the participants based on individual differences in nonverbal communication cues. © 2014 Chinese Association of Automation.",,"Behavioral research; Support vector machines; Support vector regression; Surveys; Binary classification; Computational framework; Human-human interactions; Individual Differences; Linear Support Vector Machines; Mel frequency cepstral co-efficient; Non-verbal features; Nonverbal communication cues; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: AFOSR-AOARD/FA2386-19-1-4015; Horizon 2020 Framework Programme, H2020: 737858; Horizon 2020; Ministry of Internal Affairs and Communications, MIC","Manuscript received February 11, 2020; accepted March 17, 2020. This work was supported by the EU-Japan coordinated R&D project on “Culture Aware Robots and Environmental Sensor Systems for Elderly Support,” commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme (737858). The authors are also grateful for financial supports from the Air Force Office of Scientific Research (AFOSR-AOARD/FA2386-19-1-4015). Recommended by Associate Editor Zhijun Li. (Corresponding author: Zhihao Shen.) Citation: Z. H. Shen, A. Elibol, and N. Y. Chong, “Understanding nonverbal communication cues of human personality traits in human-robot interaction,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1465–1477, Nov. 2020.","This work was supported by the EU-Japan coordinated R&D project on ?Culture Aware Robots and Environmental Sensor Systems for Elderly Support,? commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme (737858). The authors are also grateful for financial supports from the Air Force Office of Scientific Research (AFOSR-AOARD/FA2386-19-1-4015).",,,,,,,,,"Minato, T., Shimada, M., Ishiguro, H., Itakura, S., Development of an android robot for studying human-robot interaction (2004) Proc. 17th Int. Conf. Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 424-434. , Ottawa, Canada; Woods, S., Dautenhahn, K., Schulz, J., The design space of robots: Investigating children's views (2004) Proc. 13th Ieee Int. 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User Interf., 7 (1-2), pp. 39-53. , Aug; Junior, C.S.J., Guclutiirk, Y., Perez, M., Giiclii, U., Andujar, C., Baro, X., Escalante, H.J., Escalera, S., First impressions: A survey on vision-based apparent personality trait analysis (2019) Ieee Trans. Affect. Comput., , Jul; Beyan, C., Capozzi, F., Becchio, C., Murino, V., Prediction of the leadership style of an emergent leader using audio and visual nonverbal features (2018) IEEETanns.Muitimdd., 20 (2), pp. 441-456. , Feb; Hsieh, W.F., Li, Y.D., Kasano, E., Simokawara, E.S., Yamaguchi, T., Confidence identification based on the combination of verbal and non-verbal factors in human robot interaction (2019) Proc. Int. Joint Conf. Neural Networks, pp. 1-7. , Budapest, Hungary; Wu, B., Ai, H.Z., Huang, C., Lao, S.H., Fast rotation invariant multi-view face detection based on Real AdaBoost (2004) Proc. 6th Ieee Int. Conf. Automatic Face and Gesture Recognition, pp. 79-84. , Seoul, South Korea; Knapp, M.L., Hall, J.A., Horgan, T.G., (2013) Nonverbal Communication in Human Interaction, , 8th ed. Boston, USA: Cengage Learning; Stiefelhagen, R., Zhu, J., Head orientation and gaze direction in meetings (2002) Proc. Chi Extended Abstracts on Human Factors in Computing Systems, pp. 858-859. , Minneapolis, USA; Ricci, E., Odobez, J.M., Learning large margin likelihoods for realtime head pose tracking (2009) Proc. 16th Ieee Int. Conf. Image Processing, , Cairo, Egypt; Davis, J.W., Bobick, A.F., The representation and recognition of human movement using temporal templates (1997) Proc. Ieee Computer Society Conf. Computer Vision and Pattern Recognition, pp. 928-934. , San Juan, USA; Bobick, A.F., Davis, J.W., The recognition of human movement using temporal templates (2001) Ieee Trans. Pattern Anal. Machine Intell., 23 (3), pp. 257-267. , Mar; Ross, M., Shaffer, H., Cohen, A., Freudberg, R., Manley, H., Average magnitude difference function pitch extractor (1974) Ieee Trans. Acoustics, Speech, Signal Process, 22 ASSP (5), pp. 353-362. , Oct; Markel, D., The SIFT algorithm for fundamental frequency estimation (1972) Ieee Trans. Audio Electroacoust., 20 AU (5), pp. 367-377. , Dec; Mei, X.D., Pan, J., Sun, S.H., Efficient algorithms for speech pitch estimation (2001) Proc. Int. Symp. Intelligent Multimedia, pp. 421-424. , Video and Speech Processing, Hong Kong, China; Noll, A.M., Pitch determination of human speech by the harmonic product spectrum, the harmonic sum spectrum, and a maximum likelihood estimate (1969) Proc. Symp. Computer Processing in Communications, pp. 779-797. , Brooklyn, USA; Xu, M., Duan, L.Y., Cai, J.F., Chia, L.T., Xu, C.S., Tian, Q., HMM-based audio keyword generation (2004) Advances in Multimedia Information Processing, pp. 566-574. , K. Aizawa, Y. Nakamura, and S. Satoh, Eds. Berlin, Heidelberg: Springer; Sahidullah, Saha, G., Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition (2012) Speech Commun., 54 (4), pp. 543-565. , May; Beyan, C., Katsageorgiou, V., Murino, V., A sequential data analysis approach to detect emergent leaders in small groups (2019) Ieee Trans. Multimedia, 21 (8), pp. 2107-2116; Feese, S., Arnrich, B., Troster, G., Meyer, B., Jonas, K., Quantifying behavioral mimicry by automatic detection of nonverbal cues from body motion (2012) Proc. Int. Conf. Privacy, pp. 520-525. , Security, Risk and Trust and Int. Conf. Social Computing, Amsterdam, Netherlands; Jie, C., Peng, P., Recognize the most dominant person in multi-party meetings using nontraditional features (2010) Proc. Ieee Int. Conf. Intelligent Computing and Intelligent Systems, pp. 312-316. , Xiamen, China; Sanchez-Cortes, D., Jayagopi, D.B., Gatica-Perez, D., Predicting remote versus collocated group interactions using nonverbal cues (2009) Proc. Workshop on Multimodal Sensor-Based Systems and Mobile Phones for Social Computing, , Cambridge, Britain; Beyan, C., Capozzi, F., Becchio, C., Murino, V., Identification of emergent leaders in a meeting scenario using multiple kernel learning (2016) Proc. 2nd Workshop on Advancements in Social Signal Processing for Multimodal Interaction, pp. 3-10. , Tokyo Japan; Bishop, C.M., (2006) Pattern Recognition and Machine Learning, , New York, USA: Springer-Verlag","Shen, Z.; Japan Advanced Institute of Science and TechnologyJapan; email: shenzhihao@jaist.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23299266,,,,"English","IEEE CAA J. Autom. Sin.",Article,"Final","",Scopus,2-s2.0-85086240737
"Lyu S., Cheah C.C.","57193015269;7007112857;","Human-Robot Interaction Control Based on a General Energy Shaping Method",2020,"IEEE Transactions on Control Systems Technology","28","6","8897113","2445","2460",,4,"10.1109/TCST.2019.2949525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085993304&doi=10.1109%2fTCST.2019.2949525&partnerID=40&md5=8c3565b754554edba97fb14d28918b8c","School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Hangzhou Innovation Institute, Beihang University, Hangzhou, 310052, China","Lyu, S., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Hangzhou Innovation Institute, Beihang University, Hangzhou, 310052, China; Cheah, C.C., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","Coexistence of the human and robot in the same workspace requires the robot to perform robot tasks, such as trajectory tracking, and also interaction tasks, such as keeping a safe distance with the human. According to various human-robot interaction (HRI) scenarios, different interaction tasks usually require different task requirements or specifications, leading to different control strategies. Besides, due to different natures of the robot tasks and interaction tasks, different controllers may be required when the task is switched from one to another. So far, there is no theoretical framework that integrates different robot and interaction task requirements into a unified robot control strategy. In this article, a general HRI control framework is proposed for the scenario of the human and robot coexisting in the same workspace. We propose a general potential energy function that can be used to derive a stable and unified controller for various robot tasks and HRI tasks. By using the proposed method, various tasks can be specified at a user level by simply adjusting certain task parameters. Interactive weights are also defined to specify the interaction behaviors of robots according to different HRI applications. A human-dominant interaction mode is presented to demonstrate the applications of the proposed method. We show how the control framework can be applied to existing robot control systems with the velocity control or torque control mode by developing a joint velocity reference command and an adaptive controller. The stability of the adaptive control system is shown by using Lyapunov-like analysis. The control methods are implemented on two industrial manipulators and experimental results are presented to illustrate the effectiveness of the proposed control framework. © 1993-2012 IEEE.","Adaptive control; general energy shaping; human-robot interaction; potential energy function; robot control; unified control method","Adaptive control systems; Controllers; Industrial manipulators; Man machine systems; Potential energy functions; Adaptive controllers; Human robot Interaction (HRI); Human-robot interaction control; Interaction behavior; Lyapunov like analysis; Robot control systems; Theoretical framework; Trajectory tracking; Human robot interaction",,,,,"A1883c0008","Manuscript received February 20, 2019; revised August 15, 2019; accepted October 9, 2019. Date of publication November 12, 2019; date of current version October 9, 2020. Manuscript received in final form October 20, 2019. This work was supported by the Agency for Science, Technology and Research of Singapore (A∗STAR) under the Advanced Manufacturing and Engineering (AME) Individual Research Grants 2017 under Project A1883c0008. Recommended by Associate Editor E. Zergeroglu. (Corresponding author: Shangke Lyu.) S. Lyu was with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. He is now with the Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China (e-mail: lyus0001@e.ntu.edu.sg).",,,,,,,,,,"Khatib, O., Real-Time obstacle avoidance for manipulators and mobile robots (1986) Int. J. Robot. Res, 5 (1), pp. 90-98; Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 3356-3363. , Sep; Kazerooni, H., Guo, J., Human extenders (1993) Asme J. Dyn. Syst. Meas. Control, 115, pp. 281-290. , Jun; Wojtara, T., Human-robot collaboration in precise positioning of a three-dimensional object (2009) Automatica, 45 (2), pp. 333-342; Noohi, M., Žefran, J.L., Patton, E., A model for human-human collaborative object manipulation and its application to human-robot interaction (2016) Ieee Trans. Robot, 32 (4), pp. 880-896. , Aug; Mao, Y., Agrawal, S.K., Design of a cable-driven arm exoskeleton (CAREX) for neural rehabilitation (2012) Ieee Trans. Robot, 28 (4), pp. 922-931. , Aug; Huang, W.H., Fajen, B.R., Fink, J.R., Warren, W.H., Visual navigation and obstacle avoidance using a steering potential function (2006) Robot. Auton. Syst, 54 (4), pp. 288-299; Lacevic, B., Rocco, P., Zanchettin, A.M., Safety assessment and control of robotic manipulators using danger field (2013) Ieee Trans. Robot, 29 (5), pp. 1257-1270. , Oct; Hogan, N., Impedance control: An approach to manipulation (1984) Asme J. Dyn. Syst. Meas. Control, 107, pp. 304-313. , Jun; Blaya, J.A., Herr, H., Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait (2004) Ieee Trans. Neural Syst. Rehabil. Eng, 12 (1), pp. 24-31. , Mar; Morbi, A., Ahmadi, M., Safely rendering small impedances in admittance-controlled haptic devices (2016) IEEE/ASME Trans. Mechatronics, 21 (3), pp. 1272-1280. , Jun; Li, X., Liu, Y.-H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7. , Apr; Tonietti, G., Schiavi, R., Bicchi, A., Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction (2005) Proc Ieee Int. Conf. Robot. Autom, pp. 526-531. , Apr; Zinn, M., Roth, B., Khatib, O., Salisbury, J.K., A new actuation approach for human friendly robot design (2004) Int. J. Robot. Res, 23 (4-5), pp. 379-398; Choi, T.-Y., Choi, B.-S., Seo, K.-H., Position and compliance control of a pneumatic muscle actuated manipulator for enhanced safety (2011) Ieee Trans. Control Syst. Technol, 19 (4), pp. 832-842. , Jul; Gribovskaya, E., Kheddar, A., Billard, A., Motion learning and adaptive impedance for robot control during physical interaction with humans (2011) Proc Ieee Int. Conf. Robot. Automat, pp. 4326-4332. , May; Oh, S., Woo, H., Kong, K., Frequency-shaped impedance control for safe human-robot interaction in reference tracking application (2014) IEEE/ASME Trans. Mechatronics, 19 (6), pp. 1907-1916. , Dec; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) Ieee Trans. Robot, 31 (2), pp. 233-245. , Apr; Li, X., Chi, G., Vidas, S., Cheah, C.C., Human-guided robotic comanipulation: Two illustrative scenarios (2016) Ieee Trans. Control Syst. Technol, 24 (5), pp. 1751-1763. , Sep; Li, Y., Tee, K.P., Yan, R., Chan, W.L., Wu, Y., A framework of human-robot coordination based on game theory and policy iteration (2016) Ieee Trans. Robot, 32 (6), pp. 1408-1418. , Dec; Lyu, S., Cheah, C.C., Adaptive robot control for human-dominant interactions using a general task function (2016) Proc Ieee Int. Conf. Real-Time Comput. Robot, pp. 39-44. , Jun; Arimoto, S., (1996) Control Theory of Non-Linear Mechanical Systems: A Passivity-Based and Circuit-Theoretic Approach, , London, U.K.: Oxford Univ. Press; Slotine, J.-J.E., Li, W., On the adaptive control of robot manipulators (1987) Int. J. Robot. Res, 6 (3), pp. 49-59; Kelly, R., Regulation of manipulators in generic task space: An energy shaping plus damping injection approach (1999) Ieee Trans. Robot. Autom, 15 (2), pp. 381-386. , Apr; Rimon, E., Koditschek, D.E., Exact robot navigation using artificial potential functions (1992) Ieee Trans. Robot. Autom, 8 (5), pp. 501-518. , Oct; Cowan, N.J., Weingarten, J.D., Koditschek, D.E., Visual servoing via navigation functions (2002) Ieee Trans. Robot. Autom, 18 (4), pp. 521-533. , Aug; Cheah, C.C., Q.Wang, D., Region reaching control of robots: Theory and experiments (2005) Proc Ieee Int. Conf. Robot. Autom, pp. 974-979. , Apr; Haghighi, R., Cheah, C.C., Optical manipulation of multiple groups of microobjects using robotic tweezers (2016) Ieee Trans. Robot, 32 (2), pp. 275-285. , Apr; Cheah, C.C., Wang, D.Q., Sun, Y.C., Region-reaching control of robots (2007) Ieee Trans. Robot, 23 (6), pp. 1260-1264. , Dec; Cheah, C.C., Li, X., (2015) Task-Space Sensory Feedback Control of Robot Manipulators, , Springer; Hou, S.P., Cheah, C.C., Multiplicative potential energy function for swarm control (2009) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst., St. Louis, MO, Usa, pp. 4363-4368. , Oct; Slotine, J.-J.E., Li, W., (1991) Applied Nonlinear Control. Englewood Cliffs, NJ, Usa, , Prentice-Hall","Lyu, S.; School of Electrical and Electronic Engineering, Singapore; email: lyus0001@e.ntu.edu.sg",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636536,,IETTE,,"English","IEEE Trans Control Syst Technol",Article,"Final","",Scopus,2-s2.0-85085993304
"Shi Q., Yang P., Chen C.","57219971977;55613889800;24729216100;","Preference Modeling of Spatial Description in Human-Robot Interaction",2020,"2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020",,,"9238088","","",,,"10.1109/ICNSC48988.2020.9238088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096360301&doi=10.1109%2fICNSC48988.2020.9238088&partnerID=40&md5=9d5203e66b8ad131e2f13d56c9f0fa3c","Zijin College, Nanjing University of Science and Technology, Nanjing, China; School of Management and Engineering, Nanjing University, Nanjing, China","Shi, Q., Zijin College, Nanjing University of Science and Technology, Nanjing, China; Yang, P., School of Management and Engineering, Nanjing University, Nanjing, China; Chen, C., School of Management and Engineering, Nanjing University, Nanjing, China","Spatial description plays an important role in the design of human-robot interaction systems for intelligent robots. In this paper, we model the preference of the types of spatial description by collecting spatial constructions in two groups of tabletop task experiments, where the participants use spatial constructions to instruct the partner (human/robot) to pick up an indicated object. The preference modeling process is implemented by analyzing the probabilistic distribution of different types of spatial description (including different reference frames) of these participants in five typical scenarios regarding the partners of human and robot, respectively. The results provide a basis for the design of collaborative robots when interacting with people and will help improve the efficiency of human-centered human-robot interaction. © 2020 IEEE.","Human-robot interaction; preference modeling; reference frame; spatial cognition","Intelligent robots; Machine design; Man machine systems; Probability distributions; Collaborative robots; Preference modeling; Probabilistic distribution; Reference frame; Spatial construction; Spatial descriptions; Social robots",,,,,"SY201924; Fundamental Research Funds for the Central Universities: 011814380035; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province: 2019SJ2061","ACKNOWLEDGMENT This work was supported in part by the University Philosophy and Social Science Research Project of Jiangsu Province (China) under Grant 2019SJ2061, in part by the Experimental Teaching Reform Foundation of Nanjing University under Grant SY201924, and in part by the Fundamental Research Funds for the Central Universities under Grant 011814380035.",,,,,,,,,,"Mu, X., Recent development of human-robot natural interaction in spatial cognition tasks (2016) 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 446-450; Fischer, T., Demiris, Y., Markerless perspective taking for humanoid robots in unconstrained environments (2016) IEEE International Conference on Robotics and Automation, pp. 3309-3316; Li, S., Spatial references and perspective in natural language instructions for collaborative manipulation (2016) 25th International Symposium on Robot and Human Interactive Commnication, pp. 446-450; Gregory Trafton, J., Enabling effective human-robot interaction using perspective-taking in robots (2005) IEEE Transactions on Systems, Man, and Cybernetics-part A: Systems and Humans, 35 (4), pp. 460-470; Moratz, R., Fischer, K., Tenbrink, T., Congnitive modeling of spatial reference for human-robot interaction (2001) International Journal on Artificial Intelligence Tools, 10 (4), pp. 589-611; Fischer, K., The role of user's concepts of the robot in humanrobot spatial instruction (2006) International Conference Spatial Congnition, pp. 76-89; Zhao, X., Cusimano, C., Malle, B.F., Do people spotaneously take a robot's visual perspective (2016) 11th ACM/IEEE International Conference, pp. 133-134; Ros, R., Solving ambiguities with perspective-taking (2010) Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-182; Kuman Pandey, A., Ali, M., Alami, R., Towards a task-aware proactive sociable robot based on multi-state perspective-taking (2013) Internatianl Journal of Social Robotics, 5 (2), pp. 215-236; Gunalp, P., Mossaian, T., Hegarty, M., Spatial perspective taking: Effects of social, directional, and interactuve cues (2019) Memory and Cognition, 47, pp. 1031-1043; Janczyk, M., Level 2 perspective taking entails two processes: Evidence from PRP experiments (2013) Journal of Experimental Psychology Learning Memory and Cognition, 39 (6), pp. 1878-1887; Flavell, J.H., Young children's knowledge about visual perception: Further evidence for the level 1-level 2 distinction (1981) Developmental Psychology, 17, pp. 99-103; Michelon, P., Zacks, J., Two kinds of visual perspective taking (2006) Perception & Psychophysics, 68, pp. 327-337; Surtees, A.D.R., Samson, D., Apperly, I., Unintentional perspective-taking calculates whether something is seen, but not how it is seen (2016) Cognition, 148, pp. 97-105; Costello, F.J., Proximity in context: An empirically grounded computational model of proximity for processing topological spatial expressions (2006) Proceeding of the 21th International Conference on Computational Linguistics, pp. 745-752; Kelleher, J.D., Kruijff, G.J.M., Incremental generation of spatial referring expressions in situated dialog (2006) Proceedings of the International Conference on Computational Linguistics, pp. 1041-1048; Dale, R., Reiter, E., Computational interpretations of the Gricean maxims in the generation of referring expressions (1995) Cognitive Science, 19 (2), pp. 233-263; Taylor, H.A., Tversky, B., Perspective in spatial descriptions (1996) Journal of Memory and Language, 35, pp. 371-391; Schober, M.F., Sptial prespective taking in conversation (1993) Cognition, 47, pp. 1-24; Schober, M.F., (2009) Spatial Dialogue between Partners with Mismatched Abilities, pp. 23-29. , Spatial Language and Dialogue, Oxford University Press, Editors: K.R. Coventry, T. Tenbrink, J.A. Bateman; Carlson, L., Strategies for human-driven robot comprehension of spatial descriptions by older adults in robot fetch task (2014) Topics in Cognitive Science, 6, pp. 513-533; Zlotowski, J., Anthropomorphism: Opportunities and challenges in human-robot interaction (2015) International Journal of Social Robotics, 7, pp. 347-360",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020","30 October 2020 through 2 November 2020",,164651,,9781728168531,,,"English","IEEE Int. Conf. Netw., Sens. Control, ICNSC",Conference Paper,"Final","",Scopus,2-s2.0-85096360301
"Brinck I., Heco L., Sikström K., Wandsleb V., Johansson B., Balkenius C.","6506619590;57221742560;57221746869;57221740906;14024323300;6602268390;","Humans Perform Social Movements in Response to Social Robot Movements: Motor Intention in Human-Robot Interaction",2020,"ICDL-EpiRob 2020 - 10th IEEE International Conference on Development and Learning and Epigenetic Robotics",,,"9278114","","",,1,"10.1109/ICDL-EpiRob48136.2020.9278114","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100024315&doi=10.1109%2fICDL-EpiRob48136.2020.9278114&partnerID=40&md5=3b005b43a4602d932dc09c2b2decda66","Department of Philosophy, Lund University, Lund, Sweden; Lund University Cognitive Science, Lund University, Lund, Sweden","Brinck, I., Department of Philosophy, Lund University, Lund, Sweden; Heco, L., Lund University Cognitive Science, Lund University, Lund, Sweden; Sikström, K., Lund University Cognitive Science, Lund University, Lund, Sweden; Wandsleb, V., Lund University Cognitive Science, Lund University, Lund, Sweden; Johansson, B., Lund University Cognitive Science, Lund University, Lund, Sweden; Balkenius, C., Lund University Cognitive Science, Lund University, Lund, Sweden","We tested whether the observation of motor action encoding social motor intention would cause the spontaneous processing of a complementary response when performed by a humanoid robot. We designed the robot's arm and upper body movements to manifest the kinematic profiles of human individual and social motor intention and designed a simple task that involved robot and human placing blocks on a table sequentially. Our results show that the behavior of the human can be modulated by human kinematics as encoded in a robot's movement. In several cases human subjects reciprocated movement that displayed social motor intention with movements showing a similar kinematic profile while attempting to make eye contact and engaging in turn-taking behaviour during the task. This suggests a novel approach in the design of HRI based in motor processing that promises to be ecologically valid, cheap, automatic, fast, resilient, intuitive, and computationally simple. © ICDL-EpiRob 2020. All rights reserved.","Becchio; Human-robot interaction; Humanoid robot; Motor intention; Social interaction; Social motor intention","Agricultural robots; Anthropomorphic robots; Behavioral research; Eye movements; Kinematics; Robotics; Eye contact; Human kinematics; Human subjects; Humanoid robot; Motor actions; Robot movements; Social movements; Upper bodies; Social robots",,,,,,,,,,,,,,,,"Billing, E., Sciutti, A., Sandini, G., Proactive eye-gaze in humanrobot interaction (2019) Anticipation and Anticipatory Systems: Humans Meet Ai, , Ö rebro, Sweden, June 10-13, 2019; Mori, M., MacDorman, K.F., Kageki, N., The uncanny valley [from the field] (2012) Ieee Robotics & Automation Magazine, 19 (2), pp. 98-100; Heyes, C., Automatic imitation (2011) Psychological Bulletin, 137 (3), p. 463; Sartori, L., Betti, S., Complementary actions (2015) Frontiers in Psychology, 6, p. 557; Hodges, B.H., Rethinking conformity and imitation: Divergence, convergence, and social understanding (2014) Frontiers in Psychology, 5, p. 726; Reddy, V., (2008) How Infants Know Minds, , Harvard University Press; Sandini, G., Sciutti, A., (2018) Humane Robots-from Robots with a Humanoid Body to Robots with An Anthropomorphic Mind; Sciutti, A., Mara, M., Tagliasco, V., Sandini, G., Humanizing humanrobot interaction: On the importance of mutual understanding (2018) Ieee Technology and Society Magazine, 37 (1), pp. 22-29; Becchio, C., Sartori, L., Castiello, U., Toward you: The social side of actions (2010) Current Directions in Psychological Science, 19 (3), pp. 183-188; Becchio, C., Sartori, L., Bulgheroni, M., Castiello, U., Both your intention and mine are reflected in the kinematics of my reach-to-grasp movement (2008) Cognition, 106 (2), pp. 894-912; Lewkowicz, D.J., Lickliter, R., (2013) The Development of Intersensory Perception: Comparative Perspectives, , Psychology Press; Lewkowicz, D., Quesque, F., Coello, Y., Delevoye-Turrell, Y.N., Individual differences in reading social intentions from motor deviants (2015) Frontiers in Psychology, 6, p. 1175; Stapel, J.C., Hunnius, S., Bekkering, H., Online prediction of others' actions: The contribution of the target object, action context and movement kinematics (2012) Psychological Research, 76 (4), pp. 434-445; Manera, V., Becchio, C., Cavallo, A., Sartori, L., Castiello, U., Cooperation or competition? Discriminating between social intentions by observing prehensile movements (2011) Experimental Brain Research, 211 (3-4), pp. 547-556; Sartori, L., Becchio, C., Castiello, U., Cues to intention: The role of movement information (2011) Cognition, 119 (2), pp. 242-252; Bono, M.G.D., Begliomini, C., Budisavljevic, S., Sartori, L., Miotto, D., Motta, R., Castiello, U., Decoding social intentions in human prehensile actions: Insights from a combined kinematics-fmri study (2017) PloS One, 12 (8); Georgiou, I., Becchio, C., Glover, S., Castiello, U., Different action patterns for cooperative and competitive behaviour (2007) Cognition, 102 (3), pp. 415-433; Clark, H.H., Pointing and placing (2003) Pointing: Where Language, Culture, and Cognition Meet, 243, p. 268; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375-382; Nakano, Y.I., Ishii, R., Estimating user's engagement from eyegaze behaviors in human-agent conversations (2010) Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 139-148",,,"Advanced Center of Electrical and Electronic Engineering;Advanced Mining Technology Center (AMTC);Synopsys;The Clover: Ingenieria 2030;Universidad Tecnica Federico Santa Maria","Institute of Electrical and Electronics Engineers Inc.","10th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2020","26 October 2020 through 30 October 2020",,165847,,9781728173061,,,"English","ICDL-EpiRob - IEEE Int. Conf. Dev. Learn. Epigenetic Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85100024315
"Paplu S.H., Mishra C., Berns K.","57204053864;57213798792;7006119626;","Pseudo-Randomization in Automating Robot Behaviour during Human-Robot Interaction",2020,"ICDL-EpiRob 2020 - 10th IEEE International Conference on Development and Learning and Epigenetic Robotics",,,"9278115","","",,2,"10.1109/ICDL-EpiRob48136.2020.9278115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100018641&doi=10.1109%2fICDL-EpiRob48136.2020.9278115&partnerID=40&md5=9df559bfc04890c00813379424f8af4f","Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany","Paplu, S.H., Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany; Mishra, C., Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany; Berns, K., Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany","Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach. © ICDL-EpiRob 2020. All rights reserved.","Facial expressions; Gestures; Human-robot interaction; Robot behaviour","Agricultural robots; Anthropomorphic robots; Behavioral research; Man machine systems; Random processes; Robotics; Behavioural changes; Facial Expressions; Human-centered evaluation; Humanoid robot; Mental state; Pseudo-randomization; Robot behavior; Visual stimulus; Human robot interaction",,,,,,,,,,,,,,,,"Olson, J.C., Jacoby, J., Cue utilization in the quality perception process (1972) Acr Special Volumes; Stansfield, S.A., A robotic perceptual system utilizing passive vision and active touch (1988) The International Journal of Robotics Research, 7 (6), pp. 138-161; Partos, T.R., Cropper, S.J., Rawlings, D., You don't see what i see: Individual differences in the perception of meaning from visual stimuli (2016) PloS One, 11 (3), p. 150615; Powers, W.T., Feedback: Beyond behaviorism: Stimulus-response laws are wholly predictable within a control-system model of behavioral organization (1973) Science, 179 (4071), pp. 351-356; Churchland, M.M., Byron, M.Y., Cunningham, J.P., Sugrue, L.P., Cohen, M.R., Corrado, G.S., Newsome, W.T., Scott, B.B., Stimulus onset quenches neural variability: A widespread cortical phenomenon (2010) Nature Neuroscience, 13 (3), p. 369; Duffy, B.R., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 177-190; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human (-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323; Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H., Hagita, N., Footing in human-robot conversations: How robots might shape participant roles using gaze cues (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 61-68. , New York, NY, USA: ACM; Yoon, Y., Ko, W.-R., Jang, M., Lee, J., Kim, J., Lee, G., Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 4303-4309. , Montreal, QC, Canada, Canada: IEEE; Calinon, S., D'Halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G., Learning and reproduction of gestures by imitation (2010) Ieee Robotics & Automation Magazine, 17 (2), pp. 44-54; Calinon, S., Billard, A., Incremental learning of gestures by imitation in a humanoid robot (2007) Proceedings of the ACM/IEEE International Conference on Human-robot Interaction, Ser. Hri '07, pp. 255-262. , http://doi.acm.org/10.1145/1228716.1228751, New York, NY, USA: ACM, [Online]; Ikeuchi, K., Baumert, D., Kudoh, S., Takizawa, M., (2019) Design of Conversational Humanoid Robot Based on Hardware Independent Gesture Generation, , http://arxiv.org/abs/1905.08702, CoRR, vol. abs/1905.08702, [Online]; Neff, M., Kipp, M., Albrecht, I., Seidel, H.-P., Gesture modeling and animation based on a probabilistic re-creation of speaker style (2008) Acm Trans. Graph, 27 (1), pp. 51-524. , http://doi.acm.org/10.1145/1330511.1330516, Mar. [Online]; Fischer, K., Jung, M., Jensen, L.C., DerWieschen, M.V.A., Emotion expression in hri-when and why (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 29-38. , Daegu, Korea (South), Korea (South): IEEE; Gelder, B.D., Van Den Stock, J., The bodily expressive action stimulus test (beast). Construction and validation of a stimulus basis for measuring perception of whole body expression of emotions (2011) Frontiers in Psychology, 2, p. 181; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139; Morris, D., (2015) Bodytalk: A World Guide to Gestures, , London: Jonathan Cape; Ekman, P., Friesen, W., Manual of the facial action coding system (facs) (1978) Trans, , ed. Vol. Consulting Psychologists Press, Palo Alto; Zafar, Z., Venugopal, R., Berns, K., Real-time recognition of human postures for human-robot interaction (2018) Proceedings of the 11th International Conference on Advances in Computer-Human Interactions (ACHI), , Rome, Italy; Reichardt, M., Föhst, T., Berns, K., Introducing finroc: A convenient real-time framework for robotics based on a systematic design approach (2012) Robotics Research Lab, Department of Computer Science, , University of Kaiserslautern, Kaiserslautern, Germany, Technical Report; Shreve, M.A., (2013) Automatic Macro-and Micro-facial Expression Spotting and Applications, , Ph.D. dissertation, University of South Florida; Ekman, P., Basic emotions (1999) Handbook of Cognition and Emotion, 98 (45-60), p. 16; Mianowski, K., Schmitz, N., Berns, K., Mechatronics of the humanoid robot roman (2007) Robot Motion and Control 2007, pp. 341-348. , Springer; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3 (1), pp. 53-67; Hall, E.T., (1910) The Hidden Dimension, 609. , NY, USA: Garden City, NY: Doubleday; Zafar, Z., Berns, K., Recognizing hand gestures for human-robot interaction (2016) Proceedings of the 9th International Conference on Advances in Computer-Human Interactions (ACHI), pp. 333-338; Likert, R., A technique for the measurement of attitudes (1932) Archives of Psychology",,,"Advanced Center of Electrical and Electronic Engineering;Advanced Mining Technology Center (AMTC);Synopsys;The Clover: Ingenieria 2030;Universidad Tecnica Federico Santa Maria","Institute of Electrical and Electronics Engineers Inc.","10th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2020","26 October 2020 through 30 October 2020",,165847,,9781728173061,,,"English","ICDL-EpiRob - IEEE Int. Conf. Dev. Learn. Epigenetic Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85100018641
"Pinto A., Sousa S., Silva C., Coelho P.","57208733566;54883324200;55995851100;57198226876;","Adaptation and validation of the HCTM Scale into Human-robot interaction Portuguese context: A study of measuring trust in human-robot interactions",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"","",,,"10.1145/3419249.3420087","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117539840&doi=10.1145%2f3419249.3420087&partnerID=40&md5=97f7a92edfd8f5d03cd0ac94bf9057f2","CeBER, Centre for Business and Economics Research, University of Coimbra, Portugal; University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal School of Digital Technologies, Tallinn University, Tallinn, Estonia; CEMMPRE, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, Portugal","Pinto, A., CeBER, Centre for Business and Economics Research, University of Coimbra, Portugal; Sousa, S., University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal School of Digital Technologies, Tallinn University, Tallinn, Estonia; Silva, C., CeBER, Centre for Business and Economics Research, University of Coimbra, Portugal; Coelho, P., CEMMPRE, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, Portugal","As robots become increasingly common in a wide variety of domains, there is an increasing need to assess the trust humans have when interacting with robots. Specifically are two of the main objectives in this study: 1) to adapt and validate the Human-Computer Trust Model Scale (HCTM) to human-robot interaction (HRI), in a Portuguese population. This need is because trust in automation has been understood through its analogy to interpersonal trust. With the HCTM we intend to suggest an alternative to that. This measure of trust is a very recent scale, 2019, and based on the present context and research. More, this scale was subjected to robust statistical analysis tests and was tested at different scenarios of computing with successful. The final motive is that authors are not aware of a similar scale in the Portuguese context that can measure trust in HRI with COBOTS. The growth of this market is notable. For that, we used 243 undergraduate students with backgrounds on Management and Engineering and Industrial Management. Results, indicate a good measurement of the latent constructs, convergent reliability, internal consistency and discriminate validity. However, Dillon-Goldstein's rho measures for both Competency (0.696), Benevolence (0.686) and Reciprocity (0.604) constructs present scores close to 0.70. According to Hair et al [11] the composite reliability (internal consistency) should be higher than 0.7 (or >0.6 in exploratory research). This means that the model is acceptable, and the Portuguese version of the model satisfies the criteria for measure trust in human-robot interaction (HRI), however, have a poor internal consist. © 2020 Owner/Author.","Adaptation and validation; COBOTS; HRI; Trust","Human computer interaction; Students; Composite Reliability; Convergent reliability; Exploratory research; Human robot Interaction (HRI); Internal consistency; Portuguese population; Robust statistical analysis; Undergraduate students; Social robots",,,,,"825618","This project has received funding from the European Union’s Horizon 2020 research and innovation program under the NGI_TRUST grant agreement no 825618.",,,,,,,,,,"Alberdi, E., Povyakalo, A., Strigini, L., Ayton, P., Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography (2004) Academic Radiology, 11 (8), pp. 909-918; Gulati, S., Sousa, S., Lamas, D., Design, development and evaluation of a human-computer trust scale (2019) Behav. Inf. Technol, , https://doi.org/10.1080/0144929X.2019.1656779; Magalhaes, M., Hill, A., Investigaçao por questionário (2000) Lisboa; Jian, J., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) Int. J. Cogn. Ergon, , https://doi.org/10.1207/s15327566ijce0401-04; Kelly, C., Boardman, M., Goillau, P., Jeannot, E., Guidelines for trust in future ATM systems: A literature review (2003) Eur. AIR TRAFFIC Manag. Program; Lyons, J.B., Stokes, C.K., Human-human reliance in the context of automation (2012) Human Factors, , https://doi.org/10.1177/0018720811427034; Madsen, M., Gregor, S., Measuring human-computer trust (2000) 11th Australasian Conference on Information Systems, 53, pp. 6-8. , December; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Acad. Manag. Rev, , https://doi.org/10.5465/amr.1995.9508080335; Muir, B.M., Moray, N., Trust in automation part II. 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Factors, , https://doi.org/10.1518/001872097778543886; Hair, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M., A primer on partial least squares structural equation modeling (PLS-SEM) (2016) Sage Publications; Hulland, J., Use of partial least squares (PLS) in strategic management research: A review of four recent studies (1999) Strategic Management Journal, 20 (2), pp. 195-204; Bagozzi, R.P., Yi, Y., On the evaluation of structural equation models (1988) Journal of the Academy of Marketing Science, 16 (1), pp. 74-94; Fornell, C., Larcker, D.F., (1981) Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics",,,,"ICST","11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020","25 October 2020 through 29 October 2020",,164330,21531633,9781450375795,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85117539840
"Hornecker E., Bischof A., Graf P., Franzkowiak L., Krüger N.","13006172500;56227446500;57219871832;57219865921;7004442773;","The Interactive Enactment of Care Technologies and its Implications for Human-Robot-Interaction in Care",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"","",,2,"10.1145/3419249.3420103","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117539644&doi=10.1145%2f3419249.3420103&partnerID=40&md5=ab1f2e33f4beef3efce50056effb8ef3","Bauhaus-Universität Weimar, Weimar, D-99423, Germany; Computer Science, Chemnitz University of Technology, Chemnitz, Germany; Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, DK-5230, Denmark","Hornecker, E., Bauhaus-Universität Weimar, Weimar, D-99423, Germany; Bischof, A., Computer Science, Chemnitz University of Technology, Chemnitz, Germany; Graf, P., Computer Science, Chemnitz University of Technology, Chemnitz, Germany; Franzkowiak, L., Bauhaus-Universität Weimar, Weimar, D-99423, Germany; Krüger, N., Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, DK-5230, Denmark","Various technical innovations for the care sector, particularly robots, are being developed to cope with demographic change and to support nursing staff. A central issue for the successful integration of such technology into gerontological care practices has not yet been appropriately addressed from an HCI perspective. Here, we draw from observation of lifting devices, used to move residents between bed and chairs. We found that this process is always moderated and facilitated by caregivers' 'interaction work': The function(ing) of care technology is inseparable from the interactive practices of care staff enacting these functions and from the emotional labor inherent to care practice. The caregivers' verbal, manual and emotional actions, and also the residents' active cooperation in the process are important factors for safe, fluid, and pleasant human-machine interaction. We propose to understand such technical care settings as a triadic interaction, and to take account of this in the future design of care technologies, in particular for robotic solutions. © 2020 ACM.","Care robotics; Collaboration; Elderly Care; Ethnography; Healthcare; Human-robot-interaction; Residential care; Social robotics; Socio-materiality","Human computer interaction; Demographic changes; Emotional labors; Future designs; Human machine interaction; Interactive practice; Lifting devices; Robotic solutions; Technical innovation; Human robot interaction",,,,,"Volkswagen Foundation","This work was funded by Volkswagen Foundation, on the ReThiCare project. We in particular thank the staff of Diakonie Sozialdienst Thüringen at Seniorenpflegeheim Sophienhaus for their collaboration and time, in particular those two staff members who helped us with our ‘experience session’, and also all residents of Sophienhaus.",,,,,,,,,,"Ala, M., Social robots: Things or agents (2016) AI Soc, 31 (4), pp. 519-535. , https://doi.org/10.1007/s00146-015-0631-6, 2016; Ala, M., Movellan, J., Tanaka, F., When a robot is social: Spatial arrangements and multimodal semiotic engagement in the practice of social robotics (2011) Soc. Stud. Sci, 41 (6), pp. 893-926. , https://doi.org/10.1177/0306312711420565, 2011; Bartneck, C., Kanda, T., Mubin, O., Al Mahmud, A., The perception of animacy and intelligence based on a robot's embodiment (2008) Humanoid Robot. 2007 7th IEEE-RAS Int. Conf. On. 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Robot, 3 (1), pp. 53-67. , https://doi.org/10.1007/s12369-010-0081-8, 2011; https://www.liftware.com/, Retrieved December 3, 2019; Robot, P., http://www.parorobots.com/, Retrieved December 3, 2019; Robear: The Bear-shaped Nursing Robot who'Ll Look after You When You Get Old, , https://www.theguardian.com/technology/2015/feb/27/robear-bear-shaped-nursing-care-robot, Retrieved May 1, 2020; https://www.arjo.com/int/products/safe-patient-handling/floor-lifters/maxi-move, Retrieved December 4, 2019 from; (2014) Panasonic's Resyone Robotic Bed, , https://newatlas.com/panasonic-resyone-robot-bed-wheelchair-iso13482/31656/, Retrieved December 4 2019; (2019) AETHON TUG Robot, , https://aethon.com/mobile-robots-for-healthcare/, Retrieved December 4 2019; (2019) ETAC Turner Pro, , https://www.etac.com/products/manual-transfer/sit-to-stand/etac-turner-pro/%0D%0A%0D%0A, Retrieved December 4 2019; (2020) Pressemitteilung Ethikrat: Chancen für Die Pflege Durch Verantwortliche Nutzung von Robotik, , https://www.ethikrat.org/mitteilungen/2020/ethikrat-chancen-fuer-die-pflege-durch-verantwortliche-nutzung-vonrobotik/",,,,"ICST","11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020","25 October 2020 through 29 October 2020",,164330,21531633,9781450375795,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85117539644
"Hmamouche Y., Ochs M., Prévot L., Chaminade T.","57170656400;13608908400;57202367740;8339688200;","Neuroscience to investigate social mechanisms involved in human-robot interactions",2020,"ICMI 2020 Companion - Companion Publication of the 2020 International Conference on Multimodal Interaction",,,,"52","56",,,"10.1145/3395035.3425263","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099210042&doi=10.1145%2f3395035.3425263&partnerID=40&md5=dbac9615797dcb09b7c008cb0d7870a6","Aix Marseille Université, CNRS, LIS, UMR7020, Marseille, France; Aix Marseille Université, CNRS, LIS, UMR7309, Marseille, France; Aix Marseille Université, CNRS, LIS, UMR7289, Marseille, France","Hmamouche, Y., Aix Marseille Université, CNRS, LIS, UMR7020, Marseille, France; Ochs, M., Aix Marseille Université, CNRS, LIS, UMR7020, Marseille, France; Prévot, L., Aix Marseille Université, CNRS, LIS, UMR7309, Marseille, France; Chaminade, T., Aix Marseille Université, CNRS, LIS, UMR7289, Marseille, France","To what extent do human-robot interactions (HRI) rely on social processes similar to human-human interactions (HHI)? To address this question objectively, we use a unique corpus. Brain activity and behaviors were recorded synchronously while participants were discussing with a human (confederate of the experimenter) or a robotic device (controlled by the confederate). Here, we focus on two main regions of interest (ROIs), that form the core of ""the social brain"", the right temporoparietal junction [rTPJ] and right medial prefrontal cortex [rMPFC]. An new analysis approach derived from multivariate time-series forecasting is used. A prediction score describes the ability to predict brain activity for each ROI, and results identify which behavioral features, built from raw recordings of the conversations, are used for this prediction. Results identify some differences between HHI and HRI in the behavioral features predicting activity in these ROIs of the social brain, that could explain significant differences in the level of activity. © 2020 ACM.","Feature selection; Functional MRI; Human-human and human-machine interactions; Multimodal signals processing; Natural conversation; Prediction","Brain; Forecasting; Interactive computer systems; Man machine systems; Neurophysiology; Time series analysis; Analysis approach; Behavioral features; Human robot Interaction (HRI); Human-human interactions; Multivariate time series; Prefrontal cortex; Regions of interest; Robotic devices; Social robots",,,,,,"This research is supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and AAP-ID-17-46-170301-11.1 by the Aix-Marseille Université Excellence Initiative (A*MIDEX).",,,,,,,,,,"Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human-machine interaction (2012) Cognitive Behavioural Systems (Lecture Notes in Computer Science), pp. 114-130. , Anna et al. 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One, 4 (1), p. e4257. , https://doi.org/10.1371/journal.pone.0004257, Jan. 2009",,,"ACM SIGCHI","Association for Computing Machinery, Inc","2020 International Conference on Multimodal Interaction, ICMI 2020","25 October 2020 through 29 October 2020",,166127,,9781450380027,,,"English","ICMI Companion - Companion Publ. Int. Conf. Multimodal Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85099210042
"Escobedo P., Ntagios M., Dahiya R.","57188743232;57205626715;24331529200;","Electronic Skin with Energy Autonomous Proximity Sensing for Human-Robot Interaction",2020,"Proceedings of IEEE Sensors","2020-October",,"9278896","","",,,"10.1109/SENSORS47125.2020.9278896","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098698341&doi=10.1109%2fSENSORS47125.2020.9278896&partnerID=40&md5=6ec9baa1fb74ddaf8521c0062713c9bd","Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Escobedo, P., Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Ntagios, M., Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Dahiya, R., Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Electronic skin or eSkin is critical to enhance the interactive capabilities of robots through physical contact. The current approaches to develop this capability involve using multiple sensors distributed on robot's body. Energy requirement of such eSkin is currently met through batteries, which is not ideal as it may reduce the operational time and requires frequent charging. In this work, we present an eSkin consisting of distributed energy autonomous proximity sensors. The eSkin contains an array of miniaturized solar cells to generate energy needed for infrared LEDs based proximity sensors. As a proof of concept, the eSkin has been integrated in an industrial robot arm UR5 and proximity sensing has been shown to enable safe Human-Robot Interaction (HRI). © 2020 IEEE.","Electronic Skin; Energy Autonomy; Human-Robot Interaction; Proximity sensing","Industrial robots; Man machine systems; Proximity sensors; Distributed energies; Electronic skin; Energy requirements; Multiple sensors; Physical contacts; Proof of concept; Proximity sensing; Safe human-robot interaction; Human robot interaction",,,,,"H2020-Intereg-IVA5055; Engineering and Physical Sciences Research Council, EPSRC: EP/M002527/1, EP/R029644/1","ACKNOWLEDGMENT This work is supported in part by Engineering and Physical Sciences Research Council (EPSRC) through engineering fellowship for growth (EP/R029644/1 and EP/M002527/1) and North West Centre for Advanced Manufacturing (NW CAM) project supported by the European Union’s INTERREG VA Programme (H2020-Intereg-IVA5055), managed by the Special EU Programmes Body (SEUPB). The views and opinions in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).",,,,,,,,,,"Dahiya, R., E-skin: From humanoids to humans [point of view] (2019) Proc. Ieee, 107 (2), pp. 247-252. , Feb; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot, 3 (21). , Aug; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) Int. J. Soc. Robot, 5 (2), pp. 291-308. , Apr; Dahiya, R.S., Valle, M., (2013) Robotic Tactile Sensing: Technologies and System, , Dordrecht: Springer; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Rob. Auton. Syst, 58 (10), pp. 1159-1176. , Oct; Yamaguchi, T., Kashiwagi, T., Arie, T., Akita, S., Takei, K., Humandlike electronic skind integrated soft robotic hand (2019) Adv. Intell. Syst, 1 (2), p. 1900018. , Jun; Zhang, C., Liu, S., Huang, X., Guo, W., Li, Y., Wu, H., A stretchable dual-mode sensor array for multifunctional robotic electronic skin (2019) Nano Energy, 62, pp. 164-170. , no. March; Dahiya, R., Large-Area soft e-skin: The challenges beyond sensor designs (2019) Proc. Ieee, 107 (10), pp. 2016-2033. , Oct; Baek, S., Flexible piezocapacitive sensors based on wrinkled microstructures: Toward low-cost fabrication of pressure sensors over large areas (2017) Rsc Adv., 7 (63), pp. 39420-39426; Dharmasena, R.D.I.G., Energy scavenging and powering e-skin functional devices (2019) Proc. Ieee, 107 (10), pp. 2118-2136; García Núñez, C., Manjakkal, L., Dahiya, R., Energy autonomous electronic skin (2019) Npj Flex. Electron., 3 (1), p. 1. , Dec; Manjakkal, L., Núñez, C.G., Dang, W., Dahiya, R., Flexible self-charging supercapacitor based on graphene-Ag-3d graphene foam electrodes (2018) Nano Energy, 51, pp. 604-612. , no. March, Sep; Jiang, C., Choi, H.W., Cheng, X., Ma, H., Hasko, D., Nathan, A., Printed subthreshold organic transistors operating at high gain and ultralow power (2019) Science (80-.)., 363 (6428), pp. 719-723. , Feb; Jiang, C., Cheng, X., Nathan, A., Flexible ultralow-power sensor interfaces for e-skin (2019) Proc. Ieee, 107 (10), pp. 2084-2105. , Oct; Dahiya, R., Akinwande, D., Chang, J.S., Flexible electronic skin: From humanoids to humans [scanning the issue] (2019) Proc Ieee, 107 (10), pp. 2011-2015. , Oct; Yu, K., Rich, S., Lee, S., Fukuda, K., Yokota, T., Someya, T., Organic photovoltaics: Toward self-powered wearable electronics (2019) Proc. Ieee, 107 (10), pp. 2137-2154. , Oct; Min, G., Manjakkal, L., Mulvihill, D.M., Dahiya, R.S., Triboelectric nanogenerator with enhanced performance via an optimized low permittivity substrate (2019) Ieee Sens. J., PP (C), pp. 1-1; Escobedo, P., De Vargas-Sansalvador, I.M.P., Carvajal, M., Capitán-Vallvey, L.F., Palma, A.J., Martínez-Olmos, A., Flexible passive tag based on light energy harvesting for gas threshold determination in sealed environments (2016) Sensors Actuators B Chem., 236, pp. 226-232. , Nov; Sadasivuni, K.K., Kafy, A., Zhai, L., Ko, H.U., Mun, S., Kim, J., Transparent and flexible cellulose nanocrystal/reduced graphene oxide film for proximity sensing (2015) Small, 11 (8), pp. 994-1002; Sadasivuni, K.K., Kafy, A., Zhai, L., Ko, H.-U., Mun, S., Kim, J., Transparent and flexible cellulose nanocrystal/reduced graphene oxide film for proximity sensing (2015) Small, 11 (8), pp. 994-1002. , Feb; Lumelsky, V.J., Shur, M.S., Wagner, S., Sensitive skin (2001) Ieee Sens. J., 1 (1), pp. 41-51. , Jun; Escobedo, P., Ntagios, M., Shakthivel, D., Navaraj, W., Dahiya, R., Energy generating electronic skin with intrinsic tactile sensing without touch sensors (2020) Ieee Trans. Robot; Gupta, S., Vilouras, A., Dahiya, R., Polydimethylsiloxane as polymeric protective coating for fabrication of ultra-thin chips (2019) Microelectron. Eng., 301, p. 111157. , Oct; Dahiya, R., Gottardi, G., Laidani, N., Pdms residues-free micro/macrostructures on flexible substrates (2015) Microelectron. Eng., 136, pp. 57-62. , Mar; Ntagios, M., Nassar, H., Pullanchiyodan, A., Navaraj, W.T., Dahiya, R., Robotic hands with intrinsic tactile sensing via 3d printed soft pressure sensors (2019) Adv. Intell. Syst, p. 1900080; Navaraj, W., Dahiya, R., Fingerprintdenhanced capacitived piezoelectric flexible sensing skin to discriminate static and dynamic tactile stimuli (2019) Adv. Intell. Syst, p. 1900051. , 1900051, Sep; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Found. Trends Robot, 5 (3), pp. 261-349","Dahiya, R.; Bendable Electronics and Sensing Technologies (BEST), United Kingdom; email: Ravinder.Dahiya@glasgow.ac.uk",,"IEEE;IEEE Sensors Council","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE Sensors, SENSORS 2020","25 October 2020 through 28 October 2020",,165760,19300395,9781728168012,,,"English","Proc. IEEE Sens.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098698341
"Zou W., Duan P., Chen Y., Yu N., Shi L.","57191196344;57219796718;57211521668;23096713200;35230888900;","Variable stiffness control with strict frequency domain constraints for physical human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340895","7146","7151",,,"10.1109/IROS45743.2020.9340895","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102413893&doi=10.1109%2fIROS45743.2020.9340895&partnerID=40&md5=f26ffe6469f6cb668ea1dc37e3347042","Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Xeno Dynamics Co., Ltd, Shenzhen, China; Nankai University, Institute of Robotics and Automatic Information Systems, Tianjin Key Laboratory of Intelligent Robotics, Tianjin, China","Zou, W., Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Duan, P., Xeno Dynamics Co., Ltd, Shenzhen, China; Chen, Y., Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Yu, N., Nankai University, Institute of Robotics and Automatic Information Systems, Tianjin Key Laboratory of Intelligent Robotics, Tianjin, China; Shi, L., Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong","Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method. © 2020 IEEE.",,"Agricultural robots; Disturbance rejection; Frequency domain analysis; Intelligent robots; Man machine systems; Stiffness; Actuator saturations; Frequency properties; Nonsmooth optimization; Physical human-robot interactions; Scheduling functions; Specific frequencies; Variable impedance control; Variable stiffness; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: U1913208, 61873135, U1713223","This work has been supported by the National Natural Science Foundation of China (61873135, U1713223, U1913208) and the Hong Kong ITF Fund GHP/001/18SZ.",,,,,,,,,,"Hogan, N., Impedance control: An approach to manipulation (1984) American Control Conference, pp. 304-313; Roveda, L., Vicentini, F., Pedrocchi, N., Tosatti, L.M., Impedance control based force-tracking algorithm for interaction robotics tasks: An analytically force overshoots-free approach (2015) International Con-ference on Informatics in Control, Automation and Robotics, pp. 386-391; Ferraguti, F., Preda, N., Manurung, A., Bonfe, M., Lambercy, O., Gassert, R., Muradore, R., Secchi, C., An energy tankbased interactive control architecture for autonomous and teleoperated robotic surgery (2015) IEEE Transactions on Robotics, 31 (5), pp. 1073-1088; Ryu, D., Song, J.B., Kang, S., Kim, M., Frequency domain stability observer and active damping control for stable haptic interaction (2008) IET Control Theory & Applications, 2 (4), pp. 261-268; Li, X., Liu, Y.H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7; Zhang, T., Xia, J., Interconnection and damping assignment passivity-based impedance control of a compliant assistive robot for physical human-robot interactions (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 538-545; Dimeas, F., Aspragathos, N., Online stability in human-robot cooperation with admittance control (2016) IEEE Transactions on Haptics, 9 (2), pp. 267-278; Landi, C.T., Ferraguti, F., Sabattini, L., Secchi, C., Bonfe, M., Fantuzzi, C., Variable admittance control preventing undesired oscillating behaviors in physical human-robot interaction (2017) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3611-3616; Lee, K., Buss, M., Force tracking impedance control with variable target stiffness (2008) IFAC Proceedings Volumes, 41 (2), pp. 6751-6756; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) IEEE International Conference on Robotics and Automation, pp. 644-650; Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 446-451; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) IEEE International Conference on Robotics and Automation, pp. 3903-3908; Buchli, J., Stulp, F., Theodorou, E., Schaal, S., Learning variable impedance control (2011) The International Journal of Robotics Research, 30 (7), pp. 820-833; Dimeas, F., Aspragathos, N., Reinforcement learning of variable admittance control for human-robot co-manipulation (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1011-1016; Zhang, L., Li, Z., Yang, C., Adaptive neural network based variable stiffness control of uncertain robotic systems using disturbance observer (2017) IEEE Transaction on Industrial Electronics, 64 (3), pp. 2236-2245; Ferraguti, F., Secchi, C., Fantuzzi, C., A tank-based approach to impedance control with variable stiffness (2013) IEEE International Conference on Robotics and Automation, pp. 4948-4953; Kastritsi, T., Dimeas, F., Doulgeri, Z., Progressive automation with DMP synchronization and variable stiffness control (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3789-3796; Vanderborght, B., Albu-Schffer, A., Bicchi, A., Burdet, E., Caldwell, D.G., Carloni, R., Catalano, M., Ganesh, G., Variable impedance actuators: A review (2013) Robotics and Autonomous Systems, 61 (12), pp. 1601-1614; Haninger, K., Tomizuka, M., Robust passivity and passivity relaxation for impedance control of fiexible-joint robots with inner-loop torque control (2018) IEEE/ASME Transactions on Mechatronics, 23 (6), pp. 2671-2680; Yu, N., Zou, W., Sun, Y., Passivity guaranteed stiffness control with multiple frequency band specifications for a cable-driven series elastic actuator (2019) Mechanical Systems and Signal Processing, 117, pp. 709-722; Yu, N., Zou, W., Stiffness control of a series elastic actuator with restricted frequency domain specifications (2019) Control Theory & Applications, 36 (5), pp. 711-719; Colgate, J.E., (1988) The Control of Dynamically Interacting Systems, , PhD Dissertation, Massachusetts Institute of Technology; Anderson, B.D.O., The small-gain theorem, the passivity theorem and their equivalence (1972) Journal of the Franklin Institute, 293 (2), pp. 105-115; Apkarian, P., Gahinet, P., Buhr, C., Multi-model, multi-objective tuning of fixed-structure controllers (2014) European Control Conference, pp. 856-861; Gahinet, P., Apkarian, P., Automated tuning of gain-scheduled control systems (2013) IEEE Conference on Decision and Control, pp. 2740-2745; Yu, N., Zou, W., Tan, W., Yang, Z., Augmented virtual stiffness rendering of a cable-driven SEA for human-robot interaction (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 714-723; Yu, N., Zou, W., Impedance control of a cable-driven SEA with mixed H2=H1 synthesis (2017) Assembly Automation, 37 (3), pp. 296-303","Yu, N.; Nankai University, China; email: nyu@nankai.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102413893
"Rosen E., Whitney D., Fishman M., Ullman D., Tellex S.","57195424425;57211723755;57210797500;55638484600;7801643708;","Mixed reality as a bidirectional communication interface for human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340822","11431","11438",,3,"10.1109/IROS45743.2020.9340822","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102409135&doi=10.1109%2fIROS45743.2020.9340822&partnerID=40&md5=01539773722733ffe7bedbfebb2760a5","Brown University, Department of Computer Science","Rosen, E., Brown University, Department of Computer Science; Whitney, D., Brown University, Department of Computer Science; Fishman, M., Brown University, Department of Computer Science; Ullman, D., Brown University, Department of Computer Science; Tellex, S., Brown University, Department of Computer Science","We present a decision-theoretic model and robot system that interprets multimodal human communication to disambiguate item references by asking questions via a mixed reality (MR) interface. Existing approaches have either chosen to use physical behaviors, like pointing and eye gaze, or virtual behaviors, like mixed reality. However, there is a gap of research on how MR compares to physical actions for reducing robot uncertainty. We test the hypothesis that virtual deictic gestures are better for human-robot interaction (HRI) than physical behaviors. To test this hypothesis, we propose the Physio-Virtual Deixis Partially Observable Markov Decision Process (PVD-POMDP), which interprets multimodal observations (speech, eye gaze, and pointing gestures) from the human and decides when and how to ask questions (either via physical or virtual deictic gestures) in order to recover from failure states and cope with sensor noise. We conducted a between-subjects user study with 80 participants distributed across three conditions of robot communication: no feedback control, physical feedback, and MR feedback. We tested performance of each condition with objective measures (accuracy, time), as well as evaluated user experience with subjective measures (usability, trust, workload). We found the MR feedback condition was 10% more accurate than the physical condition and a speedup of 160%. We also found that the feedback conditions significantly outperformed the no feedback condition in all subjective metrics. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Feedback; Intelligent robots; Man machine systems; Markov processes; Mixed reality; User experience; Bi-directional communication; Decision-theoretic; Human communications; Human robot Interaction (HRI); Partially observable Markov decision process; Physical behaviors; Physical conditions; Robot communication; Social robots",,,,,,,,,,,,,,,,"Admoni, H., Weng, T., Scassellati, B., Modeling communicative behaviors for object references in human-robot interaction (2016) Robotics and Automation (ICRA), 2016 IEEE International Conference on, pp. 3352-3359; Bangerter, A., Using pointing and describing to achieve joint focus of attention in dialogue (2004) Psychological Science, 15 (6), pp. 415-419; Bellman, R., A markovian decision process (1957) Journal of Mathematics and Mechanics, pp. 679-684; Brooke, J., SUS-a quick and dirty usability scale (1996) Usability Evaluation in Industry, 189 (194), pp. 4-7; Chai, J.Y., She, L., Fang, R., Ottarson, S., Littley, C., Liu, C., Hanson, K., Collaborative effort towards common ground in situated human-robot dialogue (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 33-40; Goodman, N.D., Stuhlmüller, A., Knowledge and implicature: Modeling language understanding as social cognition (2013) Topics in Cognitive Science, 5; Gross, S., Krenn, B., Scheutz, M., The reliability of non-verbal cues for situated reference resolution and their interplay with language: Implications for human robot interaction (2017) Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 189-196; Hakkani-Tür, D., Slaney, M., Celikyilmaz, A., Larry, Heck., Eye gaze for spoken language understanding in multi-modal conversational interactions (2014) Proceedings of the 16th International Conference on Multimodal Interaction, pp. 263-266; Sandra, G., Hart, NASA-task load index (NASA-TLX); 20 years later (2006) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50, pp. 904-908. , Sage Publications Sage CA: Los Angeles, CA; Hart, S.G., Staveland, L.E., Development of NASA-TLX (task load index): Results of empirical and theoretical research (1988) Advances in Psychology, 52, pp. 139-183. , Elsevier; Pack Kaelbling, L., Littman, M.L., Cassandra, A.R., Planning and acting in partially observable stochastic domains (1998) Artificial Intelligence, 101 (1-2), pp. 99-134; Mehlmann, G., Häring, M., Janowski, K., Baur, T., Gebhard, P., André, E., Exploring a model of gaze for grounding in multimodal HRI (2014) Proceedings of the 16th International Conference on Multimodal Interaction, pp. 247-254; Perlmutter, L., Kernfeld, E., Cakmak, M., Situated language understanding with human-like and visualization-based transparency (2016) Robotics: Science and Systems; Prasov, Z., Chai, J.Y., What's in a gaze?: The role of eye-gaze in reference resolution in multimodal conversational interfaces (2008) Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 20-29; Qvarfordt, P., Gaze-informed multimodal interaction (2017) The Handbook of Multimodal-Multisensor Interfaces, pp. 365-402. , Association for Computing Machinery and Morgan & Claypool; Rosen, E., Whitney, D., Phillips, E., Chien, G., Tompkin, J., Konidaris, G., Tellex, S., (2017) Communicating Robot Arm Motion Intent through Mixed Reality Head-mounted Displays; Shridhar, M., Hsu, D., Interactive visual grounding of referring expressions for human-robot interaction (2018) Proceedings of Robotics: Science and Systems; Sibirtseva, E., Kontogiorgos, D., Nykvist, O., Karaoguz, H., Leite, I., Gustafson, J., Kragic, D., A comparison of visualisation methods for disambiguating verbal requests in human-robot interaction (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 43-50; Spaan, J.M.T., Vlassis, N.A., (2011) Perseus: Randomized Point-based Value Iteration for POMDPs, , http://arxiv.org/abs/1109.2145, CoRR, abs/1109. 2145; Ullman, D., Malle, B.F., What does it mean to trust a robot?: Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 263-264; Whitney, D., Rosen, E., MacGlashan, J., Wong, L.L.S., Tellex, S., Reducing errors in object-fetching interactions through social feedback (2017) International Conference on Robotics and Automation, , Singapore, May; Williams, T., Tran, N., Rands, J., Dantam, N.T., Augmented, mixed, and virtual reality enabling of robot deixis (2018) International Conference on Virtual, Augmented and Mixed Reality, pp. 257-275. , Springer; Williams, T., Bussing, M., Cabroll, S., Boyle, E., Tran, N., Mixed reality deictic gesture for multi-modal robot communication (2019) ACM/IEEE International Conference on Human-Robot Interaction (HRI), , 03",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102409135
"Rajendran V., Carreno-Medrano P., Fisher W., Werner A., Kulic D.","57222562988;56336138700;57211956135;55556945800;10244652900;","A framework for human-robot interaction user studies",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341286","6215","6222",,1,"10.1109/IROS45743.2020.9341286","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102404791&doi=10.1109%2fIROS45743.2020.9341286&partnerID=40&md5=bcfafd1fe3484b224f425803f7e5caf9","University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Monash University, Faculty of Engineering, Melbourne, Australia","Rajendran, V., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Carreno-Medrano, P., Monash University, Faculty of Engineering, Melbourne, Australia; Fisher, W., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Werner, A., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Kulic, D., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada, Monash University, Faculty of Engineering, Melbourne, Australia","Human-Robot Interaction (HRI) user studies are challenging to evaluate and compare due to a lack of standardization and the infrastructure required to implement each study. The lack of experimental infrastructure also makes it difficult to systematically evaluate the impact of individual components (e.g., the quality of perception software) on overall system performance. This work proposes a framework to ease the implementation and reproducibility of human-robot interaction user studies. The framework utilizes ROS middleware and is implemented with four modules: perception, decision, action, and metrics. The perception module aggregates sensor data to be used by the decision and action modules. The decision module is the task-level executive and can be designed by the HRI researcher for their specific task. The action module takes subtask requests from the decision module and breaks them down into motion primitives for execution on the robot. The metrics module tracks and generates quantitative metrics for the study. The framework is implemented with modular interfaces to allow for alternate implementations within each module and can be generalized for a variety of tasks and human/robot roles. The framework is illustrated through an example scenario involving a human and a Franka Emika Panda arm collaboratively assembling a toolbox together. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Man machine systems; Middleware; Quality control; Decision modules; Human robot Interaction (HRI); Individual components; Motion primitives; Quality of perception; Quantitative metrics; Reproducibilities; Specific tasks; Human robot interaction",,,,,,,,,,,,,,,,"Ajoudani, A., Progress and prospects of the human.robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Khan, A., Robots in healthcare: A survey (2019) Science and Information Conference. Springer, pp. 280-292; Zeylikman, S., The HRC model set for human-robot collaboration research (2017) CoRR, , abs/1710.11211; Murali, A., Pyrobot: An open-source robotics framework for research and benchmarking (2019) CoRR, , abs/1906.08236; Steinfeld, A., Common metrics for human-robot interaction (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-robot Interaction, pp. 33-40. , ser. HRI '06. New York, NY, USA: ACM; Hoffman, G., Evaluating fluency in human.robot collaboration (2019) Ieee Transactions on Human-Machine Systems, 49 (3), pp. 209-218. , June; Lindblom, J., Current challenges for ux evaluation of humanrobot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Springer, pp. 267-277; Belhassein, K., Towards methodological principles for user studies in human-robot interaction (2019) Test Methods and Metrics for Effective Hri in Collaborative Human-Robot Teams Workshop, , ACM/IEEE International Conference on Human-Robot Interaction; Quigley, M., Ros: An open-source robot operating system (2009) Icra Workshop on Open Source Software; Malik, A.A., (2018) Digital Twins of Human Robot Collaboration in a Production Setting, ., 17. , 06; Tsarouchi, P., Ros based coordination of human robot cooperative assembly tasks-an industrial case study (2015) Procedia Cirp, 37, pp. 254-259; Lane, I., HRItk: The human-robot interaction ToolKit rapid development of speech-centric interactive systems in ROS (2012) NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012). Montréal, pp. 41-44. , Canada: Association for Computational Linguistics, June; Clodic, A., Shary: A supervision system adapted to human-robot interaction (2009) Experimental Robotics. Springer, pp. 229-238; Paxton, C., CoSTAR: Instructing collaborative robots with behavior trees and vision (2017) Robotics and Automation (ICRA), 2017 Ieee International Conference on; Fischer, T., Icub-hri: A software framework for complex human-robot interaction scenarios on the icub humanoid robot (2018) Frontiers in Robotics and Ai, 5 (22), pp. 1-9; Fong, T., The human-robot interaction operating system (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-robot Interaction, pp. 41-48. , ser. HRI '06. New York, NY, USA: ACM; Missri, S., (2019) Tealsense Gazebo Plugin, , https://github.com/SyrianSpock/realsensegazeboplugin; Sucan, I.A., MoveIt, , https://moveit.ros.org/about/; Marco, E., Easy Handeye: Tf Nvisp Hand-Eye Calibration, , https://github.com/IFL-CAMP/easyhandeye; Toussaint, M., Relational activity processes for modeling concurrent cooperation (2016) (ICRA 2016); Cao, Z., Openpose: Realtime multi-person 2d pose estimation using part affinity fields (2018) CoRR, , http://arxiv.org/abs/1812.08008, abs/1812.08008; Goebel, P., Pi Trees, , https://github.com/pirobot/pitrees,2019",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102404791
"Weber D., Santini T., Zell A., Kasneci E.","57217014046;54881866000;7003785870;56059892600;","Distilling location proposals of unknown objects through gaze information for human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340893","11086","11093",,,"10.1109/IROS45743.2020.9340893","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102404452&doi=10.1109%2fIROS45743.2020.9340893&partnerID=40&md5=8033b0eb4a87c9bd3a699ff89ada11a7","University of Tübingen, Tübingen, 72076, Germany","Weber, D., University of Tübingen, Tübingen, 72076, Germany; Santini, T., University of Tübingen, Tübingen, 72076, Germany; Zell, A., University of Tübingen, Tübingen, 72076, Germany; Kasneci, E., University of Tübingen, Tübingen, 72076, Germany","Successful and meaningful human-robot interaction requires robots to have knowledge about the interaction context - e.g., which objects should be interacted with. Unfortunately, the corpora of interactive objects is - for all practical purposes - infinite. This fact hinders the deployment of robots with pre-trained object-detection neural networks other than in pre-defined scenarios. A more flexible alternative to pre-training is to let a human teach the robot about new objects after deployment. However, doing so manually presents significant usability issues as the user must manipulate the object and communicate the object's boundaries to the robot. In this work, we propose streamlining this process by using automatic object location proposal methods in combination with human gaze to distill pertinent object location proposals. Experiments show that the proposed method 1) increased the precision by a factor of approximately 21 compared to location proposal alone, 2) is able to locate objects sufficiently similar to a state-of-the-art pre-trained deep-learning method (FCOS) without any training, and 3) detected objects that were completely missed by FCOS. Furthermore, the method is able to locate objects for which FCOS was not trained on, which are undetectable for FCOS by definition. © 2020 IEEE.",,"Agricultural robots; Deep learning; Educational robots; Intelligent robots; Learning systems; Location; Man machine systems; Object detection; Object recognition; Interaction context; Interactive objects; Learning methods; Object location; Pre-training; State of the art; Unknown objects; Human robot interaction",,,,,,,,,,,,,,,,"Nee, A.Y., Ong, S., Chryssolouris, G., Mourtzis, D., Augmented reality applications in design and manufacturing (2012) CIRP Annals, 61 (2), pp. 657-679; Rentzos, L., Papanastasiou, S., Papakostas, N., Chryssolouris, G., Augmented reality for human-based assembly: Using product and process semantics (2013) IFAC Proceedings Volumes, 46 (15), pp. 98-101; DeVries, T., Misra, I., Wang, C., Maaten Der, L.Van, Does object recognition work for everyone? 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ACM, p. 17; Santini, T., Fuhl, W., Kasneci, E., PuRe: Robust pupil detection for real-time pervasive eye tracking (2018) Computer Vision and Image Understanding, 170, pp. 40-50. , http://www.sciencedirect.com/science/article/pii/S1077314218300146; Santini, T., Fuhl, W., Kasneci, E., PuReST: Robust pupil tracking for real-time pervasive eye tracking (2018) Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, Ser. ETRA '18, pp. 611-615. , http://doi.acm.org/10.1145/3204493.3204578, New York, NY, USA: ACM; (2019), https://www.metralabs.com/mobiler-roboter-scitos-g5/, accessed: 2020-02-09; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3 (2-3), p. 5. , Kobe, Japan; Xiang, L., Echtler, F., Kerl, C., (2016) libfreenect2: Release 0. 2, , https://doi.org/10.5281/zenodo.50641, apr; Wiedemeyer, T., (2014) IAI Kinect2, , https://github.com/code-iai/iaikinect2, Institute for Artificial Intelligence, University Bremen, accessed: 2019-11-21; Bradski, G., The opencv library (2000) Dr. Dobb's Journal of Software Tools; Niehorster, D.C., Santini, T., Hessels, R.S., Hooge, I.T., Kasneci, E., Nyström, M., The impact of slippage on the data quality of headworn eye trackers (2020) Behavior Research Methods, pp. 1-21; Henriques, J.F., Caseiro, R., Martins, P., Batista, J., High-speed tracking with kernelized correlation filters (2014) IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (3), pp. 583-596; Lukezic, A., Vojir, T., Cehovin Zajc, L., Matas, J., Kristan, M., Discriminative correlation filter with channel and spatial reliability (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6309-6318; Zitnick, C.L., Dollár, P., Edge boxes: Locating object proposals from edges (2014) European Conference on Computer Vision. Springer, pp. 391-405",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102404452
"Zahedi F., Bitz T., Phillips C., Lee H.","57219441506;57219436637;57222348277;35102589200;","Regulation of 2D arm stability against unstable, damping-defined environments in physical human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340829","11330","11336",,1,"10.1109/IROS45743.2020.9340829","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102404254&doi=10.1109%2fIROS45743.2020.9340829&partnerID=40&md5=9e2c576c300bf89a0907fb3276358714","Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States","Zahedi, F., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States; Bitz, T., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States; Phillips, C., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States; Lee, H., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States","This paper presents an experimental study to investigate how humans interact with a robotic arm simulating primarily unstable, damping-defined, mechanical environments, and to quantify lower bounds of robotic damping that humans can stably interact with. Human subjects performed posture maintenance tasks while a robotic arm simulated a range of negative damping-defined environments and transiently perturbed the human arm to challenge postural stability. Analysis of 2-dimensional kinematic responses in both the time domain and phase space allowed us to evaluate stability of the coupled human-robot system in both anterior-posterior (AP) and medial-lateral (ML) directions, and to determine the lower bounds of robotic damping for stable physical human-robot interaction (pHRI). All subjects demonstrated higher capacity to stabilize their arm against negative damping-defined environments in the AP direction than the ML direction, evidenced by all 3 stability measures used in this study. Further, the lower bound of robotic damping for stable pHRI was more than 3.5 times lower in the AP direction than the ML direction: -30.0 Ns/m and -8.2 Ns/m in the AP and ML directions, respectively. Sensitivity analysis confirmed that the results in this study were relatively insensitive to varying experimental conditions. Outcomes of this study would allow us to design a less conservative robotic impedance controller that utilizes a wide range of robotic damping, including negative damping, and achieves more transparent and agile operations without compromising coupled stability and safety of the human-robot system, and thus improves the overall performance of pHRI. © 2020 IEEE.",,"Agricultural robots; Damping; Intelligent robots; Machine design; Man machine systems; Phase space methods; Robotic arms; Robotics; Sensitivity analysis; System stability; Time domain analysis; Anterior posteriors; Experimental conditions; Human-robot systems; Impedance controllers; Mechanical environment; Physical human-robot interactions; Physical humanrobot interaction (phri); Posture maintenances; Social robots",,,,,"National Science Foundation, NSF: 1925110, 1846885","Research supported by National Science Foundation Awards #1846885 and #1925110. Hyunglae Lee is with the School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287, USA (e-mail: hyunglae.lee@asu.edu; *corresponding author: 480-727-7463; fax:",,,,,,,,,,"Haddadin, S., Suppa, M., Fuchs, S., Bodenmuller, T., Albu-Schaffer, A., Hirzinger, G., Towards the robotic co-worker (2011) Robotics Research, pp. 261-282. , Springer; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; Albu-Schaffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimbock, T., Hirzinger, G., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Industrial Robot-an International Journal, 34 (5), pp. 376-385; Yoshikawa, T., Force control of robot manipulators (2000) Proc. 2000 IEEE International Conference on Robotics and Automation, pp. 220-226; Chiaverini, S., Siciliano, B., Villani, L., A survey of robot interaction control schemes with experimental comparison (1999) IEEE/ASME Trans on Mechatronics, 4 (3), pp. 273-285; Hogan, N., Impedance control-an approach to manipulation. 1. theory (1985) Journal of Dynamic Systems Measurement and Control-Transactions of the Asme, 107 (1), pp. 1-7; Hogan, N., Buerger, S.P., Impedance and interaction control (2005) Robotics and Automation Handbook, , New York, CRC Press; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244. , Apr; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) IEEE International Conference on Robotics and Automation (ICRA), pp. 644-650. , Washington D. C., USA; Burdet, E., Osu, R., Franklin, D.W., Yoshioka, T., Milner, T.E., Kawato, M., A method for measuring endpoint stiffness during multi-joint arm movements (2000) J Biomech, 33 (12), pp. 1705-1709. , Dec; Gomi, H., Osu, R., Task-dependent viscoelasticity of human multijoint arm and its spatial characteristics for interaction with environments (1998) J Neurosci, 18 (21), pp. 8965-8978. , Nov 1; De Vlugt, E., Schouten, A.C., Van Der Helm, T.F.C., Closed-loop multivariable system identification for the characterization of the dynamic arm compliance using continuous force disturbances: A model study (2003) Journal of Neuroscience Methods, 122 (2), pp. 123-140. , Jan 30; Perreault, E.J., Kirsch, R.F., Acosta, A.M., Multiple-input, multiple-output system identification for characterization of limb stiffness dynamics (1999) Biological Cybernetics, 80 (5), pp. 327-337. , May; Franklin, D.W., Liaw, G., Milner, T.E., Osu, R., Burdet, E., Kawato, M., Endpoint stiffness of the arm is directionally tuned to instability in the environment (2007) J Neurosci, 27 (29), pp. 7705-7716. , Jul 18; Krutky, M.A., Trumbower, R.D., Perreault, E.J., Influence of environmental stability on the regulation of end-point impedance during the maintenance of arm posture (2013) Journal of Neurophysiology, 109 (4), pp. 1045-1054. , Feb; Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M., The central nervous system stabilizes unstable dynamics by learning optimal impedance (2001) Nature, 414 (6862), pp. 446-449. , Nov 22; Perreault, E.J., Kirsch, R.F., Crago, P.E., Effects of voluntary force generation on the elastic components of endpoint stiffness (2001) Exp Brain Res, 141 (3), pp. 312-323. , Dec; Hanzlick, H., Murphy, H., Lee, H., Stability of the human ankle in relation to environmental mechanics (2017) Proc. IEEE International Conference on Robotics and Automation (ICRA 2017), , Singapore; Goswami, A., Thuilot, B., Espiau, B., Compas-like biped robot Part I: Stability and bifurcation of passive gaits (1996) INRIA, 2996; Hanzlick, H., Hyunglae, L., Gender difference of ankle stability in the sagittal and frontal planes (2017) Conf Proc IEEE Eng Med Biol Soc, 2017, pp. 1621-1624. , Jul; Lee, H., Hogan, N., Essential considerations for design and control of human-interactive robots (2016) Proc. 2016 IEEE International Conference on Robotics and Automation (ICRA 2016), pp. 3069-3074. , Stockholm; Kearney, R.E., Hunter, I.W., System-identification of human joint dynamics (1990) Critical Reviews in Biomedical Engineering, 18 (1), pp. 55-87; Lee, H., Krebs, H.I., Hogan, N., Multivariable dynamic ankle mechanical impedance with active muscles (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22 (5), pp. 971-981. , Sep; Lee, H., Perreault, E.J., Stabilizing stretch reflexes are modulated independently from the rapid release of perturbation-triggered motor plans (2019) Sci Rep, 9 (1), p. 13926. , Sep 26; Arnold, J., Hanzlick, H., Lee, H., Variable damping control of the robotic ankle joint to improve trade-off between performance and stability (2019) IEEE International Conference on Robotics and Automation (ICRA), pp. 1699-1704. , Montreal, Canada; Bitz, T., Zahedi, F., Lee, H., Variable damping control of a robotic arm to improve trade-off between agility and stability and reduce user effort (2020) IEEE International Conference on Robotics and Automation (ICRA), , France","Lee, H.; Arizona State University, United States; email: hyunglae.lee@asu.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102404254
"Sommerhalder M., Zimmermann Y., Cizmeci B., Riener R., Hutter M.","57222350539;57211426393;35101660900;7003404145;35409405200;","Physical human-robot interaction with real active surfaces using haptic rendering on point clouds",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341053","9767","9773",,,"10.1109/IROS45743.2020.9341053","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102402762&doi=10.1109%2fIROS45743.2020.9341053&partnerID=40&md5=db11f2a7bfa52c90ba8883fd10f53d81","ETH Zurich, Robotic Systems Lab, Switzerland; ETH Zurich, Sensory-Motor Systems Lab, Switzerland; Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland","Sommerhalder, M., ETH Zurich, Robotic Systems Lab, Switzerland, ETH Zurich, Sensory-Motor Systems Lab, Switzerland; Zimmermann, Y., ETH Zurich, Robotic Systems Lab, Switzerland, ETH Zurich, Sensory-Motor Systems Lab, Switzerland; Cizmeci, B., ETH Zurich, Robotic Systems Lab, Switzerland; Riener, R., ETH Zurich, Sensory-Motor Systems Lab, Switzerland, Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland; Hutter, M., ETH Zurich, Robotic Systems Lab, Switzerland","During robot-assisted therapy of hemiplegic patients, interaction with the patient must be intrinsically safe. Straight-forward collision avoidance solutions can provide this safety requirement with conservative margins. These margins heavily reduce the robot's workspace and make interaction with the patient's unguided body parts impossible. However, interaction with the own body is highly beneficial from a therapeutic point of view. We tackle this problem by combining haptic rendering techniques with classical computer vision methods. Our proposed solution consists of a pipeline that builds collision objects from point clouds in real-time and a controller that renders haptic interaction. The raw sensor data is processed to overcome noise and occlusion problems. Our proposed approach is validated on the 6 DoF exoskeleton ANYexo for direct impacts, sliding scenarios, and dynamic collision surfaces. The results show that this method has the potential to successfully prevent collisions and allow haptic interaction for highly dynamic environments. We believe that this work significantly adds to the usability of current exoskeletons by enabling virtual haptic interaction with the patient's body parts in human-robot therapy. © 2020 IEEE.",,"Agricultural robots; Exoskeleton (Robotics); Intelligent robots; Patient treatment; Dynamic environments; Haptic interactions; Haptic rendering techniques; Hemiplegic patients; Intrinsically safe; Physical human-robot interactions; Robot-assisted therapies; Safety requirements; Human robot interaction",,,,,"Innosuisse - Schweizerische Agentur für Innovationsförderung, Innosuisse; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Innosuisse - Schweizerische Agentur für Innovationsförderung, Innosuisse","This research was supported in part by the Swiss National Science Foundation through the National Centre of Competence in Research Robotics (NCCR Robotics) and Innosuisse, the Swiss Innovation Agency.",,,,,,,,,,"Kaldestad, K.B., Haddadin, S., Belder, R., Hovland, G., Anisi, D.A., Collision avoidance with potential fields based on parallel processing of 3d-point cloud data on the GPU (2014) 2014 Ieee International Conference on Robotics and Automation (ICRA), pp. 3250-3257. , May; Du, G., Long, S., Li, F., Huang, X., Active collision avoidance for human-robot interaction with ukf, expert system, and artificial potential field method (2018) Frontiers in Robotics and Ai, 5, p. 125; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2016) Ieee Transactions on Automation Science and Engineering, 13 (2), pp. 882-893. , April; Minguez, J., Montano, L., Extending collision avoidance methods to consider the vehicle shape, kinematics, and dynamics of a mobile robot (2009) Ieee Transactions on Robotics, 25 (2), pp. 367-381. , April; Fu, Y., Jiang, G., Feng, W., Zhou, Y., Ou, Y., On real-time obstacle avoidance using 3-d point clouds (2014) 2014 Ieee International Conference on Robotics and Biomimetics (ROBIO 2014), pp. 631-636. , Dec; Knopp, S., Lorenz, M., Pelliccia, L., Philipp, K., Using industrial robots as haptic devices for vr-training (2018) IEEE, Conference on Virtual Reality and 3D User Interfaces (VR), , https://ieeexplore.ieee.org/abstract/document/8446614; Zimmermann, Y., Forino, A., Riener, R., Hutter, M., Anyexo: A versatile and dynamic upper-limb rehabilitation robot (2019) Ieee Robotics and Automation Letters, 4 (4), pp. 3649-3656. , Oct; Seraji, H., Bon, B., Real-time collision avoidance for positioncontrolled manipulators (1999) Ieee Transactions on Robotics and Automation, 15 (4), pp. 670-677. , Aug; Sharkawy, A., Koustoumpardis, P., Aspragathos, N., Human-robot collisoins detection for safe human-robot interaction using one multiinput-output neural network (2019) Soft Computing, , August; Salisbury, K., Conti, F., Barbagli, F., Haptic rendering: Introductory concepts (2004) Computer Graphics and Applications, 1 (4); Zilles, C.B., Salisbury, J.K., A constraint-based god-object method for haptic display (1995) Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, 3, pp. 146-151. , vol.3 Aug; Rydén, F., Chizeck, H.J., A proxy method for real-time 3-dof haptic rendering of streaming point cloud data (2013) Ieee Transactions on Haptics, 6 (3), pp. 257-267. , July; Li, X., Kesavadas, T., Surgical robot with environment reconstruction and force feedback (2018) 2018 40th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (EMBC), pp. 1861-1866. , July; Hutter, M., Sommer, H., Gehring, C., Hoepflinger, M., Bloesch, M., Siegwart, R., Quadrupedal locomotion using hierarchical operational space control (2014) The International Journal of Robotics Research, 33, pp. 1047-1062. , 07; Bellicoso, C.D., Gehring, C., Hwangbo, J., Fankhauser, P., Hutter, M., Perception-less terrain adaptation through whole body control and hierarchical optimization (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots, pp. 558-564. , Nov; Leeper, A., Chan, S., Hsiao, K., Ciocarlie, M., Salisbury, K., Constraint-based haptic rendering of point data for teleoperated robot grasping (2012) 2012 Ieee Haptics Symposium (HAPTICS), pp. 377-383. , March; Glass, K., Colbaugh, R., Lim, D., Seraji, H., Real-time collision avoidance for redundant manipulators (1995) Ieee Transactions on Robotics and Automation, 11 (3), pp. 448-457. , June; https://www.iso.org/news/2016/03/Ref2057.html, ISO/TS 15066:2017-04: Robots and robotic devices; collaborative robots, Apr. 2017; Rusu, R.B., Cousins, S., 3D is here: Point Cloud Library (PCL) (2011) Ieee International Conference on Robotics and Automation (ICRA), , Shanghai, China, May 9-13; Korn, M., Holzkothen, M., Pauli, J., Color supported generalizedicp (2014) 2014 International Conference on Computer Vision Theory and Applications (VISAPP), 3, pp. 592-599. , https://doi.ieeecomputersociety.org/, Los Alamitos, CA, USA: IEEE Computer Society, jan",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102402762
"Qiu S., Liu H., Zhang Z., Zhu Y., Zhu S.-C.","57219797230;57200626348;57211250285;57140988800;22236080700;","Human-robot interaction in a shared augmented reality workspace",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340781","11413","11418",,1,"10.1109/IROS45743.2020.9340781","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102400989&doi=10.1109%2fIROS45743.2020.9340781&partnerID=40&md5=ef60f1810b91c6d84531f62cc044759d","UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department","Qiu, S., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Liu, H., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Zhang, Z., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Zhu, Y., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Zhu, S.-C., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department","We design and develop a new shared Augmented Reality (AR) workspace for Human-Robot Interaction (HRI), which establishes a bi-directional communication between human agents and robots. In a prototype system, the shared AR workspace enables a shared perception, so that a physical robot not only perceives the virtual elements in its own view but also infers the utility of the human agent - the cost needed to perceive and interact in AR - by sensing the human agent's gaze and pose. Such a new HRI design also affords a shared manipulation, wherein the physical robot can control and alter virtual objects in AR as an active agent; crucially, a robot can proactively interact with human agents, instead of purely passively executing received commands. In experiments, we design a resource collection game that qualitatively demonstrates how a robot perceives, processes, and manipulates in AR and quantitatively evaluates the efficacy of HRI using the shared AR workspace. We further discuss how the system can potentially benefit future HRI studies that are otherwise challenging. © 2020 IEEE.",,"Agricultural robots; Augmented reality; Intelligent robots; Machine design; Man machine systems; Active agents; Bi-directional communication; Human robot Interaction (HRI); Physical robots; Prototype system; Resource collections; Virtual elements; Virtual objects; Social robots",,,,,"Defense Advanced Research Projects Agency, DARPA: XAI N66001-17-2-4029; Office of Naval Research, ONR: MURI N00014-16-1-2007, N00014-19-1-2153","‹ Shuwen Qiu and Hangxin Liu contributed equally to this work. UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA) at Statistics Department. Emails: {s.qiu, hx.liu, zeyuzhang, yixin.zhu}@ucla.edu, sczhu@stat.ucla.edu. The work reported herein was supported by ONR N00014-19-1-2153, ONR MURI N00014-16-1-2007, and DARPA XAI N66001-17-2-4029.",,,,,,,,,,"LaValle, S.M., Yershova, A., Katsev, M., Antonov, M., Head tracking for the oculus rift (2014) International Conference on Robotics and Automation (ICRA); Liu, H., Xie, X., Millar, M., Edmonds, M., Gao, F., Zhu, Y., Santos, V.J., Zhu, S.-C., A glove-based system for studying hand-object manipulation via joint pose and force sensing (2017) International Conference on Intelligent Robots and Systems (IROS); Liu, H., Zhang, Z., Xie, X., Zhu, Y., Liu, Y., Wang, Y., Zhu, S.-C., High-Idelity grasping in virtual reality using a glove-based system (2019) International Conference on Robotics and Automation (ICRA); Schatzschneider, C., Bruder, G., Steinicke, F., Who turned the clock? effects of manipulated zeitgebers, cognitive load and immersion on time estimation (2016) IEEE Transactions on Visualization & Computer Graph (TVCG), 22 (4), pp. 1387-1395; Ye, T., Qi, S., Kubricht, J., Zhu, Y., Lu, H., Zhu, S.-C., The martian: Examining human physical judgments across virtual gravity fields (2017) IEEE Transactions on Visualization & Computer Graph (TVCG), 23 (4), pp. 1399-1408; Wang, D., Kubrlcht, J., Zhu, Y., Lianq, W., Zhu, S.-C., Jiang, C., Lu, H., Spatially perturbed collision sounds attenuate perceived causality in 3d launching events (2018) Conference on Virtual Reality and 3D User Interfaces (VR); Lin, J., Guo, X., Shao, J., Jiang, C., Zhu, Y., Zhu, S.-C., A virtual reality platform for dynamic human-scene interaction (2016) SIGGRAPH ASIA 2016 Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments; Shah, S., Dey, D., Lovett, C., Kapoor, A., Airsim: High-idelity visual and physical simulation for autonomous vehicles (2018) Field and Service Robotics, , Springer; Xie, X., Liu, H., Zhang, Z., Qiu, Y., Gao, F., Qi, S., Zhu, Y., Zhu, S.-C., Vrgym: A virtual testbed for physical and interactive ai (2019) ACM Turing Celebration Conference-China; Xie, X., Li, C., Zhang, C., Zhu, Y., Zhu, S.-C., Learning virtual grasp with failed demonstrations via Bayesian inverse reinforcement learning (2019) International Conference on Intelligent Robots and Systems (IROS); 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"Lanza D., Menicatti R., Sgorbissa A.","57219625601;57197737505;6602687901;","Abductive recognition of context-dependent utterances in human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341155","10975","10981",,,"10.1109/IROS45743.2020.9341155","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102399158&doi=10.1109%2fIROS45743.2020.9341155&partnerID=40&md5=accee494a008dd404cb36d289548b1ac","University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy","Lanza, D., University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy; Menicatti, R., University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy; Sgorbissa, A., University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy","Context-dependent meaning recognition in natural language utterances is one of the key problems of computational pragmatics. Abductive reasoning seems apt for modeling and understanding these phenomena. In fact, it presents observations through hypotheses, allowing us to understand subtexts and implied meanings without exact deductions. For this reason in this paper, we are going to explore abductive reasoning and context modeling in human-robot interaction. Rather than a radical inferential approach, we assumed a conventional approach towards context-depending meanings, i.e, they are conventionally encoded rather than inferred from the utterances. In order to address the problem, a case study is presented, analyzing whether such a system could manage correctly these linguistic phenomena. The results obtained confirm the validity of a conventional approach in context modeling and, on this basis, further models are proposed to work around the limitations of the case study. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Man machine systems; Abductive reasoning; Context dependent; Context modeling; Conventional approach; In contexts; Linguistic phenomena; Natural languages; Social robots",,,,,,,,,,,,,,,,"Krämer, N.C., Von Der Pütten, A., Eimler, S., Human-agent and human-robot interaction theory: Similarities to and differences from human-human interaction (2012) Human-Computer Interaction: The Agency Perspective, Ser. Studies in Computational Intelligence, M. Zacarias and J. V. de Oliveira, Eds. 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Vancouver, pp. 567-573. , https://www.aclweb.org/anthology/P17-2090, Canada: Association for Computational Linguistics, July; Xie, Z., Wang, S.I., Li, J., Lévy, D., Nie, A., Jurafsky, D., Ng, A.Y., (2019) Data Noising As Smoothing in Neural Network Language Models, , https://collaborate.princeton.edu/en/publications/data-noising-as-smoothing-in-neural-network-language-models, Jan; Yu, S., Yang, J., Liu, D., Li, R., Zhang, Y., Zhao, S., Hierarchical data augmentation and the application in text classification (2019) Ieee Access, 7, pp. 185476-185485. , conference Name: IEEE Access",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102399158
"Nascimento H., Mujica M., Benoussaad M.","57219534655;57208247021;34167551000;","Collision avoidance in human-robot interaction using kinect vision system combined with robot's model and data",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341248","10293","10298",,,"10.1109/IROS45743.2020.9341248","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102396790&doi=10.1109%2fIROS45743.2020.9341248&partnerID=40&md5=b8bffc55a6a8fc65a208455233670ac4","University of Pernambuco, Polytechnic School, Automation and Control Department50720-001, Brazil; University of Toulouse, LGP-ENIT, Tarbes, France","Nascimento, H., University of Pernambuco, Polytechnic School, Automation and Control Department50720-001, Brazil, University of Toulouse, LGP-ENIT, Tarbes, France; Mujica, M., University of Toulouse, LGP-ENIT, Tarbes, France; Benoussaad, M., University of Toulouse, LGP-ENIT, Tarbes, France","Human-Robot Interaction (HRI) is a largely ad-dressed subject today. Collision avoidance is one of main strategies that allow space sharing and interaction without contact between human and robot. It is thus usual to use a 3D depth camera sensor which may involves issues related to occluded robot in camera view. While several works overcame this issue by applying infinite depth principle or increasing the number of cameras, we developed in the current work a new and an original approach based on the combination of a 3D depth sensor (Microsoft® Kinect V2) and the proprioceptive robot position sensors. This method uses a principle of limited safety contour around the obstacle to dynamically estimate the robot-obstacle distance, and then generate the repulsive force that controls the robot. For validation, our approach is applied in real time to avoid collision between dynamical obstacles (humans or objects) and the end-effector of a real 7-dof Kuka LBR iiwa collaborative robot.Several strategies based on distancing and its combination with dodging were tested. Results have shown a reactive and efficient collision avoidance, by ensuring a minimum obstacle-robot distance (of ≈ 240mm), even when the robot is in an occluded zone in the Kinect camera view. © 2020 IEEE.",,"Agricultural robots; Cameras; Collision avoidance; Intelligent robots; Man machine systems; Depth sensors; Human robot Interaction (HRI); Kinect cameras; Obstacle distance; Repulsive forces; Robot positions; Space-sharing; Vision systems; Social robots",,,,,,,,,,,,,,,,"Tan, J.T.C., Duan, F., Zhang, Y., Watanabe, K., Kato, R., Arai, T., Human-robot collaboration in cellular manufacturing: Design and development (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Iros 2009, pp. 29-34; Kr¨uger, J., Lien, T.K., Verl, A., Cooperation of human and machines in assembly lines (2009) Cirp Annals-Manufacturing Technology, 58 (2), pp. 628-646; Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human . robot interaction (2008) Mechanism and Machine Theory, 43, pp. 253-270; Alami, R., Albu-Schaeffer, A., Bicchi, A., Bischoff, R., Chatila, R., De Luca, A., De Santis, A., Villani, L., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1-16. , Oct; Popov, D., Klimchik, A., Mavridis, N., Collision detection, localization &classification for industrial robots with joint torque sensors (2017) RO-MAN 2017-26th Ieee International Symposium on Robot and Human Interactive Communication, 2017, pp. 838-843. , May 2018-Janua; De Luca, A., Flacco, F., Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration (2012) Proceedings of the Ieee Ras and Embs International Conference on Biomedical Robotics and Biomechatronics, pp. 288-295; Flacco, F., Kr¨oger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) Proceedings-IEEE International Conference on Robotics and Automation, pp. 338-345; Schmidt, B., Wang, L., Depth camera based collision avoidance via active robot control (2014) Journal of Manufacturing Systems, 33 (4), pp. 711-718. , http://dx.doi.org/10.1016/j.jmsy.2014.04.004; Chen, J.-H., Song, K.-T., Collision-free motion planning for human-robot collaborative safety under cartesian constraint (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA), pp. 1-7; Safeea, M., Neto, P., Kuka sunrise toolbox: Interfacing collaborative robots with matlab (2019) Ieee Robotics and Automation Magazine, 26 (1), pp. 91-96; Flacco, F., Kroeger, T., De Luca, A., Khatib, O., A depth space approach for evaluating distance to objects: With application to human-robot collision avoidance (2015) Journal of Intelligent and Robotic Systems: Theory and Applications, 80, pp. 7-22; Wasenm¨uller, O., Stricker, D., Comparison of kinect v1 and v2 depth images in terms of accuracy and precision (2017) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 34-45. , LNCS 10117; (2015) Kuka Sunrise.Connectivity Fri 1.7. Augsburg, pp. 1-59. , Germany, 1st edition Group K.R; Gaz, C., De Luca, A., Payload estimation based on identified coefficients of robot dynamics-With an application to collision detection (2017) Ieee International Conference on Intelligent Robots and Systems, 2017, pp. 3033-3040. , Septe; Zhang, W., Ma, X., Cui, L., Chen, Q., 3 points calibration method of part coordinates for arc welding robot (2008) Intelligent Robotics and Applications, C. Xiong, Y. Huang, Y. Xiong, and H. Liu, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 216-224; Chen, S.Y., Kalman filter for robot vision: A survey (2012) Ieee Transac-tions on Industrial Electronics, 59 (11), pp. 4409-4420. , Nov; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) Proceedings. 1985 Ieee International Conference on Robotics and Automation, 2, pp. 500-505. , March; Albu-Sch¨affer, A., Haddadin, S., Ott, C., Stemmer, A., Wimb¨ock, T., Hirzinger, G., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Industrial Robot, 34 (5), pp. 376-385",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102396790
"Cong M., Ma H., Liu D., Zhang J.","16306181000;57219926451;57137089900;57217993373;","Design and human-robot interaction control of rehabilitation exoskeleton compliant hip joint [康复外骨骼柔性髋关节设计与人机交互控制]",2020,"Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)","48","10",,"38","43",,,"10.13245/j.hust.201007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096116258&doi=10.13245%2fj.hust.201007&partnerID=40&md5=419d82cd4c0d3ba7938d20aceca67ce3","School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China","Cong, M., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China; Ma, H., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China; Liu, D., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China; Zhang, J., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China","To solve the problem that the auxiliary force cannot be adjusted in real-time with the parameters of the lower limbs of the human body during the rehabilitation treatment of hip joint motion,a human-robot interaction control method based on friction compensation and fuzzy impedance control was proposed.The design of the bionic mechanism was based on the biological basis of the human hip joint,and to avoid the influence of the impedance parameters of the human lower limb on the output of the compliant hip joint,the dynamic modeling of the human-robot system was carried out.Based on the EKF (extended Kalman filter),the internal friction of the motor was compensated to reduce the friction influence on the motor output position.An identification algorithm was used to estimate the impedance parameters of the human lower limbs,a closed-loop human-robot interaction controller was constructed based on fuzzy impedance control,and the contact force was adjusted in real-time to achieve gait trajectory training.The experiment verified the rationality of the design of the rehabilitation exoskeleton compliant hip joint and the effectiveness of the human-robot interaction controller method. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.","Compliant hip joint; Friction compensation; Human-robot interaction control; Rehabilitation exoskeleton; Series elastic actuators","Bioinformatics; Controllers; Educational robots; Exoskeleton (Robotics); Extended Kalman filters; Friction; Fuzzy control; Fuzzy filters; Hip prostheses; Joints (anatomy); Machine design; Man machine systems; Motion control; EKF (extended Kalman filter); Friction compensation; Human lower limbs; Human-robot interaction control; Human-robot systems; Identification algorithms; Impedance control; Impedance parameters; Social robots",,,,,,,,,,,,,,,,", (5), p. 19. , 张雷雨,李剑锋,刘钧辉,等.上肢康复外骨骼的设计与人机相容性分析[J].机械工程学报,2018,54 -28; FARKHATDINOV, I, EBERT, J, VAN OORT, G, Assisting human balance in standing with a robotic exoskeleton[J] (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 414-421; (2017), 45 (10), p. 105. , 黄剑,余晓强,陶春静.基于无线穿戴式传感系统的智能步态检测研究[J].华中科技大学学报(自然科学版),, -110; ZHANG, T, TRAN, M, HUANG, H., Design and experimental verification of hip exoskeleton with balance capacities for walking assistance (2018) IEEE/ASME Trans- actions on Mechatronics, 23 (1), pp. 274-285. , [J]; CAPPELLO, L, XILOYANNIS, M, DINH, B K, Multistable series elastic actuators:design and control [J] (2019) Robotics and Autonomous Systems, 118, pp. 167-178; ZHAO, Y, PAINE, N, JORGENSEN, S J, Impedance control and performance measure of series elastic actuators (2018) IEEE Transactions on Industrial Electronics, 65 (3), pp. 2817-2827. , [J]; PRATT, G A, WILLISSON, P, BOLTON, C, Late motor processing in low-impedance robots:impedance control of series-elastic actuators,2004 (2004) Proc of American Control Conference, pp. 3245-3251. , [C] Boston:IEEE; VALLERY, H, VENEMAN, J, VAN ASSELDONK, E, Compliant actuation of rehabilitation robots [J] (2008) IEEE Robotics & Automation Magazine, 15 (3), pp. 60-69; WYETH, G., Demonstrating the safety and performance of a velocity sourced series elastic actuator:ICRA,2008 (2008) Proc of IEEE International Conference on Robotics and Automation, pp. 3642-3647. , [C] Pasadena:IEEE; OH, S, KONG, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 71-80. , [J]; YU, H, HUANG, S, CHEN, G, Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100. , [J]; , (12), p. 1943. , 韩亚丽,许有熊,高海涛,等.基于导纳控制的膝关节外骨骼摆动控制研究[J].自动化学报,2016,42 -1950; LU, Q, GANG, T, HAO, G, Compound optimal control of harmonic drive considering hysteresis characteristic (2019) Mechanical Sciences, 10 (2), pp. 383-391. , [J]; BOVI, G, RABUFFETTI, M, MAZZOLENI, P, A multiple-task gait analysis approach:kinematic,kinetic and emg reference data for healthy young and adult subjects[J] (2010) Gait & Posture, 33 (1), pp. 6-13; LEE, H, LEE, S, SEO, K, Training for walking efficiency with a wearable hip-assist robot in patients with stroke[J] (2019) Stroke, 50 (12), pp. 3545-3552",,,,"Huazhong University of Science and Technology",,,,,16714512,,,,"Chinese","Huazhong Ligong Daxue Xuebao",Article,"Final","",Scopus,2-s2.0-85096116258
"Akgun S.A., Ghafurian M., Crowley M., Dautenhahn K.","57209982972;57190254083;16318327500;7003305185;","Using Emotions to Complement Multi-Modal Human-Robot Interaction in Urban Search and Rescue Scenarios",2020,"ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction",,,,"575","584",,1,"10.1145/3382507.3418871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096649137&doi=10.1145%2f3382507.3418871&partnerID=40&md5=a95811358f628c14b47197db8cc66506","University of Waterloo, Systems Design Engineering, Waterloo, ON, Canada; University of Waterloo, Electrical and Computer Engineering, Waterloo, ON, Canada; University of Waterloo, Electrical and Computer Engineering and Systems Design Engineering, Waterloo, Canada","Akgun, S.A., University of Waterloo, Systems Design Engineering, Waterloo, ON, Canada; Ghafurian, M., University of Waterloo, Electrical and Computer Engineering, Waterloo, ON, Canada; Crowley, M., University of Waterloo, Electrical and Computer Engineering, Waterloo, ON, Canada; Dautenhahn, K., University of Waterloo, Electrical and Computer Engineering and Systems Design Engineering, Waterloo, Canada","An experiment is presented to investigate whether there is consensus in mapping emotions to messages/situations in urban search and rescue scenarios, where efficiency and effectiveness of interactions are key to success. We studied mappings between 10 specific messages, presented in two different communication styles, reflecting common situations that might happen during search and rescue missions, and the emotions exhibited by robots in those situations. The data was obtained through a Mechanical Turk study with 78 participants. Our findings support the feasibility of using emotions as an additional communication channel to improve multi-modal human-robot interaction for urban search and rescue robots, and suggests that these mappings are robust, i.e. are not affected by the robot's communication style. © 2020 ACM.","affective communication; emotions; human-robot interaction; multi-modal communication; search and rescue; urban search and rescue","Interactive computer systems; Man machine systems; Mapping; Communication styles; Mechanical turks; Multi-modal; Search and rescue; Urban search and rescue; Social robots",,,,,,,,,,,,,,,,"https://www.fema.gov/about-agency, [n.d.]. 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"Ma C., Tan Q., Xu C., Zhao J., Wang X., Sun J.","56941211200;36816552800;57215777029;57210474184;57219095264;56352307300;","Comparative Analysis on Few Typical Algorithms of Gesture Recognition for Human-robot Interaction",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,"3425047","","",,,"10.1145/3424978.3425047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094922400&doi=10.1145%2f3424978.3425047&partnerID=40&md5=be06b5b4dc6d74286d4e4391a95846c4","Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; School of Instrument Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing, China","Ma, C., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; Tan, Q., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; Xu, C., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; Zhao, J., School of Instrument Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing, China; Wang, X., School of Instrument Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing, China; Sun, J., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China","Human-robot interaction (HRI) is considered as one of the key techniques of space intelligence robots. Few typical features of complicated human actions need to be captured and understood accurately by intelligent robot to ensure free communication and interaction between both above in real-time, especially for identifying and tracking hand state. There are four approaches for gesture recognition, including algorithm based on Kinect V2 SDK, model-based particle swarm optimization algorithm (PSO), deep learning algorithm and gesture module based on Baidu AI platform. These have been selected to compare in the form of principle, calculating time, robustness, range, environmental adaptability and correct rate respectively. The comparative results have generalized that both the second and the third algorithm have better performance than other algorithms in the above aspects such as calculating efficiency, robustness, detecting range, external disturbance and correct ratio. Particularly, the second algorithm is not only suitable for close range, but also suitable for multi-view cases. However, the third algorithm can have better performance, but depends on precise network model and weights by introducing lots of positive and negative gesture samples. © 2020 ACM.","Deep learning; Gesture recognition; Human-robot interaction; Particle swarm optimization","Deep learning; Gesture recognition; Intelligent robots; Man machine systems; Particle swarm optimization (PSO); Social robots; Calculating efficiencies; Comparative analysis; Environmental adaptability; External disturbances; Free communications; Human robot Interaction (HRI); Intelligence robots; Particle swarm optimization algorithm; Learning algorithms",,,,,"030601, HTKJ2019KL502016; National Natural Science Foundation of China, NSFC: 51975044; Beijing Information Science and Technology University, BISTU","This research was supported, in part, by the National Natural Science Foundation of China (No. 51975044), the Science and Technology on Space Intelligent Control Laboratory (No. HTKJ2019KL502016), the China Advance Research for Manned Space Project (No. 030601) and the 2019 Practice Training Program of Beijing Information Science and Technology University.",,,,,,,,,,"Xuefeng, C., Research on robot control technology based on human posture recognition (2014) Wuhan University of Science and Technology, pp. 2-5; Zhihua, H., Dawei, T., Qijie, Z., A human-robot-interaction-based navigation system for mobile service robot (2009) Robot, 31 (3), pp. 248-250; Mehrabian, A., Nonverbal communication (1977) Transaction Publishers, America; Jiewang, C., Chunxia, G., Somatosensory information: Concept, characteristics and management (2014) Journal of Intelligence, 33 (11), pp. 150-151; Lei, L., Jinling, Z., A static gesture recognition method based on data glove (2015) Journal of Computer-Aided Design & Computer Graphics, 27 (12), pp. 2410-2418; Uddin, Z.M.Z., Thang, N.D., Human activity recognition using body joint-anglefeatures and hidden markov model (2011) Etri. Journal, 33 (4), pp. 569-579; Erol, A., Bebis, G., Nicolescu, M., Vision-based Hand Pose Estimation: A review (2007) Computer Vision and Image Understanding, 108 (1-2), pp. 52-73; Romero, J., Kjellstrm, H., Kragic, D., Monocular real-time 3d articulated hand pose estimation (2009) 9th IEEE-RAS International Conference on Humanoid Robots, pp. 87-92; Shotton, J., Fitzgibbon, A., Cook, M., Real-time human pose recognition in parts from single depth images (2011) IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2011, pp. 1297-1304; Oikonomidis, I., Kyriazis, N., Markerless and effificient 26-dof hand pose recovery (2010) 10th Asian Conference on Computer Vision, ACCV, 2010, pp. 744-757; De La Gorce, M., Paragios, N., Fleet, D.J., Model-based hand tracking with texture, shading and self-occlusions (2008) IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008, pp. 1-8; Kerdvibulvech, C., Human hand motion recognition using an extended particle filter[j] (2014) AMDO, pp. 71-80; Siriborvornratanakul, T., Enhancing user experiences of mobile-based augmented reality via spatial augmented reality: Designs and architectures of projector-camera devices (2018) Advances in Multimedia, pp. 81947261-819472617; Kerdvibulvech, C., A review of augmented reality-based human-computer interaction applications of gesture-based interaction (2019) International Conference on Human-Computer Interaction, Springer, Cham, pp. 233-242; Pan, W., Wei, G., A gesture recognition research and application based on kinect (2018) Computer and Digital Engineering, 46 (8), pp. 1660-1661; Antonis, A.A., Lourakis, M., Real-time tracking of multiple skincolored objects with a possibly moving camera (2004) Lecture Notes in Computer Science, pp. 368-379; Oikonomidis, I., Kyriazis, N., Full dof tracking of a hand interacting with an object by modeling occlusions and physical constraints (2011) IEEE International Conference on Computer Vision, ICCV 2011, 1, pp. 2088-2095; Kennedy, J., Eberhart, R., Particle swarm optimization (1995) IEEE International Conference on Neural Networks, 4, pp. 1942-1948; Hongling, Y., Shibin, X., Yuanbin, M., Hand gesture recognition based on convolutional neural network (2016) Computer Technology and Development, 28 (7), pp. 13-14; https://ai.baidu.com/, Baidu AI Homepage",,"Emrouznejad A.","Association for Science and Engineering (ASciE)","ICST","4th International Conference on Computer Science and Application Engineering, CSAE 2020","20 October 2020 through 22 October 2020",,163982,21531633,9781450377720,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85094922400
"Tanevska A., Rea F., Sandini G., Cañamero L., Sciutti A.","57194323352;54386065400;35280694500;6602322296;36957816700;","A Socially Adaptable Framework for Human-Robot Interaction",2020,"Frontiers in Robotics and AI","7",,"121","","",,7,"10.3389/frobt.2020.00121","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094968227&doi=10.3389%2ffrobt.2020.00121&partnerID=40&md5=9fc2fc40881948e41ac31ed10b0c7e0f","Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy; EECAiA Lab, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology (IIT), Genova, Italy","Tanevska, A., Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy, EECAiA Lab, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom, Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology (IIT), Genova, Italy; Rea, F., Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy; Sandini, G., Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy; Cañamero, L., EECAiA Lab, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Sciutti, A., Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology (IIT), Genova, Italy","In our everyday lives we regularly engage in complex, personalized, and adaptive interactions with our peers. To recreate the same kind of rich, human-like interactions, a social robot should be aware of our needs and affective states and continuously adapt its behavior to them. Our proposed solution is to have the robot learn how to select the behaviors that would maximize the pleasantness of the interaction for its peers. To make the robot autonomous in its decision making, this process could be guided by an internal motivation system. We wish to investigate how an adaptive robotic framework of this kind would function and personalize to different users. We also wish to explore whether the adaptability and personalization would bring any additional richness to the human-robot interaction (HRI), or whether it would instead bring uncertainty and unpredictability that would not be accepted by the robot's human peers. To this end, we designed a socially adaptive framework for the humanoid robot iCub. As a result, the robot perceives and reuses the affective and interactive signals from the person as input for the adaptation based on internal social motivation. We strive to investigate the value of the generated adaptation in our framework in the context of HRI. In particular, we compare how users will experience interaction with an adaptive versus a non-adaptive social robot. To address these questions, we propose a comparative interaction study with iCub whereby users act as the robot's caretaker, and iCub's social adaptation is guided by an internal comfort level that varies with the stimuli that iCub receives from its caretaker. We investigate and compare how iCub's internal dynamics would be perceived by people, both in a condition when iCub does not personalize its behavior to the person, and in a condition where it is instead adaptive. Finally, we establish the potential benefits that an adaptive framework could bring to the context of repeated interactions with a humanoid robot. © Copyright © 2020 Tanevska, Rea, Sandini, Cañamero and Sciutti.","affective interaction; emotion recognition; human-robot interaction; personalized HRI; social adaptability",,,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC; University of Hertfordshire, UH; Horizon 2020","This work has been supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.","The authors would like to thank those who participated in the study, as well as Matthew Lewis and Imran Khan from the University of Hertfordshire for their help and availability. Funding. This work has been supported by a Starting Grant from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.",,,,,,,,,"Adam, C., Johal, W., Pellier, D., Fiorino, H., Pesty, S., Social human-robot interaction: a new cognitive and affective interaction-oriented architecture, (2016) International Conference on Social Robotics, pp. 253-263. , Kansas City, Springer; Admoni, H., Scassellati, B., Data-driven model of nonverbal behavior for socially assistive human-robot interactions, (2014) Proceedings of the 16th International Conference on Multimodal Interaction, pp. 196-199. , Istanbul, ACM; Ahmad, M.I., Mubin, O., Shahid, S., Orlando, J., Robot's adaptive emotional feedback sustains children's social engagement and promotes their vocabulary learning: a long-term child-robot interaction study (2019) Adapt. 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Pers, 40, pp. 525-543; Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F., The iCub humanoid robot: an open platform for research in embodied cognition, (2008) 8th Workshop on Performance Metrics for Intelligent Systems, pp. 50-56. , Gaithersburg, MD, ACM; Paiva, A., Leite, I., Ribeiro, T., Emotion modeling for social robots, (2014) The Oxford Handbook of Affective Computing, pp. 296-308. , Calvo R., D'Mello S., Gratch J., Kaas A., (eds), Oxford, eds; Parmiggiani, A., Maggiali, M., Natale, L., Nori, F., Schmitz, A., Tsagarakis, N., The design of the iCub humanoid robot (2012) Int. J. Human. 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FCSE, Skopje, Macedonia; Tanevska, A., Rea, F., Sandini, G., Cañamero, L., Sciutti, A., Eager to learn vs. quick to complain? how a socially adaptive robot architecture performs with different robot personalities, (2019) IEEE SMC'19 Special Session on Adaptation and Personalization in Human-Robot Interaction, , Bari, in; Tanevska, A., Rea, F., Sandini, G., Cañamero, L., Sciutti, A., A cognitive architecture for socially adaptable robots, (2019) 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 195-200. , Oslo; Tanevska, A., Rea, F., Sandini, G., Sciutti, A., Are adults sufficiently emotionally expressive to engage in adaptive interaction with an affective robot?, (2018) 2018 Social Cognition in Humans and Robots, socSMCs-EUCognition Workshop. FET Proactive H2020 Project “Socializing Sensorimotor Contingencies, , a, -,.” Hamburg; Tanevska, A., Rea, F., Sandini, G., Sciutti, A., Designing an affective cognitive architecture for human-humanoid interaction, (2018) 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 253-254. , b, Chicago, IL, ACM; Terziev, V., Nichev, N., Factors affecting the process of social adaptation, (2017) Proc. ICARBSS, pp. 452-458. , Lankawi; Thórisson, K., Helgasson, H., Cognitive architectures and autonomy: a comparative review (2012) J. Artif. Gen. Intell, 3, pp. 1-30; Vaufreydaz, D., Johal, W., Combe, C., Starting engagement detection towards a companion robot using multimodal features (2016) Robot. Auton. Syst, 75, pp. 4-16; Vernon, D., (2014) Artificial Cognitive Systems: A Primer, , MIT Press; Vernon, D., Metta, G., Sandini, G., The iCub cognitive architecture: Interactive development in a humanoid robot, (2007) 2007 IEEE 6th International Conference on Development and Learning, pp. 122-127. , London; Wood, L.J., Zaraki, A., Walters, M.L., Novanda, O., Robins, B., Dautenhahn, K., The iterative development of the humanoid robot Kaspar: an assistive robot for children with autism, (2017) International Conference on Social Robotics, pp. 53-63. , Tsukuba, Springer; Yalcin, Z.N., DiPaola, S., A computational model of empathy for interactive agents (2018) Biol. Inspir. Cogn. Architect, 26, pp. 20-25","Tanevska, A.; Department of Robotics, Italy; email: ana.tanevska@iit.it
Tanevska, A.; EECAiA Lab, United Kingdom; email: ana.tanevska@iit.it
Tanevska, A.; Cognitive Architecture for Collaborative Technologies Unit, Italy; email: ana.tanevska@iit.it",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85094968227
"Xu C., Yu X., Wang Z., Ou L.","57221160189;15764009800;57220876392;13404303100;","Multi-View Human Pose Estimation in Human-Robot Interaction",2020,"IECON Proceedings (Industrial Electronics Conference)","2020-October",,"9255211","4769","4775",,,"10.1109/IECON43393.2020.9255211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097777591&doi=10.1109%2fIECON43393.2020.9255211&partnerID=40&md5=1700cbfcd2d0ed8d277054fc85d6e6d8","Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China","Xu, C., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China; Yu, X., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China; Wang, Z., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China; Ou, L., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China","This paper contributes a real-time human-robot interaction system based on multi-human poses capture. First, we propose an iterative method for multi-human 3D poses estimation from multi-view. There are three mainly steps: (1) all independent 2D poses under each view are obtained by using the pose detector; (2) 2D poses from different perspectives are associated based on a greedy algorithm; (3) 3D skeletons are generated with the 2D poses that have been correctly associated. In the process of each iteration, the poses with the highest correspondence are always paired priority to ensure the accuracy of the results. Due to the occlusion in each view, previous methods that rely on the appearance of the human body and wearable devices are not suitable for multi-human 3D poses estimation, especially in crowded scenes. The proposed method does not required any markers or devices, which has good robustness even under occlusion or crowded. In addition, we also designed three human-robot interaction modes, including action following, target specification and dynamic obstacle avoidance. For each mode, target generation and correction methods are present. Combining human body pose estimation and robot motion, design experiments to verify the smoothness of the robot control method for dynamic target tracking and the effectiveness in different interaction modes. © 2020 IEEE.","3D skeletons; human-robot interaction; iteration; multi-human; target","Industrial electronics; Iterative methods; Machine design; Man machine systems; Motion control; Motion tracking; Target tracking; Correction method; Design experiments; Dynamic obstacle avoidance; Greedy algorithms; Human pose estimations; Interaction modes; Target specifications; Wearable devices; Social robots",,,,,"2018YF-B1308400","VII. ACKNOWLEDGEMENTS This paper was supported by National Key Research and Development Plan Intelligent Robot Key Project (2018YF-B1308400).",,,,,,,,,,"Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.-P., Xu, W., Theobalt, C., Vnect: Real-time 3d human pose estimation with a single rgb camera (2017) ACM Transactions on Graphics (TOG), pp. 1-14; Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kipman, A., Efficient human pose estimation from single depth images (2012) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 2821-2840; Dong, J., Jiang, W., Huang, Q., Bao, H., Zhou, X., Fast and robust multi-person 3d pose estimation from multiple views (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7792-7801; Bridgeman, L., Volino, M., Guillemaut, J.-Y., Hilton, A., Multiperson 3d pose estimation and tracking in sports (2019) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); Zhang, Y., An, L., Yu, T., Li, X., Li, K., Liu, Y., (2020) 4d Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras; Ershadi-Nasab, S., Noury, E., Kasaei, S., Sanaei, E., Multiple human 3d pose estimation from multiview images (2018) Multimedia Tools and Applications, pp. 15573-15601; Joo, H., Simon, T., Li, X., Liu, H., Tan, L., Gui, L., Banerjee, S., Matthews, I., Panoptic studio: A massively multiview system for social interaction capture (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 190-204; He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask r-cnn (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969; Fang, H.-S., Xie, S., Tai, Y.-W., Lu, C., Rmpe: Regional multi-person pose estimation (2017) ICCV; Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P.V., Schiele, B., Deepcut: Joint subset partition and labeling for multi person pose estimation (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4929-4937; Newell, A., Huang, Z., Deng, J., Associative embedding: End-toend learning for joint detection and grouping (2017) Advances in Neural Information Processing Systems, pp. 2277-2287; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y., (2018) Openpose: Realtime Multi-person 2d Pose Estimation Using Part Affinity Fields; Rakprayoon, P., Ruchanurucks, M., Coundoul, A., Kinect-based obstacle detection for manipulator (2011) IEEE/SICE International Symposium on System Integration",,,"IEEE Industrial Electronics Society (IES);SPECS - Smart Grid + Power Electronics Consortium Singapore;The Institute of Electrical and Electronics Engineers (IEEE)","IEEE Computer Society","46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020","19 October 2020 through 21 October 2020",,165032,,9781728154145,IEPRE,,"English","IECON Proc",Conference Paper,"Final","",Scopus,2-s2.0-85097777591
"Kim S.K., Kirchner E.A., Schloßmüller L., Kirchner F.","42761618100;23397107100;57219669704;7006011550;","Errors in Human-Robot Interactions and Their Effects on Robot Learning",2020,"Frontiers in Robotics and AI","7",,"558531","","",,1,"10.3389/frobt.2020.558531","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094680107&doi=10.3389%2ffrobt.2020.558531&partnerID=40&md5=41dfcd8d53cc44083413ebd6624a172d","Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany; Research Group Robotics, University of Bremen, Bremen, Germany","Kim, S.K., Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany; Kirchner, E.A., Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany, Research Group Robotics, University of Bremen, Bremen, Germany; Schloßmüller, L., Research Group Robotics, University of Bremen, Bremen, Germany; Kirchner, F., Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany, Research Group Robotics, University of Bremen, Bremen, Germany","During human-robot interaction, errors will occur. Hence, understanding the effects of interaction errors and especially the effect of prior knowledge on robot learning performance is relevant to develop appropriate approaches for learning under natural interaction conditions, since future robots will continue to learn based on what they have already learned. In this study, we investigated interaction errors that occurred under two learning conditions, i.e., in the case that the robot learned without prior knowledge (cold-start learning) and in the case that the robot had prior knowledge (warm-start learning). In our human-robot interaction scenario, the robot learns to assign the correct action to a current human intention (gesture). Gestures were not predefined but the robot had to learn their meaning. We used a contextual-bandit approach to maximize the expected payoff by updating (a) the current human intention (gesture) and (b) the current human intrinsic feedback after each action selection of the robot. As an intrinsic evaluation of the robot behavior we used the error-related potential (ErrP) in the human electroencephalogram as reinforcement signal. Either gesture errors (human intentions) can be misinterpreted by incorrectly captured gestures or errors in the ErrP classification (human feedback) can occur. We investigated these two types of interaction errors and their effects on the learning process. Our results show that learning and its online adaptation was successful under both learning conditions (except for one subject in cold-start learning). Furthermore, warm-start learning achieved faster convergence, while cold-start learning was less affected by online changes in the current context. © Copyright © 2020 Kim, Kirchner, Schloßmüller and Kirchner.","error-related potentials (ErrPs); human-robot interaction (HRI); learning with prior knowledge; long-term learning; reinforcement learning; robotics",,,,,,"Bundesministerium für Wirtschaft und Technologie, BMWi; Deutsches Zentrum für Luft- und Raumfahrt, DLR: FKZ 50RA1701, FKZ 50RA1703; Bundesministerium für Wirtschaft und Energie, BMWi","Funding. This work was funded by the Federal Ministry of Economics and Energy (BMWi), Project Management Agency German Aerospace Center (DLR). Funding reference number: FKZ 50RA1701 and FKZ 50RA1703.","This work was funded by the Federal Ministry of Economics and Energy (BMWi), Project Management Agency German",,,,,,,,,"Agrawal, S., Goyal, N., Thompson sampling for contextual bandits with linear payoffs (2013) International Conference on Machine Learning (ICML), pp. 127-135. , Atlanta, GA; Ahn, M., Jun, S.C., Performance variation in motor imagery brain-computer interface: a brief review (2015) J. Neurosci. Methods, 243, pp. 103-110. , 25668430; Auer, P., Cesa-Bianchi, N., Fischer, P., Finite-time analysis of the multiarmed bandit problem (2002) Mach. Learn, 47, pp. 235-256; Bargsten, V., Ferandez, J.D.G., COMPI: development of a 6-DOF compliant robot arm for human-robot cooperation (2015) Proceedings of the 8th International Workshop on Human-Friendly Robotics (HFR), , Munich, in; Betzel, R.F., Bertolero, M.A., Gordon, E.M., Gratton, C., Dosenbach, N.U., Bassett, D.S., The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability (2019) Neuroimage, 202, p. 115990. , 31291606; Blankertz, B., Sanelli, C., Halder, S., Hammer, E., Kübler, A., Müller, K.-R., Predicting BCI performance to study BCI illiteracy (2009) BMC Neurosci, 10, p. P84; Cesa-Bianchi, N., Gentile, C., Zappella, G., A gang of bandits (2013) Advances in Neural Information Processing Systems (NIPS), pp. 737-745. , Burges C.J.C., Bottou L., Welling M., Ghahramani Z., Weinberger K.Q., (eds), Lake Tahoe, CA, NIPS Proceedings, eds; Chang, C.-C., Lin, C.-J., LIBSVM: A library for support vector machines (2011) ACM Trans. Intell. Syst. Techno, 27, pp. 1-27; Chapelle, O., Li, L., An empirical evaluation of thompson sampling (2011) Advances in Neural Information Processing Systems, pp. 2249-2257. , in, eds G. J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger (NIPS Proceedings; Chavarriaga, R., Sobolewski, A., Millán, J.D.R., Errare machinale est: the use of error-related potentials in brain-machine interfaces (2014) Front. Neurosci, 8, p. 208. , 25100937; Cortes, D., Adapting multi-armed bandits policies to contextual bandits scenarios (2018) arXiv[preprint].arXiv:1811.04383; Daniel, C., Viering, M., Metz, J., Kroemer, O., Peters, J., Active reward learning (2014) Proceedings of Robotics: Science and Systems, , Berkeley, CA, in; Ehrlich, S.K., Cheng, G., Human-agent co-adaptation using error-related potentials (2018) J. Neural Eng, 15, p. 066014. , 30204127; Ehrlich, S.K., Cheng, G., A computational model of human decision making and learning for assessment of co-adaptation in neuro-adaptive human-robot interaction (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 264-271. , a, Bari, IEEE; Ehrlich, S.K., Cheng, G., A feasibility study for validating robot actions using EEG-based error-related potentials (2019) Int. J. Soc. Robot, 11, pp. 271-283. , b; Falkenstein, M., Hoormann, J., Christ, S., Hohnsbein, J., ERP components on reaction errors and their functional significance: a tutorial (2000) Biol. Psychol, 51, pp. 87-107. , 10686361; Gentile, C., Li, S., Zappella, G., Online clustering of bandits (2014) International Conference on Machine Learning (ICML), pp. 757-765. , in; Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., Millán, J.D.R., Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control (2015) Sci. Rep, 5, p. 13893. , 26354145, (Anchorage, AK; Iturrate, I., Montesano, L., Minguez, J., Robot reinforcement learning using EEG-based reward signals (2010) IEEE International Conference of on robotics and automation (ICRA), pp. 4181-4184. , Anchorage; Jeunet, C., NKaoua, B., Subramanian, S., Hachet, M., Lotte, F., Predicting mental imagery-based BCI performance from personality, cognitive profile and neurophysiological patterns (2015) PLoS ONE, 10, p. e0143962. , 26625261; Kim, S.K., Kirchner, E.A., Classifier transferability in the detection of error related potentials from observation to interaction (2013) Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3360-3365. , Manchester; Kim, S.K., Kirchner, E.A., Handling few training data: classifier transfer between different types of error-related potentials (2016) IEEE Trans. Neural Syst. Rehabil. Eng, 24, pp. 320-332. , 26701866; Kim, S.K., Kirchner, E.A., Kirchner, F., Flexible online adaptation of learning strategy using EEG-based reinforcement signals in real-world robotic applications (2020) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), , Paris, in; Kim, S.K., Kirchner, E.A., Stefes, A., Kirchner, F., Intrinsic interactive reinforcement learning–using error-related potentials for real world human-robot interaction (2017) Sci. Rep, 7, p. 17562; Kirchner, E.A., Kim, S.K., Straube, S., Seeland, A., Wöhrle, H., Krell, M.M., On the applicability of brain reading for predictive human-machine interfaces in robotics (2013) PLoS ONE, 8, p. e81732. , 24358125; Krell, M.M., Straube, S., Seeland, A., Wöhrle, H., Teiwes, J., Metzen, J.H., pySPACE–a signal processing and classification environment in Python (2013) Front. Neuroinform, 7, p. 40. , 24399965; Langford, J., Zhang, T., The epoch-greedy algorithm for multi-armed bandits with side information (2008) Advances in Neural Information Processing Systems 20, pp. 817-824. , Platt J.C., Koller D., Singer Y., Roweis S.T., (eds), Vancouver, NIPS Proceedings, eds; Li, L., Chu, W., Langford, J., Schapire, R.E., A contextual-bandit approach to personalized news article recommendation (2010) Proceedings of the 19th International Conference on World Wide Web, pp. 661-670. , Raleigh, NC; Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update (2018) J. Neural Eng, 15, p. 031005. , 29488902; Ma, B.-Q., Li, H., Zheng, W.-L., Lu, B.-L., Reducing the subject variability of EEG signals with adversarial domain generalization (2019) International Conference on Neural Information Processing, pp. 30-42. , Vancouver; Morioka, H., Kanemura, A., Hirayama, J., Shikauchi, M., Ogawa, T., Ikeda, S., Learning a common dictionary for subject-transfer decoding with resting calibration (2015) Neuroimage, 111, pp. 167-178. , 25682943; Müller, K.-R., Krauledat, M., Dornhege, G., Curio, G., Blankertz, B., Machine learning techniques for brain-computer interfaces (2004) Biomed. Eng, 49, pp. 11-21; Parra, L., Spence, C., Gerson, A., Sajda, P., Response error correction–a demonstration of improved human-machine performance using real-time EEG monitoring (2003) IEEE Trans. Neural Syst. Rehabil. Eng, 11, pp. 173-177. , 12899266; Rivet, B., Souloumiac, A., Attina, V., Gibert, G., xDAWN algorithm to enhance evoked potentials: application to brain-computer interface (2009) IEEE Trans. Biomed. Eng, 56, pp. 2035-2043. , 19174332; Salazar-Gomez, A.F., DelPreto, J., Gil, S., Guenther, F.H., Rus, D., Correcting robot mistakes in real time using EEG signal (2017) Proceedings of IEEE International Conference on Robotics and Automation (ICRA); Seghier, M.L., Price, C.J., Interpreting and utilising intersubject variability in brain function (2018) Trends Cogn. Sci, 22, pp. 517-530. , 29609894; van Schie, H.T., Mars, R.B., Coles, M.G.H., Bekkering, H., Modulation of activity in medial frontal and motor cortices during error observation (2004) Nat. Neurosci, 7, pp. 549-554. , 15107858; Vidaurre, C., Blankertz, B., Towards a cure for BCI illiteracy (2010) Brain Topogr, 23, pp. 194-198. , 19946737; Wu, Q., Wang, H., Gu, Q., Wang, H., Contextual bandits in a collaborative environment (2016) Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 529-538. , Pisa; Zhang, W., Tan, C., Sun, F., Wu, H., Zhang, B., A review of EEG-based brain-computer interface systems design (2018) Brain Science Advances, 4, pp. 156-167. , https://journals.sagepub.com/doi/full/10.26599/BSA.2018.9050010, –, Available online at","Kim, S.K.; Robotics Innovation Center, Germany; email: su-kyoung.kim@dfki.de",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85094680107
"Manjunatha H., Jujjavarapu S.S., Esfahani E.T.","57192710332;57197767379;16424554200;","Classification of Motor Control Difficulty using EMG in Physical Human-Robot Interaction",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283016","2708","2713",,1,"10.1109/SMC42975.2020.9283016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098876762&doi=10.1109%2fSMC42975.2020.9283016&partnerID=40&md5=548f35b5f4426ec68093235c7e9d4a10","State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States","Manjunatha, H., State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States; Jujjavarapu, S.S., State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States; Esfahani, E.T., State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States","In physical human-robot interaction, a variable admittance/impedance controller is desired to adjust its controller parameters to enhance the collaboration by minimizing the human effort and maximizing the stability. In this paper, we propose a physiological monitoring approach based on electroencephalogram activities to classify the motor control difficulty and use that information for adjusting an admittance controller. We designed a physical human-robot interaction experiment where the human guides the robot's end-effector across four tasks with varying motor control difficulty. Each task is a combination of high/low damping and fine/gross motor control. During the experiments, we measure the muscle activation information in terms of surface electromyogram from eight channels. Two sets of features based on Riemann geometry and time domain (Hudgins' features) are extracted every 500 ms from the EMG data. A support vector machine classifier is trained on these features to estimate whether the existing admittance parameters are comfortable for the user else an increase/decrease of the damping is suggested. Riemann geometry-based features yielded higher accuracy (85.7%) than the Hudgins' features (69.1%) across 21 participants; however, the performance of these classifiers on the new sessions degraded to 63.1% and 54.5% respectively. To address this issue, we implemented a transfer learning approach using Riemannian features that improved the inter-session detection rate to 73.95%. © 2020 IEEE.",,"Controllers; Damping; End effectors; Man machine systems; Patient monitoring; Social robots; Support vector machines; Transfer learning; Admittance parameters; Controller parameter; Muscle activation; Physical human-robot interactions; Physiological monitoring; Robot's end effectors; Support vector machine classifiers; Surface electromyogram; Classification (of information)",,,,,,,,,,,,,,,,"Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2015) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 613-624; Gopinathan, S., Ötting, S.K., Steil, J.J., A user study on personalized stiffness control and task specificity in physical human-robot interaction (2017) Frontiers in Robotics and AI, 4, p. 58; Grafakos, S., Dimeas, F., Aspragathos, N., Variable admittance control in pHRI using EMG-based arm muscles co-activation (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC, pp. 001900-001905. , IEEE; Kosuge, K., Kazamura, N., Control of a robot handling an object in cooperation with a human (1997) Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI, pp. 142-147; Memar, A.H., Esfahani, E.T., Objective assessment of human workload in physical human-robot cooperation using brain monitoring (2019) ACM Transactions on Human-Robot Interaction, 9, pp. 1-21. , Dec; Peternel, L., Tsagarakis, N., Ajoudani, A., A human-robot comanipulation approach based on human sensorimotor information (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (7), pp. 811-822; Memar, A.H., Esfahani, E.T., EEG correlates of motor control difficulty in physical human-robot interaction: A frequency domain analysis (2018) 2018 IEEE Haptics Symposium (HAPTICS, pp. 229-234. , IEEE; Novak, D., Beyeler, B., Omlin, X., Riener, R., Workload estimation in physical human-robot interaction using physiological measurements (2014) Interacting with Computers, 27 (6), pp. 616-629; Manjunatha, H., Esfahani, E.T., (2020) Application of Reinforcement and Deep Learning Techniques in Brain-Machine Interfaces, pp. 1-14. , Cham: Springer International Publishing; Rodrigues, P.L.C., Jutten, C., Congedo, M., Riemannian procrustes analysis: Transfer learning for brain-computer interfaces (2019) IEEE Transactions on Biomedical Engineering, 66 (8), pp. 2390-2401; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for EMG signal classification (2012) Expert Systems with Applications, 39 (8), pp. 7420-7431; Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Hager, A.-G.M., Elsig, S., Giatsidis, G., Müller, H., Electromyography data for non-invasive naturally-controlled robotic hand prostheses (2014) Scientific Data, 1, p. 140053; Tuzel, O., Porikli, F., Meer, P., Pedestrian detection via classification on riemannian manifolds (2008) IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (10), pp. 1713-1727; Barachant, A., Bonnet, S., Congedo, M., Jutten, C., Multiclass brain-computer interface classification by riemannian geometry (2011) IEEE Transactions on Biomedical Engineering, 59 (4), pp. 920-928; Pan, L., Zhang, D., Jiang, N., Sheng, X., Zhu, X., Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns (2015) Journal of Neuroengineering and Rehabilitation, 12 (1), p. 110; Congedo, M., Barachant, A., Bhatia, R., Riemannian geometry for EEG-based brain-computer interfaces; A primer and a review (2017) Brain-Computer Interfaces, 4 (3), pp. 155-174; Moakher, M., A differential geometric approach to the geometric mean of symmetric positive-definite matrices (2005) SIAM Journal on Matrix Analysis and Applications, 26 (3), pp. 735-747; Cohen, M.X., (2014) Analyzing Neural Time Series Data: Theory and Practice., , MIT press","Esfahani, E.T.; State University of New York at Buffalo, United States; email: ehsanesf@buffalo.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","",Scopus,2-s2.0-85098876762
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","A Model-driven Approach for the Formal Analysis of Human-Robot Interaction Scenarios",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283204","1907","1914",,3,"10.1109/SMC42975.2020.9283204","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098874643&doi=10.1109%2fSMC42975.2020.9283204&partnerID=40&md5=5ca34bd2c27f9ca2a5e4161f181217ab","Politecnico di Milano, Milan, Italy; McMaster University, Hamilton, Canada","Lestingi, L., Politecnico di Milano, Milan, Italy; Askarpour, M., McMaster University, Hamilton, Canada; Bersani, M.M., Politecnico di Milano, Milan, Italy; Rossi, M., Politecnico di Milano, Milan, Italy","Robots are currently mostly found in industrial settings. In the future, a wider range of environments will benefit from their inclusion. This calls for the development of tools that allow professionals to set up dependable robotic applications in which people productively interact with robots aware of their needs. Given the co-existence of humans and robots, the precise analysis - e.g., through formal verification techniques - of properties related to aspects such as human needs and physiology is of paramount importance. In this paper, we present a formally-based, model-driven approach to design and verify scenarios involving human-robot interactions. Some of the features of our approach are tailored to the healthcare domain, from which our case studies are derived. In our approach, the designer specifies the main parameters of the mission to generate the model of the application, which includes mobile robots, the humans to be served, including some of their physiological features, and the decision-maker that orchestrates the execution. All components are modeled through hybrid automata to capture variables with complex dynamics. The model is verified through Statistical Model Checking (SMC), using the Uppaal tool, to determine the probability of success of the mission. The results are examined by the developer, who iteratively refines the design until the probability of success is satisfactory. © 2020 IEEE.","human-robot interaction; model-driven development; service robots; statistical model checking","Decision making; End effectors; Industrial robots; Iterative methods; Machine design; Man machine systems; Model checking; Physiological models; Physiology; Healthcare domains; Industrial settings; Model driven approach; Physiological features; Probability of success; Robotic applications; Statistical model checking; Verification techniques; Human robot interaction",,,,,,,,,,,,,,,,"(2010) Safety of Machinery-General Principles for Design-Risk Assessment and Risk Reduction, , ISO 12100, ISO; ISO 13849-1Safety of machinery-Safety-related parts of control systems-Part 1: General principles for design. ISO 2006; ISO 13482 Robots and robotic devices-Safety requirements for personal care robots. ISO, 2014; Jacobs, T., Virk, G.S., ISO 13482-The new safety standard for personal care robots (2014) Intl. Symp. on Robotics. VDE, pp. 1-6; Alur, R., Courcoubetis, C., Halbwachs, N., Henzinger, T.A., Ho, P.-H., Nicollin, X., Olivero, A., Yovine, S., The algorithmic analysis of hybrid systems (1995) Theoretical Computer Science, 138 (1), pp. 3-34; Agha, G., Palmskog, K., A survey of statistical model checking (2018) ACM Transactions on Modeling and Computer Simulation (TOMACS), 28 (1), pp. 1-39; Larsen, K.G., Pettersson, P., Yi, W., (1997) Uppaal in a Nutshell, 1 (1-2), pp. 134-152. , Springer-Verlag; David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B., Uppaal SMC tutorial (2015) STTT, 17 (4), pp. 397-415; (2020) HRI Toolchain, , https://github.com/LesLivia/hritoolchain; Yagoda, R.E., Coovert, M.D., How to work and play with robots: An approach to modeling human-robot interaction (2012) Computers in Human Behavior, 28 (1), pp. 60-68; Webster, M., Dixon, C., Fisher, M., Salem, M., Saunders, J., Koay, K.L., Dautenhahn, K., Formal verification of an autonomous personal robotic assistant (2014) AAAI Spring Symposium Series; Vicentini, F., Askarpour, M., Rossi, M.G., Mandrioli, D., Safety assessment of collaborative robotics through automated formal verification (2019) IEEE Transactions on Robotics; Amor, H.B., Neumann, G., Kamthe, S., Kroemer, O., Peters, J., Interaction primitives for human-robot cooperation tasks (2014) Intl. Conf. on Robotics and Automation (ICRA), pp. 2831-2837. , IEEE; Nikolaidis, S., Ramakrishnan, R., Gu, K., Shah, J., Efficient model learning from joint-action demonstrations for human-robot collaborative tasks (2015) Proc. of HRI, pp. 189-196. , IEEE; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence, 247, pp. 45-69; Araiza-Illan, D., Pipe, T., Eder, K., (2016) Model-based Testing, Using Belief-desire-intentions Agents, of Control Code for Robots in Collaborative Human-robot Interactions, , arXiv preprint arXiv:1603.00656; Arai, R., Schlingloff, H., Model-based performance prediction by statistical model checking an industrial case study of autonomous transport robots (2017) Concurrency, Specification and Programming; Foughali, M., Ingrand, F., Seceleanu, C., Statistical model checking of complex robotic systems (2019) Proc. of SPIN, pp. 114-134. , Springer; Herd, B., Miles, S., McBurney, P., Luck, M., Quantitative analysis of multiagent systems through statistical model checking (2015) Int. Workshop on Engineering Multi-Agent Systems, pp. 109-130. , Springer; Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F., Formal model of human erroneous behavior for safety analysis in collaborative robotics (2019) Robotics and Computer-Integrated Manufacturing, 57, pp. 465-476; Alur, R., Dill, D.L., A theory of timed automata (1994) Theoretical Computer Science, 126 (2), pp. 183-235; Jaber, M.Y., Givi, Z., Neumann, W.P., Incorporating human fatigue and recovery into the learning-forgetting process (2013) Applied Mathematical Modelling, 37 (12-13), pp. 7287-7299; Chettibi, T., Haddad, M., Lehtihet, H., Khalil, W., Suboptimal trajectory generation for industrial robots using trapezoidal velocity profiles (2006) IROS, pp. 729-735. , IEEE; Tremblay, O., Dessaint, L.-A., Dekkiche, A.-I., A generic battery model for the dynamic simulation of hybrid electric vehicles (2007) Vehicle Power and Propulsion Conference, pp. 284-289. , IEEE; Payne, P., Lopetegui, M., Yu, S., A review of clinical workflow studies and methods (2019) Cognitive Informatics, pp. 47-61. , Springer; Kaemarungsi, K., Krishnamurthy, P., Modeling of indoor positioning systems based on location fingerprinting (2004) Proc. of Infocom, 2, pp. 1012-1022. , IEEE; Dong, H., Ugaldey, I., El Saddik, A., Development of a fatiguetracking system for monitoring human body movement (2014) Int. Instrumentation and Measurement Tech. Conf., pp. 786-791. , IEEE; Hadley, M., A deterministic model of the free will phenomenon (2018) Journal of Consciousness Exploration & Research, 9 (1)",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098874643
"Lyu S., Cheah C.C., Yu X., Guo L.","57193015269;7007112857;51162010800;57309250600;","Human-robot Interaction Control by using a General Sensory Model",2020,"IEEE International Conference on Control and Automation, ICCA","2020-October",,"9264397","630","635",,,"10.1109/ICCA51439.2020.9264397","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098073124&doi=10.1109%2fICCA51439.2020.9264397&partnerID=40&md5=ec2117ee638c1f16432a1328b76e00b1","Hangzhou Innovation Institute of Beihang University, Hangzhou, 310052, China; Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore, 639798, Singapore; Beihang University, Beijing, 100191, China","Lyu, S., Hangzhou Innovation Institute of Beihang University, Hangzhou, 310052, China; Cheah, C.C., Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore, 639798, Singapore; Yu, X., Beihang University, Beijing, 100191, China; Guo, L., Beihang University, Beijing, 100191, China","In recent years, there has been an increasing interest in the study of human-robot interactions (HRI). In HRI tasks, the strengths of both human and robot can be utilized in task execution in a complimentary way. According to different scenarios of human-robot interaction, the interaction task requirements and the external sensory systems or configurations adopted are usually different. Majority of the existing works focus on developing the control methods for some specific applications or with specific sensors and few results has been presented to formulate different interaction task requirements and various sensory models in a general way. In this paper, a human-robot interaction task variable that is able to describe various interaction task requirements in a unified way is integrated with a general sensory model obtained from on an offline neural network based learning algorithm so that various external sensors can be directly used in the interaction control systems to provide various sensory information so as to enhance the perception capability. We present a robot controller by combining the human-robot interaction task variable and the general sensory model so as to achieve various human-robot interaction tasks based on various external sensors by simply adjusting the task parameters and training the system, without having to modify the sensory models or controller. Convergence analysis of the proposed offline neural network based learning algorithm is shown and experimental results are presented to illustrate the performance of the proposed method. © 2020 IEEE.",,"Controllers; Educational robots; Learning algorithms; Learning systems; Man machine systems; Neural networks; Sensory perception; Convergence analysis; External sensors; Human robot Interaction (HRI); Human-robot interaction control; Interaction controls; Offline neural networks; Perception capability; Sensory information; Social robots",,,,,,,,,,,,,,,,"Vukobratovic, M., Hristic, D., Stojiljkovic, Z., Development of active anthropomorphic exoskeletons (1974) Medical and Biological Engineering, 12 (1), pp. 66-80; Kazerooni, H., Human-robot interaction via the transfer of power and information signals (1990) IEEE Transactions on Systems, Man and Cybernetics, 20 (2), pp. 450-463; Kazerooni, H., Racine, J., Huang, L., Steger, R., On the control of the Berkeley lower extremity exoskeleton (bleex) (2005) IEEE International Conference on Robotics and Automation, pp. 4353-4360; Hogan, N., Impedance control: An approach to manipulation (1984) American Control Conference, pp. 304-313; Veneman, J.F., Kruidhof, R., Hekman, E.E., Ekkelenkamp, R., Van Asseldonk, E.H., Kooij Der, H.Van, Design and evaluation of the lopes exoskeleton robot for interactive gait rehabilitation (2007) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15 (3), pp. 379-386; Kiguchi, K., Hayashi, Y., An emg-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42 (4), pp. 1064-1071; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Transactions on Robotics, 31 (2), pp. 233-245; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1986) Autonomous Robot Vehicles, pp. 396-404. , Springer; Lacevic, B., Rocco, P., Zanchettin, A.M., Safety assessment and control of robotic manipulators using danger field (2013) IEEE Transactions on Robotics, 29 (5), pp. 1257-1270; Cowan, N.J., Weingarten, J.D., Koditschek, D.E., Visual servoing via navigation functions (2002) IEEE Transactions on Robotics and Automation, 18 (4), pp. 521-533; Lyu, S., Cheah, C.C., Intention based control for physical humanrobot interaction (2018) 2018 IEEE International Conference on Realtime Computing and Robotics (RCAR), pp. 1-6. , Aug; Lyu, S., Cheah, C.C., Human-robot interaction control based on a general energy shaping method (2019) IEEE Transactions on Control Systems Technology, pp. 1-16; Lewis, F.L., Liu, K., Yesildirek, A., Neural net robot controller with guaranteed tracking performance (1995) IEEE Transactions on Neural Networks, 6 (3), pp. 703-715; Lewis, F.L., Yesildirek, A., Liu, K., Multilayer neural-net robot controller with guaranteed tracking performance (1996) IEEE Transactions on Neural Networks, 7 (2), pp. 388-399; Cheng, L., Hou, Z.-G., Tan, M., Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model (2009) Automatica, 45 (10), pp. 2312-2318; Zhao, Y., Cheah, C.C., Neural network control of multifingered robot hands using visual feedback (2009) IEEE Transactions on Neural Networks, 20 (5), pp. 758-767; Lyu, S., Cheah, C.C., Vision based neural network control of robot manipulators with unknown sensory jacobian matrix (2018) 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1222-1227; Lyu, S., Cheah, C.C., Data-driven learning for robot control with unknown jacobian (2020) Automatica, 120, p. 109120; Cheah, C.C., Li, X., (2016) Task-space Sensory Feedback Control of Robot Manipulators, , Springer; Arimoto, S., (1996) Control Theory of Nonlinear Mechanical Systems, , Oxford University Press, Inc; Qu, Z., (1998) Robust Control of Nonlinear Uncertain Systems, , John Wiley & Sons, Inc; Lewis, F.L., Dawson, D.M., Abdallah, C.T., (2003) Robot Manipulator Control: Theory and Practice, , CRC Press",,,,"IEEE Computer Society","16th IEEE International Conference on Control and Automation, ICCA 2020","9 October 2020 through 11 October 2020",,165496,19483449,9781728190938,,,"English","IEEE Int. Conf. Control Autom., ICCA",Conference Paper,"Final","",Scopus,2-s2.0-85098073124
"Alimardani M., Hiraki K.","55836794100;7102170992;","Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction",2020,"Frontiers in Robotics and AI","7",,"125","","",,4,"10.3389/frobt.2020.00125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092925232&doi=10.3389%2ffrobt.2020.00125&partnerID=40&md5=9c1419b927d5b8660cacdde85cec975f","Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands; Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan","Alimardani, M., Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands; Hiraki, K., Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan","Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls. © Copyright © 2020 Alimardani and Hiraki.","brain-computer interface (BCI); cognitive workload estimation; EEG; emotion recognition; error detection; human-robot interaction (HRI); passive BCIs; social robots",,,,,,"200800000003; Japan Society for the Promotion of Science, KAKEN: 15F15046; Cabinet Office, Government of Japan; Council for Science, Technology and Innovation, CSTI","This research was supported by Grant-in-Aid for JSPS Research Fellow 15F15046, ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan) and CREST-JST (No. 200800000003).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. 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Neurorobot, 13, p. 7. , 30983986","Alimardani, M.; Department of Cognitive Science and Artificial Intelligence, Netherlands; email: m.alimardani@uvt.nl",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85092925232
"Singh H.","57220962261;","Systematic evaluation of human robot interaction in sustainable evironment",2020,"Journal of Green Engineering","10","10",,"9779","9788",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096579592&partnerID=40&md5=97dba1523d85fa4ccd29991e7a63e60c","Department of Mechatronics Engineering, Chandigarh University, Mohali, Gharuan, Punjab, India","Singh, H., Department of Mechatronics Engineering, Chandigarh University, Mohali, Gharuan, Punjab, India","The effects of the spread of Artificial Intelligence (AI) on salaries depend both on the way in which human labour has to be integrated in the production of products and on the elasticity of substitution. A rise in robotic labour may either have positive or negative effects on wages by means of a traditional mechanism involving labour, robots and common resources. Alternatively, the aggregate output relationship can be calculated directly by using Houthakker's 1950s method without measuring capital or other fixed variables. The method of Houthakker is based on the probability distribution of the variable factor productivity. The various distributions on the US productivity are indicated for cross-sectional results. The growth of AI technologies will cause salaries to decrease, other things equal to, if the Elasticity of Replacement between Human and ROBOTIC labour is higher than about 1.9. For the processing industry, even less human-robot replacement elasticity is likely to result in lower industrial workers ' salaries as robots proliferate. © 2020 Alpha Publishers. 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(2018) Nature, 557 (7706), pp. 490-491; Floridi, L., Taddeo, M., Don’t grant robots legal personhood (2018) Nature, 557 (7705), p. 309; Ossmy, O., Hoch, J. E., MacAlpine, P., Hasan, S., Stone, P., Adolph, K. E., Variety wins: Soccer-playing robots and infant walking (2018) Front. Neurorobot, 12. , MAY","Singh, H.; Department of Mechatronics Engineering, Mohali, India",,,"Alpha Publishers",,,,,19044720,,,,"English","J. Green Eng.",Article,"Final","",Scopus,2-s2.0-85096579592
"Aspragathos N., Moulianitis V., Koustoumpardis P.","7003803146;6506258012;6507626671;","Special Issue on Human-Robot Interaction (HRI)",2020,"Robotica","38","10",,"1715","1716",,1,"10.1017/S0263574720000946","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094175066&doi=10.1017%2fS0263574720000946&partnerID=40&md5=c485875cb87df880de7ea7d72aae4e86","Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece; Department of Product and Systems Design Engineering, University of the Aegean, Greece","Aspragathos, N., Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece; Moulianitis, V., Department of Product and Systems Design Engineering, University of the Aegean, Greece; Koustoumpardis, P., Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece","Human-robot interaction (HRI) is one of the most rapidly growing research fields in robotics and promising for the future of robotics technology. Despite the fact that numerous significant research results in HRI have been presented during the last years, there are still challenges in several critical topics of HRI, which could be summarized as: (i) collision and safety, (ii) virtual guides, (iii) cooperative manipulation, (iv) teleoperation and haptic interfaces, and (v) learning by observation or demonstration. In physical HRI research, the complementarity of the human and the robot capabilities is carefully considered for the advancement of their cooperation in a safe manner. New advanced control systems should be developed so the robot will acquire the ability to adapt easily to the human intentions and to the given task. The possible applications requiring co-manipulation are cooperative transportation of bulky and heavy objects, manufacturing processes such as assembly and surgery. ©",,,,,,,,,,,,,,,,,,"Gordić, Z., Jovanović, K., Collision detection on industrial robots in repetitive tasks using modified dynamic time warping (2020) Robotica, 38 (10), pp. 1717-1736; Sharkawy, A., Koustoumpardis, P., Aspragathos, N., Neural network design for manipulator collision detection based only on the joint position sensors (2020) Robotica, 38 (10), pp. 1737-1755; Huber, G., Wollherr, D., An online trajectory generator on SE (3) for human-robot collaboration (2020) Robotica, 38 (10), pp. 1756-1777; Restrepo, S.S., Raiola, G., Guerry, J., D'Elia, E., Lamy, X., Sidobre, D., Toward an intuitive and iterative 6D virtual guide programming framework for assisted human-robot comanipulation (2020) Robotica, 38 (10), pp. 1778-1806; Žlajpah, L., Petrič, T., Unified virtual guides framework for path tracking tasks (2020) Robotica, 38 (10), pp. 1807-1823; Papageorgiou, D., Dimeas, F., Kastritsi, T., Doulgeri, Z., Kinesthetic guidance utilizing DMP synchronization and assistive virtual fixtures for progressive automation (2020) Robotica, 38 (10), pp. 1824-1841; Alevizos, K., Bechlioulis, C., Kyriakopoulos, K., Physical human-robot cooperation based on robust motion intention estimation (2020) Robotica, 38 (10), pp. 1842-1866; Koskinopoulou, M., Maniadakis, M., Trahanias, P., Speed adaptation in learning from demonstration through latent space formulation (2020) Robotica, 38 (10), pp. 1867-1879; Torabi, A., Khadem, M., Zareinia, K., Sutherland, G.R., Tavakoli, M., Using a redundant user interface in teleoperated surgical systems for task performance enhancement (2020) Robotica, 38 (10), pp. 1880-1894","Koustoumpardis, P.; Robotics Group, Greece; email: koust@upatras.gr",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Editorial,"Final","All Open Access, Bronze",Scopus,2-s2.0-85094175066
"Hu Y., Benallegue M., Venture G., Yoshida E.","56576497600;36996384200;15623574000;35276803100;","Interact with Me: An Exploratory Study on Interaction Factors for Active Physical Human-Robot Interaction",2020,"IEEE Robotics and Automation Letters","5","4","9170777","6764","6771",,2,"10.1109/LRA.2020.3017475","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090841136&doi=10.1109%2fLRA.2020.3017475&partnerID=40&md5=e1cdd2f836e70bea105d70351d820297","CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan; Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan","Hu, Y., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan; Benallegue, M., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan; Venture, G., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan, Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan; Yoshida, E., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan","In future robotic applications in environments such as nursing houses, construction sites, private homes, etc, robots might need to take unpredicted physical actions according to the state of the users to overcome possible human errors. Referring to these actions as active physical human-robot interactions (active pHRI), in this letter, the goal is to verify the possibility of identifying measurable interaction factors that could be used in future active pHRI controllers, by exploring and analyzing the state of the users during active pHRI. We hypothesize that active physical robot actions can cause measurable alterations in the physical and physiological data of the users, and that these measurements could be interpreted with users' personality and perceptions. We design an experiment where the participant uses the robot to play a visual puzzle game, during which, the robot takes unanticipated physical actions. We collect physiological and physical data, as well as outcomes of two state-of-the-art questionnaires on the perceptions of robots, CH-33 and Godspeed Series Questionnaires (GSQ), and a pre-experiment personality questionnaire, to relate the collected data with the users' perceptions and personality. The experiment outcomes show that we can extract a few factors related to personality, perception, physiological, and physical measurements. Even though we could not draw very clear correlations, these outcomes give fundamental insights for the design of novel pHRI experiments. © 2016 IEEE.","human factors and human-in-the-loop; human-centered robotics; Physical human-robot interaction","Agricultural robots; Machine design; Man machine systems; Physiology; Surveys; Construction sites; Exploratory studies; Interaction factor; Physical human-robot interactions; Physical measurement; Physiological data; Robotic applications; Users' perception; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 17H01804, 18F18754","Manuscript received February 24, 2020; accepted July 9, 2020. Date of publication August 18, 2020; date of current version August 31, 2020. This letter was recommended for publication by Associate Editor M. J. Johnson and Editor A. M. Okamura upon evaluation of the Reviewers’ comments. This work was supported in part by the Grant-in-Aid for JSPS Research Fellow under Grant 18F18754 and in part by JSPS Grant-in-Aid for Research under Grant 17H01804. (Corresponding author: Yue Hu.) Yue Hu, Mehdi Benallegue, and Eiichi Yoshida are with the CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-0046, Japan (e-mail: yuehu@ieee.org; mehdi.benallegue@aist.go.jp; e.yoshida@aist.go.jp).",,,,,,,,,,"(2016) TS (Technical Specification) 15066: 2016: Robots and Robotic Devices-Collaborative Robots., , ISO, Geneva, Switzerland: International Organization for Standardization; Haddadin, S., On making robots understand safety: Embedding injury knowledge into control (2012) Int. J. Robot. Res., 31 (13), pp. 1578-1602; Bicchi, A., Peshkin, M.A., Colgate, J.E., Safety for physical human-robot interaction (2008) Springer Handbook of Robotics. 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Robots, 40 (3), pp. 561-580; Endo, Y., Tada, M., Mochimaru, M., Dhaiba: Development of virtual ergonomic assessment system with human models (2014) Proc. 3rd Int. Digit. Human Symp., p. 58; Benallegue, M., Escande, A., Miossec, S., Kheddar, A., Fast c1 proximity queries using support mapping of sphere-torus-patches bounding volumes (2009) Proc. IEEE Int. Conf. Robot. Autom., pp. 483-488. , May; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas) development and validation (2017) Proc. ACM/IEEE Int. Conf. Human-Robot Interact., pp. 254-262","Hu, Y.; CNRS-AIST JRL (Joint Robotics Laboratory), Japan; email: yuehu@ieee.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85090841136
"Luria M., Sheriff O., Boo M., Forlizzi J., Zoran A.","56895782800;57219436931;57219432618;6602845027;26657025200;","Destruction, catharsis, and emotional release in human-robot interaction",2020,"ACM Transactions on Human-Robot Interaction","9","4","22","","",,7,"10.1145/3385007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090123724&doi=10.1145%2f3385007&partnerID=40&md5=cc3a21969080c092bcb5e39dc955946d","Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA  15206, United States; Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel","Luria, M., Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA  15206, United States; Sheriff, O., Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel; Boo, M., Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel; Forlizzi, J., Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA  15206, United States; Zoran, A., Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel","The intersection between social, technical, and economic factors biases new product development to focus on utilitarian value. However, objects that serve alternative goals, behaviors and emotions have accompanied humankind for millennia. This article speculates about robotic objects for one non-utilitarian behavior and its implications: destruction. Robots and objects for destruction have a shared history of embodiment and heavily rely on their embodiment for interaction. Yet the topic of destruction is not very common in the field of human-robot interaction (HRI). Thus, we (1) present a survey of ethnographic investigations that show modes of HRI related to destruction, and (2) develop speculative concepts of interaction that demonstrate these ideas in HRI. By exemplifying a broad range of speculative uses of destruction in HRI and grounding it in literature, we hope this theoretical and conceptual article will bring a fresh perspective on alternative interactions with robots. © 2020 Owner/Author.","catharsis; creation; destruction; embodied interaction; HRI; human-object interaction; human-robot interaction; Interaction design; material culture; social robots; speculative design","Agricultural robots; Man machine systems; Product development; Economic factors; Human robot Interaction (HRI); New product development; Human robot interaction",,,,,,,,,,,,,,,,"Alvares, C.A., (1980) Homo Faber, , M. 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Acm, pp. 324-331; Zoran, A., Buechley, L., Hybrid reassemblage: An exploration of craft, digital fabrication and artifact uniqueness (2013) Leonardo, 46 (1), pp. 4-10. , 2013; Zoran, A., Paradiso, J.A., (2013) FreeD: A Freehand Digital Sculpting Tool, , In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2613-2616",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85090123724
"Bingol M.C., Aydogmus O.","57195613088;23088079600;","Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot",2020,"Engineering Applications of Artificial Intelligence","95",,"103903","","",,7,"10.1016/j.engappai.2020.103903","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089847163&doi=10.1016%2fj.engappai.2020.103903&partnerID=40&md5=411b89ad0e9cb959565949c0d11cff36","Fırat University, Technology Faculty, Mechatronics Engineering, Elazig, Turkey","Bingol, M.C., Fırat University, Technology Faculty, Mechatronics Engineering, Elazig, Turkey; Aydogmus, O., Fırat University, Technology Faculty, Mechatronics Engineering, Elazig, Turkey","People who are not experts in robotics can easily implement complex robotic applications by using human–robot interaction (HRI). HRI systems require many complex operations such as robot control, image processing, natural speech recognition, and decision making. In this study, interactive control with an industrial robot was performed by using speech recognition software in the Turkish language. The collected voice data were converted to text data by using automatic speech recognition module based on deep neural networks (DNN). The proposed DNN (p-DNN) was compared to classic classification algorithms. Converted text data was improved in another module to select the process to be applied. According to selected process, position data were defined using image processing. The determined position information was sent to the robot using a fuzzy controller. The developed HRI system was implemented on a KUKA KR Agilus KR6 R900 sixx robot manipulator. The word accuracy rate of the p-DNN model was measured as 90.37%. The developed image processing module and fuzzy controller worked with minimal errors. The contribution of this study is that an industrial robot is easily programming using this software by people who are not experts in robotics and know Turkish. © 2020 Elsevier Ltd","Deep neural networks; Human–robot interaction; Intelligent robots; Robotic vision; Turkish speech recognition","Behavioral research; Character recognition; Complex networks; Controllers; Decision making; Deep neural networks; Image processing; Industrial robots; Manipulators; Robot applications; Robot programming; Robotics; Social robots; Automatic speech recognition; Classification algorithm; Complex operations; Image Processing Module; Interactive control; Position information; Robotic applications; Speech recognition softwares; Speech recognition",,,,,,,,,,,,,,,,"Amrouche, A., Debyeche, M., Taleb-Ahmed, A., Michel Rouvaen, J., Yagoub, M.C.E., An efficient speech recognition system in adverse conditions using the nonparametric regression (2010) Eng. Appl. Artif. 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"Atashzar S.F., Huang H.-Y., Duca F.D., Burdet E., Farina D.","12040259900;57207276734;57218488959;7004358183;7004908025;","Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction",2020,"IEEE Robotics and Automation Letters","5","4","9144407","5953","5960",,2,"10.1109/LRA.2020.3010459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089308232&doi=10.1109%2fLRA.2020.3010459&partnerID=40&md5=7fd0601f146bc21e504a080e7c12ae41","Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States; Department of Bioengineering, Imperial College London, London, United Kingdom; Department of Electrical and Computer Engineering, Technical University of Munich, München, Germany","Atashzar, S.F., Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States; Huang, H.-Y., Department of Bioengineering, Imperial College London, London, United Kingdom; Duca, F.D., Department of Electrical and Computer Engineering, Technical University of Munich, München, Germany; Burdet, E., Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States; Farina, D., Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States","The capacity of the biomechanics of human limbs to absorb energy during physical human-robot interaction (pHRI) can play an imperative role in controlling the performance of human-centered robotics systems. Using the concept of 'excess of passivity,' we have recently designed passivity signature maps for elbow and wrist joints. We have also shown that this knowledge can be exploited and extrapolated during the interaction with a robotic system by transparency-maximized algorithms. A major application is in robotic rehabilitation systems and assistive technologies. Here, for the first time, the nonlinear energy capacitance of the hip joint and the affecting factors are decoded. This can be critical for maximizing the performance of wearable exoskeletons. Knowledge regarding energy absorption behavior can significantly help to reduce the conservatism of control algorithms. In this work, the energetic behavior is studied for three different hip angles, while perturbations were provided at three different interaction speeds. The results show that the increase in agonist and antagonist muscle contractions can consistently expand the margins of the passivity map. Additionally, by separating the effects of agonist and antagonist contractions, it was identified that the passivity margins have a correlation with the subject's posture during interaction with the robot and the correlation depends on the type of muscle contraction. A preliminary design of a stabilizer is also formulated that takes into account variable passivity behavior of the joint, in the energy domain, to enhance the performance while guaranteeing pHRI stability. © 2016 IEEE.","Biomechanics; human-robot interaction; rehabilitation robotics; robot control","Agricultural robots; Decoding; Exoskeleton (Robotics); Hip prostheses; Joints (anatomy); Man machine systems; Muscle; Absorption behaviors; Assistive technology; Human centered robotics; Muscle contractions; Physical human-robot interactions; Physical humanrobot interaction (phri); Preliminary design; Robotic rehabilitation; Social robots",,,,,"National Science Foundation, NSF: 2031594; Horizon 2020 Framework Programme, H2020: 779982, 871767; European Commission, EC","Manuscript received February 24, 2020; accepted June 27, 2020. Date of publication July 20, 2020; date of current version July 31, 2020. This letter was recommended for publication by Associate Editor M. Malvezzi and Editor Allison M. Okamura upon evaluation of the reviewers’ comments. This work was supported in part by the European Commission under Grant H2020-ICT 779982 EXTEND and Grant 871767 REHYB, and in part by the USA National Science Foundation Award 2031594. (Atashzar and Huang are equally contributed to this work.) (Corresponding author: S. Farokh Atashzar.) S. Farokh Atashzar is with the Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY 10003 USA, and also with the NYU Wireless, Brooklyn, NY 11201 USA (e-mail: f.atashzar@nyu.edu).",,,,,,,,,,"Atashzar, S.F., Shahbazi, M., Patel, R.V., Haptics-enabled interactive neurorehabilitation mechatronics: Classification, functionality, challenges and ongoing research (2019) Mechatronics, 57, pp. 1-19; Avraham, C., Nisky, I., The effect of tactile augmentation on manipulation and grip force control during force-field adaptation (2020) J. NeuroEng. 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Eng., 28 (5), pp. 1138-1145. , May","Atashzar, S.F.; Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, United States; email: f.atashzar@nyu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85089308232
"Miseikis J., Caroni P., Duchamp P., Gasser A., Marko R., Miseikiene N., Zwilling F., De Castelbajac C., Eicher L., Fruh M., Fruh H.","55786158600;57218305407;57218306941;57218306940;57218307008;57218306052;56682894200;57218305681;57218305578;57218304827;6603955424;","Lio-A Personal Robot Assistant for Human-Robot Interaction and Care Applications",2020,"IEEE Robotics and Automation Letters","5","4","9134708","5339","5346",,28,"10.1109/LRA.2020.3007462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088699394&doi=10.1109%2fLRA.2020.3007462&partnerID=40&md5=a20ec1e7e520a1fa79313d39b16f2df5","F AND P Robotics Ag, Zurich, CH-8005, Switzerland","Miseikis, J., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Caroni, P., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Duchamp, P., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Gasser, A., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Marko, R., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Miseikiene, N., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Zwilling, F., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; De Castelbajac, C., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Eicher, L., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Fruh, M., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Fruh, H., F AND P Robotics Ag, Zurich, CH-8005, Switzerland","Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities. © 2016 IEEE.","automation in life sciences: biotechnology; autonomous agents; human-centered robotics; pharmaceutical and health care; physical human-robot interaction; Service robotics","Agricultural robots; Charging (batteries); Deep learning; Health care; Intelligent robots; Man machine systems; Ultrasonic applications; Visual servoing; Autonomous navigation; Autonomous operations; Collision detection; Healthcare facility; Mechanical sensors; Mobile robot platforms; Privacy requirements; Safety requirements; Human robot interaction",,,,,,"Manuscript received February 24, 2020; accepted June 15, 2020. Date of publication July 7, 2020; date of current version July 15, 2020. This letter was recommended for publication by Associate Editor J. Son and Editor Y. Choi upon evaluation of the reviewers’ comments. This work was supported by F&P Robotics AG. (Corresponding author: Justinas Miseikis.) The authors are with the F&P Robotics AG, CH-8005 Zürich, Switzerland (e-mail: j.miseikis@gmail.com; pic@fp-robotics.com; pdu@fp-robotics.com; ags@fp-robotics.com; mra@fp-robotics.com; nmi@fp-robotics.com; frz@fp-robotics.com; cdc@fp-robotics.com; lei@fp-robotics.com; mfr@fp-robotics.com; hrf@fp-robotics.com).",,,,,,,,,,"Flandorfer, P., Population ageing and socially assistive robots for elderly persons: The importance of sociodemographic factors for user acceptance (2012) Int. J. Population Res., 2012, pp. 1-13; Oliver, D., Foot, C., Humphries, R., (2014) Making Our Health and Care Systems Fit for An Ageing Population, , London, UK: King's Fund London; Suisse, C., Die zukunft des pflegeheimmarkts (2015) Pfäffikon: Schellenberg Druck AG, pp. 1-44; (2003) WHO | Violence against Health Workers, , https://www.who.int/violence_injury_prevention/violence/workplace/en/, World Health Organisation, Accessed: May13, 2020; Heinz, R., Rolf, M., Rainer, U., Themenreport pflege 2030 (2012) Was Ist zu Erwarten-was Ist zu Tun? Bertelsmann Stiftung; Ikeuchi, T., Sakurai, R., Furuta, K., Kasahara, Y., Imamura, Y., Shinkai, S., Utilizing social robot to reduce workload of healthcare professionals in psychiatric hospital: A preliminary study (2018) Innov. Aging, 2, pp. 695-696; (2019) UBTECH Shows off Massive Upgrades to Walker Humanoid Robot-IEEE Spectrum, , https://spectrum.ieee.org/automaton/robotics/humanoids/ubtech-upgrades-walker-humanoidrobot, Accessed May19, 2020; Kittmann, R., Fröhlich, T., Schäfer, J., Reiser, U., Weißhardt, F., Haug, A., Let me introducemyself: I am care-O-bot 4, a Gentleman robot (2015) Proc. Mensch Comput., pp. 223-232; Pfeiffer, S., Angulo, C., Gesture learning and execution in a humanoid robot via dynamic movement primitives (2015) Pattern Recognit. Lett., 67, pp. 100-107; Ackerman, E., Moxi prototype from diligent robotics starts helping out in hospitals (2018) IEEE Spectr., , https://spectrum.ieee.org/automaton/robotics/industrial-robots/moxiprototype-from-diligent-robotics-starts-helping-out-in-hospitals; Goeldner, M., Herstatt, C., Tietze, F., The emergence of care robotics- A patent and publication analysis (2015) Technological Forecasting Social Change, 92, pp. 115-131; Bauer, J., Camera-based fall detection system with the service robot sanbot ELF (2018) Proc. Smart Public Building, pp. 15-28; Autonomous Robots Are Helping Kill Coronavirus in Hospitals-ieee Spectrum, , https://spectrum.ieee.org/automaton/robotics/medical-robots/autonomous-robots-are-helpingkill-coronavirus-in-hospitals, Accessed Jun. 26 2020; Früh, M., Gasser, A., Erfahrungen aus dem Einsatz von Pflegerobotern für Menschen im Alter (2018) Pflegeroboter. Berlin, pp. 37-62. , Germany: Springer; Stachniss, C., Frese, U., Grisetti, G., (2007) OpenSLAM, 2. , http://www.openslam.org; Fox, D., Burgard, W., Dellaert, F., Thrun, S., Monte Carlo localization: Efficient position estimation for mobile robots (1999) Proc. 16th Nat. Conf. Artif. Intell. 11th Conf. Innovative Appl. Artif. Intell., 1999, pp. 2-2; Fischinger, D., Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016) Robot. Auton. Syst., 75, pp. 60-78; Thermal Imaging for Detecting Elevated Body Temperature | FLIR Systems, , https://www.flir.com/discover/publicsafety/thermal-imaging-for-detecting-elevated-body-temperature/, Accessed May 5 2020; Niemueller, T., Lakemeyer, G., Ferrein, A., Incremental task-level reasoning in a competitive factory automation scenario (2013) Proc. AAAI Spring Symp.-Designing Intell. Robots: Reintegrating AI, pp. 1-6; Wielemaker, J., Schrijvers, T., Triska, M., Lager, T., SWI-prolog (2012) Theory Pract. Log. Program., 12 (1-2), pp. 67-96; (2016) Applying Human Factors and Usability Engineering to Medical Devices: Guidance for Industry and Food and Drug Administration Staff, , Washington, DC: FDA D. Administration Food et al; Wirth, L., Siebenmann, J., Gasser, A., Erfahrungen aus dem Einsatz von assistenzrobotern für Menschen im alter (2020) Mensch-Roboter-Kollaboration. Berlin, pp. 257-279. , Germany: Springer; Gasser, A., Steinemann, S., Opwis, K., (2017) A Qualitative View on Elders Interacting with A Health Care Robot with Bodily Movements, , M. S. thesis, Depart. Psychol. Univ. Basel for the degree of Master of Science in Psychology; Bendel, O., Co-Robots as care robots (2020) AAAI Spring Symp. Series; Fischinger, D., HOBBIT-The mutual care robot (2013) Proc. RSJ Int. Conf. Intell. Robots Syst., pp. 1-6; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: Reviewand future directions (2009) Int. J. Social Robot., 1 (4), pp. 319-330; Syrdal, D.S., Dautenhahn, K., Walters, M.L., Koay, K.L., Sharing spaces with robots in a home scenario-anthropomorphic attributions and their effect on proxemic expectations and evaluations in a live HRI trial (2008) Proc. AAAI Fall Symp. : AI Eldercare: New Solutions Old Problems, pp. 116-123; Broadbent, E., Attitudes towards health-care robots in a retirement village (2012) Australas. J. Ageing, 31 (2), pp. 115-120","Miseikis, J.; F AND P Robotics AgSwitzerland; email: j.miseikis@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85088699394
"He W., Xue C., Yu X., Li Z., Yang C.","57218621820;57203136027;57203040999;57218308705;57204881979;","Admittance-Based Controller Design for Physical Human-Robot Interaction in the Constrained Task Space",2020,"IEEE Transactions on Automation Science and Engineering","17","4","9075439","1937","1949",,61,"10.1109/TASE.2020.2983225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085713020&doi=10.1109%2fTASE.2020.2983225&partnerID=40&md5=b4421c532687a9f5ac3ba8332f00434a","Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China; Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Department of Automation, University of Science and Technology of China, Hefei, China; Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","He, W., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China, Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Xue, C., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China, Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Yu, X., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China, Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Li, Z., Department of Automation, University of Science and Technology of China, Hefei, China; Yang, C., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners-This work is motivated by the neglect of safety in existing controller design in physical human-robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human-robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: A differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: A learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector. © 2004-2012 IEEE.","Adaptive neural network (NN) control; admittance control; integral barrier Lyapunov function (IBLF); motion constraint; physical human-robot interaction (pHRI)","Accident prevention; Adaptive control systems; Closed loop systems; Controllers; End effectors; Industrial manipulators; Integral equations; Learning systems; Lyapunov functions; Machine design; Man machine systems; Manipulators; Medical robotics; Navigation; Patient rehabilitation; Radial basis function networks; Service industry; Trajectories; Adaptive neural networks; Coordinated operation; Physical human-robot interactions; Physical humanrobot interaction (phri); Radial basis function neural networks; Radial basis function NN; Reference trajectories; Semi-globally uniformly ultimately bounded; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61873298, 61933001; Ministry of Education of the People's Republic of China, MOE: 6141A02033339; University of Science and Technology Beijing, USTB; National Key Research and Development Program of China, NKRDPC: 2017YFB1302302, 2018YFC2001600","Prof. Yang has been awarded the EU Marie Curie International Incoming Fellowship, the U.K. EPSRC UKRI Innovation Fellowship, and the Best Paper Award of the IEEE TRANSACTIONS ON ROBOTICS, as well as over ten conference best paper awards.","Manuscript received December 6, 2019; revised January 31, 2020; accepted March 1, 2020. Date of publication April 21, 2020; date of current version October 6, 2020. This article was recommended for publication by Associate Editor Z. Xiong and Editor Q. Zhao upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61933001 and Grant 61873298, in part by the Joint Funds of Equipment Pre-Research and Ministry of Education of China under Grant 6141A02033339, in part by the Beijing Top Discipline for Artificial Intelligent Science and Engineering, USTB, and in part by the National Key Research and Development Program of China under Grant 2017YFB1302302 and Grant 2018YFC2001600. (Corresponding author: Wei He.) Wei He, Chengqian Xue, and Xinbo Yu are with the Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China, and also with the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (e-mail: weihe@ieee.org).",,,,,,,,,"Pehlivan, A.U., Losey, D.P., Omalley, M.K., Minimal assist-Asneeded controller for upper limb robotic rehabilitation (2016) Ieee Trans. Robot, 32 (1), pp. 113-124. , Feb; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) Ieee Trans. Robot, 31 (5), pp. 1089-1100. , Oct; He, W., Ge, W., Li, Y., Liu, Y.-J., Yang, C., Sun, C., Model identification and control design for a humanoid robot (2017) Ieee Trans. Syst., Man, Cybern. 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"Azagra P., Civera J., Murillo A.C.","56403248100;23007888000;10040627500;","Incremental Learning of Object Models from Natural Human-Robot Interactions",2020,"IEEE Transactions on Automation Science and Engineering","17","4","9062316","1883","1900",,1,"10.1109/TASE.2020.2980246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083453649&doi=10.1109%2fTASE.2020.2980246&partnerID=40&md5=de978be5a01c3daed9d20ade7de0f33f","Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain","Azagra, P., Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain; Civera, J., Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain; Murillo, A.C., Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain","In order to perform complex tasks in realistic human environments, robots need to be able to learn new concepts in the wild, incrementally, and through their interactions with humans. This article presents an end-To-end pipeline to learn object models incrementally during the human-robot interaction (HRI). The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models. Throughout the article, we highlight the main challenges associated with this problem, such as high degree of occlusion and clutter, domain change, low-resolution data, and interaction ambiguity. This article shows the benefits of using multiview approaches and combining visual and language features, and our experimental results outperform standard baselines. Note to Practitioners-This article was motivated by challenges in recognition tasks for dynamic and varying scenarios. Our approach learns to recognize new user interactions and objects. To do so, we use multimodal data from the user-robot interaction; visual data are used to learn the objects and speech is used to learn the label and help with the interaction-Type recognition. We use state-of-The-Art deep learning (DL) models to segment the user and the objects in the scene. Our algorithm for incremental learning is based on a classic incremental clustering approach. The pipeline we propose works with all sensors mounted on the robot, so it allows mobility on the system. This article uses the data recorded from a Baxter robot, which enables the use of the manipulation arms in future steps, but it would work with any robot that can have the same sensors mounted. The sensors used are two RGB-D cameras and a microphone. The pipeline currently has high computational requirements to run the two DL-based steps. We have tested it with a desktop computer, including a GTX 1060 and 32 GB of RAM. © 2004-2012 IEEE.","Human-robot interaction (HRI); incremental learning; multimodal data; object recognition","Learning systems; Man machine systems; Object detection; Pipelines; Visual languages; Human environment; Human robot Interaction (HRI); Incremental learning; Language features; Low resolution; Object learning; Robot sensors; Target object; Human robot interaction",,,,,"DGA T45 17R/FSE; Ministerio de Ciencia, Innovación y Universidades, MCIU; European Commission, EC; European Regional Development Fund, ERDF; Agencia Estatal de Investigación, AEI: PGC2018-096367-B-I00, PGC2018-098817-A-I00, RTC-2017-6421-7","Manuscript received October 17, 2019; revised December 19, 2019; accepted February 21, 2020. Date of publication April 9, 2020; date of current version October 6, 2020. This article was recommended for publication by Associate Editor Y. Pan and Editor Y. Ding upon evaluation of the reviewers’ comments. This work was supported in part by the European Union (CHISTERA IGLU), in part by the FEDER, Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under Grant PGC2018-096367-B-I00, Grant PGC2018-098817-A-I00, and Grant RTC-2017-6421-7, and in part by the Aragón Regional Government under Grant DGA T45 17R/FSE. (Corresponding author: Pablo Azagra.) The authors are with the Departamento de Informática e Ingeniería de Sistemas and Instituto de Investigación en Ingeniería de Aragón, University of Zaragoza, 50009 Zaragoza, Spain (e-mail: pazagra@ unizar.es).",,,,,,,,,,"Azagra, P., Golemo, F., Mollard, Y., Lopes, M., Civera, J., Murillo, A.C., A multimodal dataset for object model learning from natural human-robot interaction (2017) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. 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"Li H.-Y., Dharmawan A.G., Paranawithana I., Yang L., Tan U.-X.","57191343038;56595047500;57188987711;57214359135;22235331000;","A Control Scheme for Physical Human-Robot Interaction Coupled with an Environment of Unknown Stiffness",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","1",,"165","182",,5,"10.1007/s10846-020-01176-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081936407&doi=10.1007%2fs10846-020-01176-2&partnerID=40&md5=6c1b6120fb160518cd64761b0ad122f5","Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Zhejiang University/University of Illinois at Urbana-Champaign Institute, Hangzhou, China","Li, H.-Y., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Dharmawan, A.G., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Paranawithana, I., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Yang, L., Zhejiang University/University of Illinois at Urbana-Champaign Institute, Hangzhou, China; Tan, U.-X., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore","Variable admittance control is commonly used for a collaborative robot to achieve the compliant or accurate cooperation according to human’s intention. However, existing research seldom investigates such a human-robot collaboration coupled with an extra environment with unknown stiffness. If the end-effector that is guided by a human with various intended motion contacts the unknown environment, the interaction might become unstable. Additionally, current research for this physical human-robot-environment interaction use two force sensors to address the issue, and hence the cost of the robot is likely to increase and it reduces the flexibility to many applications. Therefore, in this paper, we address the issue of physical human-robot interaction coupled with an extra environment whose stiffness is unknown. To achieve this, the condition of robot admittance is rigorously proved in accordance with different human intended motion and environmental stiffness. Moreover, a variable admittance control scheme is proposed based on human intention, environmental force and environment stiffness using the combination of a force sensor and a force observer. Simulation and experiments are conducted to demonstrate the effectiveness of the proposed control scheme. © 2020, Springer Nature B.V.","Collaborative manipulator; Physical human-robot interaction (pHRI); Variable admittance control","Man machine systems; Manipulators; Physical addresses; Stiffness; Admittance control; Collaborative robots; Environmental forces; Human intentions; Human-robot collaboration; Physical human-robot interactions; Physical humanrobot interaction (phri); Unknown environments; Human robot interaction",,,,,,,,,,,,,,,,"Colgate, E., Wannasuphoprasit, W., Peshkin, M., Cobots: Robots for collaboration with human operators (1996) Proceedings of the ASME Dynamic Systems and Control Division, 58, pp. 433-439; Khatib, O., Yokoi, K., Brock, O., Chang, K., Casal, A., Robots in human environments: Basic autonomous capabilities (1999) Int. J. Robot. 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"Tausch A., Kluge A., Adolph L.","57219250178;23397227500;55116326700;","Psychological Effects of the Allocation Process in Human–Robot Interaction – A Model for Research on ad hoc Task Allocation",2020,"Frontiers in Psychology","11",,"564672","","",,2,"10.3389/fpsyg.2020.564672","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091947852&doi=10.3389%2ffpsyg.2020.564672&partnerID=40&md5=cb26f63369d5d97e85236591a7ed4ed8","Department Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany","Tausch, A., Department Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Kluge, A., Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany; Adolph, L., Department Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany","Task allocation is immensely important when it comes to designing human–robot interaction (HRI), but although it is the shaping part of the interaction, it is merely regarded as a process with its own effects on human thinking and behavior. This study aims at linking research from different fields like psychological theory, HRI and allocation optimization to create a new process model of ad hoc task allocation in human–robot interaction. It addresses the process characteristics and psychological outcomes of a real-time allocation process that integrates the worker. To achieve this, we structured the process into steps and identified relevant psychological constructs associated with them. The model is a first step toward ergonomic research on the self-organized allocation of tasks in HRI, but may also be an inspiration for practitioners designing HRI systems. To create successful work in HRI, designing the technology is an important foundation, but a participative, thought-out process for allotting tasks could be the key to adequate autonomy, work satisfaction and successful cooperation. © Copyright © 2020 Tausch, Kluge and Adolph.","decision perception; HRI; participation; satisfaction; self-organizing sociotechnical system; theory; work psychology",,,,,,,,,,,,,,,,,"Abdallah, S., Lesser, V.R., Modeling task allocation using a decision theoretic model (2005) Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 719-726. , Pechoucek M., Steiner D., Thompson S., (eds), New York, NY, ACM; Adams, S.J., Inequity in social exchange (1965) Adv. Exp. Soc. 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"Xue T., Wang W., Ma J., Liu W., Pan Z., Han M.","57205218623;35286155000;56210799000;57020033400;57197869982;57214111483;","Progress and Prospects of Multimodal Fusion Methods in Physical Human-Robot Interaction: A Review",2020,"IEEE Sensors Journal","20","18","9094690","10355","10370",,3,"10.1109/JSEN.2020.2995271","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090213507&doi=10.1109%2fJSEN.2020.2995271&partnerID=40&md5=9e2e748d8039af560899e8c731f5cdd0","Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China","Xue, T., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Wang, W., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Ma, J., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Liu, W., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Pan, Z., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Han, M., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China","Recent advances in physical Human-Robot Interaction (pHRI) have shown the potential and feasibility of robot systems for active and safe collaboration with humans. This induces high demand for real-time perception about the dynamic unity formed by robot, human, environment and operating objects. Single sensory modality is inadequate to give a robust estimate of the state of the unity. This paper provides a comprehensive review of state-of-the-art multi-modal fusion methods exploring their classifications, feasibilities and challenges based on the fact that pHRI is spatio-temporal sharing, multi-modal and multi-task. Finally, several directions of research are recommended based on the discussion of multi-modal fusion methods. © 2020 IEEE.","multi-modal; multi-modal fusion; multi-task; Physical human-robot interaction; spatio-temporal sharing","Man machine systems; Multi-modal fusion; Physical human-robot interactions; Physical humanrobot interaction (phri); Real time perception; Robust estimate; Sensory modality; Spatio temporal; State of the art; Social robots",,,,,"YG2017QN61; National Natural Science Foundation of China, NSFC: 51675329, 51675342, 51775332, GZ2016KF001; Ministry of Science and Technology of the People's Republic of China, MOST: 2018IM020100; National Key Scientific Instrument and Equipment Development Projects of China: 2016YFC0104104, 2016YFF0101602","Manuscript received March 25, 2020; revised May 9, 2020; accepted May 11, 2020. Date of publication May 18, 2020; date of current version August 14, 2020. This work was supported in part by the Special Program for Innovation Method of the Ministry of Science and Technology, China under Grant 2018IM020100, in part by the National Key Scientific Instruments and Equipment Development Program of China under Grant 2016YFF0101602 and Grant 2016YFC0104104, in part by the National Natural Science Foundation of China under Grant 51675329, Grant 51675342, and Grant 51775332, in part by the State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System GZ2016KF001, and in part by the Cross Fund for Medical and Engineering of Shanghai Jiao Tong University under Grant YG2017QN61. The associate editor coordinating the review of this article and approving it for publication was Prof. Dongsoo Har. (Corresponding author: Weiming Wang.) 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Hum.-Robot Interact., pp. 150-159. , Feb; Kato, S., Yamanobe, N., Venture, G., Ganesh, G., Humans can predict where their partner would make a handover (2018) Proc. Companion ACM/IEEE Int. Conf. Hum.-Robot Interact., pp. 145-146. , Mar; Peternel, L., Tsagarakis, N., Caldwell, D., Ajoudani, A., Robot adaptation to human physical fatigue in human-robot co-manipulation (2018) Auto. Robots, 42 (5), pp. 1011-1021. , Jun","Wang, W.; Institute of Knowledge Based Engineering, China; email: wangweiming@sjtu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Review,"Final","",Scopus,2-s2.0-85090213507
"Maric B., Polic M., Tabak T., Orsag M.","57191051623;57195968811;57209148972;36089400400;","Unsupervised optimization approach to in situ calibration of collaborative human-robot interaction tools",2020,"IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems","2020-September",,"9235229","255","262",,3,"10.1109/MFI49285.2020.9235229","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096141225&doi=10.1109%2fMFI49285.2020.9235229&partnerID=40&md5=16b4ef5cb8229036a94f87e7e4e7b2a0","LARICS Laboratory for Robotics and Intelligent Control Systems, University of Zagreb, Faculty of Elec-trical Engineering and Computing, Unska 3, Zagreb, 10000, Croatia; Special hospital for Orthopaedic Surgery AKROMION, Ljudevita Gaja odvojak 2, Krapinske Toplice, 49217, Croatia; University of Zagreb, School of Medicine, Salata 3, Zagreb, 10000, Croatia","Maric, B., LARICS Laboratory for Robotics and Intelligent Control Systems, University of Zagreb, Faculty of Elec-trical Engineering and Computing, Unska 3, Zagreb, 10000, Croatia; Polic, M., LARICS Laboratory for Robotics and Intelligent Control Systems, University of Zagreb, Faculty of Elec-trical Engineering and Computing, Unska 3, Zagreb, 10000, Croatia; Tabak, T., Special hospital for Orthopaedic Surgery AKROMION, Ljudevita Gaja odvojak 2, Krapinske Toplice, 49217, Croatia, University of Zagreb, School of Medicine, Salata 3, Zagreb, 10000, Croatia; Orsag, M., LARICS Laboratory for Robotics and Intelligent Control Systems, University of Zagreb, Faculty of Elec-trical Engineering and Computing, Unska 3, Zagreb, 10000, Croatia","In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement sys-tem, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time. © 2020 IEEE.",,"Calibration; End effectors; Industrial manipulators; Intelligent systems; Iterative methods; Linear programming; Manipulators; Motion capture; Robot applications; Robot programming; Iterative clustering; Motion capture system; Nelder-Mead simplex methods; Optimization approach; Outlier detection procedure; Programming by demon-stration; Simplex optimization; Working environment; Human robot interaction",,,,,"Hrvatska Zaklada za Znanost, HRZZ: UIP-2017-05-4042","This work has been supported by SPECULARIA [25] (Structured Ecological CULtivation with Autonomous Robots In Agriculture) founded by Croatian Science Foundation (HRZZ), under grant number UIP-2017-05-4042.",,,,,,,,,,"Calinon, S., (2009) Robot Programming by Demonstration., , Boca Raton, FL, USA: CRC Press, Inc., 1st ed; Sheng, W., Thobbi, A., Gu, Y., An integrated framework for human-robot collaborative manipulation (2015) Ieee Transactions on Cybernetics, 45, pp. 2030-2041. , Oct; Dang, H., Allen, P.K., Robot learning of everyday object manipulations via human demonstration (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1284-1289; Lin, H.-I., Lin, Y.-H., A novel teaching system for industrial robots (2014) Sensors, 14 (4), pp. 6012-6031; Horaud, R., Dornaika, F., Hand-eye calibration (1995) The International Journal of Robotics Research, 14 (3), p. 195210; Svaco, M., Sekoranja, B., Suligoj, F., Jerbic, B., Calibration of an industrial robot using a stereo vision system (2014) Procedia Engineering, 69, pp. 459-463; Jerbic, B., Nikolic, G., Chudy, D., Svaco, M., Sekoranja, B., Robotic application in neurosurgery using intelligent visual and haptic interaction (2015) International Journal of Simulation Modelling, 14 (1), pp. 71-84; Suligoj, F., Jerbic, B., Svaco, M., Sekoranja, B., Mihalinec, D., Vidakovic, J., Medical applicability of a low-cost industrial robot arm guided with an optical tracking system (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3785-3790; Suligoj, F., Svaco, M., Jerbic, B., Sekoranja, B., Vidakovic, J., Automated marker localization in the planning phase of robotic neurosurgery (2017) Ieee Access, 5, pp. 12265-12274; Bennett, D.J., Hollerbach, J.M., Autonomous calibration of single-loop closed kinematic chains formed by manipulators with passive endpoint constraints (1991) Ieee Transactions on Robotics and Automation, 7 (5), pp. 597-606; Elatta, A., Gen, L.P., Zhi, F.L., Daoyuan, Y., Fei, L., An overview of robot calibration (2004) Information Technology Journal, 3 (1), pp. 74-78; Dornaika, F., Horaud, R., Simultaneous robot-world and hand-eye calibration (1998) Ieee Transactions on Robotics and Automation, 14 (4), pp. 617-622; Shah, M., Eastman, R.D., Hong, T., An overview of robot-sensor calibration methods for evaluation of perception systems (2012) Proceedings of the Workshop on Performance Metrics for Intelligent Systems, pp. 15-20; Renders, J.-M., Rossignol, E., Becquet, M., Hanus, R., Kinematic calibration and geometrical parameter identification for robots (1991) Ieee Transactions on Robotics and Automation, 7 (6), pp. 721-732; Koide, K., Menegatti, E., General hand-eye calibration based on reprojection error minimization (2019) Ieee Robotics and Automation Letters, 4 (2), pp. 1021-1028; Li, J., Gao, H., Zhang, F., Zheng, M., A method of human motion feature extraction and recognition based on motion capture device (2020) Transactions on Edutainment Xvi, p. 184195. , Springer; Zhou, F., De La Torre, F., Hodgins, J.K., Aligned cluster analysis for temporal segmentation of human motion (2008) 2008 8th Ieee International Conference on Automatic Face & Gesture Recognition, pp. 1-7; Han, S., Liu, B., Wang, R., Ye, Y., Twigg, C.D., Kin, K., Online optical marker-based hand tracking with deep labels (2018) Acm Transactions on Graphics (TOG), 37 (4), pp. 1-10; Shepherd, J.D., Bunting, P., Dymond, J.R., Operational large-scale segmentation of imagery based on iterative elimination (2019) Remote Sensing, 11 (6), p. 658; Zhang, Y., Liang, W., He, H., Tan, J., Kinematic chain based multi-joint capturing using monocular visual-inertial measurements (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1818-1823; Maric, B., Mutka, A., Orsag, M., Collaborative human-robot framework for delicate sanding ofcomplex shape surfaces (2020) Ieee Robotics and Automation Letters, 5 (2), p. 28482855; Pham, H., Pham, Q., A new approach to time-optimal path parameterization based on reachability analysis (2018) Ieee Transactions on Robotics, 34, pp. 645-659. , June; (2020) Laboratory for Robotics and Intelligent Control Systems Youtube Channel., , https://www.youtube.com/playlist?list=PLC0C6uwoEQ8Y5bkEnlTvUrDs-TCTGuDOZ, Accessed: May; Scherzinger, S., Roennau, A., Dillmann, R., Forward dynamics compliance control (fdcc): A new approach to cartesian compliance for robotic manipulators (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4568-4575. , Sep; Specularia (Structured Ecological Cultivation with Autonomous Robots in Agriculture, , http://specularia.fer.hr/, Accessed: 2020-05-15",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020","14 September 2020 through 16 September 2020",,164537,,9781728164229,85PRA,,"English","IEEE Int Conf Multisensor Fusion Integr Intell Syst",Conference Paper,"Final","",Scopus,2-s2.0-85096141225
"Al G.A., Estrela P., Martinez-Hernandez U.","57202987162;14630335800;55316543200;","Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors",2020,"IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems","2020-September",,"9235264","330","335",,1,"10.1109/MFI49285.2020.9235264","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096092462&doi=10.1109%2fMFI49285.2020.9235264&partnerID=40&md5=984a2ac5645f1f89fce761fa6a4c64c6","University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom","Al, G.A., University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom; Estrela, P., University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom; Martinez-Hernandez, U., University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom","In this paper, we present a multimodal sensor interface that is capable of recognizing hand gestures for human-robot interaction. The proposed system is composed of an array of proximity and gesture sensors, which have been mounted on a 3D printed bracelet. The gesture sensors are employed for data collection from four hand gesture movements (up, down, left and right) performed by the human at a predefined distance from the sensorised bracelet. The hand gesture movements are classified using Artificial Neural Networks. The proposed approach is validated with experiments in offline and real-time modes performed systematically. First, in offline mode, the accuracy for recognition of the four hand gesture movements achieved a mean of 97.86%. Second, the trained model was used for classification in real-time and achieved a mean recognition accuracy of 97.7%. The output from the recognised hand gesture in real-time mode was used to control the movement of a Universal Robot (UR3) arm in the CoppeliaSim simulation environment. Overall, the results from the experiments show that using multimodal sensors, together with computational intelligence methods, have the potential for the development of intuitive and safe human-robot interaction. © 2020 IEEE.",,"3D printers; Gesture recognition; Intelligent computing; Man machine systems; Neural networks; Palmprint recognition; Computational intelligence methods; Data collection; Hand-gesture recognition; Multimodal sensor; Real time modes; Recognition accuracy; Safe human-robot interaction; Simulation environment; Social robots",,,,,"Royal Society: RGS/R2/192346","*This work was supported by The Republic of Turkey Ministry of National Education, and the Royal Society Research Grants for the ‘Touching and feeling the immersive world’ project (RGS/R2/192346).",,,,,,,,,,"Pedersen, M.R., Nalpantidis, L., Andersen, R.S., Schou, C., Bøgh, S., Krüger, V., Madsen, O., Robot skills for manufacturing: From concept to industrial deployment (2016) Robotics and Computer-Integrated Manufacturing, 37, pp. 282-291; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Petit, M., Lallée, S., Boucher, J.-D., Pointeau, G., Cheminade, P., Ognibene, D., Chinellato, E., Martinez-Hernandez, U., The coordinating role of language in real-Time multimodal learning of cooperative tasks (2012) Ieee Transactions on Autonomous Mental Development, 5 (1), pp. 3-17; Mokaram, S., Aitken, J.M., Martinez-Hernandez, U., Eimontaite, I., Cameron, D., Rolph, J., Gwilt, I., Law, J., A rosintegrated api for the kuka lbr IIwa collaborative robot (2017) IFACPapersOnLine, 50 (1), pp. 15859-15864; Nickel, K., Stiefelhagen, R., Visual recognition of pointing gestures for human-robot interaction (2007) Image and Vision Computing, 25 (12), pp. 1875-1884; Bugmann, G., Pires, J.N., Robot-by-voice: Experiments on commanding an industrial robot using the human voice (2005) Industrial Robot: An International Journal; Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H., Safe and efficient human-robot collaboration part i: Estimation of human arm motions (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA)., pp. 1993-1999; Barattini, P., Morand, C., Robertson, N.M., A proposed gesture set for the control of industrial collaborative robots (2012) 2012 Ieee ROMAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication., pp. 132-137; Pedersen, M.R., Herzog, D.L., Krüger, V., Intuitive skill-level programming of industrial handling tasks on a mobile manipulator (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 4523-4530; Elmezain, M., Al-Hamadi, A., Appenrodt, J., Michaelis, B., A hidden markov model-based continuous gesture recognition system for hand motion trajectory (2008) 2008 19th International Conference on Pattern Recognition., pp. 1-4; Maurtua, I., Fernández, I., Tellaeche, A., Kildal, J., Susperregi, L., Ibarguren, A., Sierra, B., Natural multimodal communication for human-robot collaboration (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1729881417716043; Srimal, P.A.S., Muthugala, M.V.J., Jayasekara, A.B.P., Deictic gesture enhanced fuzzy spatial relation grounding in natural language (2017) 2017 Ieee International Conference on Fuzzy Systems (FUZZIEEE)., pp. 1-8; Fani, S., Ciotti, S., Catalano, M.G., Grioli, G., Tognetti, A., Valenza, G., Ajoudani, A., Bianchi, M., Simplifying telerobotics: Wearability and teleimpedance improves human-robot interactions in teleoperation (2018) Ieee Robotics & Automation Magazine, 25 (1), pp. 77-88; Neto, P., Norberto Pires, J., Paulo Moreira, A., High-level programming and control for industrial robotics: Using a hand-held accelerometer-based input device for gesture and posture recognition (2010) Industrial Robot: An International Journal, 37 (2), pp. 137-147; Muxfeldt, A., Haus, J.N., Cheng, J., Kubus, D., Exploring tactile surface sensors as a gesture input device for intuitive robot programming (2016) 2016 Ieee 21st International Conference on Emerging Technologies and Factory Automation (ETFA)., pp. 1-4; Saenz, J., Fritsche, M., Tactile sensors for safety and interaction with the mobile manipulator valeri (2016) Proceedings of Isr 2016: 47st International Symposium on Robotics. Vde, pp. 1-7; Hughes, D., Lammie, J., Correll, N., A robotic skin for collision avoidance and affective touch recognition (2018) Ieee Robotics and Automation Letters, 3 (3), pp. 1386-1393; Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., Static hand gesture recognition with parallel cnns for space human-robot interaction (2017) International Conference on Intelligent Robotics and Applications., pp. 462-473. , Springer; Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, L.W., Lawrence, N., Prescott, T.J., An integrated probabilistic framework for robot perception, learning and memory (2016) 2016 Ieee International Conference on Robotics and Biomimetics (ROBIO)., pp. 1796-1801; Rani, P., Liu, C., Sarkar, N., Vanman, E., An empirical study of machine learning techniques for affect recognition in human-robot interaction (2006) Pattern Analysis and Applications, 9 (1), pp. 58-69",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020","14 September 2020 through 16 September 2020",,164537,,9781728164229,85PRA,,"English","IEEE Int Conf Multisensor Fusion Integr Intell Syst",Conference Paper,"Final","",Scopus,2-s2.0-85096092462
"Sun P., Sun Y.","8443251800;57212083313;","Tracking Control for a Rehabilitative Training Walker Considering Human-Robot Interaction",2020,"Journal of Physics: Conference Series","1621","1","012052","","",,,"10.1088/1742-6596/1621/1/012052","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091564955&doi=10.1088%2f1742-6596%2f1621%2f1%2f012052&partnerID=40&md5=e98a3f355ef53703d9f5c6b18017696f","School of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China","Sun, P., School of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China; Sun, Y., School of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China","A new extended state observer combined with a Backstepping tracking control method were proposed in this paper for the omnidirectional rehabilitative training walker. The aim of this study is to obtain a stable tracking controller, and it considered the interaction forces of the user and walker to ensure that the omnidirectional walker can track accurately specified trajectory. The extended state observer was designed to estimate the unknown interaction forces of user and the walker using output position and speed states. Further, a Backstepping controller based on the observed value was constructed to resist the adverse effects of interaction forces. The asymptotic stability of the trajectory tracking error, the velocity tracking error and the state observation error were guaranteed. Through programming, simulation results show the effectiveness of the proposed design method. © 2020 Published under licence by IOP Publishing Ltd.",,"Asymptotic stability; Backstepping; Controllers; Educational robots; Errors; Navigation; State estimation; Backstepping controller; Backstepping tracking controls; Extended state observer; Interaction forces; Specified trajectories; Tracking controller; Tracking controls; Trajectory tracking errors; Human robot interaction",,,,,,,,,,,,,,,,"Lim, D H, Kim, W S, Kim, H J, Development of real-time gait phase detection system for a lower extremity exoskeleton robot (2017) International Journal of Precision Engineering & Manufacturing, 18, pp. 681-687; Wu, Z, Li, F, Mi, W, Robust controller design for the cooperative control of lower extremity rehabilitation robot (2014) International Journal of Control & Automation, 7, pp. 313-328; Chen, J, Wang, J, Ouyang, S, Adaptive sliding mode control based on a filter for four-wheel omni-directional mobile robots (2014) Cybernetics & Information Technologies, 14, pp. 140-153; Wang, J, Chen, J, Ouyang, S, Trajectory tracking control based on adaptive neural dynamics for four-wheel drive omnidirectional mobile robots (2014) Engineering Review, 34, pp. 235-243; Liu, Q, Liu, D, Meng, W, Fuzzy sliding mode control of a multi-DOF parallel robot in rehabilitation environment (2014) International Journal of Humanoid Robotics, 11, pp. 1223-1251; Meng, W, Xie, S Q, Liu, Q, Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training (2017) IEEE/ASME Transactions on Mechatronics, 22, pp. 173-184; Yu, H, Huang, S, Chen, G, Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31, pp. 1089-1100; Zhang, J, Cheah, C C, Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Transactions on Robotics, 31, pp. 233-245; Sharifi, M, Behzadipour, S, Vossoughi, G, Nonlinear model reference adaptive impedance control for human-robot interactions (2014) Control Engineering Practice, 32, pp. 9-27; Grosu, V, Multi-axis force sensor for human-robot interaction sensing in a rehabilitation robotic device (2017) Sensors, 17, pp. 1294-1307; Ka, D M, Hong, C, Toan, T H, Minimizing human-exoskeleton interaction force by using global fast sliding mode control (2016) International Journal of Control Automation and Systems, 14, pp. 1064-1073; Sun, P, Wang, S, Redundant input safety tracking for omnidirectional rehabilitative training walker with control constraints (2017) Asian Journal of Control, 19, pp. 116-130; Sun, P, Wang, S Y, Guaranteed costnon-fragile tracking control for omnidirectional rehabilitative training walker with velocity constraints (2016) International Journal of Control, Automation and Systems, 14, pp. 1340-1351",,"Zhou X.Cheng C.",,"IOP Publishing Ltd","2020 International Conference on Computer Science and Communication Technology, ICCSCT 2020","25 July 2020 through 26 July 2020",,162957,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85091564955
"Perez J., Aguilar J., Dapena E.","57222704762;55434810600;6602301492;","MIHR: A Human-robot interaction model",2020,"IEEE Latin America Transactions","18","9","9381793","1521","1529",,,"10.1109/TLA.2020.9381793","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103431079&doi=10.1109%2fTLA.2020.9381793&partnerID=40&md5=e5db4bea40966cf9e36740b4b94fa9b0","LaSDAI, Universidad de Los Andes, Venezuela; CEMISID, Universidad de Los Andes, Venezuela","Perez, J., LaSDAI, Universidad de Los Andes, Venezuela; Aguilar, J., CEMISID, Universidad de Los Andes, Venezuela; Dapena, E., LaSDAI, Universidad de Los Andes, Venezuela","The interactions between people and social robots have generated positive effects on people of different ages in diverse contexts. A model of the interaction process is important to understand the person who interacts, in order to manage the internal dynamics of interaction in the social robots. There are models that describe the interaction between humans and machines, but they don't integrate the three most important elements to be considered during the interactions by the social robots: the modalities of human communication, the capacity of adaptation, and the expression of emotions. In this paper, a review of the interaction models between people and social robots is made, in order to analyze what has been done about these three important elements of the interaction. Then, it is proposed a Human-Robot Interaction Model (MIHR) based on a Human-Human Interaction Model (MIHH) previously developed, which integrates the main elements to be considered during the interactions by the social robots. © 2003-2012 IEEE.","Human-Robot Interaction; ROBOT; Social Robot; Social Robotics","Man machine systems; Different ages; Human communications; Human-human interactions; Interaction model; Interaction process; Internal dynamics; Social robots",,,,,,,,,,,,,,,,"Ribeiro, T., Pereira, A., Di Tullio, E., Paiva, A., The sera ecosystem: Socially expressive robotics architecture for autonomous human-robot interaction (2016) Proc. Symposium on Enabling Computing Research in Socially Intelligent HumanRobot Interaction, pp. 155-163; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of HRI, 6 (1), pp. 25-63; Lasota, P., Fong, T., Shah, J., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5 (4), pp. 261-349; Siregar, R., Syahputra, R., Mustar, M., Human-robot interaction based GUI (2017) Journal of Electrical Technology UMY, 1 (1), pp. 10-19; Briggs, G., Scheutz, M., The pragmatic social robot: Toward socially-sensitive utterance generation in human-robot interactions (2016) Proc. AAAI Fall Symposium Series: Artificial Intelligence for Human-Robot Interaction, pp. 12-15; Ghazali, A., Ham, J., Barakova, E., Markopoulos, P., Effects of robot facial characteristics and gender in persuasive human-robot interaction (2018) Frontiers in Robotics and AI, 5 (73), pp. 1-16; Correia, F., Mascarenhas, S., Prada, R., Melo, F., Paiva, A., Group-based emotions in teams of humans and robots (2018) Proc. International Conference on Human-Robot Interaction, pp. 261-269; Luna, K., Palacios, E., Marin, A., A fuzzy speed controller for a guide robot using an HRI approach (2018) IEEE Latin America Transactions, 16 (8), pp. 2102-2107; Andreasson, R., Alenljung, B., Billing, E., Lowe, R., Affective touch in human-robot interaction: Conveying emotion to the nao robot (2018) Journal of Social Robotics, 10 (4), pp. 473-491; Zafar, Z., Salazar, D., Al-Darraji, S., Urukalo, D., Berns, K., Rodić, A., Human robot interaction using dynamic hand gestures (2017) Proc. International Conference on Robotics, pp. 649-656; Graaf, M., An ethical evaluation of human-robot relationships (2016) Journal of Social Robotics, 8 (4), pp. 589-598; Pérez, J., Aguilar, J., Dapena, E., MIHH: Un Modelo de Interacción Humano-Humano (2018) Revista Venezolana de Computación, 5 (1), pp. 10-19; Scassellati, B., Improving social skills in children with ASD using a long-term, in-home social robot (2018) Science Robotics, 3 (21), pp. 1-9; Pérez, J., Castro, J., LRS1: un robot social de bajo costo para la asignatura Programación 1 (2018) Revista Colombiana de Tecnologías de Avanzada, 32 (2), pp. 68-77; Noguchi, Y., Kamide, H., Tanaka, F., Effects on the self-disclosure of elderly people by using a robot which intermediates remote communication (2018) Proc. 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 612-617; Dumas, B., Lalanne, D., Oviatt, S., Multimodal interfaces: A survey of principles, models and frameworks (2009) Human Machine Interaction, pp. 3-26. , Berlin; Vinciarelli, A., Open challenges in modelling, analysis and synthesis of human behaviour in human-human and human-machine interactions (2015) Cognitive Computation, 7 (4), pp. 397-413; Hussain, J., Model-based adaptive user interface based on context and user experience evaluation (2018) Journal on Multimodal User Interfaces, 12 (1), pp. 1-16; Martins, G., Santos, L., Dias, J., User-adaptive interaction in social robots: A survey focusing on non-physical interaction (2018) International Journal of Social Robotics, pp. 1-21; Hellström, T., Bensch, S., Understandable robots. Paladyn (2018) Journal of Behavioral Robotics, 9 (1), pp. 110-123; Gil, A., Aguilar, J., Puerto, E., Dapena, E., Emergence analysis in a multi-robot system (2018) Proc. XLIV Latin American Computer Conference (CLEI), pp. 662-669; Gil, A., Aguilar, J., Dapena, E., Rivas, R., A control Architecture for Robot Swarms (AMEB) (2019) Journal Cybernetics and Systems, 50 (3), pp. 300-322; Song, S., Yamada, S., Expressing emotions through color, sound, and vibration with an appearance-constrained social robot (2017) Proc. International Conference on Human-Robot Interaction, pp. 2-11; Brinck, I., Balkenius, C., Johansson, B., Making place for social norms in the design of human-robot interaction. what social robots can and should do (2016) Proc. Robophilosophy, pp. 303-312; Pérula, R., Bioinspired decision-making for a socially interactive robot (2018) Cognitive Systems Research, pp. 1-40; Herrera, D., Roberti, F., Toibero, M., Carelli, R., Human-Robot Interaction: Legible behavior rules in passing and crossing events (2016) IEEE Latin America Transactions, 14 (6), pp. 2644-2650; Tsiakas, K., Dagioglou, M., Karkaletsis, V., Makedon, F., Adaptive robot assisted therapy using interactive reinforcement learning (2016) Pro. International Conference on Social Robotics, pp. 11-21; Strohkorb, S., Establishing sustained, supportive human-robot relationships: Building blocks and open challenges (2016) Proc. Spring Symposium on Enabling Computing Research in Socially Intelligent Human-Robot Interaction, pp. 179-182; Araujo, A., Pérez, J., Rodriguez, W., Aplicación de una Red Neuronal Convolucional para el Reconocimiento de Personas a Través de la Voz (2018) Proc. Sexta Conferencia Nacional de Computación, Informática y Sistemas, pp. 77-81; Martins, G., Ferreira, P., Santos, L., Dias, J., A context-aware adaptability model for service robots (2016) Proc. Workshop on Autonomous Mobile Service Robots, pp. 1-7; Nichola, L., Erin, W., Heather, P., Effects of voice-adaptation and social dialogue on perceptions of a robotic learning companion (2016) Proc. 11th International Conf. On Human-Robot Interaction, pp. 255-262; Shen, Q., Dautenhahn, K., Saunders, J., Kose, H., Can real-time, adaptive human-robot motor coordination improve humans overall perception of a robot? (2015) IEEE Transactions on Autonomous Mental Development, 7 (1), pp. 52-64; Ros, R., Baroni, I., Demiris, Y., Adaptive human-robot interaction in sensorimotor task instruction: From human to robot dance tutors (2014) Robotics and Autonomous Systems, 62 (6), pp. 707-720; Aylett, R., Kappas, A., Castellano, G., Bull, S., Barendregt, W., Paiva, A., Hall, L., I know how that feels-an empathic robot tutor (2015) Proc. EChallenges e-2015 Conference, pp. 1-9; Karami, A., Adaptive artificial companions learning from users feedback (2016) Adaptive Behavior, 24 (2), pp. 69-86; Rukavina, S., Affective computing and the impact of gender and age (2016) PloS One, 11 (3), pp. 1-20; Gil, A., Aguilar, J., Rivas, R., Dapena, E., Behavioral module in a control architecture for multi-robots (2016) Revista Ingeniería Al Día, 2 (1), pp. 40-57; Morán, N., Pérez, J., Rodríguez, W., Reconocimiento de Estados Emocionales de Personas Mediante la Voz Utilizando Algoritmos de Aprendizaje de Máquina (2018) Revista Venezolana de Computación, 5 (2), pp. 41-52; Bandhakavi, A., Wiratunga, N., Massie, S., Padmanabhan, D., Lexicon generation for emotion detection from text (2017) IEEE Intelligent Systems, 32 (1), pp. 102-108; Campos, V., Jou, B., Giro, X., From pixels to sentiment: Fine-tuning cnns for visual sentiment prediction (2017) Image and Vision Computing, 65, pp. 15-22; Cordero, J., Aguilar, J., Aguilar, K., Chávez, D., Puerto, E., Recognition of the driving style in vehicle drivers (2020) Sensors, 20 (9); Aguilar, J., Aguilar, K., Cordero, J., Chávez, D., Puerto, E., Different intelligent approaches for modeling the style of car driving (2017) Proc. International Conference on Informatics in Control, Automation and Robotics, pp. 284-291; Lee, W., Norman, M., Affective computing as complex systems science (2016) Procedia Computer Science, 95, pp. 18-23; Izui, T., Milleville, I., Sakka, S., Venture, G., Expressing emotions using gait of humanoid robot (2015) Proc. International Symposium on Robot and Human Interactive Communication, pp. 241-245; Samani, H., The evaluation of affection in human-robot interaction (2016) Kybernetes, 45 (8), pp. 1257-1272; Pérez, J., Aguilar, J., Dapena, E., Carrillo, G., (2020) Q-learning Based Algorithm for Learning Children's Difficulties in Multiplication Tables, , submitted to publication; Thellman, S., Physical vs. Virtual Agent Embodiment and Effects on Social Interaction (2016) Intelligent Virtual Agents. Lecture Notes in Computer Science, , D. Traum et al. (eds), Cham: Springer; Dapena, E., Pérez, J., Rivas, R., Guijarro, A., Rostro Genérico para Máquinas que Interactúan con Personas (2016) Revista Científica UNET, 28 (2), pp. 121-130; Kitchenham, B., Linkman, S., Law, D., DESMET: A methodology for evaluating software engineering methods and tools (1997) Computing & Control Engineering Journal, 8 (3), pp. 120-126",,,,"IEEE Computer Society",,,,,15480992,,,,"Spanish","IEEE. Lat. Am. Trans.",Article,"Final","",Scopus,2-s2.0-85103431079
"Alimuradov A.K., Tychkov A.Y., Mezhina V.A., Fokina E.A., Zhurina A.E., Ageykin A.V., Gorbunov V.N., Reva E.K.","56595170300;36731632400;57219973215;57219972070;57219970957;57194621365;57202754023;57219971241;","Development of Natural Emotional Speech Database for Training Automatic Recognition Systems of Stressful Emotions in Human-Robot Interaction",2020,"Conference Proceedings - 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020",,,"9216940","11","16",,,"10.1109/DCNAIR50402.2020.9216940","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096359588&doi=10.1109%2fDCNAIR50402.2020.9216940&partnerID=40&md5=b77a498ca0c4cd09e7a9c8c4da7d5996","Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Department of Instrument Engineering, Information Technology and Electronics, Penza State University, Penza, Russian Federation; Department of Microbiology, Epidemiology and Infectious Diseases, Penza State University, Penza, Russian Federation; Technology Transfer Center, Penza State University, Penza, Russian Federation; Penza State University, Department of Journalism, Penza, Russian Federation","Alimuradov, A.K., Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Tychkov, A.Y., Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Mezhina, V.A., Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Fokina, E.A., Department of Instrument Engineering, Information Technology and Electronics, Penza State University, Penza, Russian Federation; Zhurina, A.E., Department of Instrument Engineering, Information Technology and Electronics, Penza State University, Penza, Russian Federation; Ageykin, A.V., Department of Microbiology, Epidemiology and Infectious Diseases, Penza State University, Penza, Russian Federation; Gorbunov, V.N., Technology Transfer Center, Penza State University, Penza, Russian Federation; Reva, E.K., Penza State University, Department of Journalism, Penza, Russian Federation","A comparative analysis of 18 popular speech databases used in the field of affective computing was carried out. A database containing unique audio recordings of Russian speech (words, phrases, and sentences), registered in natural experience of emotions by eyewitnesses and/or participants in adverse events under stress, has been developed. Voice recordings were taken from video materials of disasters and emergencies occurred on the territory of Russia in 2015-2019. Examination results of psychological and psychiatric reactions of 293 eyewitnesses and/or participants (119 men and 174 women, aged 15 to 65 years) to traumatic factors of adverse events are presented. In accordance with the development results, the following key conclusions have been made: In the future, the database will allow researchers to objectively assess the capabilities of existing methods and approaches for analyzing emotional speech, as well as to assess the need for new mathematical devices in speech signal processing; the voice database can be successfully used as training or test sample, necessary for effective training of systems for automatic recognition of stressful emotions in human-robot interaction. © 2020 IEEE.","affective computing; artificial intelligence systems; emotion recognition; emotional speech database; speech signal; stressful emotions","Agricultural robots; Audio recordings; Audio signal processing; Complex networks; Database systems; Educational robots; Man machine systems; Robotics; Speech; Speech analysis; Speech recognition; Affective Computing; Automatic recognition; Automatic recognition system; Comparative analysis; Development results; Emotional speech; Speech database; Speech signal processing; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 18-37-00256 mol_a; Council on grants of the President of the Russian Federation: MK-490.2020.8","The article is a continuation of previous studies by the authors [2-4], carried out under the Russian Foundation for Basic Research grant, and obtained as a result of 2018 contest of projects for fundamental scientific research conducted by young scientists (My First Grant), No. 18-37-00256 mol_a.","Council for Grants of the President of the Russian Federation, project No. MK-490.2020.8.",,,,,,,,,"Schuller, B.W., Batliner, A.M., (2013) Computational Paralinguistics: Emotion Affect and Personality in Speech and Language Processing, , New York: Wiley; Alimuradov, A.K., Fokina, E.A., Zhurina, A.E., A brief review of known emotional speech databases: Current state, problems and development prospects (2020) Proc. 12th Int. Sc. Conf. Methods, Means and Technologies of Obtaining and Processing Data (Shlyandin Readings 2020), pp. 292-294. , Penza, March 16- 18, 2020, E.A. Pecherskaya, Ed. Penza: PSU Publishing House; Alimuradov, A.K., Tychkov, A.Yu., Churakov, P.P., A method for noise-robust speech signal processing to assess human psychoemotional state, "" (2019) 2019 3rd School on Dynamics of Complex Networks and Their Application in Intellectual Robotics (DCNAIR), pp. 6-8. , Sept. 9-11, Innopolis, Russia; Alimuradov, A.K., Tychkov, A.Yu., Application of improved complete ensemble empirical mode decomposition with adaptive noise in speech signal processing 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1351-1355. , Jan. 27-30 2020, Saint Petersburg and Moscow, Russia; Engberg, I.S., Hansen, A.V., (1996) Documentation of the Danish Emotional Speech Database (DES). Internal AAU Report, , Center for Person Kommunikation, Department of Communication Technology, Institute of Electronic Systems, Aalborg University, Denmark; Hansen, L.J.H., Bou-Ghazale, S.E., Getting started with susas: A speech under simulated and actual stress database 5th European Conference on Speech Communication and Technology, pp. 1743-1746. , September 22-25 1997, Rhodes, Greece; Berlin Database of Emotional Speech. Official Page, , http://emodb.bilderbar.info/docu/, [Online] Available, accessed April 20 2020); Makarova, V., Petrushin, V.A., Ruslana: A database of russian emotional utterances 7th International Conference on Spoken Language Processing (ICSLP2002-INTERSPEECH 2002), pp. 2041-2044. , September 16-20, 2002, Denver, Colorado, USA; Hozjan, V., Kacic, Z., Moreno, A., Bonafonte, A., Nogueiras, A., Interface databases: Design and collection of a multilingual emotional speech database (2002) Third International Conference on Language Resources and Evaluation (LREC02), pp. 2024-2048. , May 29-31, Las Palmas, Canary Islands-Spain; Batliner, A., Steidl, S., Nöth, E., Releasing a thoroughly annotated and processed spontaneous emotional database: The fau aibo emotion corpus International Conference on Language Resources and Evaluation (LREC 2008), pp. 28-31. , 26 May-1 June, 2008, Marrakech, Morocco; Grimm, M., Kroschel, K., Narayanan, Sh., The vera am mittag german audio-visual emotional speech database 2008 IEEE International Conference on Multimedia and Expo, pp. 865-868. , June 23-26, 2008, Hannover, Germany; Gajšek, R., Štruc, V., Miheli, F., Podlesek, A., Komidar, L., Soan, G., Bajec, B., Multi-modal emotional database: Avid (2009) Informatica (Ljubljana, 33 (1), pp. 101-106; Database. Official Page, , http://kahlan.eps.surrey.ac.uk/savee, Surrey Audio-Visual Expressed Emotion (SAVEE), [Online] Available, (accessed April 20 2020); TURkish Emotional Speech Database (TURES). Official Page, , http://www.turesdatabase.com, [Online] Available, (accessed April 20 2020); Arimoto, Y., Kawatsu, H., Ohno, S., Iida, H., Naturalistic emotional speech collection paradigm with online game and its psychological and acoustical assessment (2012) Acoustical Science and Technology, 33 (6), pp. 359-369; Wang, K.X., Zhang, Q.L., Liao, S.Y., A database of elderly emotional speech (2013) 2013 International Symposium on Signal Processing, Biomedical Engineering and Informatics (SPBEI 2013), pp. 549-553. , December 16- 18, Hangzou, China; Moriyama, T., Mori, Sh., Ozawa, Sh., A synthesis method of emotional speech using subspace constraints in prosody (2009) Journal of Information Processing, 50 (3), pp. 1181-1191; Zhang, H., Lopez, G., Shuzo, M., Omiya, Y., Mistuyoshi, Sh., Warisawa, Sh., Yamada, I., A database of japanese emotional signals elicited by real experiences (2014) Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Book Series (LNICST), Pervasive Computing Paradigms for Mental Health (4th International Symposium, MindCare, pp. 3-12. , Tokyo, Japan, May 8-9, 2014). Eds. P. Cipresso, A. Matic, G. Lopez 2014 100. Cham: Springer; Livingstone, S.R., Russo, F.A., The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english (2018) PLoS ONE, 13 (5); Geethashree, A., Ravi, D.J., Kannada emotional speech database: Design, development and evaluation (2017) Lecture Notes in Networks and Systems Book Series (LNNS), Proceedings of International Conference on Cognition and Recognition, 14, pp. 135-143. , Eds. D.S. Guru, T. Vasudev, H.K. Chethan, Y.H. Sharath Kumar, Singapore: Springer; Sapinski, T., Kaminska, D., Pelikant, A., Avots, E., Ozcinar, C., Anbarjafari, Gh., Multimodal database of emotional speech, video and gestures (2018) International Journal of Information, Control and Computer Sciences, 12 (10), pp. 809-814; Lassalle, A., Pigat, D., OReilly, H., Berggen, S., Fridenson-Hayo, S., Tal, S., Elfström, S., Lundqvist, D., The eu-emotion voice database (2019) Behavior Research Methods, 51 (2), pp. 493-506; The Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters: Official Page, , https://www.mchs.gov.ru, [Online] Available, accessed April 20 2020)",,,,"Institute of Electrical and Electronics Engineers Inc.","4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020","7 September 2020 through 9 September 2020",,163883,,9781728172866,,,"English","Conf. Proc. - Sci. Sch. Dyn. Complex Networks Appl. Intellect. Robot., DCNAIR",Conference Paper,"Final","",Scopus,2-s2.0-85096359588
"Alimuradov A.K., Tychkov A.Y., Makarova N.A.","56595170300;36731632400;57219971857;","Study of Voiced Speech Using Empirical Mode Decomposition to Detect Stressful Emotions in Human-Robot Interaction",2020,"Conference Proceedings - 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020",,,"9216904","97","100",,,"10.1109/DCNAIR50402.2020.9216904","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096358447&doi=10.1109%2fDCNAIR50402.2020.9216904&partnerID=40&md5=4747f138a04ef30dac41b6f14edede10","Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; K.R. Evgrafov Regional Psychiatric Hospital, Twelfth Children's Department, Penza, Russian Federation","Alimuradov, A.K., Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Tychkov, A.Y., Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Makarova, N.A., K.R. Evgrafov Regional Psychiatric Hospital, Twelfth Children's Department, Penza, Russian Federation","A method for studying voiced speech by means of improved complete ensemble empirical mode decomposition with adaptive noise to detect stressful emotions in human-robot interaction is presented. The informative parameters of voiced speech fully reflect the violation in functioning of organs of human speech apparatus due to emotional excitation. The key feature of the research is to decompose a signal into empirical modes, to detect modes containing periodic information on the excitation source of the vocal tract, and to construct a composite signal reflecting information on glottal activity. The research results of voiced speech, being computation of the fundamental frequency of 100 multi-harmonic signals, are presented. The pitch frequency modulation of multi-harmonic signals in the frequency range of 0-2.5 Hz/ms in 0.5 Hz/ms increments simulated the irregularity in vibration of the vocal folds (30-40 % of the nominal value) arising from the emotional excitation of a person. The results obtained allow us to conclude that the presented method for the study of voiced speech, using the improved decomposition, can be successfully tested to detect stressful emotions in human interaction with robotic devices. © 2020 IEEE.","affective computing; emotional speech; empirical mode decomposition; speech signal processing; voiced speech","Agricultural robots; Complex networks; Man machine systems; Robotics; Signal processing; Speech; Speech analysis; Speech recognition; Empirical Mode Decomposition; Ensemble empirical mode decomposition; Excitation sources; Frequency ranges; Fundamental frequencies; Human interactions; Informative parameters; Research results; Human robot interaction",,,,,"Council on grants of the President of the Russian Federation: MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8; Council on grants of the President of the Russian Federation: MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8; Council on grants of the President of the Russian Federation: MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8","Council for Grants of the President of the Russian Federation, project No. MK-490.2020.8.","The research described in this article is devoted to the study of human voiced speech to detect stressful emotions in human interaction with robotic devices. Within the framework of the research, a unique technology for adaptive analysis of non-stationary signals, namely, the method of empirical mode decomposition (EMD) is used. The research was carried out with the financial support of the Council for Grants of the President of the Russian Federation, project No. MK-490.2020.8 “Development of methods and virtual means for adaptive noise-robust processing and detection of clinically significant parameters of medical electrical and acoustic signals in patients with borderline mental disorders.”","ACKNOWLEDGEMENTS The authors are grateful to the Council for Grants of the President of the Russian Federation for support in implementation of the project “Development of methods and virtual means for adaptive noise-robust processing and detection of clinically significant parameters of medical electrical and acoustic signals in patients with borderline mental disorders” No. MK-490.2020.8.",,,,,,,,"Schuller, B.W., Batliner, A.M., (2013) Computational Paralinguistics: Emotion Affect and Personality in Speech and Language Processing, , New York: Wiley; Emotion AI Hub R&D Neurodata Lab: Official Page, , https://neurodatalab.com, [Online] Available, (accessed April 20, 2020); Alimuradov, A.K., Fokina, E.A., Zhurina, A.E., A brief review of known emotional speech databases: Current state, problems and development prospects (2020) Proc. 12th Int. Sc. Conf. Methods, Means and Technologies of Obtaining and Processing Data (Shlyandin Readings 2020), pp. 292-294. , Penza, March 16-18, 2020, E.A. Pecherskaya, Ed. Penza: PSU Publishing House; Alimuradov, A.K., Tychkov, A.Y., Ageykin, A.V., An adaptive speech segmentation algorithm to determine temporal patterns of human psycho-emotional states (2020) Advances in Automation. Lecture Notes in Electrical Engineering, 641, pp. 879-890; Huang, N.E., Zheng, Sh., Steven, R.L., The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis (1998) Proc. Of the Royal Society of London, 454, pp. 903-995; Zhaohua, W., Huang, N.E., Ensemble empirical mode decomposition: A noise-assisted data analysis method (2009) Advances in Adaptive Data Analysis, 1 (1), pp. 1-41; Yeh, J.-R., Shieh, J.-S., Huang, N.E., Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method (2010) Advances in Adaptive Data Analysis, 2 (2), pp. 135-156; Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P., A complete ensemble empirical mode decomposition with adaptive noise 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (ICASSP-11), pp. 4144-4147. , May 22-27, 2011, Prague, Czech Republic; Colominasa, M.A., Schlotthauera, G., Torres, M.E., Improved complete ensemble emd: A suitable tool for biomedical signal processing (2014) Biomedical Signal Processing and Control, 14, pp. 19-29; Lee, H.S., Improvement of decomposing results of empirical mode decomposition and its variations for sea-level records analysis (2018) Journal of Coastal Research, Special: Proceedings of the 15th International Coastal Symposium, (85), pp. 526-530; Sharma, R., Prasanna, S.R.M., Characterizing glottal activity from speech using empirical mode decomposition 2015 21st National Conference on Communicati",,,,"Institute of Electrical and Electronics Engineers Inc.","4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020","7 September 2020 through 9 September 2020",,163883,,9781728172866,,,"English","Conf. Proc. - Sci. Sch. Dyn. Complex Networks Appl. Intellect. Robot., DCNAIR",Conference Paper,"Final","",Scopus,2-s2.0-85096358447
"Jin L., Zhang H., Shen Y., Ye C.","57208013044;57031171700;7404767808;7202201245;","Human-Robot Interaction for Assisted Object Grasping by a Wearable Robotic Object Manipulation Aid for the Blind",2020,"Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",,,"9209377","","",,2,"10.1109/ICHMS49158.2020.9209377","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093963317&doi=10.1109%2fICHMS49158.2020.9209377&partnerID=40&md5=778eb73d5ac7ca7da6ed6ab87697044d","Virginia Commonwealth Univ, Richmond, VA, United States; Univ of Nevada, Reno, Reno, NV, United States","Jin, L., Virginia Commonwealth Univ, Richmond, VA, United States; Zhang, H., Virginia Commonwealth Univ, Richmond, VA, United States; Shen, Y., Univ of Nevada, Reno, Reno, NV, United States; Ye, C., Virginia Commonwealth Univ, Richmond, VA, United States","This paper presents a new hand-worn device, called wearable robotic object manipulation aid (W-ROMA), that can help a visually impaired individual locate a target object and guide the hand to take a hold of it. W-ROMA may assist the individual for navigational (e.g., grasping a chair and moving it to make path) or non-navigational purpose (e.g, grasping a mug). The device consists of a sensing unit and a guiding unit. The sensing unit uses a Structure Core sensor, comprising of an RGB-D camera and an Inertial Measurement Unit (IMU), to detect the target object and estimate the device pose. Based on the object and pose information, the guiding unit computes the Desired Hand Movement (DHM) and convey it to the user by an electro-Tactile display to guide the hand to approach the object. A speech interface is developed and used as an additional way to convey the DHM and used for human-robot interaction. A new method, called Depth Enhanced Visual-Inertial Odometry (DVIO), is proposed for 6-DOF device pose estimation. It tightly couples the camera's depth and visual data with the IMU data in a graph optimization process to produce more accurate pose estimation than the existing state-of-The-Art approach. The estimated poses are used to 'stitched' the imaging and point cloud data captured at different points to form a larger view of the scene for object detection. They can also be used to position the individual for wayfinding. Experimental results demonstrate that the DVIO method outperforms the state-of-The-Art VIO approach in 6-DOF pose estimation. © 2020 IEEE.","6-DOF pose estimation; assisted object grasping/manipulation; electro-Tactile sensation; visual-inertial odometry","Cameras; Man machine systems; Object detection; Object recognition; Robotics; Wearable sensors; 6-dof pose estimations; Electro-tactile displays; Inertial measurement unit; Object manipulation; Point cloud data; Speech interface; State-of-the-art approach; Visually impaired; Human robot interaction",,,,,"National Institutes of Health, NIH: R01EY026275; National Eye Institute, NEI","This work was supported in part by the National Eye Institute (NEI) of the National Institute of Health under Award R01EY026275. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. 978-1-7281-5871-6/20/$31.00 ©2020 IEEE",,,,,,,,,,"Bourne, R.R., Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: A systematic review and meta-Analysis (2017) The Lancet Global Health, 5 (9), pp. e888-e897; Agin, G.J., Real Time Control of a Robot with a Mobile Camera (1979) Technical Note, 179. , SRI International, Feb; Hutchinson, S.A., Hager, G.D., Corke, P.I., A tutorial on visual servo control (1996) IEEE Transaction on Robotics and Automation, 12 (5), pp. 651-670; Bigham, J.P., Jayant, C., Miller, A., White, B., Yeh, T., VizWiz:: LocateIt-enabling blind people to locate objects in their environment (2010) Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern RecognitionWorkshops; http://www.looktel.com/recognizer, LookTel Recognizer; Matusiak, K., Skulimowski, P., Strumiłło, P., Object recognition in a mobile phone application for visually impaired users (2013) Proc. International Conference on Human System Interactions, pp. 479-484; Nanayakkara, S., Shilkrot, R., Maes, P., EyeRing: A Finger-worn Assistant (2012) Proc. ACM SIGCHI Conference on Human Factors in Computing, pp. 1961-1966; Horvath, S., Galeotti, J., Wu, B., Klatzky, R., Siegel, M., Stetten, G., FingerSight: Fingertip Haptic Sensing of the Visual Environment (2014) IEEE Journal of Translational Engineering in Health and Medicine, 2; Schauerte, B., Martinez, M., Constantinescu, A., An Assistive Vision System for the Blind that Helps Find Lost Things (2012) Proceedings of the 13th International Conference on Computers Helping People with Special Needs, 2, pp. 566-572; Thakoor, K., Mante, N., Zhang, C., Siagian, C., Weiland, J., Itti, L., Medioni, G., A system for assisting the visually impaired in localization and grasp of desired objects (2014) Proceedings of European Conference on Computer Vision, pp. 643-657; Lowe, D.G., Distinctive image features from scale-invariant keypoints (2004) International Journal of Computer Vision, 60 (2), pp. 91-110; Liu, X., Zhang, H., Jin, L., Ye, C., A wearable robotic object manipulation aid for the visually impaired Proc IEEE International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics, 2018, pp. 5-9; Tensorflow Lite Examples, , https://www.tensorflow.org/lite/examples, Accessed on 12/03/2019; Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Guadarrama, S., Speed/accuracy trade-offs for modern convolutional object detectors Proc IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7310-7311; Lin, T.-Y., Microsoft coco: Common objects in context (2014) Proc. European Conference on Computer Vision, pp. 740-755; Lucas Bruce, D., Kanade, T., An iterative image registration technique with an application to stereo vision (1981) Proceedings of International Joint Conferences on Artificial Intelligence, pp. 674-679; Structure core-specs data Accessed on 12/03/2019, , https://structure.io/structure-core/specs; Davidson, G., Google Pixel: The Complete Beginner?s Guide. Van Helostein, 2017; Solomonow, M., Lyman, J., Freedy, A., Electrotactile two-point discrimination as a function of frequency, body site, laterality, and stimulation codes (1977) Annals of Biomedical Engineering, 5 (1), pp. 47-60; Rahimi, M., Jiang, F., Ye, C., Shen, Y., Dynamic Spatiotemporal Pattern Identification and Analysis Using a Fingertip-based Electro-Tactile Display Array (2019) Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 8132-8137; Dellaert, F., Kaess, M., Square root sam: Simultaneous localization and mapping via square root information smoothing (2006) The International Journal of Robotics Research, 25 (12), pp. 1181-1203; Gutierrez-Gomez, D., Mayol-Cuevas, W., Guerrero, J.J., Inverse depth for accurate photometric and geometric error minimisation in RGBD dense visual odometry (2015) Proc IEEE International Conference on Robotics and Automation, pp. 83-89; Civera, J., Davison, A.J., Montiel, J.M., Inverse depth parametrization for monocular SLAM (2008) IEEE Transactions on Robotics, 24 (5), pp. 932-945; Qin, T., Li, P., Shen, S., Vins-mono: A robust and versatile monocular visual-inertial state estimator (2018) IEEE Transactions on Robotics, 34 (4), pp. 1004-1020; Zhang, H., Jin, L., Ye, C., A depth-enhanced visual inertial odometry for a robotic navigation aid for blind people Proc. Visual-Inertial Navigation: Challenges and Applications Workshop at 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems; Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K., Siegwart, R., Keyframe-based visual-inertial SLAM using nonlinear optimization (2013) Proceedings of Robotics Science and Systems; Qin, T., Cao, S., Pan, J., Shen, S., (2019) A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors, , arXiv preprint arXiv 1901, 03642",,"Fortino G.Wang F.-Y.Nurnberger A.Kaber D.Falcone R.Mendonca D.Yu Z.Guerrieri A.",,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Human-Machine Systems, ICHMS 2020","7 September 2020 through 9 September 2020",,163564,,9781728158716,,,"English","Proc. IEEE Int. Conf. Hum.-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85093963317
"Cantucci F., Falcone R.","57205735711;57213828373;","Towards trustworthiness and transparency in social human-robot interaction",2020,"Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",,,"9209397","","",,4,"10.1109/ICHMS49158.2020.9209397","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093931780&doi=10.1109%2fICHMS49158.2020.9209397&partnerID=40&md5=eb12d433498d06e2ef573dea15c15318","Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","Cantucci, F., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy; Falcone, R., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","Cooperation between autonomous robots and humans is becoming more and more demanding. Robots have to be able to capable of possessing and expose a wide range of cognitive functions, once humans require their help. This paper describes a cognitive architecture for human-robot interaction that allows a robot to dynamically modulate its own level of social autonomy every time a human user delegates to it a task to accomplish in her/his place. The task adoption process leverages on multiple robot's cognitive capabilities (i.e.The ability to have a theory of mind of the user, to build a model of the world, to profile the user and to make an evaluation about its own skill trustworthiness for building the user's profile). On the basis of these capabilities the robot is able to adapt its own level of intelligent collaboration by adopting the task at the different levels of help defined in the theory of delegation and adoption conceived by Castelfranchi and Falcone. Besides that, the architecture enhances robot's behavior transparency because gives to it the ability to provide a comprehensive explanation of the strategy it has adopted for accomplishing the delegated task. We propose an implementation of the cognitive architecture based on JaCaMo framework, which provides support for implementing multi-Agent systems and integrates different multi-Agent programming dimensions. © 2020 IEEE.","Explainable AI; Human-Robot Interaction; Robot Adjustable Autonomy; Theory of Mind","Architecture; Cognitive systems; Intelligent agents; Man machine systems; Multi agent systems; Transparency; Adoption process; Cognitive architectures; Cognitive capability; Cognitive functions; Multi-agent programming; Social human-robot interactions; Theory of minds; User's profiles; Social robots",,,,,,,,,,,,,,,,"Kuipers, B., How can we trust a robot? Communications of the ACM, 61 (3), p. 2018; Khan, A., Anwar, Y., Robots in healthcare: A survey Science and Information Conference, 2019, pp. 280-292. , Springer; Veloso, M.M., Biswas, J., Coltin, B., Rosenthal, S., Cobots: Robust symbiotic autonomous mobile service robots (2015) IJCAI, p. 4423; Ivanov, S.H., Webster, C., Berezina, K., Adoption of robots and service automation by tourism and hospitality companies (2017) Revista Turismo & Desenvolvimento, 27 (28), pp. 1501-1517; Falcone, R., Castelfranchi, C., The human in the loop of a delegated agent: The theory of adjustable social autonomy (2001) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31 (5), pp. 406-418; Wortham, R.H., Theodorou, A., Bryson, J.J., What does the robot think? transparency as a fundamental design requirement for intelligent systems Ijcai-2016 Ethics for Artificial Intelligence Workshop, 2016; Boissier, O., Bordini, R.H., Hübner, J.F., Ricci, A., Santi, A., Multi-Agent oriented programming with jacamo (2013) Science of Computer Programming, 78 (6), pp. 747-761; Castelfranchi, C., Falcone, R., Towards a theory of delegation for agent-based systems (1998) Robotics and Autonomous Systems, 24 (3-4), pp. 141-157; Scassellati, B.M., Foundations for a theory of mind for a humanoid robot (2001), Ph.D. dissertation Massachusetts Institute of Technology; Langley, P., Explainable agency in human-robot interaction AAAI Fall Symposium Series, 2016; Goodrich, M.A., Olsen, D.R., Crandall, J.W., Palmer, T.J., Experiments in adjustable autonomy (2001) Proceedings of IJCAI Workshop on Autonomy, Delegation and Control: Interacting with Intelligent Agents. Seattle, WA: American Association for Artificial Intelligence Press, pp. 1624-1629; Mostafa, S.A., Ahmad, M.S., Mustapha, A., Adjustable autonomy: A systematic literature review (2019) Artificial Intelligence Review, 51 (2), pp. 149-186; Birk, A., Pfingsthorn, M., A hmi supporting adjustable autonomy of rescue robots (2005) Robot Soccer World Cup, pp. 255-266. , Springer; Roehr, T.M., Shi, Y., Using a self-confidence measure for a system-initiated switch between autonomy modes (2010) Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Sapporo, Japan, pp. 507-514; Petersen, K., Von Stryk, O., Towards a general communication concept for human supervision of autonomous robot teams (2011) Proceedings of the Fourth International Conference on Advances in Computer-Human Interactions (ACHI). Citeseer, pp. 228-235; Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K., Explainable agents and robots: Results from a systematic literature review Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2019, pp. 1078-1088; Sridharan, M., Meadows, B., Colaco, Z., A tale of many explanations: Towards an explanation generation system for robots (2016) Proceedings of the 31st Annual ACM Symposium on Applied Computing. ACM, pp. 260-267; Georgeff, M.P., Lansky, A.L., Reactive reasoning and planning (1987) AAAI, 87, pp. 677-682; Rao, A.S., Georgeff, M.P., Bdi agents: From theory to practice (1995) ICMAS, 95, pp. 312-319; Bordini, R.H., Hübner, J.F., Bdi agent programming in agentspeak using jason (2005) International Workshop on Computational Logic in Multi-Agent Systems, pp. 143-164. , Springer; Rao, A.S., Agentspeak (l): Bdi agents speak out in a logical computable language (1996) European Workshop on Modelling Autonomous Agents in A Multi-Agent World, pp. 42-55. , Springer; Ricci, A., Piunti, M., Viroli, M., Omicini, A., Environment programming in cartago (2009) Multi-Agent Programming, pp. 259-288. , Springer; Hübner, J.F., Sichman, J.S., Boissier, O., Developing organised multiagent systems using the moise+ model: Programming issues at the system and agent levels (2007) International Journal of Agent-Oriented Software Engineering, 1 (3-4), pp. 370-395",,"Fortino G.Wang F.-Y.Nurnberger A.Kaber D.Falcone R.Mendonca D.Yu Z.Guerrieri A.",,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Human-Machine Systems, ICHMS 2020","7 September 2020 through 9 September 2020",,163564,,9781728158716,,,"English","Proc. IEEE Int. Conf. Hum.-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85093931780
"Laudante E., Greco A., Caterino M., Fera M.","57197770710;57192181305;57209547668;23484700200;","Human-robot interaction for improving fuselage assembly tasks: A case study",2020,"Applied Sciences (Switzerland)","10","17","5757","","",,6,"10.3390/app10175757","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090577773&doi=10.3390%2fapp10175757&partnerID=40&md5=8c57f4dfb572ec23036720f8370e9071","Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy","Laudante, E., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy; Greco, A., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy; Caterino, M., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy; Fera, M., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy","In current industrial systems, automation is a very important aspect for assessing manufacturing production performance related to working times, accuracy of operations and quality. In particular, the introduction of a robotic system in the working area should guarantee some improvements, such as risks reduction for human operators, better quality results and a speed increase for production processes. In this context, human action remains still necessary to carry out part of the subtasks, as in the case of composites assembly processes. This study aims at presenting a case study regarding the reorganization of the working activity carried out in workstation in which a composite fuselage panel is assembled in order to demonstrate, by means of simulation tool, that some of the advantages previously listed can be achieved also in aerospace industry. In particular, an entire working process for composite fuselage panel assembling will be simulated and analyzed in order to demonstrate and verify the applicability and effectiveness of human-robot interaction (HRI), focusing on working times and ergonomics and respecting the constraints imposed by standards ISO 10218 and ISO TS 15066. Results show the effectiveness of HRI both in terms of assembly performance, by reducing working times and ergonomics-for which the simulation provides a very low risk index. © 2020 by the authors.","Aerospace production; Ergonomics; Human-robot interaction; Simulation",,,,,,"785419; European Commission, EC","Acknowledgments: This work was supported by the European Commission under the H2020 Framework Clean Sky JTI–project LABOR, ID no. 785419, topic manager: Leonardo SpA.","This work was supported by the European Commission under the H2020 Framework Clean Sky JTI-project LABOR, ID no. 785419, topic manager: Leonardo SpA.",,,,,,,,,"Burgess-Limerick, R., Steiner, L.J., Torma-Krajewsky, J., (2009) Ergonomics Processes: Implementation Guide and Tools for the Mining Industry, , US Department of Health and Human Services: Washington, DC, USA; Centers for Disease Control and Prevention: Atlanta, GA, USA; National Institute for Occupational Safety and Health: Washington, DC, USA; DHHS (NIOSH): Pittsburg, PA, USA; Seri Rahayu, K., Norhidayah, H., Shahrizan, O.M., A Study on Muscle Fatigue Associated with Awkward Posture among Workers in Aerospace Industry (2013) Adv. Eng. Forum, 10, pp. 287-292; Eklund, J.A., Ergonomics and Quality Management-Humans in Interaction with Technology, Work Environment and Organization (1999) Int. J. Occup. Saf. Ergo, 5, pp. 143-160; Getty, R.L., Getty, J.M., Ergonomics Oriented to Processes Becomes a Tool for Continuous Improvement (1999) Int. J. Occup. Saf. 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"Li K., Zhang J., Wang L., Zhang M., Li J., Bao S.","57193804114;57223713086;7409187628;7601556980;57218145193;57218138665;","A review of the key technologies for sEMG-based human-robot interaction systems",2020,"Biomedical Signal Processing and Control","62",,"102074","","",,19,"10.1016/j.bspc.2020.102074","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088051273&doi=10.1016%2fj.bspc.2020.102074&partnerID=40&md5=a8d695a4d93453eaa5daa8b6be345b31","School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, United States; College of Education & Human Development, Texas A&M University, College Station, United States","Li, K., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, United States; Zhang, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; Wang, L., College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, United States; Zhang, M., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; Li, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; Bao, S., College of Education & Human Development, Texas A&M University, College Station, United States","As physiological signals that are closely related to human motion, surface electromyography (sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some reviews on myoelectric control systems have focused on signal processing, feature extraction and pattern recognition methods. However, to the best of our knowledge, there is no review paper on the comprehensive description of HRISs based on sEMG. Therefore, in this paper, we present a detailed review of the key technologies related to the use of sEMG signals in HRISs. First, the advantages and difficulties of using sEMG signals in HRISs are summarized, and some typical applications are introduced. Second, the research status and existing problems are discussed for four key technologies related to sEMG-based HRISs: signal preprocessing and feature extraction, human motion intention recognition, robustness enhancement for sEMG pattern recognition, and multisource information fusion based on sEMG. Finally, we discuss the bottlenecks hindering the application of sEMG-based HRISs and present prospects for this currently active research area. © 2020 Elsevier Ltd","Human-robot interaction system (HRIS); Multisource information fusion; sEMG pattern recognition; Surface electromyography (sEMG)","Biomedical signal processing; Extraction; Feature extraction; Man machine systems; Pattern recognition systems; Human motion intention; Multi-source information fusion; Myoelectric control; Pattern recognition method; Physiological signals; Signal preprocessing; Surface electromyography; Typical application; Social robots; electromyography; feature extraction; human; motion; review; robotics",,,,,"2019HW0001; CXZZBS2019033; 19JCJQJC61600; National Key Research and Development Program of China, NKRDPC: 2018IRS04, 2018YFB1306500","This work was supported in part by National Key R&D Program of China (2018YFB1306500); Intelligent Robot and System High-precision Innovation Center Open Fund Project (2018IRS04); Tianjin Outstanding Youth Fund Project (19JCJQJC61600); Subsidized Project of Innovative Ability Training for Postgraduates in Hebei Province (CXZZBS2019033); Joint Doctoral Training Foundation of HEBUT (2019HW0001).","This work was supported in part by National Key R&D Program of China ( 2018YFB1306500 ); Intelligent Robot and System High-precision Innovation Center Open Fund Project ( 2018IRS04 ); Tianjin Outstanding Youth Fund Project ( 19JCJQJC61600 ); Subsidized Project of Innovative Ability Training for Postgraduates in Hebei Province ( CXZZBS2019033 ); Joint Doctoral Training Foundation of HEBUT ( 2019HW0001 ).",,,,,,,,,"He, Y.Q., Zhao, Y.W., Han, J.D., Yu, H.B., Wang, T.R., Integration with human beings - new trend of robot technology development (2015) Robot Ind., 5, pp. 74-80; Hu, J., Hou, Z.G., Chen, Y.X., Zhang, F., Wang, W.Q., Lower limb rehabilitation robots and interactive control methods (2014) Acta Autom. 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"Atzeni M., Reforgiato Recupero D.","57195407967;57206674454;","Multi-domain sentiment analysis with mimicked and polarized word embeddings for human–robot interaction",2020,"Future Generation Computer Systems","110",,,"984","999",,16,"10.1016/j.future.2019.10.012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074500394&doi=10.1016%2fj.future.2019.10.012&partnerID=40&md5=4e4109862de14514e7e8db0e902ce046","Università degli Studi di Cagliari, Department of Mathematics and Computer Science, Via Ospedale 72, Cagliari, 09124, Italy","Atzeni, M., Università degli Studi di Cagliari, Department of Mathematics and Computer Science, Via Ospedale 72, Cagliari, 09124, Italy; Reforgiato Recupero, D., Università degli Studi di Cagliari, Department of Mathematics and Computer Science, Via Ospedale 72, Cagliari, 09124, Italy","This paper presents a state-of-the-art approach for sentiment polarity classification. Our approach relies on an ensemble of Bidirectional Long Short-Term Memory networks equipped with a neural attention mechanism. The system makes use of pre-trained word embeddings, and is capable of predicting new vectors for out-of-vocabulary words, by learning distributional representations based on word spellings. Also, during the training process the recurrent neural network is used to perform a fine-tuning of the original word embeddings, taking into account information about sentiment polarity. This step can be particularly helpful for sentiment analysis, as word embeddings are usually built based on context information, while words with opposite sentiment polarity often occur in similar contexts. The system described in this paper is an improved version of an approach that competed in a recent challenge on semantic sentiment analysis. We evaluate the performance of the system on the same multi-domain test set used by the organizers of the challenge, showing that our approach allows reaching better results with respect to the previous top-scoring system. Last but not least, we embedded the proposed sentiment polarity approach on top of a humanoid robot to lively identify the sentiment of the speaking user. © 2019 Elsevier B.V.","Deep learning; LSTM; Natural language processing; Neural attention; RNN; Sentiment analysis; Word embeddings","Anthropomorphic robots; Deep learning; Embeddings; Human robot interaction; Semantics; Sentiment analysis; Attention mechanisms; Context information; LSTM; NAtural language processing; Neural attention; Out of vocabulary words; Polarity classification; State-of-the-art approach; Long short-term memory",,,,,"Nvidia: 812882","We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. 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"Nomura T.","7403422375;","A possibility of inappropriate use of gender studies in human-robot Interaction",2020,"AI and Society","35","3",,"751","754",,,"10.1007/s00146-019-00913-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074475382&doi=10.1007%2fs00146-019-00913-y&partnerID=40&md5=7ba987b092f7b75afcada1a69025fff2","Ryukoku University, Otsu, Shiga  520-2194, Japan","Nomura, T., Ryukoku University, Otsu, Shiga  520-2194, Japan",[No abstract available],,,,,,,"JPMJCR17A2; Japan Society for the Promotion of Science, KAKEN: 18H04121","The research was supported by JST CREST Grant number JPMJCR17A2, Japan, and the Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research no. 18H04121.",,,,,,,,,,"Alexander, E., Bank, C., Jessica, J.J., Hayes, B., Scassellati, B., Asking for help from a gendered robot (2014) Proceedings of the 36Th Annual Conference of the Cognitive Science Society (Cogsci2014)., pp. 2333-2338. , 23–26 July 2014. 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October 10–15, 2009, IEEE, St. Louis, MO, Piscataway, NJ; Strait, M., Briggs, P., Scheutz, M., Gender, more so than age, modulates positive perceptions of language-based human–robot interactions (2015) 4Th International Symposium on New Frontiers in Human Robot Interaction, , https://hrilab.tufts.edu/publications/straitetal15aisb.pdf, Salem M, Weiss A, Baxter P, Dau-tenhahn K (eds), 21–22 Apr 2015, Canterbury, United Kingdom; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: the double-edge sword of robot gender and personality in human–robot interaction (2014) Comput Hum Behav, 38, pp. 75-84; Tung, F.-W., Influence of gender and age on the attitudes of children towards humanoid robots: Human–computer interaction. Users and applications (2011) Proceedings of the 14Th Conference on human–computer Interactions: Users and Applications, pp. 637-646. , volume part IV. July 9–14. Springer, Orlando, FL. Berlin, Germany, New York, NY; Weber, J., Bath, C., ‘Social’’ robots and ‘‘emotional’’ software agents: gendering processes and de-gendering strategies for ‘‘technologies in the making’ (2007) Gender designs IT: construction and deconstruction of information society technology [in German], pp. 53-63. , Zorn I, Maass S, Rommes E, (eds), Springer, Weissbaden","Nomura, T.; Ryukoku UniversityJapan; email: nomura@rins.ryukoku.ac.jp",,,"Springer",,,,,09515666,,,,"English","AI Soc.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85074475382
"Müller F., Jäkel J., Thomas U.","57188974322;33967731400;7004226574;","Stability analysis for a passive/active human model in physical human–robot interaction with multiple users",2020,"International Journal of Control","93","9",,"2104","2119",,2,"10.1080/00207179.2018.1541508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057307857&doi=10.1080%2f00207179.2018.1541508&partnerID=40&md5=04d07a1b5460b3b51a2fdd1641a7c393","Chair of Robotics and Human-Machine-Interaction Lab, Chemnitz University of Technology, Chemnitz, Germany; Department of Electrical Engineering and Information Technology, Leipzig University of Applied Science, Leipzig, Germany","Müller, F., Chair of Robotics and Human-Machine-Interaction Lab, Chemnitz University of Technology, Chemnitz, Germany; Jäkel, J., Department of Electrical Engineering and Information Technology, Leipzig University of Applied Science, Leipzig, Germany; Thomas, U., Chair of Robotics and Human-Machine-Interaction Lab, Chemnitz University of Technology, Chemnitz, Germany","Human–robot–human interaction (HRH), understood as a physical human–robot interaction (pHRI) with two humans, can be applied when lifting heavy, bulky and large-sized objects with a robot. In combination with a virtual environment, this system can become nonlinear. In this article, we prove sufficient stability conditions for a stationary point of such a particular type of nonlinear multiple time-delay systems. In addition, a new human model consisting of a passive and an active part will be introduced and validated on experimental data. The derived stability conditions are applied to a single-user pHRI system including this human model. The results indicate that these conditions are very conservative. Then four approaches for the analysis of a multi-user pHRI are introduced and compared with each other. Finally, a potential HRH application with a nonlinear environment in the form of a potential force field is presented. © 2018 Informa UK Limited, trading as Taylor & Francis Group.","human–robot–human; impedance control; Lyapunov–Krasovskii functional; nonlinear time-delay systems; Physical human–robot interaction","Delay control systems; System stability; Time delay; Virtual reality; Impedance control; Krasovskii functional; Multiple time-delay system; Nonlinear environments; Nonlinear time delay systems; Potential force fields; Robot interactions; Stability condition; Human robot interaction",,,,,,,,,,,,,,,,"Agarwal, P., Deshpande, A.D., (2015) Impedance and force-field control of the index finger module of a hand exoskeleton for rehabilitation, , August,. 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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS), Sendai, Japan (4, 3883–3888","Müller, F.; Robotics and Human-Machine-Interaction Lab, Reichenhainer Str. 70, Germany; email: florian.mueller@etit.tu-chemnitz.de",,,"Taylor and Francis Ltd.",,,,,00207179,,IJCOA,,"English","Int J Control",Article,"Final","",Scopus,2-s2.0-85057307857
"Choi Y., Choi M., Oh M., Kim S.","55685955800;56594110900;57211928364;57209619163;","Service robots in hotels: understanding the service quality perceptions of human-robot interaction",2020,"Journal of Hospitality Marketing and Management","29","6",,"613","635",,44,"10.1080/19368623.2020.1703871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077157890&doi=10.1080%2f19368623.2020.1703871&partnerID=40&md5=0cdefb4039372e3fc880bdd2d4037f01","School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; School of Events, Tourism Hospitality Management, Leeds Beckett University, Leeds, United Kingdom","Choi, Y., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Choi, M., School of Events, Tourism Hospitality Management, Leeds Beckett University, Leeds, United Kingdom; Oh, M., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Kim, S., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong","Hotel industry started to adopt service robots, which are considered a future workforce. However, no attempt was conducted to examine the dimensionality of service quality of service robots. This paper aims to understand the influence of human–robot interaction from the viewpoint of hoteliers and guests. Two studies are conducted in this respect. Study 1 organizes focus-group interviews with hotel managers from various departments to elicit themes related to guest–robot interaction and robot-delivered services. Based on the findings in Study 1, Study 2 conducts an experiment to examine and compare hotel guests’ perceptions about the quality of services provided by human staff and service robots, as well as their joint services. Human staff services are perceived higher than the services of service robots in terms of interaction quality and physical service environment. However, no significant difference in outcome quality is noted. © 2019 Taylor & Francis Group, LLC.","artificial intelligence; human–robot interaction; service quality; Service robots",,,,,,"Hong Kong Polytechnic University, PolyU","This work was supported by the internal grant?[YBX4]?of the The Hong?Kong Polytechnic University.",,,,,,,,,,"Ariffin, A.A.M., Maghzi, A., A preliminary study on customer expectations of hotel hospitality: Influences of personal and hotel factors (2012) International Journal of Hospitality Management, 31 (1), pp. 191-198; Assaker, G., Hallak, R., Moderating effects of tourists’ novelty-seeking tendencies on destination image, visitor satisfaction, and short-and long-term revisit intentions (2013) Journal of Travel Research, 52, pp. 600-613; Bailenson, J.N., Yee, N., Digital chameleons: Automatic assimilation of nonverbal gestures in immersive virtual environments (2005) Psychological Science, 16, pp. 814-819; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction (2004) RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759), pp. 591-594. , September, IEEE, Okayama, Japan, &; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Bickmore, T.W., Pfeifer, L.M., Jack, B.W., Taking the time to care: Empowering low health literacy hospital patients with virtual nurse agents (2009) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1265-1274. , Boston: ACM, MA, &; Brady, M.K., Cronin, J.J., Some new thoughts on conceptualizing perceived service quality: A hierarchical approach (2001) Journal of Marketing, 65, pp. 34-49; Breen, R.L., A practical guide to focus-group research (2006) Journal of Geography in Higher Education, 30 (3), pp. 463-475; Chan, A.P.H., Tung, V.W.S., Examining the effects of robotic service on brand experience: The moderating role of hotel segment (2019) Journal of Travel & Tourism Marketing, 36 (4), pp. 458-468; Choi, T.Y., Chu, R., Determinants of hotel guests’ satisfaction and repeat patronage in the Hong Kong hotel industry (2001) International Journal of Hospitality Management, 20, pp. 277-297; Cronin, J.J., Taylor, S.A., Measuring service quality: A reexamination and extension (1992) Journal of Marketing, 56, pp. 55-68; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychological Review, 114 (4), pp. 864-886; Gefen, D., Karahanna, E., Straub, D.W., Trust and TAM in online shopping: An integrated model (2003) MIS Quarterly, 7, pp. 51-90; Robots: The next generation of hoteliers, , https://www.hotel-icon.com/icon-edit/robots-the-next-generation-of-hoteliers, Retrived from; Hutt, C., (1970), Specific and diverse exploration. 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Conceptions of embodiment and their implications for social human–Robot interaction (2014) Frontiers in Artificial Intelligence and Applications, 273, pp. 49-53","Choi, Y.; School of Hotel and Tourism Management, 17 Science Museum Road 818, TST East, Hong Kong; email: young.choi@polyu.edu.hk",,,"Routledge",,,,,19368623,,,,"English","J. Hosp. Mark. Manage.",Article,"Final","",Scopus,2-s2.0-85077157890
"Xue C., Qiao Y., Murray N.","57220024635;22036417400;7201513152;","Enabling Human-Robot-Interaction for Remote Robotic Operation via Augmented Reality",2020,"Proceedings - 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020",,,"9217702","194","196",,2,"10.1109/WoWMoM49955.2020.00046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096586417&doi=10.1109%2fWoWMoM49955.2020.00046&partnerID=40&md5=ab4759faa1f5a48342707cbe21af813e","Computer and Software Engineering, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland; Software Research Institute, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland","Xue, C., Computer and Software Engineering, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland; Qiao, Y., Software Research Institute, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland; Murray, N., Computer and Software Engineering, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland","Human-Robot Interaction (HRI) will be a crucial component of smart factories of the future (FoF). This demo presents a reliable and cost-effective HRI system based on Augmented Reality (AR). In this demonstration, we offer concepts and methods currently being developed to enable high-level human-robot collaboration and interaction. The user interaction and instructions are captured via AR and communicated to a Robot Operating System (ROS) powered robotic arm. © 2020 IEEE.","Augmented Reality; Human-Robot Interaction; Robot Operating System; Unity3D","Augmented reality; Cost effectiveness; Man machine systems; Robotics; Cost effective; Factories of the futures; Human robot Interaction (HRI); Human-robot collaboration; Robot operating systems (ROS); User interaction; Social robots",,,,,"Science Foundation Ireland, SFI: SFI/16/RC/3918","ACKNOWLEDGEMENT This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant number SFI/16/RC/3918, co-funded by CONFIRM Smart Manufacturing and Robotics & Drives Ireland",,,,,,,,,,"Feil-Seifer, D., Mataric, M., Human-robot interaction (2019) Encyclopedia of Complexity and Systems Science, pp. 4643-4659; Goodrich, M.A., Human-robot interaction: A survey: Foundations and trends in human-computer interaction: Vol 1, no 3 (2020) Dl.acm.org, , https://dl.acm.org/doi/10.1561/1100000005; Green, S., Billinghurst, M., Chen, X., Chase, J., Human robot collaboration: An augmented reality approach a literature review and analysis (2007) Proceeding of Mesa 07:3RD International Conference on Mechatronics and Embedded System and Applications, , Nevada,USA; De Melo, M.S.P., Da Silva, N.J.G., Da Silva, P.J.L., Teixeira, J.M.X.N., Teichrieb, V., Analysis and comparison of robotics 3d simulators (2019) 2019 21st Symposium on Virtual and Augmented Reality (SVR), pp. 242-251. , Rio de Janeiro, Brazil; (2020) Ros.org, , https://www.ros.org/about-ros/, ROS.org | About ROS; Lasota, P.A., Song, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) A Survey of Methods for Safe Human-Robot Interaction; Chacko, S.M., Kapila, V., An augmented reality interface for human-robot interaction in unconstrained environments (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3222-3228. , Macau, China; Quintero, C.P., Li, S., Pan, M.K., Chan, W.P., Der Van Loos, M.H.F., Croft, E., Robot programming through augmented trajectories in augmented reality (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1838-1844. , Madrid; (2020) Docs.microsoft.com, , https://docs.microsoft.com/en-us/windows/mixed-reality/mrtk-gettingstarted, Getting started with MRTK version 2-Mixed Reality; (2020) GitHub, , https://github.com/siemens/ros-sharp/wiki, siemens/ros-sharp; Aristidou, A., Lasenby, J., FABRIK: A fast iterative solver for the Inverse Kinematics problem (2011) Graphical Models, 73 (5), pp. 243-260; Assetstore.unity.com, 2020, , https://assetstore.unity.com/packages/tools/animation/final-ik-14290, Final IK | Animation Tools | Unity Asset Store; (2019) Tarang Shah-Blog, , https://tarangshah.com/blog/2017-04-01/rosbasics-1-nodes-topics-services-actions, ROS Basics 1-Nodes, Topics, Services &Actions; (2020) Docs.ros.org, , http://docs.ros.org/melodic/api/moveit_tutorials/html/index.html, MoveIt Tutorials - moveit tutorials Kinetic documentation; Rodrigues, T.B., An evaluation of a 3d multimodal marker-less motion analysis system | proceedings of the 10th acm multimedia systems conference (2020) Dl.acm.org, , https://dl.acm.org/doi/10.1145/3304109.3306236; Keighrey, C., Comparing user QoE via physiological and interaction measurements of immersive ar and vr speech and language therapy applications | proceedings of the on thematic workshops of acm multimedia 2017 (2019) Dl.acm.org, , https://dl.acm.org/doi/10.1145/3126686.3126747; Hynes, E., Flynn, R., Lee, B., Murray, N., A quality of experience evaluation comparing augmented reality and paper based instruction for complex task assistance (2019) 2019 Ieee 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6. , Kuala Lumpur, Malaysia",,,"IEEE;IEEE Computer Society;Missouri University of Science and Technology","Institute of Electrical and Electronics Engineers Inc.","21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020","31 August 2020 through 3 September 2020",,163852,,9781728173740,,,"English","Proc. - IEEE Int. Symp. World Wirel., Mob. Multimed. Networks, WoWMoM",Conference Paper,"Final","",Scopus,2-s2.0-85096586417
"Jensen L.C., Melissa Langedijk R., Fischer K.","56277957900;57219848313;24491633100;","Understanding the Perception of Incremental Robot Response in Human-Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223615","41","47",,2,"10.1109/RO-MAN47096.2020.9223615","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095797815&doi=10.1109%2fRO-MAN47096.2020.9223615&partnerID=40&md5=93a1bea90d10f915ec983c60d76c61e4","University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark","Jensen, L.C., University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark; Melissa Langedijk, R., University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark; Fischer, K., University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark","Incremental feedback, i.e. the timely response to human behavior while it is happening, has previously been found to potentially speed up human-robot interactions, but it is unclear how people evaluate incremental robots. In this study, we show that the evaluation of incremental robot response depends on the actual success of the incremental feedback; that is, if the feedback leads to increased efficiency, people evaluate the robot as more competent and more credible. If the robot does not use incremental feedback, no interaction between evaluation and efficiency can be found. Thus, incremental feedback draws people's attention to interaction success. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Biofeedback; Efficiency; Man machine systems; Human behaviors; Speed up; Social robots",,,,,,"*This work was carried out in the framework of the Smooth Project, supported by the Danish Innovation Foundation 1Lars Christian Jensen, Kerstin Fischer and Rosalyn Melissa Langedijk are all with the Department of design & Communication, University of southern Denmark, Universitetsparken 1, 6000 Kolding, Denmark.",,,,,,,,,,"Skantze, G., Hjalmarsson, A., Towards incremental speech generation in dialogue systems (2010) Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Association for Computational Linguistics, pp. 1-8; Skantze, G., Hjalmarsson, A., Towards incremental speech generation in conversational systems (2013) Computer Speech & Language, 27 (1), pp. 243-262; Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings of the 2014 ACM/IEEE Int. Conference on Human-robot Interaction, ACM, pp. 25-32; Kato, Y., Kanda, T., Ishiguro, H., May i help you?-design of human-like polite approaching behavior (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 35-42. , IEEE; Pitsch, K., Kuzuoka, H., Suzuki, Y., Sussenbach, L., Luff, P., Heath, C., The first five seconds : Contingent stepwise entry into an interaction as a means to secure sustained engagement in hri (2009) ROMAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, IEEE, pp. 985-991; Castro-González, Á., Castillo, J.C., Alonso-Martin, F., Olortegui-Ortega, O.V., González-Pacheco, V., Malfaz, M., Salichs, M.A., The effects of an impolite vs. a polite robot playing rock-paper-scissors (2016) International Conference on Social Robotics, pp. 306-316. , Springer; Chromik, M., Carlmeyer, B., Wrede, B., Ready for the next step?: Investigating the effect of incremental information presentation in an object fetching task (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 95-96; Carlmeyer, B., Schlangen, D., Wrede, B., Look at me!: Self-interruptions as attention booster? (2016) Proceedings of the Fourth International Conference on Human Agent Interaction, ACM, pp. 221-224; De Kok, I., Hough, J., Hülsmann, F., Botsch, M., Schlangen, D., Kopp, S., A multimodal system for real-time action instruction in motor skill learning (2015) Conference Paper; Kennington, C., Kousidis, S., Baumann, T., Buschmeier, H., Kopp, S., Schlangen, D., Better driving and recall when in-car information presentation uses situationally-aware incremental speech output generation (2014) Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 1-7; Ghigi, F., Eskenazi, M., Torres, M.I., Lee, S., Incremental dialog processing in a task-oriented dialog (2014) Fifteenth Annual Conference of the International Speech Communication Association; Manuvinakurike, R., Paetzel, M., Qu, C., Schlangen, D., DeVault, D., Toward incremental dialogue act segmentation in fast-paced interactive dialogue systems (2016) Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 252-262; Carlmeyer, B., Schlangen, D., Wrede, B., Towards closed feedback loops in hri: Integrating inprotk and pamini (2014) Proceedings of the 2014 Workshop on Multimodal, Multi-Party, Real-World Human-Robot Interaction, ACM, pp. 1-6; Baumann, T., Lindner, F., Incremental speech production for polite and natural personal-space intrusion (2015) International Conference on Social Robotics, pp. 72-82. , Springer; Buschmeier, H., Baumann, T., Dosch, B., Kopp, S., Schlangen, D., Combining incremental language generation and incremental speech synthesis for adaptive information presentation (2012) Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 295-303. , Association for Computational Linguistics; Tsai, V., Baumann, T., Pecune, F., Casell, J., Faster responses are better responses: Introducing incrementality into sociable virtual personal assistants (2018) Proceedings of the 2018 International Workshop on Spoken Dialog System Technology; Hough, J., De Kok, I., Schlangen, D., Kopp, S., Timing and grounding in motor skill coaching interaction: Consequences for the information state (2015) Proceedings of the 19th SemDial Workshop on the Semantics and Pragmatics of Dialogue (GoDIAL); Kobberholm, K.W., Carstens, K.S., Bøg, L.W., Santos, M.H., Ramskov, S., Mohamed, S.A., Jensen, L.C., The influence of incremental information presentation on the persuasiveness of a robot (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 302-304; Cialdini, R.B., (1987) Influence. A. Michel Port Harcourt, 3; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas): Development and validation (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 254-262; Pajaziti, A., Slam-map building and navigation via ros (2014) International Journal of Intelligent Systems and Applications in Engineering, 2 (4), pp. 71-75; Fischer, K., Seibt, J., Rodogno, R., Rasmussen, M.K., Weiss, A., Bodenhagen, L., Juel, W.K., Krüger, N., Integrative social robotics hands-on (2020) Interaction Studies, 21 (1), pp. 145-185; (2020) R: A Language and Environment for Statistical Computing, , https://www.R-project.org/, R Core Team, Vienna, Austria: R Foundation for Statistical Computing; Sanchez, G., Trinchera, L., Russolillo, G., (2017) Plspm: Tools for Partial Least Squares Path Modeling (Pls-pm), , https://CRAN.R-project.org/package=plspm, R package version 0. 4. 9",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095797815
"Han J., Ajaykumar G., Li Z., Huang C.-M.","57219858579;57215834400;57219859443;55193383300;","Structuring Human-Robot Interactions via Interaction Conventions",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223468","341","348",,1,"10.1109/RO-MAN47096.2020.9223468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095787678&doi=10.1109%2fRO-MAN47096.2020.9223468&partnerID=40&md5=cc2a2861fb08b788982378ae282154fb","Johns Hopkins University, Baltimore, MD  21218, United States; Tsinghua University, Beijing, 100084, China","Han, J., Johns Hopkins University, Baltimore, MD  21218, United States; Ajaykumar, G., Johns Hopkins University, Baltimore, MD  21218, United States; Li, Z., Tsinghua University, Beijing, 100084, China; Huang, C.-M., Johns Hopkins University, Baltimore, MD  21218, United States","Interaction conventions (e.g., using pinch gestures to zoom in and out) are designed to structure how users effectively work with an interactive technology. We contend in this paper that successful human-robot interactions may be achieved through an appropriate use of interaction conventions. We present a simple, natural interaction convention - Put That Here - for instructing a robot partner to perform pick-and-place tasks. This convention allows people to use common gestures and verbal commands to select objects of interest and to specify their intended location of placement. We implement an autonomous robot system capable of parsing and operating through this convention. Through a user study, we show that participants were easily able to adopt and use the convention to provide task specifications. Our results show that participants using this convention were able to complete tasks faster and experienced significantly lower cognitive load than when using only verbal commands to give instructions. Furthermore, when asked to give natural pick-and-place instructions to a human collaborator, the participants intuitively used task specification methods that paralleled our convention, incorporating both gestures and verbal commands to provide precise task-relevant information. We discuss the potential of interaction conventions in enabling productive human-robot interactions. © 2020 IEEE.",,"Agricultural robots; Man machine systems; Specifications; Cognitive loads; Interactive technology; Natural interactions; Pick and place; Robot Partners; Task relevant; Task specifications; User study; Social robots",,,,,"National Science Foundation, NSF: DGE-1746891; Johns Hopkins University, JHU: 1840088","We thank Yuxiang Gao, Jaimie Patterson, Eden Metzger, Anthonia Duru, and Erica Hwang for their help with this work. This work was partially supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1746891, the Nursing/Engineering joint fellowship from the Johns Hopkins University, and the National Science Foundation award #1840088.",,,,,,,,,,"Krämer, N.C., Von Der Pütten, A., Eimler, S., Humanagent and human-robot interaction theory: Similarities to and differences from human-human interaction (2012) Human-computer Interaction: The Agency Perspective, pp. 215-240. , Springer; Aylett, R.S., Castellano, G., Raducanu, B., Paiva, A., Hanheide, M., Long-term socially perceptive and interactive robot companions: Challenges and future perspectives (2011) Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 323-326; Norman, D.A., Affordance, conventions, and design (1999) Interactions, 6 (3), pp. 38-43; Raskin, J., Intuitive equals familiar (1994) Communications of the ACM, 37 (9), pp. 17-19; Lozano-Pérez, T., Jones, J.L., Mazer, E., O'Donnell, P.A., Task-level planning of pick-and-place robot motions (1989) Computer, 22 (3), pp. 21-29; Bolt, R.A., (1980) Put-that-there: Voice and Gesture at the Graphics Interface, 14. , ACM; Wiedenbeck, S., Davis, S., The influence of interaction style and experience on user perceptions of software packages (1997) International Journal of Human-Computer Studies, 46 (5), pp. 563-588; Shyam Sundar, S., Bellur, S., Oh, J., Xu, Q., Jia, H., User experience of on-screen interaction techniques: An experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping (2014) Human-Computer Interaction, 29 (2), pp. 109-152; Rauterberg, M., Fjeld, M., Krueger, H., Bichsel, M., Leonhardt, U., Meier, M., Build-it: A computer vision-based interaction technique for a planning tool (1997) People and Computers XII, pp. 303-314. , Springer; Schmidt, A., Implicit human computer interaction through context (2000) Personal Technologies, 4 (2-3), pp. 191-199; Oviatt, S., DeAngeli, A., Kuhn, K., Integration and synchronization of input modes during multimodal human-computer interaction (1997) Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 415-422; Surakka, V., Illi, M., Isokoski, P., Gazing and frowning as a new human-computer interaction technique (2004) ACM Transactions on Applied Perception (TAP), 1 (1), pp. 40-56; Jacob, R.J.K., The use of eye movements in human-computer interaction techniques: What you look at is what you get (1991) ACM Transactions on Information Systems (TOIS), 9 (2), pp. 152-169; Jacob, R.J.K., Eye movement-based human-computer interaction techniques: Toward non-command interfaces (1993) Advances in Humancomputer Interaction, 4, pp. 151-190; Rempel, D., Camilleri, M.J., Lee, D.L., The design of hand gestures for human-computer interaction: Lessons from sign language interpreters (2014) International Journal of Human-computer Studies, 72 (10-11), pp. 728-735; Kendon, A., Do gestures communicate? A review (1994) Research on Language and Social Interaction, 27 (3), pp. 175-200; McNeill, D., (1992) Hand and Mind: What Gestures Reveal about Thought, , University of Chicago press; Ann Graham, J., Heywood, S., The effects of elimination of hand gestures and of verbal codability on speech performance (1975) European Journal of Social Psychology, 5 (2), pp. 189-195; Goldin-Meadow, S., (2005) Hearing Gesture: How Our Hands Help Us Think, , Harvard University Press; Matuszek, C., Bo, L., Zettlemoyer, L., Fox, D., Learning from unscripted deictic gesture and language for humanrobot interactions (2014) AAAI, pp. 2556-2563; Williams, T., Acharya, S., Schreitter, S., Scheutz, M., Situated open world reference resolution for human-robot dialogue (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 311-318. , IEEE Press; Zender, H., Kruijff, G.M., Kruijff-Korbayová, I., Situated resolution and generation of spatial referring expressions for robotic assistants (2009) IJCAI, pp. 1604-1609; Li, S., Scalise, R., Admoni, H., Rosenthal, S., Srinivasa, S.S., Spatial references and perspective in natural language instructions for collaborative manipulation (2016) Robot and Human Interactive Communication (RO-MAN) 2016 25th IEEE International Symposium on, pp. 44-51. , IEEE; Rogalla, O., Ehrenmann, M., Zollner, R., Becher, R., Dillmann, R., Using gesture and speech control for commanding a robot assistant (2002) Robot and Human Interactive Communication, 2002. Proceedings. 11th IEEE International Workshop on, pp. 454-459. , IEEE; Jones, G., Berthouze, N., Bielski, R., Julier, S., Towards a situated, multimodal interface for multiple uav control (2010) Robotics and Automation (ICRA) 2010 IEEE International Conference on, pp. 1739-1744. , IEEE; Takahashi, T., Nakanishi, S., Kuno, Y., Shirai, Y., Human-robot interface by verbal and nonverbal behaviors (1998) Intelligent Robots and Systems, 1998. 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(IROS 2005). 2005 IEEE/RSJ International Conference on, pp. 708-713. , IEEE; Huang, C., Thomaz, A.L., Effects of responding to, initiating and ensuring joint attention in human-robot interaction (2011) RO-MAN, 2011 IEEE, pp. 65-71. , IEEE; Hart, S.G., Nasa-task load index (nasa-tlx); 20 years later (2006) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50, pp. 904-908. , Sage Publications Sage CA: Los Angeles, CA; Nomura, T., Takagi, S., Exploring effects of educational backgrounds and gender in human-robot interaction (2011) 2011 International Conference on User Science and Engineering (I-user), pp. 24-29. , IEEE; Kaplan, F., Who is afraid of the humanoid? Investigating cultural differences in the acceptance of robots (2004) International Journal of Humanoid Robotics, 1 (3), pp. 465-480; Archer, D., Unspoken diversity: Cultural differences in gestures (1997) Qualitative Sociology, 20 (1), pp. 79-105; Kemp, C.C., Anderson, C.D., Nguyen, H., Trevor, A.J., Xu, Z., A point-and-click interface for the real world: Laser designation of objects for mobile manipulation (2008) Human-robot Interaction (HRI), 2008 3rd ACM/IEEE International Conference on, pp. 241-248. , IEEE; Ishii, K., Zhao, S., Inami, M., Igarashi, T., Imai, M., Designing laser gesture interface for robot control (2009) IFIP Conference on Human-Computer Interaction, pp. 479-492. , Springer",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095787678
"Wengefeld T., Hochemer D., Lewandowski B., Kohler M., Beer M., Gross H.-M.","56731144600;57219856177;57201701924;57219849705;57219848498;7202668837;","A Laser Projection System for Robot Intention Communication and Human Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223517","259","265",,2,"10.1109/RO-MAN47096.2020.9223517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095787314&doi=10.1109%2fRO-MAN47096.2020.9223517&partnerID=40&md5=f6571c3c09492916cb9744b38f2424b9","Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany","Wengefeld, T., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Hochemer, D., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Lewandowski, B., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Kohler, M., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Beer, M., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Gross, H.-M., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany","In order to deploy service robots in environments where they encounter and/or cooperate with persons, one important key factor is human acceptance. Hence, information on which upcoming actions of the robot are based has to be made transparent and understandable to the human. However, considering the restricted power resources of mobile robot platforms, systems for visualization not only have to be expressive but also energy efficient. In this paper, we applied the well-known technique of laser scanning on a mobile robot to create a novel system for intention visualization and human-robot-interaction. We conducted user tests to compare our system to a low-power consuming LED video projector solution in order to evaluate the suitability for mobile platforms and to get human impressions of both systems. We can show that the presented system is preferred by most users in a dynamic test setup on a mobile platform. © 2020 IEEE.",,"Agricultural robots; Drilling platforms; Energy efficiency; Man machine systems; Mobile robots; Visualization; Energy efficient; Human impressions; Laser projection system; Laser scanning; Mobile platform; Mobile robot platforms; Power resources; Service robots; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0437D, 03ZZ0460, 16SV7829K","This work has received funding from the German Federal Ministry of Education and Research (BMBF) to the project 3D-PersA2 (grant agreement no. 03ZZ0460), the project ROTATOR (grant agreement no. 03ZZ0437D) as part of the research alliance 3DSensation and the project FRAME (grant agreement no. 16SV7829K).",,,,,,,,,,"Lewandowski, B., Socially compliant human-robot interaction for autonomous scanning tasks in supermarket environments (2020) Proc. of RO-MAN; Gross, H.-M., TOOMAS: Interactie shopping guide robots in everyday use. final implementation and experiences from long-term field trials (2009) Proc. of IROS, pp. 2005-2012; Trinh, T.Q., Autonomous mobile gait training robot for orthopedic rehabilitation in a clinical environment (2020) Proc. of RO-MAN; Gross, H.-M., Mobile robot companion for walking training of stroke patients in clinical post-stroke rehabilitation (2017) Proc. of ICRA, pp. 1028-1035; Gross, H.M., Living with a mobile companion robot in your own apartment-final implementation and results of a 20-weeks field study with 20 seniors (2019) Proc. of ICRA, pp. 2253-2259; Gross, H.-M., Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments (2015) Proc. of IROS, pp. 5992-5999; Foukarakis, M., Applying a multimodal user interface development framework on a domestic service robot (2017) Proc. of the 10th Int. Conf. on PErvasive Technologies Related to Assistive Environments, pp. 378-384; Stricker, R., R2D2 reloaded: Dynamic video projection on a mobile service robot (2015) Proc. of ECMR, pp. 1-6; Chadalavada, R.T., That's on my mind! robot to human intention communication through on-board projection on shared oor space (2015) Proc. of ECMR, pp. 1-6; Panek, P., User acceptance of a mobile led projector on a socially assistive robot (2012) Ambient Assisted Living, pp. 77-91. , Springer; Sasai, T., Development of a guide robot interacting with the user using information projection. basic system (2011) IEEE Int. Conf. on Mechatronics and Automation, pp. 1297-1302; Matsumaru, T., Mobile robot with preliminary-announcement and display function of forthcoming motion using projection equipment (2006) Proc. of RO-MAN, pp. 443-450; Yamashita, S., Evaluation of robots that signals a pedestrian using face orientation based on moving trajectory analysis (2019) Proc. of RO-MAN, pp. 1-8; Neggers, M., Investigating the understandability and ef-ciency of directional cues in robot navigation (2019) Proc. of RO-MAN; Van, G.D.T., Designing an expressive head for a help requesting socially assistive robot (2020) Human-Friendly Robotics (HFR) 2019, pp. 88-102. , Springer; Hietanen, A., Proof of concept of a projection-based safety system for human-robot collaborative engine assembly (2019) Proc. of RO-MAN; Bolano, G., Transparent robot behavior using augmented reality in close human-robot interaction (2019) Proc. of RO-MAN, pp. 1-7; Sharpe, L.T., A luminous ef-ciency function, v (-) for daylight adaptation (2005) Journal of Vision, 5 (11), p. 3; Hall, E.T., A system for the notation of proxemic behavior (1963) American Anthropologist, 65 (5), pp. 1003-1026; Wengefeld, T., A multi modal people tracker for real time human robot interaction (2019) Proc. of RO-MAN, p. 8; Wengefeld, T., Real-time person orientation estimation using colored pointclouds (2019) Proc. of ECMR. IEEE, p. 7; Lewandowski, B., Deep orientation: Fast and robust upper body orientation estimation for mobile robotic applications (2019) Proc. of IROS, pp. 441-448; Philippsen, R., An interpolated dynamic navigation function (2005) Proc. of ICRA, pp. 3782-3789; Dijkstra, E.W., A note on two problems in connexion with graphs (1959) Numerische Mathematik, 1 (1), pp. 269-271; Mueller, S., Local real-time motion planning using evolutionary optimization (2017) Towards Autonomous Robotic Systems (TAROS) Ser. LNCS, pp. 211-221. , 10454. Springer; Fox, D., The dynamic window approach to collision avoidance (1997) IEEE Robotics Automation Magazine, 4 (1), pp. 23-33. , March; Liebner, J., Now i need help! passing doors and using elevators as an assistance requiring robot (2019) Int. Conf. on Social Robotics (ICSR), pp. 527-537. , Springer",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095787314
"Sorrentino A., Fiorini L., Fabbricotti I., Sancarlo D., Ciccone F., Cavallo F.","57194094756;56902334800;6506027040;34880746900;56399235300;14628324200;","Exploring Human attitude during Human-Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223527","195","200",,,"10.1109/RO-MAN47096.2020.9223527","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095778642&doi=10.1109%2fRO-MAN47096.2020.9223527&partnerID=40&md5=3d0f9d955cfc00ce5a32d56b2590ce94","Scuola Superiore Sant'Anna, Pisa, Italy; Erasmus University, Erasmus School of Health Policy and Management, Rotterdam, Netherlands; Fondazione Casa Sollievo della Sofferenza - Irccs, Complex Unit of Geriatrics, Foggia, Italy","Sorrentino, A., Scuola Superiore Sant'Anna, Pisa, Italy; Fiorini, L., Scuola Superiore Sant'Anna, Pisa, Italy; Fabbricotti, I., Erasmus University, Erasmus School of Health Policy and Management, Rotterdam, Netherlands; Sancarlo, D., Fondazione Casa Sollievo della Sofferenza - Irccs, Complex Unit of Geriatrics, Foggia, Italy; Ciccone, F., Fondazione Casa Sollievo della Sofferenza - Irccs, Complex Unit of Geriatrics, Foggia, Italy; Cavallo, F., Scuola Superiore Sant'Anna, Pisa, Italy","The aim of this work is to provide an automatic analysis to assess the user attitude when interacts with a companion robot. In detail, our work focuses on defining which combination of social cues the robot should recognize so that to stimulate the ongoing conversation and how. The analysis is performed on video recordings of 9 elderly users. From each video, low-level descriptors of the behavior of the user are extracted by using open-source automatic tools to extract information on the voice, the body posture, and the face landmarks. The assessment of 3 types of attitude (neutral, positive and negative) is performed through 3 machine learning classification algorithms: k-nearest neighbors, random decision forest and support vector regression. Since intra- and intersubject variability could affect the results of the assessment, this work shows the robustness of the classification models in both scenarios. Further analysis is performed on the type of representation used to describe the attitude. A raw and an auto-encoded representation is applied to the descriptors. The results of the attitude assessment show high values of accuracy (>0.85) both for unimodal and multimodal data. The outcome of this work can be integrated into a robotic platform to automatically assess the quality of interaction and to modify its behavior accordingly. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Learning algorithms; Machine learning; Nearest neighbor search; Support vector regression; Video recording; Automatic analysis; Classification models; Extract informations; K-nearest neighbors; Low level descriptors; Machine learning classification; Quality of interaction; Robotic platforms; Social robots",,,,,,,,,,,,,,,,"Horstmann, A.C., Krämer, N.C., Great expectations? Relation of previous experiences with social robots in real life or in the media and expectancies based on qualitative and quantitative assessment (2019) Front. Psychol., 10, p. 939. , Apr; Vinciarelli, A., Pantic, M., Bourlard, H., Social signal processing: Survey of an emerging domain (2009) Image Vis. Comput., 27 (12), pp. 1743-1759; Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., Cavallo, F., (2019) A Survey of Behavioural Models for Social Robots, , May; De Carolis, B., Ferilli, S., Palestra, G., Simulating empathic behavior in a social assistive robot (2017) Multimed. Tools Appl.; Tanevska, A., Rea, F., Sandini, G., Cañamero, L., Sciutti, A., (2020) A Socially Adaptable Framework for Human-Robot Interaction, pp. 1-23; Rudovic, O., Lee, J., Dai, M., Schuller, B., Picard, R.W., Personalized machine learning for robot perception of affect and engagement in autism therapy (2018) Sci. Robot.; Bengio, Y., Courville, A., Vincent, P., Representation learning: A review and new perspectives (2013) IEEE Trans. Pattern Anal. Mach. Intell.; Hinton, G.E., Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks (2006) Science (80-.).; Cao, Z., Simon, T., Wei, S.E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7291-7299; Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P., OpenFace 2. 0: Facial behavior analysis toolkit (2018) Proceedings-13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018; Eyben, F., Weninger, F., Gross, F., Schuller, B., Recent developments in opensmile, the munich open-source multimedia feature extractor (2013) MM 2013-Proceedings of the 2013 ACM Multimedia Conference; Chollet, F., Keras documentation (2015) Keras. Io; Abadi, M., TensorFlow: A system for large-scale machine learning (2016) Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016; Pedregosa, F., Scikit-learn: Machine learning in python (2011) J. Mach. Learn. Res.; Bisong, E., Bisong, E., Google colaboratory (2019) Building Machine Learning and Deep Learning Models on Google Cloud Platform",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095778642
"Dromnelle R., Girard B., Renaudo E., Chatila R., Khamassi M.","57211624716;36945382400;56303861600;7003451196;6508136456;","Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223451","612","617",,2,"10.1109/RO-MAN47096.2020.9223451","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095773376&doi=10.1109%2fRO-MAN47096.2020.9223451&partnerID=40&md5=1ee991132786f5880fe4f37eb974c489","Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Universitat Innsbruck, Intelligent and Interactive Systems Group, Innsbruck, Austria","Dromnelle, R., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Girard, B., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Renaudo, E., Universitat Innsbruck, Intelligent and Interactive Systems Group, Innsbruck, Austria; Chatila, R., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Khamassi, M., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France","An important current challenge in Human-Robot Interaction (HRI) is to enable robots to learn on-the-fly from human feedback. However, humans show a great variability in the way they reward robots. We propose to address this issue by enabling the robot to combine different learning strategies, namely model-based (MB) and model-free (MF) reinforcement learning. We simulate two HRI scenarios: a simple task where the human congratulates the robot for putting the right cubes in the right boxes, and a more complicated version of this task where cubes have to be placed in a specific order. We show that our existing MB-MF coordination algorithm previously tested in robot navigation works well here without retuning parameters. It leads to the maximal performance while producing the same minimal computational cost as MF alone. Moreover, the algorithm gives a robust performance no matter the variability of the simulated human feedback, while each strategy alone is impacted by this variability. Overall, the results suggest a promising way to promote robot learning flexibility when facing variable human feedback. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Educational robots; Learning systems; Man machine systems; Reinforcement learning; Computational costs; Coordination algorithms; Human robot Interaction (HRI); Learning strategy; Model-based OPC; Robot navigation; Robust performance; Specific ordering; Social robots",,,,,"ANR11 LABX65; Horizon 2020 Framework Programme, H2020: 731761; Délégation Générale pour l'Armement, DGA","* This work has been funded by a DGA (French National Defence Armaments Procurement Agency) scholarships (RD) and by Labex SMART (ANR11 LABX65). The research leading to these results has received funding from the European Unions Horizon 2020 research and innovation programme (grant agreement number 731761, IMAGINE).",,,,,,,,,,"Najar, A., (2017) Shaping Robot Behaviour with Unlabeled Human Instructions, , PhD thesis, Universit de Cercy Pontoise, Paris, FR; Ho, M.K., Littman, M.L., Cushman, F., Austerweil, J.L., Teaching with rewards and punishments: Reinforcement or communication? (2015) CogSci; Roe, A., Siegelman, M., A parent-child relations questionnaire (1963) Child Development, pp. 355-369; Palminteri, S., Khamassi, M., Joffily, M., Coricelli, G., Contextual modulation of value signals in reward and punishment learning (2015) Nature Communications, 6 (1), pp. 1-14; Knox, W.B., Stone, P., Combining manual feedback with subsequent mdp reward signals for reinforcement learning (2010) Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1-Volume 1, pp. 5-12. , Citeseer; Griffith, S., Subramanian, K., Scholz, J., Isbell, C.L., Thomaz, A.L., Policy shaping: Integrating human feedback with reinforcement learning (2013) Advances in Neural Information Processing Systems, pp. 2625-2633; Fournier, P., (2019) Intrinsically Motivated and Interactive Reinforcement Learning: A Developmental Approach, , PhD thesis, EDITE de Paris; Sutton, R.S., Barto, A.G., (1998) Introduction to Reinforcement Learning, , Cambridge, MA, USA: MIT Press, 1st ed; Dromnelle, R., Renaudo, E., Pourcel, G., Chatila, R., Girard, B., Khamassi, M., How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning (2020) Living Machines 2020 : The 9th International Conference on Biomimetic and Biohybrid Systems.; Gat, E., On three-layer architectures (1998) Artificial Intelligence and Mobile Robots, , MIT Press; Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F., An architecture for autonomy (1998) IJRR Journal, 17, pp. 315-337; Daw, N., Niv, Y., Dayan, P., Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control (2005) Nat. Neurosci., 8 (12), pp. 1704-1711; Viejo, G., Khamassi, M., Brovelli, A., Girard, B., Modelling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning (2015) Frontiers in Behavioral Neuroscience, 9 (225); Powell, T., Sammut-Bonnici, T., (2015) Pareto Analysis. 01; Dzeroski, S., De Raedt, L., Driessens, K., Relational reinforcement learning (2001) Machine Learning, 43 (1-2), pp. 7-52; Knox, W.B., Stone, P., Reinforcement learning from simultaneous human and mdp reward (2012) AAMAS",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095773376
"Lewandowski B., Wengefeld T., Muller S., Jenny M., Glende S., Schroter C., Bley A., Gross H.-M.","57201701924;56731144600;57219847251;57219858211;16306855100;14016701500;56469346000;7202668837;","Socially Compliant Human-Robot Interaction for Autonomous Scanning Tasks in Supermarket Environments",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223568","363","370",,3,"10.1109/RO-MAN47096.2020.9223568","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095766367&doi=10.1109%2fRO-MAN47096.2020.9223568&partnerID=40&md5=aa349aa9339e80ad8ca4dfc9537fa876","Technische Universität Ilmenau, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, 98694, Germany; Youse GmbH, Berlin, 13187, Germany; MetraLabs GmbH, Ilmenau, 98693, Germany","Lewandowski, B., Technische Universität Ilmenau, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, 98694, Germany; Wengefeld, T., Technische Universität Ilmenau, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, 98694, Germany; Muller, S., Youse GmbH, Berlin, 13187, Germany; Jenny, M., Youse GmbH, Berlin, 13187, Germany; Glende, S., Youse GmbH, Berlin, 13187, Germany; Schroter, C., MetraLabs GmbH, Ilmenau, 98693, Germany; Bley, A., MetraLabs GmbH, Ilmenau, 98693, Germany; Gross, H.-M., Technische Universität Ilmenau, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, 98694, Germany","In this paper, we present a system for socially aware robot navigation for a wide range of service tasks in supermarkets. It comprises modules for real-time person detection and tracking to gain situation awareness, modules to react to situations, and means for human-robot communication. The technical performance of the situation awareness was evaluated in a shelf out-of-stock (SOOS) detection scenario under real-world conditions in a supermarket in Germany. Furthermore, in order to investigate whether and to what extent our social navigation strategy can improve the acceptance and application of a mobile service robot in a supermarket, we have conducted surveys with N = 60 participants and usability tests with N =8 participants during a three-day field test. We can show that a robot for SOOS detection operating in a supermarket during the opening hours is generally accepted by customers and that the integration of a real-time person perception is crucial, especially for keeping appropriate distances to persons and for improving user-centered communication. Furthermore, our results indicate that various communication channels (e.g. speech, a video projector, and LED lights) are beneficial in order to address a wider user group in the targeted supermarket setting. © 2020 IEEE.",,"Acceptance tests; Agricultural robots; Retail stores; Speech communication; Surveys; Appropriate distances; Human-robot communication; Mobile service robots; Robot navigation; Situation awareness; Social navigation; Technical performance; Video projectors; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0437D, 16SV7829K","This work has received funding from the German Federal Ministry of Education and Research (BMBF) to the project ROTATOR (grant agreement no. 03ZZ0437D) in the program Zwanzig20 and to the project FRAME (grant agreement no. 16SV7829K).",,,,,,,,,,"Gross, H.-M., TOOMAS: Interactive shopping guide robots in everyday use. final implementation and experiences from long-term field trials (2009) Proc. of IROS, pp. 2005-2012; Endres, H., Field test of a navigation system: Autonomous cleaning in supermarkets (1998) Proc. of ICRA, pp. 1779-1781; Garrett, J.J., Elements of user experience, the: User-centered design for the web and beyond (2010) Pearson Education; Gross, H.-M., Mobile robot companion for walking training of stroke patients in clinical post-stroke rehabilitation (2017) Proc. of ICRA, pp. 1028-1035; Gross, H.M., Living with a mobile companion robot in your own apartment-final implementation and results of a 20-weeks field study with 20 seniors (2019) Proc. of ICRA, pp. 2253-2259; Marin-Hernandez, A., Conception and implementation of a supermarket shopping assistant system (2012) 11th Mexican Int. Conf. on Artificial Intelligence, pp. 26-31; Gruen, T.W., Retail out-of-stocks: A worldwide examination of extent, causes and consumer responses (2002) Grocery Manufacturers of America Washington, DC; Paolanti, M., Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning (2017) Proc. of ECMR, pp. 1-6; Kumar, S., Remote retail monitoring and stock assessment using mobile robots (2014) Int. Conf. on Technologies for Practical Robot Applications (TePRA), pp. 1-6; Frontoni, E., Design and test of a real-time shelf out-of-stock detector system (2018) Microsystem Technologies, 24 (3), pp. 1369-1377; (2020) Zebra Smartsight, , https://connect.zebra.com/smartsight, Zebra Technologies Corp; (2020) Bossa Nova Out-of-stock, , https://www.bossanova.com/out-of-stocks, Bossa Nova Robotics; (2020) Tory Inventory Robot, , https://www.metralabs.com/en/tory-shelf-automated-shelfscanning-inventory/, MetraLabs GmbH; (2020) Simbe Robotics Out-of-stock Management, , https://www.simberobotics.com/solutions/out-of-stock-management/, Simbe Robotics, Inc; Hawes, N., The strands project: Long-term autonomy in everyday environments (2017) IEEE Robotics Automation Magazine, 24 (3), pp. 146-156; Triebel, R., Spencer: A socially aware service robot for passenger guidance and help in busy airports (2016) Field and Service Robotics, pp. 607-622. , Springer; Sucar, O.M.-E., From hci to hri-usability inspection in multimodal human-robot interactions (2003) 12th Int. Workshop on Robot and Human Interactive Communication, ROMAN, pp. 37-41; Doering, N., User-centered design and evaluation of a mobile shopping robot (2015) Int. Journal on Social Robotics (JSR), 7, pp. 203-225; (1998) ISO 9241-11: Ergonomic Requirements for Of-ce Work with Visual Dis-play Terminals (VDTs)-Part 11: Guidance on Usability, , International Organization for Standardization; Weiss, A., The usus evaluation framework for human-robot interaction (2009) Symp. on New Frontiers in Human-robot Interaction, 4 (1), pp. 11-26; Fox, D., The dynamic window approach to collision avoidance (1997) IEEE Robotics & Automation Magazine, 4 (1), pp. 23-33. , March; Gross, H.-M., ROREAS-robot coach for walking and orientation training in clinical post-stroke rehabilitation: Prototype implementation and evaluation in fields trials (2017) Autonomous Robots (AR), 41 (3), pp. 679-698; Lewandowski, B., A fast and robust 3d person detector and posture estimator for mobile robotic application (2019) Proc. of ICRA, pp. 4869-4875; Cao, Z., Realtime multi-person 2d pose estimation using part af-nity fields (2017) Proc. of CVPR, pp. 7291-7299; Spinello, L., Human detection using multimodal and multidimensional features (2008) Proc. of ICRA, pp. 3264-3269; Weinrich, C., People detection and distinction of their walking AIDS in 2d laser range data based on generic distance-invariant features (2014) Proc. of RO-MAN, pp. 767-773; Wengefeld, T., A multi modal people tracker for real time human robot interaction (2019) Proc. of RO-MAN, pp. 1-8; Lewandowski, B., Deep orientation: Fast and robust upper body orientation estimation for mobile robotic applications (2019) Proc. of IROS, pp. 441-448; Müller, S., A multi-modal person perception framework for socially interactive mobile service robots (2020) Sensors, 20 (3), p. 722; Trinh, T.Q., Go ahead, please. : Recognition and resolution of con-ict situations in narrow passages for polite mobile robot navigation (2015) Social Robotics, pp. 643-653. , Springer International Publishing; Hall, E.T., (1966) The Hidden Dimension, 609. , Garden City, NY: Doubleday; Vega-Magro, A., Socially acceptable robot navigation over groups of people (2017) Proc. of RO-MAN, pp. 1182-1187; Papadakis, P., Social mapping of human-populated environments by implicit function learning (2013) Proc. of IROS, pp. 1701-1706; Kirby, R., COMPANION: A constraint-optimizing method for person-acceptable navigation (2009) Proc. of RO-MAN, pp. 607-612; Clarkson, E., Applying heuristic evaluation to human-robot interaction systems (2007) Flairs Conference, pp. 44-49; Buss, M., Towards proactive human-robot interaction in human environments (2011) Int. Conf. on Cognitive Infocommunications (CogInfoCom), pp. 1-6; Hassenzahl, M., The inference of perceived usability from beauty (2010) HumanComputer Interaction, 25 (3), pp. 235-260; Frederiks, A.D., Towards participatory design of social robots (2019) IFIP Conf. on Human-Computer Interaction, pp. 527-535. , Springer; Diefenbach, S., Handbuch zur fun-ni toolbox (2010) User Experience Evaluation Auf Drei Ebenen, 27, p. 2019. , Retrieved May; Wengefeld, T., A laser projection system for robot intention communication and human robot interaction (2020) Proc. of RO-MAN",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095766367
"Lagerstedt E., Thill S.","57210616605;36624312800;","Benchmarks for evaluating human-robot interaction: Lessons learned from human-animal interactions",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223347","137","143",,1,"10.1109/RO-MAN47096.2020.9223347","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095758350&doi=10.1109%2fRO-MAN47096.2020.9223347&partnerID=40&md5=fcc0e3f1950b1efd6a0a3ff852bf3b82","University of Skövde, School of Informatics, Skövde, Sweden; Radboud University, Donders Institute for Brain Cognition, and Behaviour, Nijmegen, Netherlands","Lagerstedt, E., University of Skövde, School of Informatics, Skövde, Sweden; Thill, S., Radboud University, Donders Institute for Brain Cognition, and Behaviour, Nijmegen, Netherlands","Human-robot interaction (HRI) is fundamentally concerned with studying the interaction between humans and robots. While it is still a relatively young field, it can draw inspiration from other disciplines studying human interaction with other types of agents. Often, such inspiration is sought from the study of human-computer interaction (HCI) and the social sciences studying human-human interaction (HHI). More rarely, the field also turns to human-animal interaction (HAI).In this paper, we identify two distinct underlying motivations for making such comparisons: to form a target to recreate or to obtain a benchmark (or baseline) for evaluation. We further highlight relevant (existing) overlap between HRI and HAI, and identify specific themes that are of particular interest for further trans-disciplinary exploration. At the same time, since robots and animals are clearly not the same, we also discuss important differences between HRI and HAI, their complementarity notwithstanding. The overall purpose of this discussion is thus to create an awareness of the potential mutual benefit between the two disciplines and to describe opportunities that exist for future work, both in terms of new domains to explore, and existing results to learn from. © 2020 IEEE.",,"Agricultural robots; Animals; Human computer interaction; Human computer interaction (HCI); Human interactions; Human robot Interaction (HRI); Human-animal interactions; Human-human interactions; Mutual benefit; Social robots",,,,,,,,,,,,,,,,"Han, Z., Yanco, H., The effects of proactive release behaviors during human-robot handovers (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 440-448; Moosaei, M., Das, S.K., Popa, D.O., Riek, L.D., Using facially expressive robots to calibrate clinical pain perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. 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Workshop at HRI2015, pp. 1-2; Morgan, L., (1894) An Introduction to Comparative Psychology, , Fellingon-Tyne: The Walter Scott Publishing Co., Ltd., 1903 original work published; Karin-D'Arcy, M., The modern role of morgan's canon in comparative psychology (2005) International Journal of Comparative Psychology, 18 (3), pp. 179-201",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095758350
"Wijnen L., Bremner P., Lemaignan S., Giuliani M.","57193755961;57206776256;18234152100;23396512600;","Performing Human-Robot Interaction User Studies in Virtual Reality",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223521","794","799",,2,"10.1109/RO-MAN47096.2020.9223521","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095753006&doi=10.1109%2fRO-MAN47096.2020.9223521&partnerID=40&md5=2ff691264bfc3928dfb72c79a769cf1c","University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom","Wijnen, L., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom; Bremner, P., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom; Lemaignan, S., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom; Giuliani, M., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom","This study investigated whether virtual reality could be used as platform for conducting human-robot interaction user studies. It was investigated whether user studies performed in virtual reality elicited realistic responses from participants. To answer this question, a real world study was replicated as closely as possible in virtual reality, where a robot tour guide asked participants to keep a secret. The experiment consisted of a virtual museum tour where the robot acted as the tour guide while displaying either social or non-social behaviour.The measurements taken in this study were the objective measurement whether the participants kept the robot's secret or not. Questionnaires were taken to investigate participants' perception of the robot and its feelings, as well as their experienced level of presence and their tendency to become immersed in the virtual environment. Results show that the participants responded differently in the virtual reality study when compared to the original real world study, where the secret was kept more often for the non-social robot, but less often for the social robot. In both the original and replicated study a strong, positive correlation was found between participants' perception of the robot as a social being and their tendency to keep the robot's secret.These inconclusive findings, some changes that were required for the virtual environment compared to the original study, and different participant demographics indicate that more work is needed to determine whether virtual reality can be used as a tool to conduct human-robot interaction experiments. © 2020 IEEE.",,"Agricultural robots; End effectors; Man machine systems; Surveys; Virtual reality; Objective measurement; Positive correlations; Real-world; Social beings; Tour guide; User study; Virtual museum tours; Social robots",,,,,"Engineering and Physical Sciences Research Council, EPSRC; University of Bristol","*This project was funded by the EPSRC and part of the FARSCOPE CDT at the Bristol Robotics Laboratory, University of the West of England and University of Bristol.",,,,,,,,,,"Kahn, P.H., Jr., Kanda, T., Ishiguro, H., Gill, B.T., Shen, S., Gary, H.E., Ruckert, J.H., Will people keep the secret of a humanoid robot?: Psychological intimacy in hri (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. 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IEEE, pp. 431-439",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095753006
"Mou W., Ruocco M., Zanatto D., Cangelosi A.","56684766800;57201548087;55463260500;6701387796;","When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223551","956","962",,2,"10.1109/RO-MAN47096.2020.9223551","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095744596&doi=10.1109%2fRO-MAN47096.2020.9223551&partnerID=40&md5=7f363e382006cd13e8d88687a302976d","University of Manchester, Department of Computer Science, United Kingdom; University of Bristol, Department of Computer Science, United Kingdom","Mou, W., University of Manchester, Department of Computer Science, United Kingdom; Ruocco, M., University of Manchester, Department of Computer Science, United Kingdom; Zanatto, D., University of Bristol, Department of Computer Science, United Kingdom; Cangelosi, A., University of Manchester, Department of Computer Science, United Kingdom","Trust is a critical issue in human-robot interactions (HRI) as it is the core of human desire to accept and use a non-human agent. Theory of Mind (ToM) has been defined as the ability to understand the beliefs and intentions of others that may differ from one's own. Evidences in psychology and HRI suggest that trust and ToM are interconnected and interdependent concepts, as the decision to trust another agent must depend on our own representation of this entity's actions, beliefs and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we investigated whether the exposure to the ToM abilities of a robot could affect humans' trust towards the robot. To this end, participants played a Price Game with a humanoid robot (Pepper) that was presented having either low-level ToM or high-level ToM. Specifically, the participants were asked to accept the price evaluations on common objects presented by the robot. The willingness of the participants to change their own price judgement of the objects (i.e., accept the price the robot suggested) was used as the main measurement of the trust towards the robot. Our experimental results showed that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Man machine systems; Critical issues; Human agent; Human robot Interaction (HRI); Humanoid robot; Price games; Theory of minds; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR; U.S. Air Force, USAF: FA9550-19-1-7002; Engineering and Physical Sciences Research Council, EPSRC: EP/R004757/1; University of Bristol; Thales Group, Thales","ACKNOWLEDGEMENT This material is based upon work supported by the Air Force Office of Scientific Research, USAF under Award No. FA9550-19-1-7002. The work of Debora Zanatto was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’.",,,,,,,,,,"Vinanzi, S., Patacchiola, M., Chella, A., Cangelosi, A., Would a robot trust you? Developmental robotics model of trust and theory of mind (2019) Philosophical Transactions of the Royal Society B, 374 (1771), p. 20180032; Zanatto, D., Patacchiola, M., Goslin, J., Cangelosi, A., Investigating cooperation with robotic peers (2019) PloS One, 14 (11); Romeo, M., Hernández Garca, D., Jones, R., Cangelosi, A., Deploying a deep learning agent for hri with potential (2019) Proceedings of the 7th International Conference on Human-Agent Interaction, pp. 81-88; Hancock, P.A., Billings, D.R., Schaefer, K.E., Can you trust your robot? (2011) Ergonomics in Design, 19 (3), pp. 24-29; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708-713; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust (2015) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 1-8; Shu, P., Min, C., Bodala, I., Nikolaidis, S., Hsu, D., Soh, H., Human trust in robot capabilities across tasks (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 241-242; Zanatto, D., Phd thesis: When do we cooperate with robots ? Investigations in human-robot interaction and trust (2019) University of Plymouth; Ham, J., Bokhorst, R., Cuijpers, R., Van, P.D.D., Cabibihan, J.-J., Making robots persuasive: The influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power (2011) International Conference on Social Robotics, pp. 71-83; Huang, C.-M., Thomaz, A.L., Joint attention in human-robot interaction (2010) 2010 AAAI Fall Symposium Series; Leslie, A.M., Pretense and representation: The origins of theory of mind (1987) Psychological Review, 94 (4), p. 412; Vanderbilt, K.E., Liu, D., Heyman, G.D., The development of distrust (2011) Child Development, 82 (5), pp. 1372-1380; Scassellati, B., Theory of mind for a humanoid robot (2002) Autonomous Robots, 12 (1), pp. 13-24; Rau, P.P., Li, Y., Li, D., Effects of communication style and culture on ability to accept recommendations from robots (2009) Computers in Human Behavior, 25 (2), pp. 587-595; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Academy of Management Review, 20 (3), pp. 709-734; Schaefer, K., (2013) The Perception and Measurement of Human-robot Trust; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Desai, M., Medvedev, M., Vázquez, M., McSheehy, S., Gadea-Omelchenko, S., Bruggeman, C., Steinfeld, A., Yanco, H., Effects of changing reliability on trust of robot systems (2012) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 73-80; Lohani, M., Stokes, C., McCoy, M., Bailey, C.A., Rivers, S.E., Social interaction moderates human-robot trust-reliance relationship and improves stress coping (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 471-472; Bellas, F., Duro, R.J., Faiña, A., Souto, D., Multilevel darwinist brain (mdb): Artificial evolution in a cognitive architecture for real robots (2010) IEEE Transactions on Autonomous Mental Development, 2 (4), pp. 340-354; Blum, C., Winfield, A.F., Hafner, V.V., Simulation-based internal models for safer robots (2018) Frontiers in Robotics and AI, 4, p. 74; Chella, A., Dindo, H., Infantino, I., A cognitive framework for imitation learning (2006) Robotics and Autonomous Systems, 54 (5), pp. 403-408; Augello, A., Cipolla, E., Infantino, I., Manfré, A., Pilato, G., Vella, F., Social signs processing in a cognitive architecture for an humanoid robot (2018) Procedia Computer Science, 123, pp. 63-68; Lazzeri, N., Mazzei, D., Cominelli, L., Cisternino, A., De Rossi, D.E., Designing the mind of a social robot (2018) Applied Sciences, 8 (2), p. 302; Baron-Cohen, S., Leslie, A.M., Frith, U., Does the autistic child have a theory of mind (1985) Cognition, 21 (1), pp. 37-46; Sturgeon, S., Palmer, A., Blankenburg, J., Feil-Seifer, D., Perception of social intelligence in robots performing false-belief tasks (2019) IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7; Faul, F., Erdfelder, E., Buchner, A., Lang, A.-G., Statistical power analyses using g power 3. 1: Tests for correlation and regression analyses (2009) Behavior Research Methods, 41 (4), pp. 1149-1160; Zanatto, D., Patacchiola, M., Cangelosi, A., Goslin, J., Generalisation of anthropomorphic stereotype (2019) International Journal of Social Robotics, pp. 1-10; Zanatto, D., Phd thesis: When do we cooperate with robots? (2019) University of Plymouth; Reysen, S., Construction of a new scale: The reysen likability scale (2005) Social Behavior and Personality: An International Journal, 33 (2), pp. 201-208; McCroskey, J.C., Young, T.J., Ethos and credibility: The construct and its measurement after three decades (1981) Communication Studies, 32 (1), pp. 24-34; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Zanatto, D., Patacchiola, M., Goslin, J., Cangelosi, A., Priming anthropomorphism: Can the credibility of humanlike robots be transferred to non-humanlike robots? (2016) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 543-544; Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., Schmitt, M., A meta-analysis on the correlation between the implicit association test and explicit self-report measures (2005) Personality and Social Psychology Bulletin, 31 (10), pp. 1369-1385",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095744596
"Scheunemann M.M., Cuijpers R.H., Salge C.","56549821200;6602683032;31767711200;","Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223478","1340","1347",,2,"10.1109/RO-MAN47096.2020.9223478","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095742220&doi=10.1109%2fRO-MAN47096.2020.9223478&partnerID=40&md5=b2675f85f54c58338810c0c5bec140e0","University of Hertfordshire, School of Computer Science, Hatfield, AL10 0UT, United Kingdom; Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands","Scheunemann, M.M., University of Hertfordshire, School of Computer Science, Hatfield, AL10 0UT, United Kingdom; Cuijpers, R.H., Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands; Salge, C., University of Hertfordshire, School of Computer Science, Hatfield, AL10 0UT, United Kingdom","A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Man machine systems; Human perception; Human robot Interaction (HRI); Physical human-robot interactions; Predictive power; Robot behavior; Social attributes; Social cognition; Visual observations; Social robots",,,,,,,,,,,,,,,,"Fiske, S.T., Cuddy, A.J.C., Glick, P., Universal dimensions of social cognition: Warmth and competence (2007) Trends in Cognitive Sciences, 11, pp. 77-83; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceeding of the 1st ACM SIGCHI/SIGART Conference on Humanrobot interaction-HRI'06, pp. 33-40. , ACM. ACM Press; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Weiss, A., Bartneck, C., Meta analysis of the usage of the godspeed questionnaire series (2015) 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 381-388; Ho, C.-C., MacDorman, K.F., Revisiting the uncanny valley theory: Developing and validating an alternative to the godspeed indices (2010) Computers in Human Behavior, 26 (6), pp. 1508-1518; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas): Development and validation (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '17, pp. 254-262. , New York, NY, USA: ACM; Judd, C.M., James-Hawkins, L., Yzerbyt, V., Kashima, Y., Fundamental dimensions of social judgment: Understanding the relations between judgments of competence and warmth (2005) Journal of Personality and Social Psychology, 89 (6), pp. 899-913; Cuddy, A.J.C., Fiske, S.T., Glick, P., The bias map: Behaviors from intergroup affect and stereotypes (2007) Journal of Personality and Social Psychology, 92, pp. 631-648. , 4; Stroessner, S.J., On the social perception of robots: Measurement, moderation, and implications (2020) Living with Robots, pp. 21-47. , R. Pak, E. J. de Visser, and E. Rovira, Eds. Academic Press; Goldman, A., De Vignemont, F., Is social cognition embodied? (2009) Trends in Cognitive Sciences, 13 (4), pp. 154-159; Dautenhahn, K., I could be you: The phenomenological dimension of social understanding (1997) Cybernetics and Systems, 28 (5), pp. 417-453; Beer, J.M., Fisk, A.D., Rogers, W.A., Toward a framework for levels of robot autonomy in human-robot interaction (2014) Journal of Human-Robot Interaction, 3 (2), pp. 74-99; Scheunmann, M.M., (2020) Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction, , https://mms.ai/ro-man2020, supplementary material for this paper; Scheunemann, M.M., Salge, C., Dautenhahn, K., Intrinsically motivated autonomy in human-robot interaction: Human perception of predictive information in robots (2019) Towards Autonomous Robotic Systems, pp. 325-337. , Cham: Springer International Publishing; (2020) Sphero Support & Help, , https://support.sphero.com/category/kxwbdyqeyq-bb-8, Sphero, Inc; Scheunemann, M.M., Dautenhahn, K., Salem, M., Robins, B., Utilizing bluetooth low energy to recognize proximity, touch and humans (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 362-367; Oudeyer, P.-Y., Kaplan, F., What is intrinsic motivation? A typology of computational approaches (2009) Frontiers in Neurorobotics, 1, p. 6; Martius, G., Der, R., Ay, N., Information driven self-organization of complex robotic behaviors (2013) PLOS ONE, 8 (5), pp. 1-14; Der, R., Martius, G., (2012) The Playful Machine: Theoretical Foundation and Practical Realization of Self-Organizing Robots, Ser. Cognitive Systems Monographs, 15. , Springer-Verlag Berlin Heidelberg; Scheunemann, M.M., Salge, C., Polani, D., Dautenhahn, K., (2020) Human Perception of Intrinsically Motivated Autonomy in Human-Robot Interaction, , https://arxiv.org/pdf/2002.05936; Nehmzow, U., (2006) Scientific Methods in Mobile Robotics, 24 (1-2). , Springer-Verlag GmbH",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095742220
"Ye S., Feigh K., Howard A.","57193499397;15831610200;26643170000;","Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223437","1186","1189",,,"10.1109/RO-MAN47096.2020.9223437","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095735091&doi=10.1109%2fRO-MAN47096.2020.9223437&partnerID=40&md5=74bfb142371d9fab0e75afbaa05d3b0c","Georgia Institute of Technology, Atlanta, GA, United States","Ye, S., Georgia Institute of Technology, Atlanta, GA, United States; Feigh, K., Georgia Institute of Technology, Atlanta, GA, United States; Howard, A., Georgia Institute of Technology, Atlanta, GA, United States","Embodiment of actions and tasks has typically been analyzed from the robot's perspective where the robot's embodiment helps develop and maintain trust. However, we ask a similar question looking at the interaction from the human perspective. Embodied cognition has been shown in the cognitive science literature to produce increased social empathy and cooperation. To understand how human embodiment can help develop and increase trust in human-robot interactions, we created conducted a study where participants were tasked with memorizing greek letters associated with dance motions with the help of a humanoid robot. Participants either performed the dance motion or utilized a touch screen during the interaction. The results showed that participants' trust in the robot increased at a higher rate during human embodiment of motions as opposed to utilizing a touch screen device. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Man machine systems; Touch screens; Cognitive science; Dynamic interaction; Embodied cognition; Greek letters; Human perspectives; Humanoid robot; Social robots",,,,,,,,,,,,,,,,"Barsalou, L.W., Niedenthal, P.M., Barbey, A.K., Ruppert, J.A., Social embodiment (2003) The Psychology of Learning and Motivation: Advances in Research and Theory, 43, pp. 43-92. , Elsevier Science, Barsalou, Lawrence W. : Emory University, Dept of Psychology, Atlanta, GA, US, 30322 barsalou@emory. edu; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2008) International Journal of Social Robotics, 1, pp. 71-81; Breazeal, C., Gray, J., Berlin, M., An embodied cognition approach to mindreading skills for socially intelligent robots (2009) The International Journal of Robotics Research, 28 (5), pp. 656-680; Dijksterhuis, A., Bargh, J.A., The perception-behavior expressway: Automatic effects of social perception on social behavior (2001) Volume 33 of Advances in Experimental Social Psychology, pp. 1-40. , Academic Press; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, C.J.Y., Visser De, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Madhavan, P., Wiegmann, D.A., Similarities and differences between human-human and human-automation trust: An integrative review (2007) Theoretical Issues in Ergonomics Science, 8 (4), pp. 277-301; Meltzoff, A.N., Elements of a developmental theory of imitation (2002) The Imitative Mind: Development, Evolution, and Brain Bases., Cambridge Studies in Cognitive Perceptual Development., pp. 19-41. , Cambridge University Press, New York, NY, US; Rambusch, J., Ziemke, T., The role of embodiment in situated learning (2005) Proceedings of the 27th Annual Conference of the Cognitive Science Society : CogSci05, pp. 1803-1808. , University of Skövde, School of Humanities and Informatics; Schaefer, K.E., (2016) Measuring Trust in Human Robot Interactions: Development of the Trust Perception Scale-HRI, pp. 191-218. , Springer US, Boston, MA; Gregory Trafton, J., Hiatt, L.M., Harrison, A.M., Tamborello, F.P., Khemlani, S.S., Schultz, A.C., ACT-r/e: An embodied cognitive architecture for human-robot interaction (2013) J. Hum.-Robot Interact., 2 (1), pp. 30-55. , feb; Wainer, J., Feil-Seifer, D.J., Shell, D.A., Mataric, M.J., The role of physical embodiment in human-robot interaction (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 117-122; Wilson, R.A., Foglia, L., Embodied cognition (2017) Edward N Zalta, Editor, the Stanford Encyclopedia of Philosophy, , Metaphysics Research Lab, Stanford University, spring 2017 edition",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095735091
"Zhang Y., Zhang G., Du Y., Yu Wang M.","57209275594;57205668441;57219605770;54959897800;","VTacArm. A Vision-based Tactile Sensing Augmented Robotic Arm with Application to Human-robot Interaction",2020,"IEEE International Conference on Automation Science and Engineering","2020-August",,"9217019","35","42",,1,"10.1109/CASE48305.2020.9217019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094141371&doi=10.1109%2fCASE48305.2020.9217019&partnerID=40&md5=f05f6cc39bd3f0a1505b73b1b435e4aa","Hong Kong University of Science and Technology, Department of Mechanical and Aerospace Engineering, Department of Electronic and Computer Engineering, Hong Kong, 852-34692544, Hong Kong","Zhang, Y., Hong Kong University of Science and Technology, Department of Mechanical and Aerospace Engineering, Department of Electronic and Computer Engineering, Hong Kong, 852-34692544, Hong Kong; Zhang, G., Hong Kong University of Science and Technology, Department of Mechanical and Aerospace Engineering, Department of Electronic and Computer Engineering, Hong Kong, 852-34692544, Hong Kong; Du, Y., Hong Kong University of Science and Technology, Department of Mechanical and Aerospace Engineering, Department of Electronic and Computer Engineering, Hong Kong, 852-34692544, Hong Kong; Yu Wang, M., Hong Kong University of Science and Technology, Department of Mechanical and Aerospace Engineering, Department of Electronic and Computer Engineering, Hong Kong, 852-34692544, Hong Kong","Endowing artificial sense of touch comparable to human's has been challenging, yet significant to enabling adaptive and collaborative interaction in contact-rich tasks. This work is dedicated to proposing a novel vision-based tactile sensor augmented robot arm (VTacArm) design with full surface coverage and developing algorithms to retrieve contact information which is essential for down-stream feedback control. We first introduce the robot arm design and its accompanying fabrication process. Then, to convert the contact signals in the image space to the arm coordinate system, a calibration procedure and method are proposed. Finally, the tactile robot arm and the contact information extraction algorithm are integrated into a control system for collaborative interaction tasks. Bumping detection/reaction and contact motion following experiments are presented to justify that the designed tactile robot arm and proposed contact sensing method are beneficial and give robot capabilities to adapt to human contacts, which is vital for workers' safety. Our work can be informative for developing novel full-body vision-based tactile sensing on robots as a new concept with significantly lower cost and manageable fabrication complexity. © 2020 IEEE.",,"Biofeedback; Machine design; Man machine systems; Robotic arms; Robotics; Visual servoing; Calibration procedure; Co-ordinate system; Collaborative interaction; Contact sensing; Fabrication process; Surface coverages; Tactile sensors; Vision-based tactile sensing; Human robot interaction",,,,,"Innovation and Technology Fund, ITF: ITS-018-17FP","This work was supported by the Hong Kong Innovation and Technology Fund (ITF) ITS-018-17FP.",,,,,,,,,,"Dahiya, R.S., Metta, G., Valle, M., Sandini, G., Tactile sensing-from humans to humanoids (2010) Ieee Trans. Robotics, 26 (1), pp. 1-20; Mittendorfer, P., Yoshida, E., Cheng, G., Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot (2015) Advanced Robotics, 29 (1), pp. 51-67; Sato, K., Kamiyama, K., Kawakami, N., Tachi, S., Finger-shaped gelforce: Sensor for measuring surface traction fields for robotic hand (2010) Ieee Transactions on Haptics, 3 (1), pp. 37-47; Yamaguchi, A., Atkeson, C.G., Combining finger vision and optical tactile sensing: Reducing and handling errors while cutting vegetables (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 1045-1051. , ieee; Yuan, W., Li, R., Srinivasan, M.A., Adelson, E.H., Measurement of shear and slip with a gelsight tactile sensor (2015) 2015 Ieee International Conference on Robotics and Automation (ICRA). 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Ieee, pp. 1927-1934; Zhang, Y., Kan, Z., Yang, Y., Tse, Y.A., Wang, M.Y., Effective estimation of contact force and torque for vision-based tactile sensors with helmholtz-hodge decomposition (2019) Ieee Robotics and Automation Letters, 4 (4), pp. 4094-4101; Zhang, Y., Yuan, W., Kan, Z., Wang, M.Y., (2019) Towards Learning to Detect and Predict Contact Events on Vision-Based Tactile Sensors, , arXiv preprint; Johansson, R.S., Vallbo, A., Tactile sensibility in the human hand: Relative and absolute densities of four types of mechanoreceptive units in glabrous skin (1979) The Journal of Physiology, 286 (1), pp. 283-300; Mukai, T., Onishi, M., Odashima, T., Hirano, S., Luo, Z., Development of the tactile sensor system of a human-interactive robot ""ri-man"" (2008) Ieee Transactions on Robotics, 24 (2), pp. 505-512; Kumagai, I., Kobayashi, K., Nozawa, S., Kakiuchi, Y., Yoshikai, T., Okada, K., Inaba, M., Development of a full body multi-Axis soft tactile sensor suit for life sized humanoid robot and an algorithm to detect contact states (2012) 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids2012), pp. 526-531; Ohmura, Y., Kuniyoshi, Y., Humanoid robot which can lift a 30kg box by whole body contact and tactile feedback (2007) 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1136-1141; Johnson, M.K., Adelson, E.H., Retrographic sensing for the measurement of surface texture and shape (2009) 2009 Ieee Conference on Computer Vision and Pattern Recognition, pp. 1070-1077; Dong, S., Yuan, W., Adelson, E.H., Improved gelsight tactile sensor for measuring geometry and slip (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 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Conf. Autom. Sci. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85094141371
"Ben Allouch S., de Graaf M., Šabanović S.","25928331900;55919980400;14036199300;","Introduction to the Special Issue on the Mutual Shaping of Human–Robot Interaction",2020,"International Journal of Social Robotics","12","4",,"843","845",,,"10.1007/s12369-020-00681-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089159590&doi=10.1007%2fs12369-020-00681-6&partnerID=40&md5=daca88c3d5975df14f74a03b0f67d0f5","Amsterdam University of Applied Sciences, Amsterdam, Netherlands; University of Utrecht, Utrecht, Netherlands; Indiana University Bloomington, Bloomington, United States","Ben Allouch, S., Amsterdam University of Applied Sciences, Amsterdam, Netherlands; de Graaf, M., University of Utrecht, Utrecht, Netherlands; Šabanović, S., Indiana University Bloomington, Bloomington, United States",[No abstract available],,,,,,,,,,,,,,,,,,,"de Graaf, M.; University of UtrechtNetherlands; email: m.m.a.degraaf@uu.nl",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Editorial,"Final","All Open Access, Bronze",Scopus,2-s2.0-85089159590
"Lindblom J., Alenljung B.","7006805787;24402835300;","The anemone: Theoretical foundations for UX evaluation of action and intention recognition in human-robot interaction",2020,"Sensors (Switzerland)","20","15","4284","1","49",,9,"10.3390/s20154284","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088948958&doi=10.3390%2fs20154284&partnerID=40&md5=3df110e0ea1f318acc3e80ab4acb0001","School of Informatics, University of Skövde, Box 408, Skövde, 541 28, Sweden","Lindblom, J., School of Informatics, University of Skövde, Box 408, Skövde, 541 28, Sweden; Alenljung, B., School of Informatics, University of Skövde, Box 408, Skövde, 541 28, Sweden","The coexistence of robots and humans in shared physical and social spaces is expected to increase. A key enabler of high-quality interaction is a mutual understanding of each other’s actions and intentions. In this paper, we motivate and present a systematic user experience (UX) evaluation framework of action and intention recognition between humans and robots from a UX perspective, because there is an identified lack of this kind of evaluation methodology. The evaluation framework is packaged into a methodological approach called ANEMONE (action and intention recognition in human robot interaction). ANEMONE has its foundation in cultural-historical activity theory (AT) as the theoretical lens, the seven stages of action model, and user experience (UX) evaluation methodology, which together are useful in motivating and framing the work presented in this paper. The proposed methodological approach of ANEMONE provides guidance on how to measure, assess, and evaluate the mutual recognition of actions and intentions between humans and robots for investigators of UX evaluation. The paper ends with a discussion, addresses future work, and some concluding remarks. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Action recognition; Activity theory; Evaluation; Human-robot collaboration; Human-robot interaction; Intention recognition; Seven stages of action; User experience (UX); User-centered","Air navigation; Man machine systems; User experience; Action and intention; Cultural-historical; Evaluation framework; Evaluation methodologies; Methodological approach; Mutual understanding; Theoretical foundations; User experiences (ux); Social robots; behavior; human; robotics; Humans; Intention; Recognition, Psychology; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 637107; European Commission, EC; Stiftelsen för Kunskaps- och Kompetensutveckling, KKS: 20140220","Funding: This work was financially supported by the Knowledge Foundation, Stockholm, under SIDUS grant agreement no. 20140220 (AIR, action and intention recognition in human interaction with autonomous systems) as well as the European Commission under the Horizon 2020 Programme under grant agreement no. 637107 (SYMBIO-TIC).",,,,,,,,,,"Stephanidis, C., Salvendy, G., Antona, M., Chen, J.Y., Dong, J., Duffy, V.G., Guo, Y., Seven HCI Grand Challenges (2019) Int. 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"Dong J., Xu J., Zhou Q., Hu S.","57217493071;56260858000;57215325244;57217485746;","Physical human–robot interaction force control method based on adaptive variable impedance",2020,"Journal of the Franklin Institute","357","12",,"7864","7878",,4,"10.1016/j.jfranklin.2020.06.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087315934&doi=10.1016%2fj.jfranklin.2020.06.007&partnerID=40&md5=66c9a158e1c27e25b13102f8afe53591","College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China","Dong, J., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China; Xu, J., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China; Zhou, Q., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China; Hu, S., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China","This study presents a method for extracting the actual interaction force from a force sensor signal by measuring the gravity of the end-effector. The variable impedance control problem of commercial industrial robots is studied, to investigate the uncertain contact impedance characteristics between the human palm and a robot end-effector in the physical human-robot interaction process. A speed-based variable impedance adaptive interaction control method is proposed. The damping parameters of the admittance controller are adaptively adjusted according to the interactive force tracking error. The desired speed of the robot is also given, which is tracked in the Cartesian space speed mode of a Stäubli TX-90 robot to achieve interactive force control. An experimental comparison is made between the control strategies of the constant impedance and adaptive variable impedance. The experimental results show that the proposed method is effective. © 2020",,"End effectors; Force control; Industrial robots; Adaptive interaction; Control strategies; Experimental comparison; Force sensor signals; Interaction forces; Physical human-robot interactions; Robot interactions; Variable impedance control; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61374103, U1709213","This work was supported in part by the National Natural Science Foundation of China under Grants U1709213 and 61374103 .",,,,,,,,,,"Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Tsukuba, Japan, pp. 446-451; Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A., Collaborative human-humanoid carrying using visionand haptic sensing (2014) IEEE International Conference on Robotics and Automation, Hong Kong, China, pp. 607-612; Gams, A., Nemec, B., Ijspeert, A.J., Ude, A., Coupling movement primitives: interaction with the environment and bimanual tasks (2014) IEEE Trans. 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Manuf., 27 (1), pp. 177-185; Tian, Z., Gao, X., Su, S., Qiu, J., Du, X., Guizani, M., Evaluating reputation management schemes of internet of vehicles based on evolutionary game theory (2019) IEEE Trans. Veh. Technol., 68 (6), pp. 5971-5980; Tian, Z., Su, S., Shi, W., Du, X., Guizani, M., Yu, X., A data-driven model for future internet route decision modeling (2019) Future Gener. Comput. Syst., 95, pp. 212-220; Tian, Z., Shi, W., Wang, Y., Zhu, C., Du, X., Su, S., Real-time lateral movement detection based on evidence reasoning network for edge computing environment (2019) IEEE Trans. Ind. Inform., 15 (7), pp. 4285-4294; Li, H., Stable and compliant motion of physical human-robot interaction coupled with a moving environment using variable admittance and adaptive control (2018) IEEE Robot. Autom. Lett., 3, pp. 2493-2500; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 089-1100; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human–robot physical interaction (2015) IEEE Trans. Robot., 31, pp. 850-863; Hogan, N., Impedance control - an approach to manipulation.I - theory. II - implementation. III- applications (1985) Asme Trans. J. Dyn. Syst. Meas. Control, 107, pp. 304-313; Craig, J.J., Raibert, M.H., A systematic method of hybrid position/force control of a manipulator (1979) IEEE International Computer Software and Applications Conference, Chicago, United States, pp. 446-451; Zhen, Y., (2015), An overview on compliance control of robot based on the impedance control, nanjing; Bowyer, S., Rodriguez, Y.B.F., Dissipative control for physical human–robot interaction (2017) IEEE Trans. Robot., 31 (6), pp. 1281-1293; Aydin, Y., Tokatli, O., Patoglu, V., Stable physical human–robot interaction using fractional order admittance control (2018) IEEE Trans. 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Technol., 12 (3), pp. 474-483; Lecours, A., Mayerstonge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) IEEE International Conference on Robotics and Automation, Saint Paul minnesota, United States, pp. 3903-3908; John, J.C., Introduction to Robotics: Mechanics and Control (2003), Pearson Education International; Zhao, M., Application and Software Development of Process Control System for Assembly Robot (2016), Southeast University","Xu, J.; College of Information Engineering, 288 Liuhe Road, China; email: xujm@zjut.edu.cn",,,"Elsevier Ltd",,,,,00160032,,JFINA,,"English","J Franklin Inst",Article,"Final","",Scopus,2-s2.0-85087315934
"Yang J., Peng C.","56549263100;57201132375;","Adaptive neural impedance control with extended state observer for human–robot interactions by output feedback through tracking differentiator",2020,"Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering","234","7",,"820","833",,1,"10.1177/0959651819898936","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079156959&doi=10.1177%2f0959651819898936&partnerID=40&md5=933fcc3f0a0e94ddabc62a22c5a969f2","State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China","Yang, J., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China; Peng, C., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China","Although impedance control has huge application potential in human–robot cooperation, its engineering application is still quite limited, owing to the high nonlinearity of the human–robot dynamics and disturbances. This article presents a novel adaptive neural network controller with extended state observer for the human–robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human–machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human–machine interactions to enhance productivity and efficiency. © IMechE 2020.","Adaptive neural network; extended state observer; human–robot interaction; impedance control; output feedback","Adaptive control systems; Closed loop systems; Controllers; Human robot interaction; Neural networks; State estimation; Adaptive neural networks; Extended state observer; Impedance control; Output feedback; Robot interactions; Feedback",,,,,,,,,,,,,,,,"Kazerooni, H., Human-robot interaction via the transfer of power and information signals (1990) IEEE T Syst Man Cyb, 20 (2), pp. 450-463; Xing, C., Yan, Z., Yin, Y., Improving the transparency of an exoskeleton knee joint based on the understanding of motor intent using energy kernel method of EMG (2017) IEEE T Neur Sys Reh, 25 (6), pp. 577-588; Kosuge, K., Hirata, Y., Human-robot interaction (2003) IEEE Robot Autom Mag, 17 (2), pp. 85-89; Fan, Y., Yin, Y., Active and progressive exoskeleton rehabilitation using multisource information fusion from EMG and force-position EPP (2013) IEEE T Bio-Med Eng, 60 (12), pp. 3314-3321; Hogan, N., Impedance control: an approach to manipulation: part II—implementation (1985) J Dyn Syst-T ASME, 107 (1), pp. 8-16; Lu, Z., Goldenberg, A.A., Robust impedance control and force regulation: theory and experiments (1995) Int J Robot Res, 14, pp. 225-254; Carelli, R., Kelly, R., An adaptive impedance/force controller for robot manipulators (1991) IEEE T Automat Contr, 36 (8), pp. 967-971; Sharifi, M., Behzadipour, S., Vossoughi, G., Nonlinear model reference adaptive impedance control for human–robot interactions (2014) Control Eng Pract, 32, pp. 9-27; Liu, Y.J., Lu, S., Tong, S., Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints (2018) Automatica, 87, pp. 83-93; Yao, B., (1996) Adaptive robust control of nonlinear systems with application to control of mechanical systems, , Berkeley, CA, University of California, Berkeley; Yao, B., Tomizuka, M., Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form (1997) Automatica, 33 (5), pp. 893-900; 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Liu, L., Liu, Y.J., Tong, S., Fuzzy-based multierror constraint control for switched nonlinear systems and its applications (2019) IEEE T Fuzzy Syst, 27, pp. 1519-1531; Ge, S.S., Hang, C.C., Woon, L.C., Impedance control of robot manipulators using adaptive neutral network (1998) Int J Intell Control Syst, 2 (3), pp. 433-452; Yang, C., Peng, G., Li, Y., Neural networks enhanced adaptive admittance control of optimized robot-environment interaction (2019) IEEE T Cybernetics, 49, pp. 2568-2579; He, W., Ge, S.S., Li, Y., Neural network control of a rehabilitation robot by state and output feedback (2015) J Intell Robot Syst, 80 (1), pp. 15-31; Chen, M., Ge, S.S., Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer (2015) IEEE T Ind Electron, 62 (12), pp. 7706-7716; Ren, B., Ge, S.S., Tee, K.P., Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function (2010) IEEE T Neural Networ, 21 (8), pp. 1339-1345; Guo, B.Z., Zhao, Z.L., On the convergence of an extended state observer for nonlinear systems with uncertainty (2011) Syst Control Lett, 60 (6), pp. 420-430; Wang, Y., Shi, R., Wang, H., ESO-Based fuzzy sliding-mode control for a 3-DOF serial-parallel hybrid humanoid arm (2014) J Control Sci Eng, 2014, p. 304590; Han, J.Q., Nonlinear tracking-differentiator (1994) J Syst Sci Math Sci, 14 (2), pp. 177-183. , (,):, –, (in Chinese; Meng, W., Yang, Q., Pan, D., NN-based asymptotic tracking control for a class of strict-feedback uncertain nonlinear systems with output constraints, pp. 5410-5415. , Proceedings of the 51st IEEE conference on decision and control (CDC), Maui, HI, 10–13 December 2012, New York, IEEE, In; Slotine, J.J.E., Li, W., (1991) Applied nonlinear control, , Englewood Cliffs, NJ, Prentice Hall; Wilson, M., Spong, M.W., Robot modeling and control (2006) Ind Robot Int J, 17 (5), pp. 709-737; Wang, J., Li, S., Yang, J., Extended state observer-based sliding mode control for PWM-based DC–DC buck power converter systems with mismatched disturbances (2015) IET Control Theory A, 9 (4), pp. 579-586; Yao, J., Jiao, Z., Ma, D., Adaptive robust control of DC motors with extended state observer (2014) IEEE T Ind Electron, 61, pp. 3630-3637; Tan, C.P., Yu, X., Man, Z., Terminal sliding mode observers for a class of nonlinear systems (2010) Automatica, 46 (8), pp. 1401-1404","Yang, J.; State Key Laboratory of Mechanical System and Vibration, China; email: JTYang@sjtu.edu.cn",,,"SAGE Publications Ltd",,,,,09596518,,PMJEE,,"English","Proc Inst Mech Eng Part I J Syst Control Eng",Article,"Final","",Scopus,2-s2.0-85079156959
"Fortunati L., Cavallo F., Sarrica M.","16244589600;14628324200;22952040200;","Multiple Communication Roles in Human–Robot Interactions in Public Space",2020,"International Journal of Social Robotics","12","4",,"931","944",,4,"10.1007/s12369-018-0509-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064480199&doi=10.1007%2fs12369-018-0509-0&partnerID=40&md5=050df40d3835884a4fb28453a0104efe","Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze, 206, Udine, 33100, Italy; The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera, Pisa, 56025, Italy; Department of Communication and Social Research, Sapienza, University of Rome, Via Salaria 113, Rome, 00198, Italy","Fortunati, L., Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze, 206, Udine, 33100, Italy; Cavallo, F., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera, Pisa, 56025, Italy; Sarrica, M., Department of Communication and Social Research, Sapienza, University of Rome, Via Salaria 113, Rome, 00198, Italy","This study looks at robots as media and aims to explore the multiple communication roles that they can play in public space. We have analyzed three scenarios: European Researchers’ Night in Pisa; the inauguration of the University of Udine’s 39th academic year; and the official video of the inauguration in Udine. These three scenarios represent three types of media interactions. The first is a human–robot interaction based on a one-to-one or circular communication model; the second is a robot–human interaction based on a one-to-many communication model in-presence; and the third is a robot–human interaction, based on the classical one-to-many communication model mediated through a television screen. Results show that public patterns of behavior toward the robot tend to replicate the ritualization of encounters between humans in the one-to-one model, and audience rituals in public events toward human characters in the one-to-many model. Second, greater proximity and familiarity with the robot increases the respondents’ positive evaluations of all the aspects of the interactions. These results are in line with results coming from research on Information and Communication Technologies (ICT) use. © 2018, Springer Nature B.V.","Human–robot communication; Public interaction with social robots; Robots as public figures; Robots in public space; Robots’ multiple communication roles; Robot–humans communication","Agricultural robots; Information theory; Communication modeling; Human interactions; Information and Communication Technologies; Public figure; Public interactions; Public space; Robot communication; Robot interactions; Social robots",,,,,,,,,,,,,,,,"de Graaf, M.M.A., Ben Allouch, S., Jan van Dijk, J.A.G.M., Long-term evaluation of a social robot in real homes (2017) Interact Stud, 17 (3), pp. 462-491; Coeckelbergh, M., Humans, animals, and robots: a phenomenological approach to human–robot relations (2010) Int J Soc Robot, 3 (2), pp. 197-204; Cabibihan, J.J., Williams, M.A., Simmons, R., When robots engage humans (2014) Int J Soc Robot, 6 (3), pp. 311-313; Wolbring, G., Yumakulov, S., Social robots: views of staff of a disability service organization (2014) Int J Soc Robot, 6 (3), pp. 457-468; Stafford, R.Q., MacDonald, B.A., Jayawardena, C., Wegner, D.M., Broadbent, E., Does the robot have a mind? Mind perception and attitudes towards robots predict use of an eldercare robot (2014) Int J Soc Robot, 6 (1), pp. 17-32; Fortunati, L., Esposito, A., Sarrica, M., Ferrin, G., Children’s knowledge and imaginary about robots (2015) Int J Soc Robot, 7, pp. 685-695; Zhao, S., Humanoid social robots as a medium of communication (2006) New Media Soc, 8, pp. 401-419; Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A., Siegwart, R., Robots meet humans—interaction in public spaces (2005) IEEE Trans Industr Electron, 52, pp. 1-17; Sone, Y., (2016) Japanese robot culture, , Springer, New York; Niemelä, M., Heikkilä, P., Lammi, H., A social service robot in a shopping mall: Expectations of the management, retailers and consumers (2017) Proceeding HRI ‘17. Proceedings of the Companion of the 2017 ACM/IEEE International Conference on human–robot Interaction, pp. 227-228; Salvini, P., Urban robotics: towards responsible innovations for our cities (2017) Robot Auton Syst, 100, pp. 278-286; Cavallo, F., Limosani, R., Manzi, A., Bonaccorsi, M., Esposito, R., Di Rocco, M., Dario, P., Development of a socially believable multi-robot solution from town to home (2014) Cogn Comput, 6, pp. 954-967; Höflich, J.R., Relationships to social robots (2013) Intervalla Platf Intellect Exchange, 1, pp. 35-48; Krotz, F., (2007) Mediatisierung: Fallstudien zum Wandel von Kommunikation, , VS-Verlag, Wiesbaden; Reeves, B., Nass, C., (1996) The media equation: how people treat computers, television, and the new media like real people, , Cambridge University Press, Cambridge; Kanda, T., Hirano, T., Eaton, D., Ishiguro, H., Interactive robots as social partners and peer tutors for children: a field trial (2004) Hum Comput Interact, 19, pp. 61-84; European Parliament Resolution of 16 February 2017 with Recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)), , http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML+TA+P8-TA-2017-0051+0+DOC+PDF+V0//EN; Delvaux, M., (2016) REPORT with Recommendations to the Commission on Civil Law Rules on Robotics, , http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//TEXT+REPORT+A8-2017-0005+0+DOC+XML+V0//EN; Gittleson, B., Saudi Arabia criticized for giving female robot citizenship, while it restricts women’s rights (2017) ABC News, , October 26. Retrieved October 28, 2017; Maza, C., (2017) Saudi Arabia Gives Citizenship to a Non-Muslim, , English-speaking robot, Newsweek, Retrieved October 26, 2017; Fong, T., Nourbakhsh, I., Dautenbaun, K., A survey of socially interactive robots (2003) Robot Auton Syst, 42, pp. 143-166; Dautenhahn, K., Billard, A., Bringing up robots or—the psychology of socially intelligent robots: From theory to implementation (1999) 3Rd Annual Conference on Autonomous Agents, pp. 366-367; Jovchelovitch, S., Priego-Hernandez, J., Cognitive polyphasia, knowledge encounters and public spheres (2015) The Cambridge handbook of social representations, pp. 163-178. , Sammut G, Andreouli E, Gaskell G, Valsiner J, (eds), Cambridge University Press, Cambridge; Goffman, E., (1959) The presentation of self in everyday life, , Doubleday Anchor Books, New York: (It. transl.: La vita quotidiana come rappresentazione, Bologna 1969; Bagnasco, A., (2001) Organizzazione sociale dello spazio. Entry of the Enciclopedia delle Scienze Sociali. I Supplement, , Treccani, Roma; Di Masso, A., Grounding citizenship: toward a political psychology of public space (2012) Polit Psychol, 33 (1), pp. 123-143; Purini, F., (2007) Spazio pubblico. Entry of the Enciclopedia Italiana, VII. Appendix, , Treccani, Roma; Fortunati, L., Robotization and the domestic sphere (2017) New Media Soc, 20 (8), pp. 2673-2690; Thompson, L.F., Gillan, D.J., Social factors in human–robot interaction (2010) Human–robot interaction in future military operations, pp. 67-81. , Barnes M, Jentsch F, (eds), Ashgate, Farnham; Gehl, R.W., Bakardjieva, M., (2017) Sociabots and their friends. Digital media and the automation of sociality, , (eds), Routledge, New York; Bicchi, A., Tamburrini, G., Social robotics and societies of robots (2015) Inf Soc, 31, pp. 237-243; Bonaccorsi, M., Fiorini, L., Cavallo, F., Saffiotti, A., Dario, P., A cloud robotics solution to improve social assistive robots for active and healthy aging (2016) Int J Soc Robot, 8, pp. 393-408; Di Nuovo, A., Broz, F., Belpaeme, T., Cangelosi, A., Cavallo, F., Esposito, R., Dario, P., A web based multi-modal interface for elderly users of the robot-era multi-robot services (2014) Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on IEEE, pp. 2186-2191. , October 2017; Taipale, S., Sarrica, M., de Luca, F., Fortunati, L., Europeans’ perception of robots implications for social policies (2015) Social robots from a human perspective, pp. 11-24. , Vincent J, Taipale S, Sapio B, Lugano G, Fortunati L, (eds), Springer, Berlin; Glas, D.F., Kanda, T., Ishiguro, H., Hagita, N., Simultaneous people tracking and localization for social robots using external laser range finders : Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on IEEE, 2009, pp. 846-853. , October 2009; Chen, Y., Wu, F., Shuai, W., Chen, X., Robots serve humans in public places—KeJia robot as a shopping assistant (2017) Int J Adv Robot Syst; Mussakhojayeva, S., Zhanbyrtayev, M., Agzhanov, Y., Sandygulova, A., Who should robots adapt to within a multi-party interaction in a public space? The Eleventh ACM/IEEE International Conference on Human Robot Interaction, 2016, pp. 483-484. , IEEE Press, March 2016; Ferrari, G.A., Meccanica ‘allargata’ (1984) La scienza ellenistica, pp. 225-296. , Giannantoni G, Vegetti M, (eds), Bibliopolis, Napoli; Parlato, E., Il Volto dell’Utopia: modi e significato dell’automa rinascimentale (1991) Il mito dell’automa, pp. 26-30. , Artioli U, Bartoli F, (eds), Artificio, Firenze; Rinaldi, A., La ricerca della ‘terza’ natura: artificialia e naturalia nel giardino toscano del ‘500 (1979) Natura e artificio, pp. 154-175. , Fagiolo M, (ed), Officina Edizioni, Roma; Zanca, A., Il mondo degli automi tra manierismo e secolo dei lumi (1991) Il mito dell’automa, pp. 31-39. , Artioli U, Bartoli F, (eds), Artificio, Firenze; Baron, N., Authenticity, emotions, and ICTs (2013) Intervalla Platf Intellect Exchange, 1, pp. 7-16; Fortunati, L., Manganelli, A., The social representations of telecommunications (2008) Pers Ubiquit Comput, 12, pp. 421-431; Elster, J., (1989) Nuts and bolts for the social sciences, , Cambridge University Press, Cambridge; Contarello, A., Fortunati, L., Sarrica, M., Social thinking and the mobile phone: a study of social change with the diffusion of mobile phones, using a social representations framework (2007) Mob Phone Cult Special Issue Contin J Media Cult, 21, pp. 149-163. , (edited by Gerard Goggin","Fortunati, L.; Department of Mathematics, Via delle Scienze, 206, Italy; email: Leopoldina.Fortunati@uniud.it",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85064480199
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","Statistical model checking of human-robot interaction scenarios",2020,"Electronic Proceedings in Theoretical Computer Science, EPTCS","319",,,"9","17",,1,"10.4204/eptcs.319.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094671954&doi=10.4204%2feptcs.319.2&partnerID=40&md5=8a6838c760cfe9ee724372accf5343fd","Politecnico di Milano, Italy; McMaster University, Canada","Lestingi, L., Politecnico di Milano, Italy; Askarpour, M., McMaster University, Canada; Bersani, M.M., Politecnico di Milano, Italy; Rossi, M., Politecnico di Milano, Italy","Robots are soon going to be deployed in non-industrial environments. Before society can take such a step, it is necessary to endow complex robotic systems with mechanisms that make them reliable enough to operate in situations where the human factor is predominant. This calls for the development of robotic frameworks that can soundly guarantee that a collection of properties are verified at all times during operation. While developing a mission plan, robots should take into account factors such as human physiology. In this paper, we present an example of how a robotic application that involves human interaction can be modeled through hybrid automata, and analyzed by using statistical modelchecking. We exploit statistical techniques to determine the probability with which some properties are verified, thus easing the state-space explosion problem. The analysis is performed using the Uppaal tool. In addition, we used Uppaal to run simulations that allowed us to show non-trivial time dynamics that describe the behavior of the real system, including human-related variables. Overall, this process allows developers to gain useful insights into their application and to make decisions about how to improve it to balance efficiency and user satisfaction. © 2020 Open Publishing Association. All rights reserved.",,"Autonomous agents; Industrial robots; Model checking; Physiology; Robotics; Human interactions; Industrial environments; Related variables; Robotic applications; State-space explosion; Statistical model checking; Statistical techniques; User satisfaction; Social robots",,,,,,,,,,,,,,,,"Alur, Rajeev, Courcoubetis, Costas, Halbwachs, Nicolas, Henzinger, Thomas A, Ho, Pei-Hsin, Nicollin, Xavier, Olivero, Alfredo, Yovine, Sergio, The algorithmic analysis of hybrid systems (1995) Theoretical computer science, 138 (1), pp. 3-34; Aniculaesei, Adina, Arnsberger, Daniel, Howar, Falk, Rausch, Andreas, (2016) Towards the verification of safety-critical autonomous systems in dynamic environments, , arXiv preprint arXiv:1612.04977; Arai, Ryota, Schlingloff, H, Model-based performance prediction by statistical model checking an industrial case study of autonomous transport robots (2017) Proc. 25th CS&P 2017-Concurrency, Specification and Programming; Behrmann, Gerd, David, Alexandre, Larsen, Kim G, A tutorial on Uppaal (2004) Formal methods for the design of real-time systems, pp. 200-236. , Springer; Chettibi, Taha, Haddad, Moussa, Lehtihet, HE, Khalil, Wisama, Suboptimal trajectory generation for industrial robots using trapezoidal velocity profiles (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 729-735. , IEEE; David, Alexandre, Larsen, Kim G, Legay, Axel, Mikǔcionis, Marius, Poulsen, Danny Bøgsted, Uppaal SMC tutorial (2015) International Journal on Software Tools for Technology Transfer, 17 (4), pp. 397-415; Foughali, Mohammed, Toward a correct-and-scalable verification of concurrent robotic systems: Insights on formalisms and tools (2017) 2017 17th International Conference on Application of Concurrency to System Design (ACSD), pp. 29-38. , IEEE; Foughali, Mohammed, Ingrand, Félix, Seceleanu, Cristina, Statistical model checking of complex robotic systems (2019) International Symposium on Model Checking Software, pp. 114-134. , Springer; Furia, Carlo A, Mandrioli, Dino, Morzenti, Angelo, Rossi, Matteo, (2012) Modeling time in computing, , Springer Science & Business Media; Gordienko, Yuri, Stirenko, Sergii, Kochura, Yuriy, Alienin, Oleg, Novotarskiy, Michail, Gordienko, Nikita, (2017) Deep learning for fatigue estimation on the basis of multimodal human-machine interactions, , arXiv preprint arXiv:1801.06048; Halder, Raju, Proencą, José, Macedo, Nuno, Santos, André, Formal verification of ROS-based robotic applications using timed-automata (2017) 2017 IEEE/ACM 5th International FMEWorkshop on Formal Methods in Software Engineering (FormaliSE), pp. 44-50. , IEEE; (2014) Robots and robotic devices-Safety requirements for personal care robots, , ISO 13482 International Organization for Standardization; Jaber, Mohamad Y, Givi, ZS, Patrick Neumann, W, Incorporating human fatigue and recovery into the learning-forgetting process (2013) Applied Mathematical Modelling, 37 (12-13), pp. 7287-7299; Jacobs, Theo, Virk, Gurvinder Singh, ISO 13482-The new safety standard for personal care robots (2014) ISR/Robotik 2014; 41st International Symposium on Robotics, pp. 1-6. , VDE; Konz, Stephan, Work/rest: Part ii-the scientific basis (knowledge base) for the guide 1 (2000) Ergonomics Guidelines and Problem Solving, 1 (401), p. 38; Li, Xinxin, Wang, Rui, Jiang, Yu, Guan, Yong, Li, Xiaojuan, Song, Xiaoyu, Formal modeling and automatic code synthesis for robot system (2017) 2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS), pp. 146-149. , IEEE; Molnar, Levente, Veres, Sandor M, Hybrid automata dicretising agents for formal modelling of robots (2011) IFAC Proceedings Volumes, 44 (1), pp. 49-54; Tremblay, Olivier, Dessaint, Louis-A, Dekkiche, Abdel-Illah, A generic battery model for the dynamic simulation of hybrid electric vehicles (2007) 2007 IEEE Vehicle Power and Propulsion Conference, pp. 284-289. , Ieee; Virk, GS, Moon, S, Gelin, R, ISO standards for service robots (2008) Advances In Mobile Robotics, pp. 133-138. , World Scientific; Wang, Rui, Guan, Yong, Song, Houbing, Li, Xinxin, Li, Xiaojuan, Shi, Zhiping, Song, Xiaoyu, A formal model-based design method for robotic systems (2018) IEEE Systems Journal, 13 (1), pp. 1096-1107; Webster, Matt, Dixon, Clare, Fisher, Michael, Salem, Maha, Saunders, Joe, Koay, Kheng Lee, Dautenhahn, Kerstin, Formal verification of an autonomous personal robotic assistant (2014) 2014 AAAI Spring Symposium Series; Zhou, Yuchen, Maity, Dipankar, Baras, John S, Timed automata approach for motion planning using metric interval temporal logic (2016) 2016 European Control Conference (ECC), pp. 690-695. , IEEE",,"Cardoso R.C.Ferrando A.Briola D.Menghi C.Ahlbrecht T.",,"Open Publishing Association","1st Workshop on Agents and Robots for Reliable Engineered Autonomy, AREA 2020","4 September 2020",,161993,20752180,,,,"English",,Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85094671954
"Tian Y., Wang G., Li L., Jin T., Xi F., Yuan G.","15836338500;57218704714;57188975448;57208208649;7102360062;55990982500;","A Universal Self-Adaption Workspace Mapping Method for Human-Robot Interaction Using Kinect Sensor Data",2020,"IEEE Sensors Journal","20","14","9042278","7918","7928",,1,"10.1109/JSEN.2020.2981091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090151555&doi=10.1109%2fJSEN.2020.2981091&partnerID=40&md5=936c59776949c5d533d475652901ab8b","Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada","Tian, Y., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Wang, G., Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada; Li, L., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Jin, T., Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada; Xi, F., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Yuan, G., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China","Human-robot interactions (HRIs) are a key technology to combine human and robot capabilities. Though many feasible methods have been proposed in this field, the current workspace mapping methods are deficient in terms of generality. In this paper, a universal control system based on the self-adaption workspace mapping method for HRI is designed. In normal circumstances, human movement is adjusted adaptively in a safe, obedient, stable and efficient way to the external environment. The universal control system could naturally carry out an object manipulation task by allowing a human operator to communicate his motions to the robot manipulators. More importantly, by applying this system, people no longer need to find parameters by trial-and-error because they are not affected by changes in the human and robot model. The function of similarity that is used to evaluate the similarity between a human arm and the robot is introduced. Moreover, experiments are carried out, and an analysis of results verifies the feasibility and excellent performance of the universal control system. Finally, a demonstration is applied to display the flexible implementation of the object manipulation task that is collaboratively done by the universal robot through this system, and the method is evaluated by comparing it with other method. © 2001-2012 IEEE.","Gesture recognition; human-robot interaction; robotics; similarity; skeleton data; workspace mapping","Control systems; Flexible displays; Man machine systems; Manipulators; Mapping; Robot applications; External environments; Human movements; Key technologies; Kinect sensors; Object manipulation; Robot manipulator; Trial and error; Universal control; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 51775322, 61603237, 61704102","Manuscript received December 16, 2019; revised March 9, 2020 and March 10, 2020; accepted March 10, 2020. Date of publication March 19, 2020; date of current version June 18, 2020. This work was supported in part by the Special Plan of Major Scientific Instruments and Equipment of the State under Grant 2018YFF01013101, in part by the National Natural Science Foundation of China under Grant 51775322, Grant 61704102, and Grant 61603237, and in part by the Project Key Technology Research and Demonstration Line Construction of Advanced Laser Intelligent Manufacturing Equipment from Shanghai Lingang Area Development Administration. The associate editor coordinating the review of this article and approving it for publication was Dr. Marco J. Da Silva. (Corresponding author: Guangjie Yuan.) Yingzhong Tian, Guopeng Wang, Long Li, Tao Jin, and Guangjie Yuan are with the Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai 200072, China (e-mail: troytian@shu.edu.cn; 18721018328@163.com; lil@shu.edu.cn; jt233@shu.edu.cn; guangjie@shu.edu.cn).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auto. Robots, 42 (5), pp. 957-975. , Jun; Liang, P., Ge, L., Liu, Y., Zhao, L., Li, R., Wang, K., An augmented discrete-Time approach for human-robot collaboration (2016) Discrete Dyn. Nature Soc, 2016, pp. 1-13; Tian, Y., Research on layout and operational pose optimization of robot grinding system based on optimal stiffness performance (2017) J. Adv. Mech. Design, Syst. , Manuf, 11 (2). , Art. no. 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(URAI, pp. 692-695; Ajoudani, A., Tsagarakis, N., Bicchi, A., Tele-impedance: Teleoperation with impedance regulation using a body-machine interface (2012) Int. J. Rob. Res, 31 (13), pp. 1642-1655; Zanchettin, A.M., Rocco, P., Bascetta, L., Symeonidis, I., Peldschus, S., Kinematic analysis and synthesis of the human arm motion during a manipulation task (2011) Proc Ieee Int. Conf. Robot. Autom, pp. 2692-2697. , May; Koenemann, J., Burget, F., Bennewitz, M., Real-Time imitation of human whole-body motions by humanoids (2014) Proc Ieee Int. Conf. Robot. Autom. ICRA, pp. 2806-2812. , May; Nikolakis, N., Maratos, V., Makris, S., A cyber physical system (CPS) approach for safe human-robot collaboration in a shared workplace (2019) Robot. Comput.-Integr. Manuf, 56, pp. 233-243. , Apr; Zuher, F., Romero, R., Recognition of human motions for imitation and control of a humanoid robot (2012) Proc. Brazilian Robot. Symp. Latin Amer. Robot. 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Mechatronics, pp. 572-577. , Dec; Miicke, E.P., Three-dimensional alpha shapes introduct ion alpha shapes in space related geometric concepts (1992) Acm Trans. Graph, 13 (1), pp. 43-72; Peidró, A., Reinoso, O., Gil, A., Marín, J.M., Payá, L., An improved Monte Carlo method based on Gaussian growth to calculate the workspace of robots (2017) Eng. Appl. Artif. Intell, 64, pp. 197-207. , Sep; Huang, Q., Planning walking patterns for a biped robot (2001) Ieee Trans. Robot. Autom, 17 (3), pp. 280-289. , Jun; Magermans, D.J., Chadwick, E.K.J., Veeger, H.E.J., Helm Der Van, T.F.C., Requirements for upper extremity motions during activities of daily living (2005) Clin. Biomech, 20 (6), pp. 591-599. , Jul","Yuan, G.; Laboratory of Intelligent Mechanism and Advanced Robotics, China; email: guangjie@shu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","",Scopus,2-s2.0-85090151555
"Ye L., Xiong G., Zeng C., Zhang H.","57201975390;34769433000;57218991943;57192485618;","Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction",2020,"Science Progress","103","3",,"","",,2,"10.1177/0036850420953642","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090986206&doi=10.1177%2f0036850420953642&partnerID=40&md5=4d06933f081a9a132e2627960dac489c","School of Mechanical Engineering, Nanchang University, Nanchang, China; School of Mechatronic Engineering, Shanghai University of Engineering Science, Shanghai, China; School of mechanical and vehicle engineering, Nanchang Institute of Science and Technology, Nanchang, China","Ye, L., School of Mechanical Engineering, Nanchang University, Nanchang, China, School of mechanical and vehicle engineering, Nanchang Institute of Science and Technology, Nanchang, China; Xiong, G., School of Mechanical Engineering, Nanchang University, Nanchang, China; Zeng, C., School of Mechanical Engineering, Nanchang University, Nanchang, China; Zhang, H., School of Mechanical Engineering, Nanchang University, Nanchang, China, School of Mechatronic Engineering, Shanghai University of Engineering Science, Shanghai, China","Collaborative robot has been widespread application prospect, such as homes, manufacturing, and health-care etc. In physical human-robot interaction, the external force appears inevitably in contact with environment or human, especially the interactive tasks such as trajectory tracking requirements and force compliance control. In this article, a method based on interaction intention estimation, which solve the problem of trajectory tracking accuracy and force compliance control in the same direction for the 7-DOF robot, is proposed. The increased virtual force depended on the manipuility performance index and inverse kinematic solution used the kinematic decoupling method based on the redundant angle avoid the singularity of redundant robot. Then, based on interactive intention estimation, a control strategy of variable impedance sliding mode theory in the presence of virtual force and contact force is proposed to achieve the trajectory tracking. We adopted hyperbolic tangent function to alleviate the chattering problem caused by switch function and validated the control system stability by Lyapunov theorem. Finally, Matlab simulations exhibit a 97.8% of high tracking accuracy amid the external force is 43% less than variable impedance parameters. It is therefore proved that the proposed method can achieve asymptotic tracking and the compliant behavior in physical human-robot interaction. © The Author(s) 2020.","7-DOF redundant robot; Physical human-robot interaction; sliding mode control; trajectory tracking control; variable impedance control","article; control strategy; control system; human; impedance; robotics; simulation; theoretical study",,,,,"National Natural Science Foundation of China, NSFC: 61763030; National Key Research and Development Program of China, NKRDPC: 2018YFB1305300","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported in part by the National Key Research and Development Program of China under Grant 2018YFB1305300, and in part by the National Natural Science Foundation of China under Grant 61763030.",,,,,,,,,,"Aristidou, A., Lasenby, J., Chrysanthou, Y., Inverse kinematics techniques in computer graphics: a survey (2018) Comput Graph Forum, 37 (6), pp. 35-58; Yoon, J.G., Jung, M.G., Lee, M.C., Inverse kinematic analysis of 5 DOF manipulator using numerical method, , 2017 Changwon International Conference on Intelligent Robot and Convergence Industry (CICIRO 2017), Changwon, Korea, 30 November–1 December 2017, In; Shimizu, M., Kakuya, H., Yoon, W.K., Analytical inverse kinematic computation for 7-DOF redundant manipulators with joint limits and its application to redundancy resolution (2008) IEEE Trans Robot, 24 (5), pp. 1131-1142; Wan, Y., Kou, Y., Liang, X., (2017) Closed-loop inverse kinematic analysis of redundant manipulators with joint limits, pp. 1241-1255. , International Conference on Mechanical Design (ICMD): Advances Mechanical Design, Singapore, Springer Nature Singapore Pte Ltd, In; Zhou, D., Ji, L., Zhang, Q., Practical analytical inverse kinematic approach for 7-DOF space manipulators with joint and attitude limits (2015) Intell Serv Robot, 8 (4), pp. 215-224; Gong, M., Li, X., Zhang, L., Analytical inverse kinematics and self-motion application for 7-DOF redundant manipulator (2019) IEEE Access, 7, pp. 18662-18674; Ficuciello, F., Villani, L., Siciliano, B., Redundancy resolution in human-robot co-manipulation with cartesian impedance control (2016) Experimental robotics, volume 109 of the series springer tracts in advanced robotics, pp. 165-176. , Switzerland, Springer International Publishing, In; Ficuciello, F., Romano, A., Villani, L., Cartesian impedance control of redundant manipulators for human-robot co-manipulation, pp. 2120-2125. , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, 14–18 September 2014, In; Nageshrao, S., Lopes, G., Jeltsema, D., Passivity based reinforcement learning control of a 2-DOF manipulator arm (2014) Mechatronics, 24 (8), pp. 1001-1007; Tang, L., Liu, Y., Tong, S., Adaptive neural control using reinforcement learning for a class of robot manipulator (2014) Neural Comput Appl, 25 (1), pp. 135-141; Zhu, Y., Barth, E.J., Impedance control of a pneumatic actuator for contact tasks, pp. 987-992. , Proceedings of the 2005 IEEE international conference on robotics and automation, Barcelona, Spain, April 2005, In; 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Jung, S., Improvement of tracking control of a sliding mode controller for robot manipulators by a neural network (2018) Int J Control Autom Syst, 16 (2), pp. 937-943; Hogan, N., Impedance control: an approach to manipulation (1985) J Dyn Syst Meas Control, 107 (1), pp. 8-16; Kazerooni, H., Sheridan, T.B., Houpt, P.K., Robust compliant motion for manipulators, part I: the fundamental concepts of compliant motion (1986) IEEE J Rob Autom, 2 (2), pp. 83-92; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans Robot, 31 (4), pp. 850-863; Sharifi, M., Behzadipour, S., Vossoughi, G.R., Model reference adaptive impedance control in Cartesian coordinates for physical human-robot interaction (2014) Adv Robot, 28 (19), pp. 1277-1290; Xiong, G.L., Chen, H.C., Xiong, P.W., Cartesian impedance control for physical human-robot interaction using virtual decomposition control approach (2019) Iran J Sci Technol Trans Mech Eng, 43 (S1), pp. 983-994; Li, Y., Ganesh, G., Jarrasse, N., Force, impedance, and trajectory learning for contact tooling and haptic identification (2018) IEEE Trans Robot, 34 (5), pp. 1170-1182; Losey, D.P., O’Malley, M.K., Trajectory deformations from physical human-robot interaction (2018) IEEE Trans Robot, 34 (1), pp. 126-138; Craig, J.J., (2018) Introduction to robotics, mechanics and control, pp. 132-137. , 4th ed., Beijing, China, China machine Press; Mao, D., Yang, W., Du, Z., Fuzzy variable impedance control based on stiffness identification for human-robot cooperation (2017) IOP Conf Ser Earth Environ Sci, 69, p. 012090; Liu, J.K., (2008) Design of robot control system and MATLAB simulation, pp. 438-447. , Beijing, China, Tsinghua University Press","Xiong, G.; School of Mechanical Engineering, China; email: xionggenliang@ncu.edu.cn",,,"SAGE Publications Ltd",,,,,00368504,,,"32924809","English","Sci. Prog.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090986206
"Mizanoor Rahman S.M.","37039570500;","Admittance-based bio-inspired cognitive pid control to optimize human-robot interaction in power-assisted object manipulation",2020,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2020-July",,"9158811","679","684",,2,"10.1109/AIM43001.2020.9158811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090387278&doi=10.1109%2fAIM43001.2020.9158811&partnerID=40&md5=b6e6b1606e8de5c983b4b6c18bee85f3","Hal Marcus College of Science and Engineering, University of West Florida, Dept. of Intelligent Systems and Robotics, Pensacola, FL  32514, United States; Dept. of Mechanical Engineering, Mie University, Tsu, Mie, Japan","Mizanoor Rahman, S.M., Hal Marcus College of Science and Engineering, University of West Florida, Dept. of Intelligent Systems and Robotics, Pensacola, FL  32514, United States, Dept. of Mechanical Engineering, Mie University, Tsu, Mie, Japan","A novel approach to PID control for optimizing human-robot interaction (HRI) in manipulating objects with a power assist robotic system (PARS) was presented. The PID control was developed in the form of the admittance control. To take bio-inspiration and fit the control with human cognition, a novel method of inclusion of weight perception in the dynamics and control of the system was proposed. Then, a comprehensive evaluation and optimization scheme was proposed to determine the optimum HRI and co-manipulation performance. A 1-DOF testbed PARS was developed to experimentally verify the proposed control in a pilot study. During the experiments, human subjects lifted objects with the system and the HRI and system characteristics were investigated and analyzed. The results showed that the proposed PID control aligned with human cognition through inclusion of weight perception in the dynamics and control was effective to optimize the HRI and co-manipulation performance. The results can be used to design human-centered control methods for PARSs to assist humans manipulating heavy objects in industries and at homes that can provide optimum HRI and manipulation performance. © 2020 IEEE.",,"Biomimetics; Intelligent mechatronics; Man machine systems; Three term control systems; Admittance control; Comprehensive evaluation; Dynamics and control; Human robot Interaction (HRI); Human-centered control; Object manipulation; Optimization scheme; System characteristics; Social robots",,,,,,,,,,,,,,,,"Rahman, S.M.M., Ikeura, R., Improving interactions between a power assist robot system and its human user in horizontal transfer of objects using a novel adaptive control method (2012) Advances in Human-Computer Interaction, 2012; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Study on optimum maneuverability in horizontal manipulation of objects with power-assist based on weight perception (2010) Proc. of SPIE, 7500, p. 75000P; Rahman, S.M.M., Ikeura, R., Hayakawa, S., Novel human-centric force control methods of power assist robots for object manipulation (2013) Proc. of 2013 IEEE Int. Conf. on Robotics and Biomimetics (IEEE ROBIO 2013), pp. 340-345. , Dec 12-14, Shenzhen, China; Gosselin, C., Laliberte, T., Mayer-St-Onge, B., Foucault, S., Lecours, A., Duchaine, V., Paradis, N., Menassa, R., A friendly beast of burden: A human-assistive robot for handling large payloads (2013) IEEE Robotics & Automation Magazine, 20 (4), pp. 139-147; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Unimanual and bimanual weight discrimination in lifting objects with a power assist system (2009) Proc. of ICROS-SICE Int. Joint Conf., pp. 4787-4792; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., A psychophysical model of the power assist system for lifting objects Proc. of 2009 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 4125-4130; Hara, H., Sankai, Y., Hal equipped with passive mechanism (2012) Proc. of IEEE Int. Symp. on System Integration, pp. 1-6; Doi, T., Yamada, H., Ikemoto, T., Naratani, H., Simulation of a pneumatic hand crane power-assist system (2008) Journal of Robotics and Mechatronics, 20 (6), pp. 896-902; Rahman, S.M.M., Ikeura, R., Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system (2018) Int. Journal of Advanced Robotic Systems, 15 (4), pp. 1-15; Rahman, S.M.M., Ikeura, R., Mpc to optimise performance in power-assisted manipulation of industrial objects (2017) IET Electric Power Applications, 11 (7), pp. 1235-1244; Rahman, S.M.M., Ikeura, R., Weight-prediction-based predictive optimal position and force controls of a power assist robotic system for object manipulation (2016) IEEE Transactions on Industrial Electronics, 63 (9), pp. 5964-5975. , September; Sheng, G., Gao, G., Research on the attitude control of civil quadrotor uav based on fuzzy pid control (2019) Proc. of 2019 Chinese Control and Decision Conference, pp. 4566-4569. , China; Zhou, X., A rolling pid control approach and its applications (2019) Proc. of 2019 Chinese Control Conference, pp. 2863-2867. , China; Rahman, S.M.M., Ikeura, R., Optimizing perceived heaviness and motion for lifting objects with a power assist robot system considering change in time constant (2012) International Journal on Smart Sensing and Intelligent Systems, 5 (2), pp. 458-486. , June; Rahman, S.M.M., Ikeura, R., Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation (2016) Journal of Information Science and Engineering, 32 (5), pp. 1325-1344; Rahman, S.M.M., Trustworthy power assistance in object manipulation with a power assist robotic system (2019) Proc. of IEEE SoutheastCon 2019, pp. 1-6. , April 11-14, USA; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Controlling a power assist robot for lifting objects considering human's unimanual, bimanual and cooperative weight perception (2010) Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 2356-2362. , May 3-8","Mizanoor Rahman, S.M.; Hal Marcus College of Science and Engineering, United States; email: smrahman@uwf.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020","6 July 2020 through 9 July 2020",,162314,,9781728167947,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85090387278
"Andriella A., Torras C., Alenyà G.","57202547412;7003421114;6508265237;","Cognitive System Framework for Brain-Training Exercise Based on Human-Robot Interaction",2020,"Cognitive Computation","12","4",,"793","810",,7,"10.1007/s12559-019-09696-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088605524&doi=10.1007%2fs12559-019-09696-2&partnerID=40&md5=c036b6871e6e7f31c18a5ebf073e7b26","Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","Andriella, A., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Torras, C., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Alenyà, G., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","Every 3 seconds, someone develops dementia worldwide. Brain-training exercises, preferably involving also physical activity, have shown their potential to monitor and improve the brain function of people affected by Alzheimer disease (AD) or mild cognitive impairment (MCI). This paper presents a cognitive robotic system designed to assist mild dementia patients during brain-training sessions of sorting tokens, an exercise inspired by the Syndrom KurzTest neuropsychological test (SKT). The system is able to perceive, learn and adapt to the user’s behaviour and is composed of two main modules. The adaptive module based on representing the human-robot interaction as a planning problem, that can adapt to the user performance offering different encouragement and recommendation actions using both verbal and gesture communication in order to minimize the time spent to solve the exercise. As safety is a very important issue, the cognitive system is enriched with a safety module that monitors the possibility of physical contact and reacts accordingly. The cognitive system is presented as well as its embodiment in a real robot. Simulated experiments are performed to (i) evaluate the adaptability of the system to different patient use-cases and (ii) validate the coherence of the proposed safety module. A real experiment in the lab, with able users, is used as preliminary evaluation to validate the overall approach. Results in laboratory conditions show that the two presented modules effectively provide additional and essential functionalities to the system, although further work is necessary to guarantee robustness and timely response of the robot before testing it with patients. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.","Adaptive robot; Cognitive robotic system; Cognitive training; HRI; Robot; Safety; Socially assistive robotics","Accident prevention; Brain; Cognitive systems; Intelligent robots; Man machine systems; Neurodegenerative diseases; Robotics; Robots; Social robots; Assistive robotics; Cognitive robotics; Cognitive training; Laboratory conditions; Mild cognitive impairments (MCI); Neuropsychological tests; Simulated experiments; Training exercise; Robot programming",,,,,"Horizon 2020 Framework Programme, H2020: 721619; Ministerio de Ciencia e Innovación, MICINN: TIN2017-90086-R; Agencia Estatal de Investigación, AEI: MDM-2016-0656","This project has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement SOCRATES (No 721619), by the Spanish Ministry of Science and Innovation HuMoUR (TIN2017-90086-R), and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Acknowledgements","We would like to thank Carla Abdelnour, Joan Hernandez and Natalia Tantinya from Fundaci? ACE for the fruitful discussions and the help in the design of the sorting tokens exercise.",,,,,,,,,"(2018) Alzheimer’s Disease International (ADI), pp. 1-48. , https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf; Abdelnour, C., Tantyna, N., Hernandez, J., Giakoumis, D., Ribes, J.C., Gerlowska, J., Skrobas, U., Boada, M., Are there differences in the opinion of patients with Alzheimer’s disease and their caregivers about having support from a service robot at home? 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"Bartneck C., Moltchanova E.","6602256988;6508318897;","Expressing uncertainty in Human-Robot Interaction",2020,"PLoS ONE","15","7 July","e0235361","","",,,"10.1371/journal.pone.0235361","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088155155&doi=10.1371%2fjournal.pone.0235361&partnerID=40&md5=5a1014f72523c99c7464b4e085385f9a","University of Canterbury, Christchurch, New Zealand","Bartneck, C., University of Canterbury, Christchurch, New Zealand; Moltchanova, E., University of Canterbury, Christchurch, New Zealand","Most people struggle to understand probability which is an issue for Human-Robot Interaction (HRI) researchers who need to communicate risks and uncertainties to the participants in their studies, the media and policy makers. Previous work showed that even the use of numerical values to express probabilities does not guarantee an accurate understanding by laypeople. We therefore investigate if words can be used to communicate probability, such as “likely” and “almost certainly not”. We embedded these phrases in the context of the usage of autonomous vehicles. The results show that the association of phrases to percentages is not random and there is a preferred order of phrases. The association is, however, not as consistent as hoped for. Hence, it would be advisable to complement the use of words with numerical expression of uncertainty. This study provides an empirically verified list of probabilities phrases that HRI researchers can use to complement the numerical values. © 2020 Bartneck, Moltchanova. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",,"article; human; probability; robotics; uncertainty; brain computer interface; ethics; risk factor; robotics; Brain-Computer Interfaces; Humans; Probability; Risk Factors; Robotics",,,,,,,,,,,,,,,,"Ulph, F, Townsend, E, Glazebrook, C., How should risk be communicated to children: a cross-sectional study comparing different formats of probability information (2009) BMC medical informatics and decision making, 9 (1), p. 26. , https://doi.org/10.1186/1472-6947-9-26, PMID: 19500337; Fuller, R, Dudley, N, Blacktop, J., Risk communication and older people—understanding of probability and risk information by medical inpatients aged 75 years and older (2001) Age and Ageing, 30 (6), pp. 473-476. , https://doi.org/10.1093/ageing/30.6.473, PMID: 11742775; Zlotowski, J, Yogeeswaran, K, Bartneck, C., Can We Control It? 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A Characterization of MTurk Workers Using the Personality Assessment Inventory (2019) Assessment, 26 (5), pp. 759-766. , https://doi.org/10.1177/1073191118760709, PMID: 29490470; Chmielewski, M, Kucker, SC., An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results (2020) Social Psychological and Personality Science, 11 (4), pp. 464-473. , https://doi.org/10.1177/1948550619875149; Barclay, S, Brown, RV, CW, K, Peterson, CR, Philips, LD, Selvidge, J., (1977) Handbook for Decision Analysis, , Office of Naval Research; Lin, M, Lucas, HC, Shmueli, G., Research commentary—too big to fail: large samples and the p-value problem (2013) Information Systems Research, 24 (4), pp. 906-917. , https://doi.org/10.1287/isre.2013.0480; Nuzzo, R., Statistical errors: P values, the’gold standard’of statistical validity, are not as reliable as many scientists assume (2014) Nature, 506 (7487), pp. 150-153. , https://doi.org/10.1038/506150a, PMID: 24522584; Merkhofer, MW., Quantifying judgmental uncertainty: Methodology, experiences, and insights (1987) IEEE Transactions on Systems, Man, and Cybernetics, 17 (5), pp. 741-752. , https://doi.org/10.1109/TSMC.1987.6499281; Bunn, D, Wright, G., Interaction of judgemental and statistical forecasting methods: issues & analysis (1991) Management science, 37 (5), pp. 501-518. , https://doi.org/10.1287/mnsc.37.5.501","Bartneck, C.; University of CanterburyNew Zealand; email: christoph.bartneck@canterbury.ac.nz",,,"Public Library of Science",,,,,19326203,,POLNC,"32673326","English","PLoS ONE",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85088155155
"Chen L., Su W., Wu M., Pedrycz W., Hirota K.","55774663200;57196287282;35231682600;57210775415;7402829702;","A Fuzzy Deep Neural Network with Sparse Autoencoder for Emotional Intention Understanding in Human-Robot Interaction",2020,"IEEE Transactions on Fuzzy Systems","28","7","8957487","1252","1264",,7,"10.1109/TFUZZ.2020.2966167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087843300&doi=10.1109%2fTFUZZ.2020.2966167&partnerID=40&md5=54c3e7ff26bb2034fff574856643442f","School of Automation, China University of Geosciences, Wuhan, 430074, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  T6R 2G7, Canada; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  T6R 2G7, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e., age, gender, and region), in which the fuzzy C-means (FCM) is used to cluster the input data, and deep neural network with sparse autoencoder (DNNSA) is designed for emotional intention understanding in human-robot interaction. It aims to make robots capable of recognizing human emotions and understanding related emotional intention, the FCM is suitable for gathering similar information so that the calculations of dimensionality of DNNSA will be reduced, and the sparse autoencoder of DNNSA can make the neuron of DNNSA sparse to reduce the complexity of the network in such a way human-robot interaction is running smoothly. To validate the proposal, simulation experiments based on benchmark databases such as facial expression database of CK+, and speech emotion corpus of CASIA were completed. The experimental results show that the proposal outperforms the baseline algorithms of Softmax regression (SR), DNNSA, FCM-based SR (FSR), Softplus, Gath Geva-based DNNSA (GDNNSA), and ensemble DNNSA (EDNNSA). Preliminary application experiments are performed in the development of emotional social robot system, where volunteers experience the scenario of 'drinking at the bar'. The obtained results indicate that the proposed FDNNSA can promote robot understanding of emotional intention of human. © 1993-2012 IEEE.","Candide3 model; deep learning; fuzzy C-means; human-robot interaction; intention understanding","Deep neural networks; Economic and social effects; Fuzzy inference; Fuzzy logic; Fuzzy neural networks; Learning systems; Man machine systems; Auto encoders; Benchmark database; Facial Expressions; Human emotion; Intention understanding; Recognizing Human Emotion; Softmax regressions; Speech emotions; Human robot interaction",,,,,"201839; National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61973286; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","Manuscript received November 29, 2018; revised March 2, 2019 and August 30, 2019; accepted December 9, 2019. Date of publication January 13, 2020; date of current version July 1, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61973286, Grant 61603356, and Grant 61733016, in part by the 111 project under Grant B17040, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant No. 201839. (Corresponding author: Min Wu.) L. Chen, W. Su, and M. Wu are with the School of Automation, China University of Geosciences, Wuhan 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China (e-mail: chenluefeng@cug.edu.cn; suwanjuan@cug.edu.cn; wumin@cug.edu.cn).",,,,,,,,,,"Angel, F.J.M., Bonarini, A., Studying people's emotional responses to robot's movements in a small scene (2014) Proc. 23th Ieee Int. Symp. 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Humaniod Robots, pp. 270-275","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636706,,IEFSE,,"English","IEEE Trans Fuzzy Syst",Article,"Final","",Scopus,2-s2.0-85087843300
"Oliff H., Liu Y., Kumar M., Williams M.","57195531830;35206193200;55389194300;57202877303;","Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach",2020,"IEEE Transactions on Automation Science and Engineering","17","3","9007403","1597","1610",,4,"10.1109/TASE.2020.2967093","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087544170&doi=10.1109%2fTASE.2020.2967093&partnerID=40&md5=6a50e35f110f684af940ea14d20dd8ab","High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Olympus Surgical Technologies Europe, Cardiff, CF3 0LT, United Kingdom","Oliff, H., High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Liu, Y., High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Kumar, M., Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Williams, M., Olympus Surgical Technologies Europe, Cardiff, CF3 0LT, United Kingdom","Several decades of development in the fields of robotics and automation have resulted in human-robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. Note to Practitioners-This article presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated, and used within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behavior to reduce the impact of human performance variation and improve human-machine interaction (HMI). This article also presents a simulation-based case study to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine-learning approach to identify the complex influence of several identified human factors on human performance. This knowledge can be used to adjust the robotic behavior to match the predicted performance of multiple different operators over different scenarios. This adaptability reduces performance disparity by reducing idle times and enabling leaner production through workpiece-in-progress reduction. Future work will focus on expanding the intelligent capabilities of the proposed system to deal with uncertainty and improve decision-making ability. © 2004-2012 IEEE.","Collaborative robotics; human factors; human-machine interaction (HMI); intelligent manufacturing; machine learning; neural networks","Concurrency control; Control systems; Data handling; Decision making; Human engineering; Industrial research; Intelligent robots; Learning systems; Man machine systems; Manufacture; Robotics; Collaborative interaction; Concurrent development; Human-machine interaction; Intelligent Manufacturing; Machine learning approaches; Manufacturing operations; Network-based learning; Predicted performance; Social robots",,,,,,,,,,,,,,,,"Moniz, A., Krings, B.-J., Robots working with humans or humans working with robots? 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"Oliff H., Liu Y., Kumar M., Williams M., Ryan M.","57195531830;35206193200;55389194300;57202877303;55439044200;","Reinforcement learning for facilitating human-robot-interaction in manufacturing",2020,"Journal of Manufacturing Systems","56",,,"326","340",,19,"10.1016/j.jmsy.2020.06.018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087334279&doi=10.1016%2fj.jmsy.2020.06.018&partnerID=40&md5=98f568f03d4c161ea9f4abfcf250233c","Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Olympus Surgical Technologies Europe, OSTE, Cardiff, United Kingdom","Oliff, H., Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Liu, Y., Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Kumar, M., Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Williams, M., Olympus Surgical Technologies Europe, OSTE, Cardiff, United Kingdom; Ryan, M., Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom","For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments. © 2020 The Society of Manufacturing Engineers","Adaptability; Human factors; Human-robot interaction; Intelligent manufacturing; Reinforcement learning","Autonomous agents; Decision making; Intelligent robots; Knowledge management; Learning systems; Manufacture; Reinforcement learning; Robotics; Autonomous decision; Autonomous robotics; Contemporary manufacturing; Human performance; Intelligent Manufacturing; Novel methodology; Performance variations; Reinforcement learning agent; Human robot interaction",,,,,,,,,,,,,,,,"Xu, L.D., Xu, E.L., Li, L., Industry 4.0: state of the art and future trends (2018) Int J Prod Res, 56 (8), pp. 2941-2962; Schönsleben, P., Fantana, F., Duchi, A., What benefits do initiatives such as industry 4.0 offer for production locations in high-wage countries? 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"Simon O., Neuhofer B., Egger R.","57217058925;55582240100;55782025300;","Human-robot interaction: Conceptualising trust in frontline teams through LEGO® Serious Play®",2020,"Tourism Management Perspectives","35",,"100692","","",,8,"10.1016/j.tmp.2020.100692","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085968932&doi=10.1016%2fj.tmp.2020.100692&partnerID=40&md5=61e3514d0ab352655d6675099bf4f164","University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria","Simon, O., University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria; Neuhofer, B., University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria; Egger, R., University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria","Due to fast-paced developments in the technological sphere, we witness robots becoming commonplace in hospitality. While research has focused on technical and functional aspects of robots, the ways in which robots could become effective members of human service teams are less understood. By taking a human-robot interaction (HRI) lens, this study investigates the impact of integrating non-human service providers into human teams with a focus on trust. A qualitative focus group approach with the LEGO® Serious Play® method is employed to open novel avenues in tourism research, and to build, visualise and distil what human-robot interaction in hospitality frontline teams could look like. The study's contribution is a conceptual framework that reveals three dominant factors of trust and their sub-dimensions necessary for successful future human-robot interaction in frontline teams in tourism, hospitality and the wider service sector. © 2020 Elsevier Ltd","Co-creation; Collaboration; Hospitality; Human-robot interaction; LEGO® serious play®; Robotics; Trust",,,,,,,,,,,,,,,,,"Agarwal, T., Autonomous robots and their types with applications (2015), https://www.elprocus.com/different-types-of-autonomous-robots-and-real-time-applications/, (online) (Accessed 20 August 2019); Amineh, R.J., Asl, D., Review of constructivism and social constructivism (2015) Journal of Social Sciences, Literature and Languages, 1 (1), pp. 9-16; Bainbridge, W.A., Hart, J., Kim, E.S., Scassellati, B., The effect of presence on human-robot interaction (2008) Proceedings of the 17th IEEE symposium on robot and human interactive community, Munich, Germany, pp. 701-706; Baker, A., Phillips, E., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction (2018) Current research and future directions. 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"Sanchez M.R.V., Mishima S., Fujiwara M., Ai G., Jouaiti M., Kobryn Y., Rimbert S., Bougrain L., Hénaff P., Wagatsuma H.","57217006645;57217006157;57216947084;56592209500;57202645483;57217005426;56809245600;6506616972;6602868734;6603005439;","Methodological design for integration of human eeg data with behavioral analyses into human-human/robot interactions in a real-world context",2020,"ICIC Express Letters","14","7",,"693","701",,,"10.24507/icicel.14.07.693","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085703781&doi=10.24507%2ficicel.14.07.693&partnerID=40&md5=a6a7d1ecb4afb9b21dd5e5016ee856cf","Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan; School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, 923-1211, Japan; Department of Computer Science and Technology, Neusoft Institute Guangdong, Nanhai Software Science Park, Foshan, 528225, China; RIKEN Center for Brain Science (RIKEN CBS), 2-1 Hirosawa, Wako, Saitama, 351-0106, Japan; Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-Ku, Tokyo, 135-0064, Japan; Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54000, France; National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, 03056, Ukraine","Sanchez, M.R.V., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan; Mishima, S., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan; Fujiwara, M., School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, 923-1211, Japan; Ai, G., Department of Computer Science and Technology, Neusoft Institute Guangdong, Nanhai Software Science Park, Foshan, 528225, China; Jouaiti, M., Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54000, France; Kobryn, Y., Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54000, France, National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, 03056, Ukraine; Rimbert, S., Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54000, France; Bougrain, L., Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54000, France; Hénaff, P., Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54000, France; Wagatsuma, H., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), 2-4 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0196, Japan, RIKEN Center for Brain Science (RIKEN CBS), 2-1 Hirosawa, Wako, Saitama, 351-0106, Japan, Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-Ku, Tokyo, 135-0064, Japan","Analysis of human activities is a complex task that needs multifactorial considerations. So an electroencephalographic (EEG) data analysis can be improved by a conjunction of devices that monitor time courses of multiple types of physiological factors of the subject and counterparts when interactions are on-going in the environment. In this article, we propose a method to provide the complementary hardware and software treatment that associate devices to be able to synchronize simultaneous data recording to fit the high sampling rate of the EEG signal, such as more than 512 Hz. This method of synchronizing physiological data gathered from three different devices through the use of trigger signals is crucial for an accurate post-analysis and is validated in the experiment. The proposed method is widely applicable in various cases accompanied with EEG measurements and offers a wide possibility in device developments for rehabilitation and communications. ICIC International © 2020.","Electroencephalography; Eye-tracker; Human-human interaction; Human-robot interaction; Motion capture system; Synchronization",,,,,,"Japan Society for the Promotion of Science, KAKEN: 15H05874, 16H01616, 17H06383; New Energy and Industrial Technology Development Organization, NEDO","Acknowledgment. This work was supported in part by JSPS KAKENHI (15H05874, 16H01616, 17H06383), JSPS Bilateral Open Partnership Joint Research Projects and the New Energy and Industrial Technology Development Organization (NEDO).",,,,,,,,,,"Baker, M., Over half of psychology studies fail reproducibility test (2015) Nature; Ahn, S., Nguyen, T., Jang, H., Kim, J., Jun, S., Exploring neuro-physiological correlates of drivers' mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data (2016) Frontiers in Human Neuroscience, 10; Goebl, W., Palmer, C., Temporal control and hand movement efficiency in skilled music performance (2013) PLoS ONE, 8 (1), pp. 1-10; Muraskin, J., Sherwin, J., Sajda, P., Knowing when not to swing: EEG evidence that enhanced perception-action coupling underlies baseball batter expertise (2015) NeuroImage, 123, pp. 1-10; Thompson, T., Steffert, T., Ros, T., Leach, J., Gruzelier, J., EEG applications for sport and performance (2008) Methods, 45 (4), pp. 279-288; Peterson, S., Ferris, D., Differentiation in theta and beta electrocortical activity between visual and physical perturbations to walking and standing balance (2018) ENeuro, 5 (4); Noureddin, B., Lawrence, P., Birch, G., Online removal of eye movement and blink EEG artifacts using a high-speed eye tracker (2012) IEEE Transactions on Biomedical Engineering, 59 (8), pp. 2103-2110; Plöchl, M., Ossandón, J., König, P., Combining EEG and eye tracking: Identification, characterization, and correction of eye movement artifacts in electroencephalographic data (2012) Frontiers in Human Neuroscience, 6; Ai, G., Sato, N., Singh, B., Wagatsuma, H., Direction and viewing area-sensitive influence of EOG artifacts revealed in the EEG topographic pattern analysis (2016) Cognitive Neurodynamics, 10 (4), pp. 301-314; Singh, B., Wagatsuma, H., A removal of eye movement and blink artifacts from EEG data using morphological component analysis (2017) Computational and Mathematical Methods in Medicine, 2017; Ai, G., Hagio, M., Ichiki, M., Wagatsuma, H., Simultaneous analysis of EEGs and movements in interactive hand shaking required skills to synchronize cooperatively in game (2018) 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 590-594; Delorme, A., Mullen, T., Kothe, C., Acar, Z.A., Bigdely-Shamlo, N., Vankov, A., Makeig, S., EEGLAB, SIFT, NFT, BCILAB and ERICA: New tools for advanced EEG processing (2011) Computational Intelligence and Neuroscience, 2011; López-Gil, J.-M., Virgili-Gomá, J., Gil, R., Guilera, T., Batalla, I., Soler-González, J., García, R., Method for improving EEG based emotion recognition by combining it with synchronized biometric and eye tracking technologies in a non-invasive and low cost way (2016) Frontiers in Computational Neuroscience, 10; Reis, P.M.R., Hebenstreit, F., Gabsteiger, F., Von Tscharner, V., Lochmann, M., Methodological aspects of EEG and body dynamics measurements during motion (2014) Frontiers in Human Neuroscience, 8; Jouaiti, M., Hénaff, P., Motor coordination learning for rhythmic movements 2019 Joint (2019) IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob 2019), pp. 290-295. , Oslo, Norway; TEA CAPTIV T-Sens Respiration, , https://teaergo.com/wp/product/t-sens-respiration-tea/, Accessed on June 21, 2019; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2D pose estimation using part affinity fields (2017) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1302-1310; Jouaiti, M., Caron, L., Henaff, P., Hebbian plasticity in CPG controllers facilitates selfsynchronization for human-robot handshaking (2018) Frontiers in Neurorobotics, 12; Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A., OpenViBE: An open-source software platform to design, test and use brain-computer interfaces in real and virtual environments (2010) Presence: Teleoperators and Virtual Environments, Massachusetts Institute of Technology Press, 19 (1), pp. 35-53. , MIT Press",,,,"ICIC International",,,,,1881803X,,,,"English","ICIC Express Lett.",Article,"Final","",Scopus,2-s2.0-85085703781
"Zacharaki A., Kostavelis I., Gasteratos A., Dokas I.","57224559553;35484641300;6701673537;6508286517;","Safety bounds in human robot interaction: A survey",2020,"Safety Science","127",,"104667","","",,30,"10.1016/j.ssci.2020.104667","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080879374&doi=10.1016%2fj.ssci.2020.104667&partnerID=40&md5=249dcb0dddbc32cfde094143169de5c8","Democritus University of Thrace, Dept. of Civil Engineering, Building A- Campus, Greece; Democritus University of Thrace, Laboratory of Robotics and Automation, Production and Management Engineering Dept., 12, Vas. Sofias Str, Xanthi, GR-671 00, Greece","Zacharaki, A., Democritus University of Thrace, Dept. of Civil Engineering, Building A- Campus, Greece; Kostavelis, I., Democritus University of Thrace, Laboratory of Robotics and Automation, Production and Management Engineering Dept., 12, Vas. Sofias Str, Xanthi, GR-671 00, Greece; Gasteratos, A., Democritus University of Thrace, Laboratory of Robotics and Automation, Production and Management Engineering Dept., 12, Vas. Sofias Str, Xanthi, GR-671 00, Greece; Dokas, I., Democritus University of Thrace, Dept. of Civil Engineering, Building A- Campus, Greece","In the era of industrialization and automation, safety is a critical factor that should be considered during the design and realization of each new system that targets operation in close collaboration with humans. Of such systems are considered personal and professional service robots which collaborate and interact with humans at diverse applications environments. In this collaboration, human safety is an important factor in the wider field of human-robot interaction (HRI) since it facilitates their harmonic coexistence. The paper at hand aims to systemize the recent literature by describing the required levels of safety during human-robot interaction, focusing on the core functions of the collaborative robots when performing specific processes. It is also oriented towards the existing methods for psychological safety during human-robot collaboration and its impact at the robot behaviour, while also discusses in depth the psychological parameters of robots incorporation in industrial and social environments. Based on the existing works on safety features that minimize the risk of HRI, a classification of the existing works into five major categories namely, Robot Perceptions for Safe HRI, Cognition-enabled robot control in HRI, Action Planning for safe navigation close to humans, Hardware safety features, and Societal and Psychological factors is also applied. Finally, the current study further discusses the existing risk assessment techniques as methods to offer additional safety in robotic systems presenting thus a holistic analysis of the safety in contemporary robots, and proposes a roadmap for safety compliance features during the development of a robotic system. © 2020 Elsevier Ltd","Human-robot interaction; Psychological safety issues; Robot safety; Safe collaboration; Safety standards; Safety techniques","Accident prevention; Human robot interaction; Man machine systems; Risk assessment; Risk perception; Robotics; Safety factor; Social robots; Psychological safety; Robot safety; Safe collaboration; Safety standard; Safety techniques; Robot programming; behavior; classification; human; human robot interaction; industry; man machine interaction; planning; priority journal; psychological aspect; psychological parameters; psychology; Review; risk assessment; risk reduction; robot behavior; robotics; safety; social environment",,,,,,,,,,,,,,,,"Ahmad, R., Plapper, P., Human-robot collaboration: twofold strategy algorithm to avoid collisions using tof sensor (2016) Int. 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Magaz., 11 (2), pp. 12-21","Zacharaki, A.; Democritus University of Thrace, Building A- Campus, Greece; email: azachara@civil.duth.gr",,,"Elsevier B.V.",,,,,09257535,,SSCIE,,"English","Saf. Sci.",Review,"Final","",Scopus,2-s2.0-85080879374
"Andriella A., Torras C., Alenyà G.","57202547412;7003421114;6508265237;","Short-Term Human–Robot Interaction Adaptability in Real-World Environments",2020,"International Journal of Social Robotics","12","3",,"639","657",,4,"10.1007/s12369-019-00606-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076511966&doi=10.1007%2fs12369-019-00606-y&partnerID=40&md5=43421a4de20a33f733ff8ef60f8b44fe","CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","Andriella, A., CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Torras, C., CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Alenyà, G., CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","In recent years there has been an increasing interest in deploying robotic systems in public environments able to effectively interact with people. To properly work in the wild, such systems should be robust and be able to deal with complex and unpredictable events that seldom happen in controlled laboratory conditions. Moreover, having to deal with untrained users adds further complexity to the problem and makes the task of defining effective interactions especially difficult. In this work, a Cognitive System that relies on planning is extended with adaptive capabilities and embedded in a Tiago robot. The result is a system able to help a person to complete a predefined game by offering various degrees of assistance. The robot may decide to change the level of assistance depending on factors such as the state of the game or the user performance at a given time. We conducted two days of experiments during a public fair. We selected random users to interact with the robot and only for one time. We show that, despite the short-term nature of human–robot interactions, the robot can effectively adapt its way of providing help, leading to better user performances as compared to a robot not providing this degree of flexibility. © 2019, Springer Nature B.V.","Adaptive Behaviour; Cognitive System; Human–Robot Interaction; Short-Term Human–Robot Interaction; Social Robotics","Agricultural robots; Cognitive systems; Social robots; Adaptive capabilities; Controlled laboratories; Degree of flexibility; Effective interactions; Public environment; Real world environments; Robot interactions; User performance; Robot programming",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 721619; Ministerio de Ciencia e Innovación, MICINN: TIN2017-90086-R; Agencia Estatal de Investigación, AEI: MDM-2016-0656","This project has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement SOCRATES (No 721619), by the Spanish Ministry of Science and Innovation HuMoUR (TIN2017-90086-R), and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).",,,,,,,,,,"Aaltonen, I., Arvola, A., Heikkilä, P., Lammi, H., Hello pepper, may i tickle you?: Children’s and adults’ responses to an entertainment robot at a shopping mall (2017) Proceedings of the Companion of the 12Th ACM/IEEE International Conference on human–robot Interaction, pp. 53-54. , NY, USA; 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Gordon, G., Spaulding, S., Westlund, J.K., Lee, J.J., Plummer, L., Martinez, M., Das, M., Breazeal, C., Affective personalization of a social robot tutor for children’s second language skills (2016) Proceedings of the 30Th Conference on Artificial Intelligence, pp. 3951-3957; Hemminghaus, J., Kopp, S., Towards adaptive social behavior generation for assistive robots using reinforcement learning (2017) Proceedings of the 12Th ACM/IEEE International Conference on human–robot Interaction, pp. 332-340. , ACM Press, New York, USA; Hoffmann, J., The metric-FF planning system: translating “ignoring delete lists” to numeric state variables (2003) J Artif Intell Res, 20, pp. 291-341; Hong, A., Igharoro, O., Liu, Y., Niroui, F., Nejat, G., Benhabib, B., Investigating human–robot teams for learning-based semi-autonomous control in urban search and rescue environments (2019) J Intell Robot Syst, 94 (3), pp. 669-686; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., An affective guide robot in a shopping mall (2009) Proceedings of the 4Th ACM/IEEE International Conference on Human Robot Interaction, pp. 173-180; 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Tapus, A., Ţapus, C., Matarić, M.J., The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia (2009) IEEE International Conference on Rehabilitation Robotics, pp. 924-929; Tomic, S., Pecora, F., Saffiotti, A., Too cool for school—adding social constraints in human aware planning (2014) Proceedings of the 9Th International Workshop on Cognitive Robotics, Cogrob (ECAI); Tonkin, M., Vitale, J., Ojha, S., Williams, M.A., Fuller, P., Judge, W., Wang, X., Would you like to sample’l Robot engagement in a shopping centre (2017) 26Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 42-49; Tsiakas, K., Abujelala, M., Makedon, F., Task engagement as personalization feedback for socially-assistive robots and cognitive training (2018) Technologies, 6 (2), p. 49; Yuen, H.K., Illingworth, J., Kittler, J., Ellipse detection using the hough transform (1988) Procedings of the Alvey Vision Conference, 8, pp. 1-41","Andriella, A.; CSIC-UPC, C/Llorens i Artigas 4-6, Spain; email: aandriella@iri.upc.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85076511966
"Bian F., Ren D., Li R., Liang P., Wang K., Zhao L.","57195946919;57204532133;57219000160;56341925400;57157579400;57191521088;","Dynamical system based variable admittance control for physical human-robot interaction",2020,"Industrial Robot","47","4",,"623","635",,1,"10.1108/IR-12-2019-0258","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084550792&doi=10.1108%2fIR-12-2019-0258&partnerID=40&md5=4c0e71511a4552f92b6ee63ef18ea1e2","Harbin Institute of Technology, Harbin, China; Fujian (Quanzhou) HIT Institute of Engineering and TechnologyQuanzhou, China; State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China","Bian, F., Harbin Institute of Technology, Harbin, China; Ren, D., Fujian (Quanzhou) HIT Institute of Engineering and TechnologyQuanzhou, China; Li, R., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China; Liang, P., Fujian (Quanzhou) HIT Institute of Engineering and TechnologyQuanzhou, China; Wang, K., Harbin Institute of Technology, Harbin, China; Zhao, L., Harbin Institute of Technology, Harbin, China","Purpose: The purpose of this paper is to enable robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction. Design/methodology/approach: This paper exploits a combination of the dynamical system and the admittance model to create robot behaviors. The reference trajectories are generated by dynamical systems while the admittance control enables robots to compliantly follow the reference trajectories. To determine how control is divided between the two models, a collaborative arbitration algorithm is presented to change their contributions to the robot motion based on the contact forces. In addition, the authors investigate to model the robot’s impedance characteristics as a function of the task requirements and build a novel artificial damping field (ADF) to represent the virtual damping at arbitrary robot states. Findings: The authors evaluate their methods through experiments on an UR10 robot. The result shows promising performances for the robot to achieve complex tasks in collaboration with human partners. Originality/value: The proposed method extends the dynamical system approach with an admittance control law to allow a robot motion being adjusted in real time. Besides, the authors propose a novel ADF method to model the robot’s impedance characteristics as a function of the task requirements. © 2020, Emerald Publishing Limited.","Artificial damping field; Compliant control; Compliant mechanisms; Cooperative robots; Dynamical systems; Haptic devices; Man machine interface (MMI); Physical human–robot interaction; Robotics","Agricultural robots; Compliance control; Damping; Dynamical systems; Machine design; Arbitration algorithm; Damping characteristics; Design/methodology/approach; Dynamical system approaches; Impedance characteristics; Physical human-robot interactions; Reference trajectories; Robot interactions; Social robots",,,,,"2017ZT015; National Natural Science Foundation of China, NSFC: 61673136; National Key Research and Development Program of China, NKRDPC: 2017YFB1301600","This work was partially supported by National Key R&D Program of China (2017YFB1301600), the National Natural Science Foundation of China (61673136) and the High Level Talent Project of Quanzhou City under Grant No.2017ZT015.",,,,,,,,,,"Abut, T., Soyguder, S., Real-time control of bilateral teleoperation system with adaptive computed torque method (2017) Industrial Robot: An International Journal, 44 (3); 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Yang, C., Zeng, C., Liang, P., Li, Z., Li, R., Su, C.-Y., Interface design of a physical human–robot interaction system for human impedance adaptive skill transfer (2017) IEEE Transactions on Automation Science and Engineering, 15 (1), pp. 329-340; Zhu, Y., Yang, C., Wei, Q., Wu, X., Yang, W., Human–robot shared control for humanoid manipulator trajectory planning (2020) Industrial Robot: The International Journal of Robotics Research and Application; Khoramshahi, M., Billard, A., A dynamical system approach to task-adaptation in physical human–robot interaction (2019) Autonomous Robots, 43 (4), pp. 927-946","Li, R.; State Key Laboratory of Robotics and System, China; email: lrf100@hit.edu.cn",,,"Emerald Group Holdings Ltd.",,,,,0143991X,,IDRBA,,"English","Ind. Robot",Article,"Final","",Scopus,2-s2.0-85084550792
"Zong C., Ji Z., Yu J., Yu H.","55413844400;8555087100;7405525050;36164668500;","An angle-changeable tracked robot with human-robot interaction in unstructured environments",2020,"Assembly Automation","40","4",,"565","575",,5,"10.1108/AA-11-2018-0231","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083660970&doi=10.1108%2fAA-11-2018-0231&partnerID=40&md5=577faad2a3dd2b9a10b20f5c16ac3d31","College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China; College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; College of Automation, Qingdao University, Qingdao, China; College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China","Zong, C., College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China, College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China; Ji, Z., College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China; Yu, J., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Yu, H., College of Automation, Qingdao University, Qingdao, China","Purpose: The purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs. Design/methodology/approach: The paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs. Findings: This paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method. Originality/value: The theoretical analysis of this paper provides basic reference for the structural design of tracked robots. © 2020, Emerald Publishing Limited.","Human–robot interaction; Mechanical structure; Passively adaptive device; Tracked robot; Unstructured environments","Control systems; Landforms; Machine design; Man machine systems; Stairs; Structural design; Adaptive devices; Design/methodology/approach; Mass distribution; Mechanical structures; Robot interaction control; Robot interactions; Unstructured environments; Working environment; Social robots",,,,,"JQ201419; 201702038; National Natural Science Foundation of China, NSFC: 61374062, 61603288, 61873136","This work was supported by the National Natural Science Foundation of China (Nos. 61873136, 61374062, 61603288), Science Foundation of Shandong Province for Distinguished Young Scholars (No. JQ201419), the Special Funds of Shandong Province for Post-doctoral Innovation Projects under Grant 201702038, Post-doctoral Application Research Project of Qingdao.",,,,,,,,,,"Ben-Tzvi, P., Ito, S., Goldenberg, A., Autonomous stair climbing with reconfigurable tracked mobile robot (2007) IEEE International Workshop on Robotic and Sensors Environments, pp. 1-6. , Ottawa; 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College of Automation, China; email: jizhijian@pku.org.cn",,,"Emerald Group Holdings Ltd.",,,,,01445154,,ASAUD,,"English","Assem. Autom.",Article,"Final","",Scopus,2-s2.0-85083660970
"Kim S., Chung W.K., Kim K.","57203917002;57204958534;16550039200;","sEMG-based Static Force Estimation for Human-Robot Interaction using Deep Learning",2020,"2020 17th International Conference on Ubiquitous Robots, UR 2020",,,"9144869","81","86",,,"10.1109/UR49135.2020.9144869","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094321697&doi=10.1109%2fUR49135.2020.9144869&partnerID=40&md5=459f7e2a466635779a10ae4b0dab3734","POSTECH, Robotics Laboratory, School of Mechanical Engineering, South Korea; POSTECH, Faculty of Mechanical Engineering, South Korea","Kim, S., POSTECH, Robotics Laboratory, School of Mechanical Engineering, South Korea; Chung, W.K., POSTECH, Faculty of Mechanical Engineering, South Korea; Kim, K., POSTECH, Faculty of Mechanical Engineering, South Korea","Human-robot interaction (HRI) is a rapidly growing research area and it occurs in many applications including human-robot collaboration, human power augmentation, and rehabilitation robotics. As it is hard to exactly calculate intended motion trajectory, generally, interaction control is applied in HRI instead of pure motion control. To implement the interaction control, force information is necessary and force sensor is widely used in force feedback. However, force sensor has some limitations as 1) it is subject to breakdown, 2) it imposes additional volume and weight to the system, and 3) its applicable places are constrained. In this situation, force estimation can be a good solution. However, if force in static situation should be measured, using position and velocity is not sufficient because they are not influenced by the exerted force anymore. Therefore, we proposed sEMG-based static force estimation using deep learning. sEMG provides a useful information about human-exerting force because it reflects the human intention. Also, to extract the complex relationship between sEMG and force, deep learning approach is used. Experimental results show that when force with maximal value of 63.2 N is exerted, average force estimation error was 3.67 N. Also, the proposed method shows that force onset timing of estimated force is faster than force sensor signal. This result would be advantageous for faster human intention recognition. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Man machine systems; Social robots; Complex relationships; Force sensor signals; Human robot Interaction (HRI); Human-robot collaboration; Interaction controls; Learning approach; Motion trajectories; Rehabilitation robotics; Deep learning",,,,,"2014M3C1B2048632; NRF-2017M3A9F6029736; National Research Foundation of Korea, NRF","This research was supported by the convergence technology development program for bionic arm through the National Research Foundation of Korea(NRF) funded by the Ministry of Science & ICT (No. 2014M3C1B2048632).","This research was supported by the Bio Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (NRF-2017M3A9F6029736)",,,,,,,,,"Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; 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"Garello L., Grella F., Castagnetta S., Bruno B., Recchiuto C.T., Sgorbissa A.","57219626207;57219626827;57219625009;36922823500;36452602300;6602687901;","Robot Agreeableness and User Engagement in Verbal Human-Robot Interaction",2020,"2020 17th International Conference on Ubiquitous Robots, UR 2020",,,"9144864","256","263",,1,"10.1109/UR49135.2020.9144864","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094314877&doi=10.1109%2fUR49135.2020.9144864&partnerID=40&md5=ce373e80f22e2c9cb0a82e436580a384","University of Genova, DIBRIS, Genova, Italy; Swiss Federal Institute of Technology in Lausanne (EPFL), CHILI Lab BioRob Lab, Switzerland","Garello, L., University of Genova, DIBRIS, Genova, Italy; Grella, F., University of Genova, DIBRIS, Genova, Italy; Castagnetta, S., University of Genova, DIBRIS, Genova, Italy; Bruno, B., University of Genova, DIBRIS, Genova, Italy, Swiss Federal Institute of Technology in Lausanne (EPFL), CHILI Lab BioRob Lab, Switzerland; Recchiuto, C.T., University of Genova, DIBRIS, Genova, Italy; Sgorbissa, A., University of Genova, DIBRIS, Genova, Italy","It is a well-established fact in the field of Human-Robot Interaction that the personality a social robot is endowed with (i.e., the one it expresses by its gestures and words) plays a key role towards the engagement experienced by any person interacting with it. In this article, we address the problem of designing the conversational style of a social robot to convey different degrees of Agreeableness, which is one of the Big Five personality traits, and investigate the relation between the person's engagement and the robot's degree of agreeableness.We propose to use subjective perception of time elapsed as an indicator for engagement and design two robot behaviours, one making the robot always agree with the person's opinion on topics of discussion (the compliant behaviour) and one making the robot always disagree (the contrasting behaviour). In an experiment involving 14 participants, we assess whether the robot adopting the contrasting behaviour is perceived as more engaging than the one adopting the compliant behaviour, and use participants' previous interactions with vocal assistants as a reference for estimating the naturalness, enjoyability and politeness of the conversation with the robot. Results suggest that designing social robots that are able to disagree with the person they're interacting with might be key to make them more engaging and entertaining. © 2020 IEEE.",,"Agricultural robots; Machine design; Man machine systems; Big five; Established facts; Personality traits; Subjective perceptions; User engagement; Verbal human-robot interactions; Social robots",,,,,"737858","This work has been partially supported by the European Commission Horizon2020 Research and Innovation Programme under grant agreement No. 737858.",,,,,,,,,,"Sidner, C., Dzikovska, M., Human-robot interaction: Engagement between humans and robots for hosting activities (2002) Proceedings. Fourth Ieee Int. Conf. On Multimodal Interfaces, pp. 123-128. , no. May. 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Conf. On., pp. 255-262; Rau, P.P., Li, Y., Li, D., Effects of communication style and culture on ability to accept recommendations from robots (2009) Computers in Human Behavior, 25 (2), pp. 587-595; Wang, L., Rau, P.-L.P., Evers, V., Robinson, B.K., Hinds, P., When in Rome: The role of culture & context in adherence to robot recommendations (2010) Proceedings of the 5th ACM/IEEE Int. Conf. On Human-robot Interaction., pp. 359-366. , IEEE Press; Bruno, B., Recchiuto, C.T., Papadopoulos, I., Saffiotti, A., Koulouglioti, C., Menicatti, R., Mastrogiovanni, F., Sgorbissa, A., Knowledge representation for culturally competent personal robots: Requirements, design principles, implementation, and assessment (2019) International Journal of Social Robotics; Madden, D., (1975) Harlequin's Stick, Charlie's Cane: A Comparative Study of Commedia dell'Arte and Silent Slapstick Comedy, , Popular Press; Guarino, N., Formal ontology and information systems (1998) Proceedings of FOIS'98., pp. 3-15. , Trento, Italy: IOS Press","Sgorbissa, A.; University of Genova, Italy; email: antonio.sgorbissa@unige.it",,,"Institute of Electrical and Electronics Engineers Inc.","17th International Conference on Ubiquitous Robots, UR 2020","22 June 2020 through 26 June 2020",,161946,,9781728157153,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85094314877
"Ye L., Sun C., Qiu Z.","57201975390;57198057519;57219023252;","Model-based fuzzy variable impedance sliding-mode control for trajectory tracking in physical human-robot interaction",2020,"Proceedings of 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC 2020",,,"9141937","871","876",,,"10.1109/ITOEC49072.2020.9141937","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091123045&doi=10.1109%2fITOEC49072.2020.9141937&partnerID=40&md5=c74b91fc5ba4226e019484921c7126ef","School of Mechanical Engineering, Nanchang University, Nanchang, China; Jiangxi Engineering Laboratory of Digital Manufacturing for Automobile Parts, Nanchang Institute of Science and Technology, Nanchang, China; Jiangxi Cigarette Factory of China Tobacco Nanchang Industrial CO.,LTD., Nanchang, China","Ye, L., School of Mechanical Engineering, Nanchang University, Nanchang, China, Jiangxi Engineering Laboratory of Digital Manufacturing for Automobile Parts, Nanchang Institute of Science and Technology, Nanchang, China; Sun, C., Jiangxi Cigarette Factory of China Tobacco Nanchang Industrial CO.,LTD., Nanchang, China; Qiu, Z., Jiangxi Engineering Laboratory of Digital Manufacturing for Automobile Parts, Nanchang Institute of Science and Technology, Nanchang, China","This paper mainly presents a nonlinear fuzzy variable impedance sliding-mode controller. The controller provides asymptotic tracking for the robot end-effector in Cartesian coordinates applicable to human-robot interaction. Firstly, the forward and analytical inverse kinematics for a 7-DOF redundant manipulator were proposed. The optimal analytical inverse kinematic solution used redundant angle is chosen according to the joint angle minimum increment. Secondly, the controller based on dynamics model of the physical human-robot interaction was designed. Finally, using simulations with Matlab software, the effectiveness of the proposed controller is investigated. © 2020 IEEE.","7-DOF robot; impedance control; physical human-robot interaction; sliding-mode control","Controllers; End effectors; Inverse kinematics; Man machine systems; MATLAB; Sliding mode control; 7-dof redundant manipulators; Analytical inverse kinematics; Asymptotic tracking; Cartesian coordinate; Physical human-robot interactions; Robot end effector; Sliding mode controller; Trajectory tracking; Human robot interaction",,,,,,"ACKNOWLEDGMENT This work was supported by the Educational hall science and technology research project of JiangXi province under Grant No.GJJ161226 and technology research for Nanchang Institute of Science and Technology under Grant No. SZZX-17-13.",,,,,,,,,,"Raibert, M.H., Craig, J.J., Hybrid position/force control of manipulators (1981) Trans. ASME J Dyn Syst Meas Control., 102, pp. 126-133. , Jun; Hou, Y.F., Mason, M.T., Robust execution of contact-rich motion plans by hybrid force-velocity control International Conference on Robotics and Automation ICRA, pp. 1933-1939. , Montreal, Canada, May. 2019; Yilmaz, N., Bazman, M., Alassi, A., Gur, B., Tumerdem, U., 6-Axis Hybrid Sensing and Estimation of Tip Forces/Torques on a Hyper-Redundant Robotic Surgical Instrument (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, pp. 2990-2997. , Macau, China, Nov; Hogan, N., Impedance Control: An approach to manipulation:Part ITheory (1985) J. Dyn. Syst. Meas. Contr., 107, pp. 1-7. , Mar; Kazerooni, H., Sheridan, T.B., Houpt, P.K., Robust compliant motion for manipulators, part I: The fundamental concepts of compliant motion (1986) IEEE Journal on Robotics and Automation, 2 (2), pp. 83-92. , Jun; Ficuciello, F., Romano, A., Villani, L., Siciliano, B., Cartesian impedance control of redundant manipulators for human-robot co-manipulation (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, pp. 2120-2125. , Chicago, USA, Sep; Sharifi, M., Behzadipour, S., Vossoughi, G.R., Model reference adaptive impedance control in Cartesian coordinates for physical human-robot interaction (2014) Adv. Robotics, 28 (19), pp. 1277-1290. , Sep; Johannsmeier, L., Gerchow, M., Haddadin, S., A framework for robot manipulation: Skill formalism, meta learning and adaptive control (2019) International Conference on Robotics and Automation ICRA, pp. 5844-5850. , Montreal, Canada, May; Chien, M., Huang, A., Adaptive Impedance Control of Robot Manipulators based on Function Approximation Technique (2004) Robotica, 22 (4), pp. 395-403. , Nov; Zhu, W.H., Schutter, J.D., Experimental verifications of virtual decomposition-based motion/force control (2002) IEEE Trans. Robo. Autom., 18 (3), pp. 379-386. , Jun; Li, Y., Ganesh, G., Jarrasse, N., Haddadin, S., Albu-Schaeffer, A., Burdet, E., Force, Impedance, and trajectory learning for contact tooling and haptic identification (2018) IEEE Trans. Robot., 34 (5), pp. 1170-1182; Zhu, Y., Barth, E.J., Impedance control of a pneumatic actuator for contact tasks (2005) Proc. IEEE Int. Conf. Robotics Automat., pp. 987-992. , Barcelona, Spain, Apr; Nicolis, D., Allevi, F., Rocco, P., Robust impedance shaping of redundant teleoperators with timedelay via sliding mode control (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, pp. 2740-2747. , Macau, China, Nov; Aristidou, A., Lasenby, J., Chrysanthou, Y., Shamir, A., Inverse kinematics techniques in computer graphics: A Survey (2018) Comput. Graph. Forum, 37 (6), pp. 35-58; Shimizu, M., Kakuya, H., Yoon, W.K., Kitagaki, K., Kosuge, K., Analytical inverse kinematic computation for 7dof redundant manipulators with joint limits and its application to Redundancy Resolution (2008) IEEE T. Robot., 24 (5), pp. 1131-1142. , Oct; Gong, M., Li, X., Zhang, L., Analytical inverse kinematics and self-motion application for 7dof redundant manipulator (2019) IEEE Access, 7, pp. 18662-18674. , Feb; Huo, W., (2005) Robot Dynamics and Control, , Beijing, China: Higher Education Press",,"Xu B.Mou K.","Chengdu Global Union Academy of Science and Technology;Chongqing Geeks Education Technology Co., Ltd;Chongqing Global Union Academy of Science and Technology;Global Union Academy of Science and Technology;IEEE Harbin Section","Institute of Electrical and Electronics Engineers Inc.","5th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2020","12 June 2020 through 14 June 2020",,161902,,9781728143224,,,"English","Proc. IEEE Inf. Technol. Mechatronics Eng. Conf., ITOEC",Conference Paper,"Final","",Scopus,2-s2.0-85091123045
"Mousavi S.A., Golkar N.","57218580996;57218580843;","Management of human-robot interaction in order to control of ankle knee during walking",2020,"HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings",,,"9152867","","",,1,"10.1109/HORA49412.2020.9152867","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089706630&doi=10.1109%2fHORA49412.2020.9152867&partnerID=40&md5=fbdc1b0e93caab6742dedbfcb12e890d","Islamic Azad University, Department of Biomedical Engineering, Mashhad, Iran","Mousavi, S.A., Islamic Azad University, Department of Biomedical Engineering, Mashhad, Iran; Golkar, N., Islamic Azad University, Department of Biomedical Engineering, Mashhad, Iran","Patients who have had a stroke or a traumatic brain injury are usually motor-impaired. Nerve damage can cause lower limb paralysis and impair walking. Survivors of stroke with varying degrees of motor dysfunction not only experience the discomfort of daily living, but also feel a great deal of stress along with the economic burden on the family and society. Many types of rehabilitation robots have been developing to aid rehabilitation in people with stroke. In this study, a new method for knee joint control using rehabilitation robots based on human-robot interaction process is present. The control process is based on quadriceps data when walking for a healthy human, using fuzzy rules designed to control the contribution of humans and robots to the walking process. The main purpose of this study was to present and simulate a smart solution to determine the amount of human and robot presence in knee motion correction in patients with stroke and cerebral palsy. Dynamic modeling using two-dimensional mapping of Hunan is use instead of humans and the robot model is a PID controller whose coefficients are determined using the Ziegler-Nigel's method. The results show that the proposed model has been able to follow the knee angle curve with fuzzy system. Moreover, our results show that using a fuzzy system can lift the pressure on a person and help him walk. The proposed PID system can also play a significant role in reducing the fuzzy system error. The proposed control system without the presence of fuzzy system has an RMS error of 181/3 and also the RMS error in the presence of fuzzy system is equal to 0.698 indicating that the proposed control system is functioning properly. © 2020 IEEE.","component; Fuzzy Control; Knee Angle Control; Rehabilitation","Agricultural robots; Brain; Errors; Fuzzy inference; Fuzzy systems; Human computer interaction; Joints (anatomy); Process control; Robotics; Three term control systems; Control process; Economic burden; Healthy humans; PID controllers; Rehabilitation robot; Smart solutions; Traumatic Brain Injuries; Two dimensional mapping; Human robot interaction",,,,,,,,,,,,,,,,"Meng, W., Design and modelling of a compliant ankle rehabilitation robot redundantly driven by pneumatic muscles (1892) 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Ieee, 2019.j. Clerk Maxwell, a Treatise on Electricity and Magnetism, 3rd Ed., 2, pp. 68-73. , Oxford: Clarendon; Shen, Z., Design and test of admittance control with inner adaptive robust position control for a lower limb rehabilitation robot (2020) International Journal of Control, Automation and Systems, 18 (1), pp. 134-142; Elissa, K., Title of Paper if Known, , unpublished; Xu, J., Adaptive human-robot interaction control of the lower extremity robotic exoskeleton with magnetorheological actuators (2019) 2019 Ieee 4th International Conference on Advanced Robotics and Mechatronics (ICARM), , ieee; Bingjing, G., Jianhai, H., Xiangpan, L., Lin, Y., Human-robot interactive control based on reinforcement learning for gait rehabilitation training robot (2019) International Journal of Advanced Robotic Systems, 16, pp. 1729881419839584; Yan, H., Human-in-the-loop optimization control for the ankle exoskeleton during walking based on iterative learning and particle swarm optimization algorithm (2019) 2019 Ieee 4th International Conference on Advanced Robotics and Mechatronics (ICARM), , ieee; Gopinath, D., Jain, S., Argall, B.D., Human-in-the-loop optimization of shared autonomy in assistive robotics (2016) Ieee Robotics and Automation Letters, 2, pp. 247-254; Miller, D.J., Stergiou, N., Kurz, M.J., An improved surrogate method for detecting the presence of chaos in gait (2016) Journal of Biomechanics, 39, pp. 2873-2876; Simpson, R.C., Levine, S.P., Bell, D.A., Jaros, L.A., Koren, Y., Borenstein, J., Navchair: An assistive wheelchair navigation system with automatic adaptation (2014) Assistive Technology and Artificial Intelligence, pp. 235-255. , ed: Springer; Li, Q., Chen, W., Wang, J., Dynamic shared control for human-wheelchair cooperation (2011) 2011 Ieee International Conference on Robotics and Automation, pp. 4278-4283; Driessen, B., Kate, T.T., Liefhebber, F., Versluis, A., Van Woerden, J., Collaborative control of the manus manipulator (2015) Universal Access in the Information Society, 4, pp. 165-173; Lerner, Z.F., A robotic exoskeleton for treatment of crouch gait in children with cerebral palsy: Design and initial application (2016) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 25 (6), pp. 650-659; Li, Z., Kang, Y., Xiao, Z., Song, W., Human-robot coordination control of robotic exoskeletons by skill transfers (2016) Ieee Transactions on Industrial Electronics, 64, pp. 5171-5181; Hénon, M., A two-dimensional mapping with a strange attractor (1976) The Theory of Chaotic Attractors, pp. 94-102. , ed: Springer; Khansari-Zadeh, S.M., Billard, A., Learning Stable Nonlinear Dynamical; Liu, D.F., Tang, Z.Y., Pei, Z.C., Swing motion control of lower extremity exoskeleton based on admittance method (2015) J Beijing Univ Aero Astro; Åström, K.J., Hägglund, T., Revisiting the ziegler-nichols step response method for pid control (2004) Journal of Process Control, 14 (6), pp. 635-650; Bovi, G., A multiple-task gait analysis approach: Kinematic kinetic and emg reference data for healthy young and adult subjects (2011) Gait & Posture, 33 (1), pp. 6-13","Mousavi, S.A.; Islamic Azad University, Iran; email: mousavibme@mshdiau.ac.ir",,,"Institute of Electrical and Electronics Engineers Inc.","2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020","26 June 2020 through 27 June 2020",,162106,,9781728193526,,,"English","HORA - Int. Congr. Human-Comput. Interact., Optim. Robot. Appl., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85089706630
"She Y., Song S., Su H.-J., Wang J.","56545216500;57200117296;8379272100;7701333092;","A Comparative Study on the Effect of Mechanical Compliance for a Safe Physical Human-Robot Interaction",2020,"Journal of Mechanical Design, Transactions of the ASME","142","6","4046068","","",,5,"10.1115/1.4046068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087990633&doi=10.1115%2f1.4046068&partnerID=40&md5=9451f39fcc6966265766a728bdc60601","Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","She, Y., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","In this paper, we study the effects of mechanical compliance on safety in physical human-robot interaction (pHRI). More specifically, we compare the effect of joint compliance and link compliance on the impact force assuming a contact occurred between a robot and a human head. We first establish pHRI system models that are composed of robot dynamics, an impact contact model, and head dynamics. These models are validated by Simscape simulation. By comparing impact results with a robotic arm made of a compliant link (CL) and compliant joint (CJ), we conclude that the CL design produces a smaller maximum impact force given the same lateral stiffness as well as other physical and geometric parameters. Furthermore, we compare the variable stiffness joint (VSJ) with the variable stiffness link (VSL) for various actuation parameters and design parameters. While decreasing stiffness of CJs cannot effectively reduce the maximum impact force, CL design is more effective in reducing impact force by varying the link stiffness. We conclude that the CL design potentially outperforms the CJ design in addressing safety in pHRI and can be used as a promising alternative solution to address the safety constraints in pHRI. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.","compliant mechanisms, design theory and methodology, variable stiffness design, robot design, robotic systems","Man machine systems; Stiffness; Universal joints; Alternative solutions; Comparative studies; Design parameters; Mechanical compliance; Physical human-robot interactions; Physical humanrobot interaction (phri); Safety constraint; Variable stiffness; Human robot interaction",,,,,"National Science Foundation, NSF: 1637656, CMMI-1637656","This material is based upon the work supported by the National Science Foundation under Grant No: CMMI-1637656. 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L., Johnson, K. L., (1987) Contact Mechanics, , Cambridge University Press, Cambridge, UK; López-Martínez, J., García-Vallejo, D., Giménez-Fernández, A., Torres-Moreno, J., A Flexible Multibody Model of a Safety Robot Arm for Experimental Validation and Analysis of Design Parameters (2014) ASME J. Comput. Nonlinear Dyn, 9 (1), p. 011003; Book, W. J., Recursive Lagrangian Dynamics of Flexible Manipulator Arms (1984) Int. J. Rob. Res, 3 (3), pp. 87-101; Wensing, P. M., Wang, A., Seok, S., Otten, D., Lang, J., Kim, S., Proprioceptive Actuator Design in the MIT Cheetah: Impact Mitigation and High-Bandwidth Physical Interaction for Dynamic Legged Robots (2017) IEEE Trans. Rob, 33 (3), pp. 509-522","She, Y.; Department of Mechanical and Aerospace Engineering, United States; email: yushe@mit.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,10500472,,JMDEE,,"English","J Mech Des, Trans ASME",Article,"Final","",Scopus,2-s2.0-85087990633
"Benli E., Motai Y., Rogers J.","57194856620;9734468900;57207977901;","Visual Perception for Multiple Human-Robot Interaction from Motion Behavior",2020,"IEEE Systems Journal","14","2","8944061","2937","2948",,1,"10.1109/JSYST.2019.2958747","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086070002&doi=10.1109%2fJSYST.2019.2958747&partnerID=40&md5=8a8fd94956c631b00b276223eae38a71","Department of Electrical and Electronics Engineering, Gümüşhane University, Gümüşhane, 29100, Turkey; Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States; U.S. Army Research Laboratory, Adelphi, MD, United States","Benli, E., Department of Electrical and Electronics Engineering, Gümüşhane University, Gümüşhane, 29100, Turkey; Motai, Y., Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States; Rogers, J., U.S. Army Research Laboratory, Adelphi, MD, United States","Visual perception is an important component for human-robot interaction processes in robotic systems. Interaction between humans and robots depends on the reliability of the robotic vision systems. The variation of camera sensors and the capability of these sensors to detect many types of sensory inputs improve the visual perception. The analysis of activities, motions, skills, and behaviors of humans and robots have been addressed by utilizing the heat signatures of the human body. The human motion behavior is analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target in the omnidirectional (O-D) thermal images. The process of human target identification and gesture recognition by traditional sensors have problem for multitarget scenarios since these sensors may not keep all targets in their narrow field of view (FOV) at the same time. O-D thermal view increases the robots' line-of-sights and ability to obtain better perception in the absence of light. The human target is informed of its position, surrounding objects and any other human targets in its proximity so that humans with limited vision or vision disability can be assisted to improve their ability in their environment. The proposed method helps to identify the human targets in a wide FOV and light independent conditions to assist the human target and improve the human-robot and robot-robot interactions. The experimental results show that the identification of the human targets is achieved with a high accuracy. © 2007-2012 IEEE.","Command cognition; human motion analysis; multiple human targets; multiple robots; omnidirectional (O-D) camera; robotic perception; target identification; thermal vision; visual perception; walking behavior","Behavioral research; Man machine systems; Robotics; Vision; Field of views; Heat signatures; Motion behavior; Robot interactions; Robotic systems; Target identification; Thermal images; Visual perception; Human robot interaction",,,,,"Army Research Laboratory, ARL; Naval Surface Warfare Center, NSWC; U.S. Navy","Manuscript received February 20, 2019; revised September 9, 2019 and November 24, 2019; accepted December 5, 2019. Date of publication December 27, 2019; date of current version June 3, 2020. This work was supported in part by the U.S. Navy, Naval Surface Warfare Center Dahlgren, in part by the U.S. Army Research Laboratory, and in part by the Ministry of National Education of Turkey. (Corresponding author: Emrah Benli.) E. Benli is with the Department of Electrical and Electronics Engineering, Gümüs¸hane University, Gümüs¸hane 29100, Turkey (e-mail: benlie@vcu.edu).",,,,,,,,,,"Benli, E., Motai, Y., Rogers, J., Human behavior-based target tracking with an omni-directional thermal camera (2019) IEEE Trans. Cogn. Develop. Syst, 11 (1), pp. 36-50. , Mar; Cavaliere, D., Senatore, S., Loia, V., Proactive UAVs for cognitive contextual awareness (2019) IEEE Syst. J, 13 (3), pp. 3568-3579. , Sep; Ju, Z., Ji, X., Li, J., Liu, H., An Integrative framework of human hand gesture segmentation for human-robot interaction (2017) IEEE Syst. 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Man Cybern., Syst, 47 (7), pp. 1169-1180. , Jul; Qu, X., Zhang, D., Lu, G., Guo, Z., Door knob hand recognition system (2017) IEEE Trans. Syst. Man Cybern., Syst, 47 (11), pp. 2870-2881. , Nov; Caron, G., Mouaddib, E.M., Marchand, E., 3D model based tracking for omnidirectional vision:Anewspherical approach (2012) Robot.Auton. Syst, 60 (8), pp. 1056-1068. , Aug; Mariottini, G.L., Scheggi, S., Morbidi, F., Prattichizzo, D., An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras (2012) Robot. Auton. Syst, 60 (9), pp. 1179-1190. , Sep; Bauzano, E., Estebanez, B., Garcia-Morales, I., Muñoz, V.F., Collaborative human-robot system for HALS suture procedures (2016) IEEE Syst. J, 10 (3), pp. 957-966. , Sep; Tung, T., Gomez, R., Kawahara, T., Matsuyama, T., Multiparty interaction understanding using smart multimodal digital signage (2014) IEEE Trans. Human-Mach. 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J, 10 (3), pp. 922-932. , Sep; Wei, P., Zhao, Y., Zheng, N., Zhu, S.C., Modeling 4D human-object interactions for joint event segmentation, recognition, and object localization (2017) IEEE Trans. Pattern Anal. Mach. Intell, 39 (6), pp. 1165-1179. , 1 Jun; Zaraki, A., Design and evaluation of a unique social perception system for human-robot interaction (2017) IEEE Trans. Cogn. Develop. Syst, 9 (4), pp. 341-355. , Dec; Qiu, S., Li, Z., He, W., Zhang, L., Yang, C., Su, C.Y., Brain-machine interface and visual compressive sensing-based teleoperation control of an exoskeleton robot (2017) IEEE Trans. Fuzzy Syst, 25 (1), pp. 58-69. , Feb; Kawanishi, Y., Voting-based hand-waving gesture spotting from a low-resolution far-infrared image sequence (2018) Proc. IEEE Visual Commun. Image Process, pp. 1-4; Hussain, S., Saxena, R., Han, X., Khan, J.A., Shin, H., Hand gesture recognition using deep learning (2017) Proc. Int. SoC Design Conf, pp. 48-49; Islam, M.R., Mitu, U.K., Bhuiyan, R.A., Shin, J., Hand gesture feature extraction using deep convolutional neural network for recognizing American sign language (2018) Proc. 4th Int. Conf. Frontiers Signal Process, pp. 115-119","Benli, E.; Department of Electrical and Electronics Engineering, Turkey; email: benlie@vcu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,19328184,,,,"English","IEEE Syst. J.",Article,"Final","",Scopus,2-s2.0-85086070002
"Céspedes N., Múnera M., Gómez C., Cifuentes C.A.","57210636629;6505875786;57197795128;36717243800;","Social Human-Robot Interaction for Gait Rehabilitation",2020,"IEEE Transactions on Neural Systems and Rehabilitation Engineering","28","6","9064601","1299","1307",,11,"10.1109/TNSRE.2020.2987428","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086051908&doi=10.1109%2fTNSRE.2020.2987428&partnerID=40&md5=dcb7d158b7c1159f8bb20609707dd7cb","Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, 111166, Colombia; Physical Rehabilitation Department, Clínica Universidad de la Sabana, Bogotá, 250008, Colombia","Céspedes, N., Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, 111166, Colombia; Múnera, M., Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, 111166, Colombia; Gómez, C., Physical Rehabilitation Department, Clínica Universidad de la Sabana, Bogotá, 250008, Colombia; Cifuentes, C.A., Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, 111166, Colombia","Robot-assisted therapy for gait rehabilitation of patients with neurological disorders usually combines a body weight support system with a treadmill system. Lokomat is one of the most used devices for gait rehabilitation. This device allows therapists to focus on the patient and the therapy. However, this therapy session is based on multi-tasking processes, which are often difficult for a therapist to manage. In this work, a Socially Assistive Robot (SAR) was integrated into a neurorehabilitation program as a collaborator agent to promote patient engagement and performance during the therapy. This short-term study presents the effects comparing the social robot condition and control condition with a group of four neurological patients using repeated measurement design. As a remarkable result, patients improved thoracic 18.44% and cervical 32.23% posture on average with SAR assistance. This study demonstrated the feasibility of the integration of a social robot as a complement of gait rehabilitation programs. © 2001-2011 IEEE.","biofeedback; Gait rehabilitation; human-robot interaction; Lokomat; socially assistive robotics","Economic and social effects; Machine design; Neurology; Patient rehabilitation; Patient treatment; Body weight support systems; Neurological disorders; Neurological patient; Neurorehabilitation; Repeated measurements; Robot-assisted therapies; Social human-robot interactions; Socially assistive robots; Social robots; adult; Article; behavior; body position; clinical article; cognition; controlled study; feasibility study; female; Guillain Barre syndrome; heart rate; heart rehabilitation; human; male; middle aged; motivation; neurologic disease; neurorehabilitation; patient care; questionnaire; robot assisted surgery; robotics; spinal cord injury; exercise test; gait; neurologic gait disorder; social interaction; stroke rehabilitation; Exercise Test; Gait; Gait Disorders, Neurologic; Humans; Robotics; Social Interaction; Stroke Rehabilitation",,,,,"801-2017; Royal Academy of Engineering: IAPP/1516/137/2016-2018","Manuscript received June 20, 2019; revised November 11, 2019 and December 12, 2019; accepted January 6, 2020. Date of publication April 13, 2020; date of current version June 5, 2020. This work was supported in part by the Industry Academia Partnership Programme Colombia, U.K., in part by the Royal Academy of Engineering through Newton Fund, human-robot interaction strategies for rehabilitation based on socially assistive robotics, under Grant IAPP/1516/137/2016-2018, in part by Colciencias Colombia under Grant 801-2017, and in part by the Colombia School of Engineering Julio Garavito Funds. (Corresponding author: Carlos A. Cifuentes.) Nathalia Céspedes, Marcela Múnera, and Carlos A. Cifuentes are with the Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia (e-mail: nathalia. cespedes@mail.escuelaing.edu.co; marcela.munera@escuelaing.edu. co; carlos.cifuentes@escuelaing.edu.co).",,,,,,,,,,"Dua, T., Janca, A., Muscetta, A., Public health principles and neurological disorders (2006) Neurological Disorders: Public Health Challenges, (2), pp. 7-25; (2016) Who What Are Neurological Disorders?, , WHO, Geneva, Switzerland; O'Sullivan, S.B., Schmitz, T.J., Fulk, G.D., (2014) Physical Rehabilitation., , Philadelphia, PA, USA: F. A. Davis Company; Visintin, M., Barbeau, H., Korner-Bitensky, N., Mayo, N.E., A new approach to retrain gait in stroke patients through body weight support and treadmill stimulation (1998) Stroke, 29 (6), pp. 1122-1128; (2018) Adherence to Long-Term Therapies-Evidence for Action: Section I-Setting the Scene: Chapter I-Defining Adherence: 1. 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Accessed: Jan. 15, [Online]","Cifuentes, C.A.; Department of Biomedical Engineering, Colombia; email: carlos.cifuentes@escuelaing.edu.co",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15344320,,ITNSB,"32287000","English","IEEE Trans. Neural Syst. Rehabil. Eng.",Article,"Final","",Scopus,2-s2.0-85086051908
"Jiang C., Ni Z., Guo Y., He H.","56422994300;38862477100;35205046400;24802077000;","Pedestrian Flow Optimization to Reduce the Risk of Crowd Disasters Through Human-Robot Interaction",2020,"IEEE Transactions on Emerging Topics in Computational Intelligence","4","3","8805280","298","311",,1,"10.1109/TETCI.2019.2930249","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085726199&doi=10.1109%2fTETCI.2019.2930249&partnerID=40&md5=5df700c002bce7eb317ce95c62c7bb67","Stevens Institute of Technology, Hoboken, NJ  07030, United States; South Dakota State University, Brookings, SD, United States; University of Rhode Island, Kingstown, RI, United States","Jiang, C., Stevens Institute of Technology, Hoboken, NJ  07030, United States; Ni, Z., South Dakota State University, Brookings, SD, United States; Guo, Y., Stevens Institute of Technology, Hoboken, NJ  07030, United States; He, H., University of Rhode Island, Kingstown, RI, United States","Pedestrian flow in densely populated or congested areas usually presents irregular or turbulent motion state due to competitive behaviors of individual pedestrians, which reduces flow efficiency and raises the risk of crowd accidents. Effective pedestrian flow regulation strategies are highly valuable for flow optimization. Existing studies seek for optimal design of indoor architectural features and spatial placement of pedestrian facilities for the purpose of flow optimization. However, once placed, the stationary facilities are not adaptive to real-time flow changes. In this paper, we investigate the problem of regulating two merging pedestrian flows in a bottleneck area using a mobile robot moving among the pedestrian flows. The pedestrian flows are regulated through dynamic human-robot interaction (HRI) during their collective motion. We adopt an adaptive dynamic programming (ADP) method to learn the optimal motion parameters of the robot in real time, and the resulting outflow through the bottleneck is maximized with the crowd pressure reduced to avoid potential crowd disasters. The proposed algorithm is a data-driven approach that only uses camera observation of pedestrian flows without explicit models of pedestrian dynamics and HRI. Extensive simulation studies are performed in both MATLAB and a robotic simulator to verify the proposed approach and evaluate the performances. © 2017 IEEE.","and pedestrian crowd pressure; human-robot interaction; learning-based optimal control; Pedestrian flow regulation","Disaster prevention; Disasters; Dynamic programming; Man machine systems; MATLAB; Robot programming; Adaptive dynamic programming; Architectural features; Collective motions; Competitive behavior; Data-driven approach; Extensive simulations; Human robot Interaction (HRI); Pedestrian dynamics; Human robot interaction",,,,,"CMMI-1526835, CMMI-1527016, CMMI-1825709, IIS-1838799, OIA-1833005; National Science Foundation, NSF: 1527016, 1825709, 1838799","Manuscript received February 7, 2019; revised May 5, 2019 and June 24, 2019; accepted July 13, 2019. Date of publication August 19, 2019; date of current version May 23, 2020. The work of C. Jiang and Y. Guo was supported by the U.S. National Science Foundation under Grant CMMI-1527016, Grant CMMI-1825709, and Grant IIS-1838799. The work of Z. Ni was supported by the U.S. National Science Foundation under Grant OIA-1833005. The work of H. He was supported by the U.S. National Science Foundation under Grant CMMI-1526835. (Corresponding author: Yi Guo.) C. Jiang and Y. Guo are with the Stevens Institute of Technology, Hoboken, NJ 07030 USA (e-mail: cjiang6@stevens.edu; yi.guo@stevens.edu).",,,,,,,,,,"Luh, P.B., Wilkie, C.T., Chang, S.-C., Marsh, K.L., Olderman, N., Modeling and optimization of building emergency evacuation considering blocking effects on crowd movement (2012) IEEE Trans. Autom. Sci. 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"Henschel A., Hortensius R., Cross E.S.","57214603084;35519731900;56497637800;","Social Cognition in the Age of Human–Robot Interaction",2020,"Trends in Neurosciences","43","6",,"373","384",,25,"10.1016/j.tins.2020.03.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084225891&doi=10.1016%2fj.tins.2020.03.013&partnerID=40&md5=19a917203a5769c66b1497351705efec","Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom; Department of Cognitive Science, Macquarie UniversityNSW  2109, Australia","Henschel, A., Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom; Hortensius, R., Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom; Cross, E.S., Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom, Department of Cognitive Science, Macquarie UniversityNSW  2109, Australia","Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desire to perceive social cues in the environment, as well as the promise of robots enhancing our daily lives. However, a strong mismatch still exists between our expectations and the reality of social robots. We argue that careful delineation of the neurocognitive mechanisms supporting human–robot interaction will enable us to gather insights critical for optimising social encounters between humans and robots. To achieve this, the field must incorporate human neuroscience tools including mobile neuroimaging to explore long-term, embodied human–robot interaction in situ. New analytical neuroimaging approaches will enable characterisation of social cognition representations on a finer scale using sensitive and appropriate categorical comparisons (human, animal, tool, or object). The future of social robotics is undeniably exciting, and insights from human neuroscience research will bring us closer to interacting and collaborating with socially sophisticated robots. © 2020 The Author(s)","artificial intelligence; fMRI; human–robot interaction; social cognition; social robotics","artificial intelligence; controlled study; expectation; functional magnetic resonance imaging; human; human experiment; neuroimaging; neuroscience; review; robotics; social cognition; social competence; artificial intelligence; cognition; neuroscience; Artificial Intelligence; Cognition; Humans; Neuroimaging; Neurosciences; Robotics; Social Cognition",,,,,"Horizon 2020 Framework Programme, H2020; Leverhulme Trust: PLP-2018-152; European Research Council, ERC; Fundação Bial; Horizon 2020: 677270","The authors thank Michaela Kent, Te-Yi Hsieh, Laura Jastrzab, Ben Jones, Richard Ramsey, and Kohinoor Darda for comments on earlier versions of this piece. This paper originates from projects in the Social Brain in Action Lab that have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.), the Leverhulme Trust (PLP-2018-152 to E.S.C), and the Bial Foundation (to R.H.).","The authors thank Michaela Kent, Te-Yi Hsieh, Laura Jastrzab, Ben Jones, Richard Ramsey, and Kohinoor Darda for comments on earlier versions of this piece. This paper originates from projects in the Social Brain in Action Lab that have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.), the Leverhulme Trust ( PLP-2018-152 to E.S.C), and the Bial Foundation (to R.H.).",,,,,,,,,"Natale, S., Ballatore, A., Imagining the thinking machine (2020) Convergence Int. J. Res. 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"Wright J.L., Chen J.Y.C., Lakhmani S.G.","55638691300;23003275100;43261397400;","Agent Transparency and Reliability in Human-Robot Interaction: The Influence on User Confidence and Perceived Reliability",2020,"IEEE Transactions on Human-Machine Systems","50","3","8795544","254","263",,12,"10.1109/THMS.2019.2925717","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079421610&doi=10.1109%2fTHMS.2019.2925717&partnerID=40&md5=dd7f346e4c438803ed998fbb03a70fbb","U.S. Army Research Laboratory, Human Research and Engineering Directorate, Orlando, FL  32826, United States; Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, United States","Wright, J.L., U.S. Army Research Laboratory, Human Research and Engineering Directorate, Orlando, FL  32826, United States; Chen, J.Y.C., Human Research and Engineering Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, United States; Lakhmani, S.G., U.S. Army Research Laboratory, Human Research and Engineering Directorate, Orlando, FL  32826, United States","Agent transparency is an important contributor to human performance, situation awareness (SA), and trust in human-agent teaming. However, agent transparency's effects on human performance when the agent is unreliable have yet to be examined. This paper examined how the transparency and reliability of an autonomous robotic squad member (ASM) affected a human observer's task performance, workload, SA, trust in the robot, and perceptions of the robot. In a 2 (ASM transparency) × 2 (ASM reliability) within-subject design experiment, participants monitored a simulated soldier squad that included an ASM as it traversed a simulated training environment, while concurrently monitoring the environment for targets. There was no difference in participants' performance on the target detection task, workload, or SA due to either ASM transparency or reliability. ASM reliability influenced participant trust and perceptions of the robot. Results suggest that reliability may be a stronger influence on the human's perceptions of the robot than transparency. Robot errors had a profound and lasting effect on the participants' perception of the robot's future reliability and resulted in reduced confidence in their assessments of the robot's reliability. These findings could have important implications for the continued use of automated systems when the user is aware of system errors. © 2013 IEEE.","Agent transparency; confidence; human-robot interaction; reliability; situation awareness (SA); trust","Automation; Patient monitoring; Reliability; Transparency; Automated systems; Autonomous robotics; Design experiments; Human performance; Robot interactions; Simulated trainings; Situation awareness; Task performance; Human robot interaction",,,,,"U.S. Department of Defense, DOD; Army Research Laboratory, ARL","Manuscript received October 9, 2018; revised February 12, 2019 and May 30, 2019; accepted June 11, 2019. Date of publication August 13, 2019; date of current version May 18, 2020. This work was supported in part by the U.S. Department of Defense Autonomy Research Pilot Initiative (Program Manager Dr. D. H. Kim) and in part by the US Army Research Laboratory - Human-Robot Interaction program (Manager Dr. Susan Hill). This paper was recommended by Associate Editor J. Lyons. (Corresponding author: Julia L. Wright.) J. L. Wright and S. G. Lakhmani are with the U.S. Army Research Laboratory, Human Research and Engineering Directorate, Orlando, FL 32826 USA (e-mail: Julia.l.wright8.civ@mail.mil; shan.g.lakhmani.civ@mail.mil).",,,,,,,,,,"(2013), http://www.acq.osd.mil/chieftechnologist/arpi.html, Autonomy research pilot initiative Department of Defense; Lyons, J.B., Being transparent about transparency: A model for humanrobot interaction (2013) The 2013 AAAI Spring Symp. Ser., , Palo Alto, CA, USA; Chen, J.Y.C., Procci, K., Boyce, M.W., Wright, J.L., Garcia, A., Barnes, M.J., (2014) Situation Awareness-based Agent Transparency, , U.S. Army Res. 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ARL-TR-7466; Hoff, K.A., Bashir, M.M., Trust in automation: Integrating empirical evidence on factors that influence trust (2015) Human Factors, 57 (3), pp. 407-434; Schaefer, K.E., Chen, J.Y.C., Szalma, J.L., Hancock, P.A., A metaanalysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems (2016) Human Factors, 58 (3), pp. 377-400; Balfe, N., Sharples, S., Wilson, J.R., Understanding is key: An analysis of factors pertaining to trust in a real-world automation system (2018) Human Factors, 60 (4), pp. 477-495; Chen, J.Y.C., Terrence, P.I., Effects of tactile cueing on concurrent performance of military and robotics tasks in a simulated multitasking environment (2008) Ergonomics, 51 (8), pp. 1137-1152","Wright, J.L.; U.S. Army Research Laboratory, United States; email: Julia.l.wright8.civ@mail.mil",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85079421610
"de Kervenoael R., Hasan R., Schwob A., Goh E.","57196059833;57204513876;55911596800;57200792631;","Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots",2020,"Tourism Management","78",,"104042","","",,69,"10.1016/j.tourman.2019.104042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075628508&doi=10.1016%2fj.tourman.2019.104042&partnerID=40&md5=f56ef158af2d44659553121590311519","Department of Marketing, Rennes School of Business, Rennes, France; Department of Marketing, La Rochelle Business School, La Rochelle, France; Birmingham University, United Kingdom; Singapore Institute of Management (SIM), Singapore","de Kervenoael, R., Department of Marketing, Rennes School of Business, Rennes, France; Hasan, R., Department of Marketing, Rennes School of Business, Rennes, France; Schwob, A., Department of Marketing, La Rochelle Business School, La Rochelle, France; Goh, E., Birmingham University, United Kingdom, Singapore Institute of Management (SIM), Singapore","Social robots have become pervasive in the tourism and hospitality service environments. The empirical understanding of the drivers of visitors' intentions to use robots in such services has become an urgent necessity for their sustainable deployment. Certainly, using social androids within hospitality services requires organisations' attentive commitment to value creation and fulfilling service quality expectations. In this paper, via structural equation modelling (SEM) and semi-structured interviews with managers, we conceptualise and empirically test visitors' intentions to use social robots in hospitality services. With data collected in Singapore's hospitality settings, we found visitors' intentions to use social robots stem from the effects of technology acceptance variables, service quality dimensions leading to perceived value, and two further dimensions from human robot interaction (HRI): empathy and information sharing. Analysis of these dimensions' importance provides a deeper understanding of novel opportunities managers may take advantage of to position social robot-delivered services in tourism and hospitality strategies. © 2019 Elsevier Ltd","Artificial intelligence; Hospitality services; Human-robot interaction; Intention to use robots; Social robots","artificial intelligence; conceptual framework; ecotourism; hospitality industry; numerical model; robotics; service quality; tourism management; Singapore [Southeast Asia]",,,,,,,,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63; Aguirre-Urreta, M.I., Marakas, G.M., Modeling choice between competing technologies: A comparison of mechanisms and information integration (2018) Technology adoption and social issues: Concepts, methodologies, tools, and applications, pp. 177-209. , IGI Global Hershey, PA; Albert, N., Merunka, D., The role of brand love in consumer‐brand relationships (2013) Journal of Consumer Marketing, 30 (3), pp. 258-266; Bagozzi, R.P., The legacy of the technology acceptance model and proposal for a paradigm shift (2007) Journal of the Association for Information Systems, 8 (4), pp. 244-254; Baird, N., Robots, automation and retail: Not so cut and dried (2018), https://www.forbes.com/sites/nikkibaird/2018/06/19/robots-automation-and-retail-not-so-cut-and-dried/, June 19 Forbes Retrieved from; Batson, C.D., These things called empathy: Eight related but distinct phenomena (2009) The social neuroscience of empathy, pp. 3-15. , J. 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"Zahedi E., Khosravian F., Wang W., Armand M., Dargahi J., Zadeh M.","57213036379;57211668159;57211670337;35236429300;6603763357;15520363400;","Towards Skill Transfer via Learning-Based Guidance in Human-Robot Interaction: An Application to Orthopaedic Surgical Drilling Skill",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","3-4",,"667","678",,3,"10.1007/s10846-019-01082-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074756992&doi=10.1007%2fs10846-019-01082-2&partnerID=40&md5=54eb9229bd5fd84057bb67122a06a943","Department of Mechanical, Industrial, Aerospace Engineering, Concordia University, Montreal, QC  H3G 1M8, Canada; Johns Hopkins University, Baltimore, MD  21218, United States","Zahedi, E., Department of Mechanical, Industrial, Aerospace Engineering, Concordia University, Montreal, QC  H3G 1M8, Canada; Khosravian, F., Johns Hopkins University, Baltimore, MD  21218, United States; Wang, W., Johns Hopkins University, Baltimore, MD  21218, United States; Armand, M., Johns Hopkins University, Baltimore, MD  21218, United States; Dargahi, J., Department of Mechanical, Industrial, Aerospace Engineering, Concordia University, Montreal, QC  H3G 1M8, Canada; Zadeh, M., Johns Hopkins University, Baltimore, MD  21218, United States","This paper presents a machine learning-based guidance (LbG) approach for kinesthetic human-robot interaction (HRI) that can be used in virtual training simulations. Demonstrated positional and force skills are learned to both discriminate the skill levels of users and produce LbG forces. Force information is obtained from virtual forces, which developed based on real computed tomography (CT) data, rather than force sensors. A femur bone drilling simulation is developed to provide a practice environment for orthopaedic residents. The residents are provided with haptic feedback that enable them to feel the variable stiffness of bone layers. The X-ray views of the bone are also presented to them for better tracking of a pre-defined path inside the bone. The simulation is capable of planning a drill path, generating X-rays based on user defined orientation, and recording motion data for user assessment and skill modeling. The knowledge of expert surgeons is also incorporated into the simulation to provide LbG forces for improving the unpredictable motions of the residents. To discriminate the skill level of users, machine learning tools are used to develop surgical expert and resident models. In addition, to improve residents performance, the expert HCRF is used to generate adaptive LbG forces regarding the similarities between residents motions and the expert model. Experimental results show that the learning-based approach is able to assess the skill of users and improve residents performance. © 2019, Springer Nature B.V.","Human-robot interaction; Machine learning-based guidance; Virtual surgical simulation","Air navigation; Bone; Computerized tomography; Infill drilling; Learning algorithms; Machine learning; Man machine systems; Robotic surgery; Surgery; Virtual reality; X rays; Haptic feedbacks; Human robot Interaction (HRI); Learning-based approach; Skill transfer; Surgical drilling; Surgical simulation; Variable stiffness; Virtual training; Human robot interaction",,,,,"Johns Hopkins University, JHU","We thank Russell H. 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"Yu X., Li Y., Zhang S., Xue C., Wang Y.","57203040999;35174564100;45761577400;57203136027;55969140000;","Estimation of human impedance and motion intention for constrained human–robot interaction",2020,"Neurocomputing","390",,,"268","279",,6,"10.1016/j.neucom.2019.07.104","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075464952&doi=10.1016%2fj.neucom.2019.07.104&partnerID=40&md5=583775a889972d3f4a02bed7d2700ad0","School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China; Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Department of Engineering and Design, University of Sussex, Brighton, BN1 9RH, United Kingdom; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China","Yu, X., School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China, Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Li, Y., Department of Engineering and Design, University of Sussex, Brighton, BN1 9RH, United Kingdom; Zhang, S., School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China, Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Xue, C., School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China, Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Wang, Y., Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China","In this paper, a complete framework for safe and efficient physical human-robot interaction (pHRI) is developed for robot by considering both issues of adaptation to the human partner and ensuring the motion constraints during the interaction. We consider the robot's learning of not only human motion intention, but also the human impedance. We employ radial basis function neural networks (RBFNNs) to estimate human motion intention in real time, and least square method is utilized in robot learning of human impedance. When robot has learned the impedance information about human, it can adjust its desired impedance parameters by a simple tuning law for operative compliance. An adaptive impedance control integrated with RBFNNs and full-state constraints is also proposed in our work. We employ RBFNNs to compensate for uncertainties in the dynamics model of robot and barrier Lyapunov functions are chosen to ensure that full-state constraints are not violated in pHRI. Results in simulations and experiments show the better performance of our proposed framework compared with traditional methods. © 2019","Adaptive neural network control; Barrier Lyapunov functions; Full-state constraints; Human motion intention estimation; Impedance learning","Adaptive control systems; Least squares approximations; Lyapunov functions; Motion estimation; Radial basis function networks; Adaptive neural network control; Human motion intention; Impedance parameters; Least square methods; Motion constraints; Physical humanrobot interaction (phri); Radial basis function neural networks; State constraints; Human robot interaction; article; human; human experiment; impedance; learning; least square analysis; motion; radial basis function neural network; robotics; simulation; uncertainty",,,,,"National Natural Science Foundation of China, NSFC: 61873297; China Postdoctoral Science Foundation: 2018M630074, 2019T120048; Fundamental Research Funds for the Central Universities: FRF-GF-18-027B; Beijing Science and Technology Planning Project: Z181100003118006","The authors would like to thank the Editor-in-Chief, the Associate Editor, and the anonymous reviewers for their constructive comments which helped to improve the quality and presentation of this paper. This work was supported by the National Natural Science Foundation of China under Grant No. 61873297 , the China Postdoctoral Science Foundation under Grant No. 2019T120048 , 2018M630074 , the Fundamental Research Funds for the Central Universities under grant No. FRF-GF-18-027B and Beijing Science and Technology Project (Grant Z181100003118006 ).",,,,,,,,,,"He, W., Li, Z., Chen, C.P., A survey of human-centered intelligent robots: issues and challenges (2017) IEEE/CAA J. Autom. Sin., 4 (4), pp. 602-609; Medina, J.R., Lawitzky, M., Mörtl, A., Lee, D., Hirche, S., An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation (2011) Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2416-2422. , IEEE; Yang, C., Huang, K., Cheng, H., Li, Y., Su, C.-Y., Haptic identification by ELM-controlled uncertain manipulator (2017) IEEE Trans. Syst. 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"Gao Q., Liu J., Ju Z.","56391647100;7410106559;25655056800;","Robust real-time hand detection and localization for space human–robot interaction based on deep learning",2020,"Neurocomputing","390",,,"198","206",,19,"10.1016/j.neucom.2019.02.066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075445505&doi=10.1016%2fj.neucom.2019.02.066&partnerID=40&md5=bd79ab3d5ef4fc381934e9a1ceea9f00","State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; University of Chinese Academy of Sciences, Beijing, China; School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Gao, Q., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, University of Chinese Academy of Sciences, Beijing, China; Liu, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; Ju, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Hand gestures are quite suitable for space human–robot interaction (SHRI) because of their natural and convenient features. While the detection and localization of hands are the premise and foundation for SHRI based on hand gestures. But hand gestures are very complicated and hand sizes are very small in some images. These problems make the robust real-time hand detection and localization very difficult. In this paper, a feature-map-fused single shot multibox detector (FF-SSD) which is a deep learning network is designed to deal with the problems of hand detection and localization in SHRI. First, the background of the method is introduced in this paper, including an astronaut assistant robot platform, the difficulties of hand detection and localization, and introduction of the state-of-the-art deep learning networks for object detection and localization. Then, the FF-SSD is proposed for detecting and localizing hands especially pony-size hands. This network takes into consideration both accuracy and speed with balanced performance. And in the experiment part, the FF-SSD is trained and tested on hand databases which include a homemade database and two public databases. At last, the superiority of the proposed method is demonstrated compared with the state-of-the-art methods. © 2019 Elsevier B.V.","Astronaut assistant robot; Deep learning; Hand detection and localization; SSD","Database systems; Human robot interaction; Object detection; Palmprint recognition; Assistant robot; Detection and localization; Hand detection; Learning network; Object detection and localizations; Robot interactions; State of the art; State-of-the-art methods; Deep learning; article; controlled study; cosmonaut; deep learning; human; nonhuman; pony; robotics; velocity",,,,,"JCTD-2018-11; 611391; 2018YFB1304600; National Natural Science Foundation of China, NSFC: 51575338, 5157540, 51575412","The authors would like to acknowledge the support from National Key R&D Program of China (Grant No. 2018YFB1304600), CAS Interdisciplinary Innovation Team (Grant No. JCTD-2018-11), DREAM project of EU FP7-ICT (grant no. 611391) and National Natural Science Foundation of China (grant no. 51575412 , 51575338 and 5157540 ). Qing Gao was born in Tangshan, China. He received the B.S. degree in automation from Electrical Engineering and Automation School, Liaoning Technology University, China in 2013. And is currently working toward the Ph.D. degree in the State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, China. His main research interests include space robot, artificial intelligence, machine vision and human robot interaction. Jinguo Liu received the Ph.D. degree in mechatronics from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), in 2007, where he has been a Full Professor, since January 2011, has also been the Assistant Director of the State Key Laboratory of Robotics, since 2008, and has also been the Associate Director of the Center for Space Automation Technologies and Systems, since 2015. His research interests include bio-inspired robotics and space robot. He has authored or coauthored three books, over 100 articles and holds 50 patents in above areas. Prof. Liu has authored and coauthored over 100 papers and thirty patents in above areas. He is the Senior Member of IEEE, the Senior Member of China Mechanical Engineering Society, the lead guest editor of international journal of Advances in Mechanical Engineering. Zhaojie Ju (M’08SM’16) received the B.S. in automatic control and the M.S. in intelligent robotics both from Huazhong University of Science and Technology, China, and the Ph.D. degree in intelligent robotics at the University of Portsmouth, UK. He held a research appointment at the University College London, London, U.K., before he started his independent academic position at the University of Portsmouth, U.K., in 2012. His research interests include machine intelligence, pattern recognition, and their applications on human motion analysis, multi-fingered robotic hand control, human-robot interaction and collaboration, and robot skill learning. He has authored or co-authored over 170 publications in journals, book chapters, and conference proceedings and received four best paper awards and one Best AE Award in ICRA2018. Dr. Ju is an Associate Editor of the IEEE TRANSACTIONS ON CYBERNETICS, Journal of Intelligent & Fuzzy Systems, International Journal of Fuzzy Systems, and Chinese Journal of Mechanical Engineering.",,,,,,,,,,"Dorais, G.A., Gawdiak, Y., The personal satellite assistant: an internal spacecraft autonomous mobile monitor (2003) Proceedings of the 2003 IEEE Aerospace Conference, 1, pp. 1-348. , IEEE; Fong, T., Micire, M.J., Morse, T., Park, E., Provencher, C., To, V., Wheeler, D., Smith, E., (2013), Smart Spheres: A Telerobotic Free-flyer for Intravehicular Activities in Space; Bualat, M., Barlow, J., Fong, T., Provencher, C., Smith, T., Astrobee: developing a free-flying robot for the international space station (2015) Proceedings of AIAA SPACE 2015 Conference and Exposition, p. 4643; Smith, T., Barlow, J., Bualat, M., Fong, T., Provencher, C., Sanchez, H., Smith, E., (2016), Astrobee: A New Platform for Free-flying Robotics on the International Space Station; Murphy, T.I., (2018), http://www.airbus.com/newsroom/press-releases/en/2018/02/hello–i-am-cimon-.html, Hello, I Am Cimon!, (). Accessed February; Liu, J., Gao, Q., Liu, Z., Li, Y., Attitude control for astronaut assisted robot in the space station (2016) Int. J. Control Autom. Syst., 14 (4), pp. 1082-1095; Gao, Q., Liu, J., Tian, T., Li, Y., Free-flying dynamics and control of an astronaut assistant robot based on fuzzy sliding mode algorithm (2017) Acta Astron., 138, pp. 462-474; Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., Static hand gesture recognition with parallel cnns for space human-robot interaction (2017) Proceedings of International Conference on Intelligent Robotics and Applications, pp. 462-473. , Springer; He, W., Dong, Y., Adaptive fuzzy neural network control for a constrained robot using impedance learning (2018) IEEE Trans. Neural Netw. Learn. Syst., 29 (4), pp. 1174-1186; He, W., Ge, S.S., Cooperative control of a nonuniform gantry crane with constrained tension (2016) Automatica, 66, pp. 146-154; He, W., Meng, T., He, X., Ge, S.S., Unified iterative learning control for flexible structures with input constraints (2018) Automatica, 96, pp. 326-336; He, W., Li, Z., Chen, C.P., A survey of human-centered intelligent robots: issues and challenges (2017) IEEE/CAA J. Autom. Sin., 4 (4), pp. 602-609; Lenc, K., Vedaldi, A., R-CNN minus R (2015) British Machine Vision Conference (BMVC); Girshick, R., Fast r-cnn (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: towards real-time object detection with region proposal networks (2015) Proceedings of Advances in Neural Information Processing Systems, pp. 91-99; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: unified, real-time object detection (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C., Ssd: single shot multibox detector (2016) Proceedings of European Conference on Computer Vision, pp. 21-37. , Springer; Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., Berg, A.C., (2017), Dssd: Deconvolutional single shot detector, arXiv preprint arXiv:; Li, Z., Zhou, F., (2017), Fssd: feature fusion single shot multibox detector, arXiv preprint arXiv:; Zheng, L., Fu, C., Zhao, Y., Extend the shallow part of single shot multibox detector via convolutional neural network (2018) In Tenth International Conference on Digital Image Processing (ICDIP 2018), p. 1080613. , International Society for Optics and Photonics; Liu, J., Luo, Y., Ju, Z., An interactive astronaut-robot system with gesture control (2016) Comput. 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Big Data, 3 (1), p. 9; Jones, M.J., Rehg, J.M., Statistical color models with application to skin detection (2002) Int. J. Comput. Vis., 46 (1), pp. 81-96","Liu, J.; State Key Laboratory of Robotics, China; email: liujinguo@sia.cn",,,"Elsevier B.V.",,,,,09252312,,NRCGE,,"English","Neurocomputing",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85075445505
"Bingol M.C., Aydogmus O.","57195613088;23088079600;","Practical application of a safe human-robot interaction software",2020,"Industrial Robot","47","3",,"359","368",,2,"10.1108/IR-09-2019-0180","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078963315&doi=10.1108%2fIR-09-2019-0180&partnerID=40&md5=043a205a0b8cc2d81b0a423f58e43b20","Department of Mechatronics Engineering, Faculty of Technology, Firat University, Elazig, Turkey","Bingol, M.C., Department of Mechatronics Engineering, Faculty of Technology, Firat University, Elazig, Turkey; Aydogmus, O., Department of Mechatronics Engineering, Faculty of Technology, Firat University, Elazig, Turkey","Purpose: Because of the increased use of robots in the industry, it has become inevitable for humans and robots to be able to work together. Therefore, human security has become the primary noncompromising factor of joint human and robot operations. For this reason, the purpose of this study was to develop a safe human-robot interaction software based on vision and touch. Design/methodology/approach: The software consists of three modules. Firstly, the vision module has two tasks: to determine whether there is a human presence and to measure the distance between the robot and the human within the robot’s working space using convolutional neural networks (CNNs) and depth sensors. Secondly, the touch detection module perceives whether or not a human physically touches the robot within the same work environment using robot axis torques, wavelet packet decomposition algorithm and CNN. Lastly, the robot’s operating speed is adjusted according to hazard levels came from vision and touch module using the robot’s control module. Findings: The developed software was tested with an industrial robot manipulator and successful results were obtained with minimal error. Practical implications: The success of the developed algorithm was demonstrated in the current study and the algorithm can be used in other industrial robots for safety. Originality/value: In this study, a new and practical safety algorithm is proposed and the health of people working with industrial robots is guaranteed. © 2020, Emerald Publishing Limited.","Artificial intelligence; Autonomous robots; Convolutional neural network; Human-robot interaction; Safety","Accident prevention; Agricultural robots; Application programs; Convolutional neural networks; Industrial manipulators; Industrial robots; Man machine systems; Manipulators; Robot applications; Software testing; Wavelet decomposition; Design/methodology/approach; Detection modules; Human securities; Robot manipulator; Robot operations; Safe human-robot interaction; Wavelet Packet Decomposition; Work environments; Human robot interaction",,,,,"Firat Üniversitesi, FU","Information note : This study was performed at Firat University within the scope of the doctoral thesis titled ?The Development of an Artificial Intelligence-Based Self-Programmable Robot Software Using Human-Robot Interaction.?",,,,,,,,,,"Avanzini, G.B., Ceriani, N.M., Zanchettin, A.M., Rocco, P., Bascetta, L., Safety control of industrial robots based on a distributed distance sensor (2014) IEEE Transactions on Control Systems Technology, 22 (6), pp. 2127-2140; Bingol, M.C., Aydogmus, O., Implementation of speech recognition for robot control using support vector machine (2018) International Eurasian Conference on Science, Engineering and Technology (EurasianSciEnTech), pp. 814-819; Bingol, M.C., Aydoğmuş, Ö., İnsan-robot etkileşiminde insan güvenliği için çok kanallı iletişim kullanarak evrişimli sinir ağı tabanlı bir yazılımının geliştirilmesi ve uygulaması (2019) Fırat Üniversitesi Müh. Bil. 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Robot",Article,"Final","",Scopus,2-s2.0-85078963315
"Chame H.F., Tani J.","55941039900;56072068100;","Cognitive and motor compliance in intentional human-robot interaction",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196896","11291","11297",,2,"10.1109/ICRA40945.2020.9196896","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092739956&doi=10.1109%2fICRA40945.2020.9196896&partnerID=40&md5=a52c5d56e427c7128dd5d015d7842e15","Okinawa Institute of Science and Technology (OIST), Cognitive Neurorobotics Research Unit (CNRU), Okinawa, 904-0495, Japan","Chame, H.F., Okinawa Institute of Science and Technology (OIST), Cognitive Neurorobotics Research Unit (CNRU), Okinawa, 904-0495, Japan; Tani, J., Okinawa Institute of Science and Technology (OIST), Cognitive Neurorobotics Research Unit (CNRU), Okinawa, 904-0495, Japan","Embodiment and subjective experience in humanrobot interaction are important aspects to consider when studying both natural cognition and adaptive robotics to human environments. Although several researches have focused on nonverbal communication and collaboration, the study of autonomous physical interaction has obtained less attention. From the perspective of neurorobotics, we investigate the relation between intentionality, motor compliance, cognitive compliance, and behavior emergence. We propose a variational model inspired by the principles of predictive coding and active inference to study intentionality and cognitive compliance, and an intermittent control concept for motor deliberation and compliance based on torque feed-back. Our experiments with the humanoid Torobo portrait interesting perspectives for the bio-inspired study of developmental and social processes. © 2020 IEEE.",,"Agricultural robots; Biomimetics; Compliance control; Robotics; Human environment; Intermittent controls; Natural cognition; Non-verbal communications; Physical interactions; Subjective experiences; Torque feed backs; Variational modeling; Human robot interaction",,,,,,,,,,,,,,,,"Pellegrinelli, S., Admoni, H., Javdani, S., Srinivasa, S., Humanrobot shared workspace collaboration via hindsight optimization (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 831-838; Landi, C.T., Ferraguti, F., Fantuzzi, C., Secchi, C., A passivitybased strategy for coaching in human-robot interaction (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1-6; Kollmitz, M., Büscher, D., Schubert, T., Burgard, W., Wholebody sensory concept for compliant mobile robots (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1-5; Ishi, C.T., Minato, T., Ishiguro, H., Analysis and generation of laughter motions, and evaluation in an android robot (2019) APSIPA Transactions on Signal and Information Processing, 8; Hu, Y., Hoffman, G., Using skin texture change to design emotion expression in social robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 2-10; Vygotsky, L.S., (1980) Mind in Society: The Development of Higher Psychological Processes, , Harvard university press; Ikemoto, S., Amor, H.B., Minato, T., Jung, B., Ishiguro, H., Physical human-robot interaction: Mutual learning and adaptation (2012) IEEE Robotics Automation Magazine, 19 (4), pp. 24-35. , Dec; Friston, K., The free-energy principle: A unified brain theory? 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IEEE, pp. 1-9; Ahmadi, A., Tani, J., A novel predictive-coding-inspired variational rnn model for online prediction and recognition (2019) Neural Computation, 31 (11), pp. 2025-2074; Spaulding, S., Introduction to debates on embodied social cognition (2012) Phenomenology and the Cognitive Sciences, 11 (4), pp. 431-448; Asimov, I., (1950) I, Robot, 1. , Gnome Press; Gawthrop, P., Loram, I., Lakie, M., Gollee, H., Intermittent control: A computational theory of human control (2011) Biological Cybernetics, 104 (1-2), pp. 31-51; Tanabe, H., Fujii, K., Suzuki, Y., Kouzaki, M., Effect of intermittent feedback control on robustness of human-like postural control system (2016) Scientific Reports, 6, p. 22446; Chame, H.F., Ahmadi, A., Tani, J., (2020) Towards Hybrid Primary Intersubjectivity: A Neural Robotics Library for Human Science, , preprint, in press; Chame, H.F., Chevallereau, C., Grounding humanoid visually guided walking: From action-independent to action-oriented knowledge (2016) Information Sciences, 352, pp. 79-97; Chame, H.F., Mota, F.P., Da Costa Botelho, S.S., A dynamic computational model of motivation based on self-determination theory and cann (2019) Information Sciences, 476, pp. 319-336",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85092739956
"Huang I., Bajcsy R.","57205425555;7006078015;","High Resolution Soft Tactile Interface for Physical Human-Robot Interaction",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197365","1705","1711",,,"10.1109/ICRA40945.2020.9197365","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092713879&doi=10.1109%2fICRA40945.2020.9197365&partnerID=40&md5=3831ef1789f33fcb3cae6edf5e7be5f3","University of California, Department of Electrical Engineering and Computer Sciences, Berkeley, CA  94270, United States","Huang, I., University of California, Department of Electrical Engineering and Computer Sciences, Berkeley, CA  94270, United States; Bajcsy, R., University of California, Department of Electrical Engineering and Computer Sciences, Berkeley, CA  94270, United States","If robots and humans are to coexist and cooperate in society, it would be useful for robots to be able to engage in tactile interactions. Touch is an intuitive communication tool as well as a fundamental method by which we assist each other physically. Tactile abilities are challenging to engineer in robots, since both mechanical safety and sensory intelligence are imperative. Existing work reveals a trade-off between these principles - tactile interfaces that are high in resolution are not easily adapted to human-sized geometries, nor are they generally compliant enough to guarantee safety. On the other hand, soft tactile interfaces deliver intrinsically safe mechanical properties, but their non-linear characteristics render them difficult for use in timely sensing and control. We propose a robotic system that is equipped with a completely soft and therefore safe tactile interface that is large enough to interact with human upper limbs, while producing high resolution tactile sensory readings via depth camera imaging of the soft interface. We present and validate a data-driven model that maps point cloud data to contact forces, and verify its efficacy by demonstrating two real-world applications. In particular, the robot is able to react to a human finger's pokes and change its pose based on the tactile input. In addition, we also demonstrate that the robot can act as an assistive device that dynamically supports and follows a human forearm from underneath. © 2020 IEEE.",,"Agricultural robots; Economic and social effects; Intelligent robots; Robotics; Assistive devices; Communication tools; Data-driven model; Intrinsically safe; Mechanical safety; Nonlinear characteristics; Physical human-robot interactions; Tactile interaction; Social robots",,,,,"CA 94270; National Science Foundation, NSF: 1545126","*This work was supported by NSF Award #1545126 1The authors are with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94270, USA",,,,,,,,,,"Bajcsy, R., Huang, I., Liu, J., A depth camera-based soft fingertip device for contact regionestimation and perception-action coupling (2019) 2019 International Conference on Robotics and Automation (ICRA; Dargahi, J., A three sensing element piezoelectric tactile sensor for robotic and prosthetic applications (2000) Sensors and Actuators A-physical, 80, pp. 23-30; Engel, J., Chen, J., Liu, C., Development of polyimide flexible tactile sensor skin (2003) J. 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No. 03CH37453, 4, pp. 3459-3464. , Oct. 3; Mukai, T., Development of the tactile sensor system of a human-interactive robot ""ri-man"" (2008) IEEE Transactions on Robotics, 24 (2), pp. 505-512. , Apr; Stiehl, W.D., (2005) The Design of the Huggable: A Therapeutic Robotic Companion for Relational, Affective Touch, 2005, pp. 408-415. , Sept; Hennersperger, C., Towards mri-based autonomous robotic us acquisitions: A first feasibility study (2017) IEEE Transactions on Medical Imaging, 36 (2), pp. 538-548; Beeson, P., Ames, B., Trac-ik: An opensource library for improved solving of generic inverse kinematics (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids, pp. 928-935. , Nov; Rahman, M., Development and control of a robotic exoskeleton for shoulder, elbow and forearm movement assistance (2012) Applied Bionics and Biomechanics, 9, pp. 275-292. , July",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092713879
"Ostanin M., Mikhel S., Evlampiev A., Skvortsova V., Klimchik A.","57203783042;57203955171;57211553454;57218706409;37016245900;","Human-robot interaction for robotic manipulator programming in Mixed Reality",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196965","2805","2811",,5,"10.1109/ICRA40945.2020.9196965","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092710443&doi=10.1109%2fICRA40945.2020.9196965&partnerID=40&md5=bcc50d4b215f2d4c7126470c04b6e7a2","Innopolis University, Center for Technologies in Robotics and Mechatronics Components, Innopolis, Russian Federation","Ostanin, M., Innopolis University, Center for Technologies in Robotics and Mechatronics Components, Innopolis, Russian Federation; Mikhel, S., Innopolis University, Center for Technologies in Robotics and Mechatronics Components, Innopolis, Russian Federation; Evlampiev, A., Innopolis University, Center for Technologies in Robotics and Mechatronics Components, Innopolis, Russian Federation; Skvortsova, V., Innopolis University, Center for Technologies in Robotics and Mechatronics Components, Innopolis, Russian Federation; Klimchik, A., Innopolis University, Center for Technologies in Robotics and Mechatronics Components, Innopolis, Russian Federation","The paper presents an approach for interactive programming of the robotic manipulator using mixed reality. The developed system is based on the HoloLens glasses connected through Robotic Operation System to Unity engine and robotic manipulators. The system gives a possibility to recognize the real robot location by the point cloud analysis, to use virtual markers and menus for the task creation, to generate a trajectory for execution in the simulator or on the real manipulator. It also provides the possibility of scaling virtual and real worlds for more accurate planning. The proposed framework has been tested on pick-and-place and contact operations execution by UR10e and KUKA iiwa robots. © 2020 IEEE.",,"Agricultural robots; Flexible manipulators; Mixed reality; Robot programming; Robotics; Accurate planning; Operation system; Pick and place; Point cloud; Real robot; Robotic manipulators; Virtual Markers; Human robot interaction",,,,,"Russian Science Foundation, RSF: 17-19-01740","The work presented in this paper was supported by the grant of the Russian Science Foundation 17-19-01740.",,,,,,,,,,"Friedrich, H., Dillmann, R., Rogalla, O., Interactive robot programming based on human demonstration and advice (1999) Sensor Based Intelligent Robots, pp. 96-119. , Springer; Milgram, P., Kishino, F., A taxonomy of mixed reality visual displays (1994) IEICE TRANSACTIONS on Information and Systems, 77 (12), pp. 1321-1329; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., Virtual, augmented, and mixed reality for human-robot interaction (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 403-404; Fang, H., Ong, S.K., Nee, A.Y.-C., Robot programming using augmented reality (2009) 2009 International Conference on CyberWorlds, IEEE, pp. 13-20; Wassermann, J., Vick, A., Krüger, J., Intuitive robot programming through environment perception, augmented reality simulation and automated program verification (2018) Procedia CIRP, 76, pp. 161-166; Lambrecht, J., Krüger, J., Spatial programming for industrial robots: Efficient, effective and useroptimised through natural communication and augmented reality (2014) Advanced Materials Research, Trans Tech Publ, 1018, pp. 39-46; Whitney, D., Rosen, E., Ullman, D., Phillips, E., Tellex, S., Ros reality: A virtual reality framework using consumer-grade hardware for ros-enabled robots (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9. , IEEE; Lipton, J.I., Fay, A.J., Rus, D., Baxter's homunculus: Virtual reality spaces for teleoperation in manufacturing (2017) IEEE Robotics and Automation Letters, 3 (1), pp. 179-186; Hoenig, W., Milanes, C., Scaria, L., Phan, T., Bolas, M., Ayanian, N., Mixed reality for robotics (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5382-5387. , IEEE; Rosen, E., Whitney, D., Phillips, E., Chien, G., Tompkin, J., Konidaris, G., Tellex, S., (2017) Communicating Robot Arm Motion Intent Through Mixed Reality Headmounted Displays; Gadre, S.Y., Rosen, E., Chien, G., Phillips, E., Tellex, S., Konidaris, G., End-user robot programming using mixed reality (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 2707-2713. , IEEE; Guhl, J., Tung, S., Kruger, J., Concept and architecture for programming industrial robots using augmented reality with mobile devices like microsoft hololens (2017) 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-4. , IEEE; Quintero, C.P., Li, S., Pan, M.K., Chan, W.P., Der Van Loos, H.M., Croft, E., Robot programming through augmented trajectories in augmented reality (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1838-1844. , IEEE; Ostanin, M., Klimchik, A., Interactive robot programing using mixed reality (2018) IFAC-PapersOnLine, 51 (22), pp. 50-55; Brandstötter, M., Angerer, A., Hofbaur, M., An analytical solution of the inverse kinematics problem of industrial serial manipulators with an ortho-parallel basis and a spherical wrist (2014) Proceedings of the Austrian Robotics Workshop, pp. 7-11; Franco, M.I.S., Preparata, P., (2008) Computational Geometry Algorithms and Applications Third Edition, p. 388; Martin, R.C.B., Fischler, A., Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography (1981) Comm. ACM, pp. 381-395; Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X., Dbscan revisited, revisited: Why and how you should (still) use dbscan (2017) ACM Transactions on Database Systems (TODS), 42 (3), p. 19; Pomerleau, F., Colas, F., Siegwart, R., A review of point cloud registration algorithms for mobile robotics (2015) Foundations and Trends in Robotics, 4 (1), pp. 1-104; LaValle, S.M., (1998) Rapidly-exploring Random Trees: A New Tool for Path Planning; Nakhaei, A., Lamiraux, F., Motion planning for humanoid robots in environments modeled by vision (2008) Humanoids 2008-8th IEEE-RAS International Conference on Humanoid Robots, pp. 197-204. , Dec",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092710443
"Martin A.E.H., Dean-Leon E., Cheng G.","57215572222;9736968400;10243873000;","TACTO-Selector: Enhanced Hierarchical Fusion of PBVS with Reactive Skin Control for Physical Human-Robot Interaction",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196979","9121","9127",,,"10.1109/ICRA40945.2020.9196979","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092691569&doi=10.1109%2fICRA40945.2020.9196979&partnerID=40&md5=67d436067909baf332ea3168301ad180","Technical University of Munich, Institute for Cognitive Systems, Germany","Martin, A.E.H., Technical University of Munich, Institute for Cognitive Systems, Germany; Dean-Leon, E., Technical University of Munich, Institute for Cognitive Systems, Germany; Cheng, G., Technical University of Munich, Institute for Cognitive Systems, Germany","In a physical Human-Robot Interaction for industrial scenarios is paramount to guarantee the safety of the user while keeping the robot's performance. Hierarchical task approaches are not sufficient since they tend to sacrifice the low priority tasks in order to guarantee the consistency of the main task. To handle this problem, we enhance the standard hierarchical fusion by introducing a novel interactive task-reconfiguring approach (TACTO-Selector) that uses the information of the tactile interaction to adapt the dimension of the tasks, therefore guaranteeing the execution of the safety task while performing the other task as good as possible. In this work, we hierarchically combine a 6 DOF Position-Based Visual Servoing (PBVS) task with a reactive skin control. This approach was evaluated on a 6 DOF industrial robot showing an improvement of 36.37% on average in tracking error reduction compared with a standard approach. © 2020 IEEE.",,"Accident prevention; Agricultural robots; Man machine systems; Robotics; Visual servoing; Hierarchical fusions; Industrial scenarios; Low priorities; Main tasks; Physical human-robot interactions; Position based visual servoing; Tactile interaction; Tracking errors; Human robot interaction",,,,,,,,,,,,,,,,"Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , Sep; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31 (13), pp. 1578-1602; Povse, B., Koritnik, D., Bajd, T., Munih, M., Correlation between impact-energy density and pain intensity during robot-man collision (2010) 2010 3rd IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 179-183. , Sep; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Foundations and TrendsR in Robotics, 5 (4), pp. 261-349; Heinzmann, J., Zelinsky, A., Quantitative safety guarantees for physical human-robot interaction (2003) The International Journal of Robotics Research, 22 (7-8), pp. 479-504; Kock, S., Bredahl, J., Eriksson, P., Myhr, M., Behnisch, K., (2006) Taming the Robot, pp. 11-14. , 01; Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C., Simmons, R., Sensor fusion for human safety in industrial workcells (2012) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , October; Flacco, F., Krger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) 2012 IEEE Interna-tional Conference on Robotics and Automation, pp. 338-345. , May; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots Proceedings. 1985 IEEE International Conference on Robotics and Automation, 2, pp. 500-505. , March 1985; Vogel, C., Walter, C., Elkmann, N., A projection-based sensor system for safe physical human-robot collaboration (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5359-5364. , Nov; Lasota, P.A., Rossano, G.F., Shah, J.A., Toward safe closeproximity human-robot interaction with standard industrial robots (2014) 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp. 339-344. , Aug; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2016) IEEE Transactions on Automation Science and Engineering, 13 (2), pp. 882-893. , April; Mansfeld, N., Djellab, B., Veuthey, J.R., Beck, F., Ott, C., Haddadin, S., Improving the performance of biomechanically safe velocity control for redundant robots through reflected mass minimization (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5390-5397. , Sep; Buizza Avanzini, G., Ceriani, N.M., Zanchettin, A.M., Rocco, P., Bascetta, L., Safety control of industrial robots based on a distributed distance sensor (2014) IEEE Transactions on Control Systems Technology, 22 (6), pp. 2127-2140. , Nov; Lacevic, B., Rocco, P., Kinetostatic danger field-a novel safety assessment for human-robot interaction (2010) 2010 IEEE/RSJ Interna-tional Conference on Intelligent Robots and Systems, pp. 2169-2174. , Oct; Lacevic, B., Rocco, P., Safety-oriented control of robotic manipulators a kinematic approach (2011) IFAC Proceedings Volumes, 44 (1), pp. 11508-11513. , 18th IFAC World Congress; Wosch, T., Feiten, W., Reactive motion control for human-robot tactile interaction (2002) Proceedings 2002 IEEE International Confer-ence on Robotics and Automation (Cat. No. 02CH37292), 4, pp. 3807-3812. , May; Fritzsche, M., Elkmann, N., Schulenburg, E., Tactile sensing: A key technology for safe physical human robot interaction (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 139-140. , March; Torres-Jara, E., Natale, L., Sensitive manipulation: Manipulation through tactile feedback (2018) International Journal of Humanoid Robotics, 15 (1), p. 1850012; Suwanratchatamanee, K., Matsumoto, M., Hashimoto, S., A simple tactile sensing foot for humanoid robot and active ground slope recognition (2009) 2009 IEEE International Conference on Mechatronics, pp. 1-6. , April; Fondahl, K., Kuehn, D., Beinersdorf, F., Bernhard, F., Grimminger, F., Schilling, M., Stark, T., Kirchner, F., An adaptive sensor foot for a bipedal and quadrupedal robot (2012) 2012 4th IEEE RAS EMBS In-ternational Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 270-275. , June; Rogelio Guadarrama-Olvera, J., Bergner, F., Dean, E., Cheng, G., Enhancing biped locomotion on unknown terrain using tactile feedback (2018) 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1-9. , Nov; Grunwald, G., Schreiber, G., Albu-Schaffer, A., Hirzinger, G., Programming by touch: The different way of human-robot interaction (2003) IEEE Transactions on Industrial Electronics, 50 (4), pp. 659-666. , Aug; Massa, D., Callegari, M., Cristalli, C., Manual guidance for industrial robot programming (2015) Industrial Robot: An International Journal, 42 (5), pp. 457-465; Cheng, G., Dean-Leon, E., Bergner, F., Olvera, J.R.G., Leboutet, Q., Mittendorfer, P., A comprehensive realization of robot skin: Sensors, sensing, control, and applications (2019) Proceedings of the IEEE, pp. 1-18; Ding, Y., Wilhelm, F., Faulhammer, L., Thomas, U., Proximity servoing methods for safe human-robot-interaction (2019) Accepted at RSJ/IROS International Conference on Intelligent Robots and Sys-tems; Lacevic, B., Rocco, P., Zanchettin, A.M., Safety assessment and control of robotic manipulators using danger field (2013) IEEE Transactions on Robotics, 29 (5), pp. 1257-1270. , Oct; Parigi Polverini, M., Zanchettin, A.M., Rocco, P., Real-time collision avoidance in human-robot interaction based on kinetostatic safety field (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4136-4141. , Sep; Sadeghian, H., Villani, L., Kamranian, Z., Karami, A., Visual servoing with safe interaction using image moments (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5479-5485. , Sep; Kermorgant, O., Chaumette, F., Combining ibvs and pbvs to ensure the visibility constraint (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2849-2854; Dean-Leon, E., Bergner, F., Ramirez-Amaro, K., Cheng, G., From multi-modal tactile signals to a compliant control (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 892-898. , Nov; Dietrich, A., Ott, C., Albu-Schffer, A., An overview of null space projections for redundant, torque-controlled robots (2015) The International Journal of Robotics Research, 34 (11), pp. 1385-1400; Dean-Leon, E., Pierce, B., Bergner, F., Mittendorfer, P., Ramirez-Amaro, K., Burger, W., Cheng, G., Tomm: Tactile omnidirectional mobile manipulator (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2441-2447. , May; Mittendorfer, P., Yoshida, E., Cheng, G., Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot (2015) Advanced Robotics, 29 (1), pp. 51-67; Romero Ramirez, F., Muoz-Salinas, R., Medina-Carnicer, R., Speeded up detection of squared fiducial markers (2018) Image and Vision Computing, 76, p. 06",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092691569
"Xiao J., Wang P., Lu H., Zhang H.","49964924600;57213429804;55729801700;56979541200;","A three-dimensional mapping and virtual reality-based human–robot interaction for collaborative space exploration",2020,"International Journal of Advanced Robotic Systems","17","3",,"","",,2,"10.1177/1729881420925293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086001420&doi=10.1177%2f1729881420925293&partnerID=40&md5=bbcc562bb685db7cf8ccb83bf391aa3c","Department of Automation, National University of Defense Technology, Changsha, China; School of Computer Science, University of Lincoln, Lincoln, United Kingdom; China Aerodynamics Research and Development Center, Changsha, China","Xiao, J., Department of Automation, National University of Defense Technology, Changsha, China, School of Computer Science, University of Lincoln, Lincoln, United Kingdom; Wang, P., China Aerodynamics Research and Development Center, Changsha, China; Lu, H., Department of Automation, National University of Defense Technology, Changsha, China; Zhang, H., Department of Automation, National University of Defense Technology, Changsha, China","Human–robot interaction is a vital part of human–robot collaborative space exploration, which bridges the high-level decision and path planning intelligence of human and the accurate sensing and modelling ability of the robot. However, most conventional human–robot interaction approaches rely on video streams for the operator to understand the robot’s surrounding, which lacks situational awareness and force the operator to be stressed and fatigued. This research aims to improve efficiency and promote the natural level of interaction for human–robot collaboration. We present a human–robot interaction method based on real-time mapping and online virtual reality visualization, which is implemented and verified for rescue robotics. At the robot side, a dense point cloud map is built in real-time by LiDAR-IMU tightly fusion; the resulting map is further transformed into three-dimensional normal distributions transform representation. Wireless communication is employed to transmit the three-dimensional normal distributions transform map to the remote control station in an incremental manner. At the remote control station, the received map is rendered in virtual reality using parameterized ellipsoid cells. The operator controls the robot with three modes. In complex areas, the operator can use interactive devices to give low-level motion commands. In the less unstructured region, the operator can specify a path or even a target point. Afterwards, the robot follows the path or navigates to the target point autonomously. In other words, these two modes rely more on the robot’s autonomy. By virtue of virtual reality visualization, the operator can have a more comprehensive understanding of the space to be explored. In this case, the high-level decision and path planning intelligence of human and the accurate sensing and modelling ability of the robot can be well integrated as a whole. Although the method is proposed for rescue robots, it can also be used in other out-of-sight teleoperation-based human–robot collaboration systems, including but not limited to manufacturing, space, undersea, surgery, agriculture and military operations. © The Author(s) 2020.","3D mapping; human–robot interaction; Human–robot space exploration; rescue robotics; virtual reality","Agricultural robots; Intelligent robots; Mapping; Military operations; Normal distribution; Remote control; Robot programming; Space research; Virtual reality; Visualization; Collaboration systems; Collaborative spaces; Reality visualization; Remote-control stations; Robot interactions; Situational awareness; Three dimensional mapping; Wireless communications; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61773393, U1813205; China Scholarship Council, CSC; National Key Research and Development Program of China, NKRDPC: 2017YFC0803300","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by National Key R&D Program of China (no. 2017YFC0803300) and National Science Foundation of China (nos. U1813205 and 61773393).","The support provided by China Scholarship Council (CSC) during a visit of Junhao Xiao to University of Lincoln is acknowledged.","The support provided by China Scholarship Council (CSC) during a visit of Junhao Xiao to University of Lincoln is acknowledged. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by National Key R&D Program of China (no. 2017YFC0803300) and National Science Foundation of China (nos. U1813205 and 61773393).",,,,,,,,"Kitano, H., Asada, M., Kuniyoshi, Y., Robocup: the robot world cup initiative Proceedings of the first international conference on autonomous agents, pp. 340-347. , New York, ACM, In; Kitano, H., Tadokoro, S., Noda, I., Robocup rescue: search and rescue in large-scale disasters as a domain for autonomous agents research, pp. 739-743. , IEEE international conference on systems, man, and cybernetics, Tokyo, 12–15 October 1999, In; Spenko, M., Buerger, S., Iagnemma, K., Editorial, special issue: the Darpa robotics challenge finals (2017) J Fiel Robot, 34 (2), pp. 227-228; Murphy, R.R., Tadokoro, S., Kleiner, A., Disaster robotics (2016) Springer handbook of robotics, pp. 1577-1604. , Berlin, Springer, In; Doroodgar, B., Ficocelli, M., Mobedi, B., The search for survivors: cooperative human–robot interaction in search and rescue environments using semi-autonomous robots, pp. 2858-2863. , IEEE international conference on robotics and automation, Anchorage, 3–7 May 2010, In; Wang, P., Xiao, J., Lu, H., A novel human-robot interaction system based on 3d mapping and virtual reality, pp. 5888-5894. , 2017 Chinese automation congress (CAC), IEEE, Jinan, 20–22 October 2017, In; Murphy, R.R., Human–robot interaction in rescue robotics (2004) IEEE Trans Syst, Man, Cybernet Part C (Applications and Reviews), 34 (2), pp. 138-153; Nomura, T., Kanda, T., Suzuki, T., Experimental investigation into influence of negative attitudes toward robots on human–robot interaction (2006) Ai Society, 20 (2), pp. 138-150; Goodrich, M.A., Schultz, A.C., Human–robot interaction: a survey (2007) Found Trends Human-Comput Interac, 1 (3), pp. 203-275; Amor, H.B., Neumann, G., Kamthe, S., Interaction primitives for human–robot cooperation tasks, pp. 2831-2837. , IEEE international conference on robotics and automation, Hong Kong, 31 May–7 June 2014, IEEE, In; Valero, A., Randelli, G., Botta, F., Operator performance in exploration robotics (2011) J Intellig Robot Syst, 64 (3-4), pp. 365-385; Sheridan, T.B., Human–robot interaction: status and challenges (2016) Human Fact, 58 (4), pp. 525-532; Wang, Z., Liu, S., Zhang, J., A spatio-temporal CRF for human interaction understanding (2016) IEEE Transactions on Circu Syst Video Technol, 27 (8), pp. 1647-1660; Birk, A., Schwertfeger, S., Pathak, K., A networking framework for teleoperation in safety, security, and rescue robotics (2009) IEEE Wirel Commun, 16 (1), pp. 6-13; Yanco, H.A., Drury, J.L., Rescuing interfaces: a multi-year study of human–robot interaction at the AAAI robot rescue competition (2007) Auto Robot, 22 (4), pp. 333-352; Yanco, H.A., Norton, A., Ober, W., Analysis of human–robot interaction at the Darpa robotics challenge trials (2015) J Fiel Robot, 32 (3), pp. 420-444; Norton, A., Ober, W., Baraniecki, L., Analysis of human–robot interaction at the Darpa robotics challenge finals (2017) Int J Robot Res, 36 (5-7), pp. 483-513; Zhang, J., Singh, S., LOAM: lidar odometry and mapping in real-time, 2, pp. 1-9. , Robotics: science and systems, Berkeley, CA, July 2014, In; Stoyanov, T., Magnusson, M., Almqvist, H., On the accuracy of the 3D normal distributions transform as a tool for spatial representation, pp. 4080-4085. , Robotics and automation (ICRA), 2011 IEEE international conference on IEEE, Shanghai, 9-13 May 2011, In; Quigley, M., Conley, K., Gerkey, B., ROS: an open-source robot operating system, pp. 5-10. , Workshop on open source software, IEEE international conference on robotics and automation, Kobe, Japan, 12 May 2009, In; Durrant-Whyte, H., Bailey, T., Simultaneous localization and mapping: part I (2006) IEEE Robot Auto Mag, 13 (2), pp. 99-110; Bailey, T., Durrant-Whyte, H., Simultaneous localization and mapping (slam): part II (2006) IEEE Robot Auto Mag, 13 (3), pp. 108-117; Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M., Visual simultaneous localization and mapping: a survey (2015) Artif Intellig Rev, 43 (1), pp. 55-81; Zhang, J., Gui, M., Wang, Q., Hierarchical topic model based object association for semantic slam (2019) IEEE Tran Visual Comput Graph, 25 (11), pp. 3052-3062; Xiao, J., Xiong, D., Yu, Q., A real-time sliding window based visual-inertial odometry for MAVs (2020) IEEE Tran Indust Inf, 16, pp. 4049-4058; Xiao, J., Adler, B., Zhang, J., Planar segment based three-dimensional point cloud registration in outdoor environments (2013) J Field Robot, 30 (4), pp. 552-582; Pomerleau, F., Colas, F., Siegwart, R., A review of point cloud registration algorithms for mobile robotics (2015) Found Tren Robot, 4 (1), pp. 1-104; Hornung, A., Wurm, K.M., Bennewitz, M., Octomap: an efficient probabilistic 3D mapping framework based on octrees (2013) Auto Robot, 34 (3), pp. 189-206; Magnusson, M., (2009) The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection, , Örebro, PhD Thesis, Örebro universitet; Bray, T., (2017) The javascript object notation (JSON) data interchange format, , https://www.json.org/","Xiao, J.; Department of Automation, China; email: junhao.xiao@ieee.org",,,"SAGE Publications Inc.",,,,,17298806,,,,"English","Int. J. Adv. Rob. Syst.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85086001420
"Wendong W., Hanhao L., Menghan X., Yang C., Xiaoqing Y., Xing M., Bing Z.","55713621900;57215826317;57215829968;57215826574;7402202189;57215834146;57215826960;","Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation",2020,"Medical Engineering and Physics","79",,,"19","25",,5,"10.1016/j.medengphy.2020.01.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082002884&doi=10.1016%2fj.medengphy.2020.01.016&partnerID=40&md5=72b3e82eef2a30adf40e163bb72f2dcd","School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; School of Mechanical Engineering, Dongguan University of Technology, China","Wendong, W., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Hanhao, L., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Menghan, X., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Yang, C., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Xiaoqing, Y., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Xing, M., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Bing, Z., School of Mechanical Engineering, Dongguan University of Technology, China","This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human–robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed for the control system to mitigate the noise and time delay of the collected signal. The clipping filtering method was used to filter the accidental error and avoid the safety problem caused by a mistrigger. A modified adaptive Kalman filter was used to account for the sudden change of the motion state during rehabilitation training. The results show that the intent recognition system designed herein can accurately recognize the human–robot interaction information, and estimate the intent of human motion in time. Therefore, it can be concluded that the designed system effectively follows the predicted motion intent with the proposed method, which is a significant improvement for human–robot interaction control of upper limb extremity rehabilitation robots. © 2020 IPEM","Exoskeleton robot; Human–robot interaction; Mixed filter; Motion prediction","Adaptive control systems; Adaptive filtering; Adaptive filters; Biomedical signal processing; Delay control systems; Exoskeleton (Robotics); Kalman filters; Machine design; Man machine systems; Motion estimation; Motion sensors; Neuromuscular rehabilitation; Adaptive kalman filter; Exoskeleton robots; Mixed filter; Motion prediction; Rehabilitation robot; Rehabilitation training; Robot interaction control; Robot interactions; Human robot interaction; algorithm; Article; artificial membrane; device safety; human computer interaction; information processing; mathematical computing; mathematical model; movement (physiology); performance; priority journal; rehabilitation medicine; upper limb; devices; human; movement (physiology); physiology; procedures; rehabilitation; robotics; upper limb; Humans; Movement; Rehabilitation; Robotics; Upper Extremity",,,,,"KCYCXPT2017006; National Natural Science Foundation of China, NSFC: 51605385; Northwestern Polytechnical University, NPU: 6101030222595-202001001; Natural Science Foundation of Shaanxi Province: 2017JQ5026, 2018JM5107","This work is supported by National Natural Science Foundation of China (Grant no. 51605385 ), Natural Science Foundation of Shaanxi Province (Grant nos. 2017JQ5026 and 2018JM5107 ), the DGUT Innovation Center of Robotics and Intelligent Equipment (Grant no. KCYCXPT2017006).","This work is supported by National Natural Science Foundation of China (Grant no. 51605385), Natural Science Foundation of Shaanxi Province (Grant nos. 2017JQ5026 and 2018JM5107), the DGUT Innovation Center of Robotics and Intelligent Equipment (Grant no. KCYCXPT2017006). 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Chai, W., Chen, C., Edwan, E., Zhang, J., Loffeld O.INS/Wi-Fi based indoor navigation using adaptive Kalman filtering and vehicle constraints. 2012:36–41","Wendong, W.; School of Mechanical Engineering, China; email: wdwang@nwpu.edu.cn",,,"Elsevier Ltd",,,,,13504533,,MEPHE,"32205023","English","Med. Eng. Phys.",Article,"Final","",Scopus,2-s2.0-85082002884
"Kim W., Kim N., Lyons J.B., Nam C.S.","56517258500;57194709022;15623605700;8421659200;","Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach",2020,"Applied Ergonomics","85",,"103056","","",,14,"10.1016/j.apergo.2020.103056","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078050068&doi=10.1016%2fj.apergo.2020.103056&partnerID=40&md5=aac34a90f9ba06478f84f1ca11a045bd","Department of Industrial & Management Engineering, Sungkyul University, South Korea; Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States; Air Force Research Laboratory, United States","Kim, W., Department of Industrial & Management Engineering, Sungkyul University, South Korea; Kim, N., Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States; Lyons, J.B., Air Force Research Laboratory, United States; Nam, C.S., Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States","The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automation schema (PAS), and affect. A video stimulus depicting an autonomous guard robot interacting with humans was employed as a stimulus via Amazon's Mechanical Turk to recruit 233 participants. Human-related and robot-related metrics were found to affect trustworthiness that subsequently affected trust. In particular, ability (as a trustworthiness facet) was a dominant factor affecting trust in HRI. Integrity was found to mediate the relationships between robot- and human-related metrics and trustworthiness. This study also showed a correlation between intelligence and trustworthiness, as well as between PAS and trustworthiness. The findings of the present study have significant implications for both theory and practice on factors and levels that affect trust in HRI. © 2020 Elsevier Ltd","Amazon's mechanical turk; Autonomous guard robot; Human-robot interaction; Structural equation modelling; Trustworthiness","Intelligent robots; Man machine systems; Amazon's mechanical turks; Dominant factor; Human likeness; Human robot Interaction (HRI); Structural equation modeling; Structural equation modelling; Theory and practice; Trustworthiness; Human robot interaction; adult; article; female; human; human experiment; intelligence; major clinical study; male; robotics; structural equation modeling; theoretical study; trust; videorecording; artificial intelligence; beneficence; computer interface; latent class analysis; man machine interaction; middle aged; young adult; Adult; Artificial Intelligence; Beneficence; Female; Humans; Latent Class Analysis; Male; Man-Machine Systems; Middle Aged; Robotics; Trust; User-Computer Interface; Young Adult",,,,,"Air Force Research Laboratory, AFRL","This research was partly supported by the U.S. Air Force Research Laboratory Summer Faculty Fellowship Program. 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Fitts Department of Industrial & Systems Engineering, United States; email: csnam@ncsu.edu",,,"Elsevier Ltd",,,,,00036870,,AERGB,"32174344","English","Appl. Ergon.",Article,"Final","",Scopus,2-s2.0-85078050068
"Lin K., Li Y., Sun J., Zhou D., Zhang Q.","56431757700;57211796940;57211779729;13105639000;55624487967;","Multi-sensor fusion for body sensor network in medical human–robot interaction scenario",2020,"Information Fusion","57",,,"15","26",,16,"10.1016/j.inffus.2019.11.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074973742&doi=10.1016%2fj.inffus.2019.11.001&partnerID=40&md5=6c641503fc7058ae389124fdadd483af","School of Computer Science and Technology, Dalian University of Technology, Dalian, China; Lanzhou Institute of Physics, China Academy of Space Technology, Lanzhou, China; Key Laboratory of Advanced Design and Intelligent Computing(Dalian University), Ministry of Education, Dalian, China","Lin, K., School of Computer Science and Technology, Dalian University of Technology, Dalian, China; Li, Y., School of Computer Science and Technology, Dalian University of Technology, Dalian, China; Sun, J., Lanzhou Institute of Physics, China Academy of Space Technology, Lanzhou, China; Zhou, D., Key Laboratory of Advanced Design and Intelligent Computing(Dalian University), Ministry of Education, Dalian, China; Zhang, Q., School of Computer Science and Technology, Dalian University of Technology, Dalian, China","With the development of sensor and communication technologies, body sensor networks(BSNs) have become an indispensable part of smart medical services by monitoring the real-time state of users. Due to introducing of smart medical robots, BSNs are not related to users, but also responsible for data acquisition and multi-sensor fusion in medical human–robot interaction scenarios. In this paper, a hybrid body sensor network architecture based on multi-sensor fusion(HBMF) is designed to support the most advanced smart medical services, which combines various sensor, communication, robot, and data processing technologies. The infrastructure and system functions are described in detail and compared with other architectures. Especially, A multi-sensor fusion method based on interpretable neural network(MFIN) for BSNs in medical human–robot interaction scenario is designed and analyzed to improve the performance of fusion decision-making. Compared with the current multi-sensor fusion methods, our design guarantees both the flexibility and reliability of the service in the medical human–robot interaction scenario. © 2019 Elsevier B.V.","Body sensor network; Fusion decision; Medical human–robot interaction; Multi-sensor fusion; Neural network","Computer architecture; Data acquisition; Data handling; Decision making; Human robot interaction; Machine design; Network architecture; Neural networks; Sensor data fusion; Body sensor networks (BSNs); Communication technologies; Data processing technologies; Fusion decision makings; Medical services; Multi-sensor fusion; Multi-sensor fusion method; Robot interactions; Body sensor networks",,,,,"Dalian Science and Technology Bureau: 2018J12GX036; Fundamental Research Funds for the Central Universities: 2017RD11; Program for Liaoning Innovative Talents in University","This work was partially supported by the Liaoning Province Higher Education Innovative Talent Support Program, and by Fundamental Research Funds for the Central Universities under grant no. DUT19JC22 . Program for the Liaoning Distinguished Professor, Program for Dalian High-level Talent Innovation Support under grant no. 2017RD11, and the Science and Technology Innovation Fund of Dalian under grant no. 2018J12GX036.",,,,,,,,,,"Zhang, Y., Gravina, R., Lu, H., Villari, M., Fortino, G., PEA: Parallel electrocardiogram-based authentication for smart healthcare systems (2018) J. Netw. Comput. Appl., 117, pp. 10-16; Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., Jafari, R., Enabling effective programming and flexible management of efficient body sensor network applications (2012) IEEE Trans. Hum.-Mach. Syst., 43 (1), pp. 115-133; Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G., Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges (2017) Inf. 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"Sharkawy A.-N., Koustoumpardis P.N., Aspragathos N.","57188818569;6507626671;7003803146;","Human–robot collisions detection for safe human–robot interaction using one multi-input–output neural network",2020,"Soft Computing","24","9",,"6687","6719",,14,"10.1007/s00500-019-04306-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072043740&doi=10.1007%2fs00500-019-04306-7&partnerID=40&md5=c9692d74fc5c657531cc90601e71d477","Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena, 83523, Egypt; Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26504, Greece","Sharkawy, A.-N., Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena, 83523, Egypt, Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26504, Greece; Koustoumpardis, P.N., Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26504, Greece; Aspragathos, N., Department of Mechanical Engineering and Aeronautics, University of Patras, Rio, 26504, Greece","In this paper, a multilayer feedforward neural network-based approach is proposed for human–robot collision detection taking safety standards into consideration. One multi-output neural network is designed and trained using data from the coupled dynamics of the manipulator with and without external contacts to detect unwanted collisions and to identify the collided link using only the intrinsic joint position and torque sensors of the manipulator. The proposed method is applied to the collaborative robots, which will be very popular in the near future, and is implemented and evaluated in 3D space motion taking into account the effect of the gravity. KUKA LWR manipulator is an example of the collaborative robots, and it is used for doing the experiments. The experimental results prove that the developed system is considerably efficient and very fast in detecting the collisions in the safe region and identifying the collided link along the entire workspace of the three-joint motion of the manipulator. Separate/uncoupled neural networks, one for each joint, are also designed and trained using the same data, and their performance is compared with the coupled one. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Collided link identification; Coupled system; Coupled/uncoupled neural network; Levenberg–Marquardt; Robot collision detection; Safe human–robot interaction","Feedforward neural networks; Light water reactors; Manipulators; Multilayer neural networks; Collaborative robots; Coupled dynamics; Coupled systems; Multilayer feedforward neural networks; Robot collisions; Robot interactions; Safety standard; Torque sensors; Human robot interaction",,,,,,"The authors would like to thank Mr. Nikitas Xanthopoulos, External Expert, Department of Mechanical Engineering and Aeronautics, University of Patras, for his help in programming with C++. Abdel-Nasser Sharkawy is funded by the ?Egyptian Cultural Affairs & Missions Sector? and ?Hellenic Ministry of Foreign Affairs Scholarship? for PhD study in Greece.","The authors would like to thank Mr. Nikitas Xanthopoulos, External Expert, Department of Mechanical Engineering and Aeronautics, University of Patras, for his help in programming with C++. Abdel-Nasser Sharkawy is funded by the “Egyptian Cultural Affairs & Missions Sector” and “Hellenic Ministry of Foreign Affairs Scholarship” for PhD study in Greece.",,,,,,,,,"Albu-Schaffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimbock, T., Hirzinger, G., The DLR lightweight robot: design and control concepts for robots in human environments (2007) Ind Robot Int J, 34, pp. 376-385; Anton, F.D., Anton, S., Borangiu, T., Human–robot natural interaction with collision avoidance in manufacturing operations (2013) Service Orientation in Holonic and Multi Agent Manufacturing and Robotics, pp. 375-388. , https://doi.org/10.1007/978-3-642-35852-4, Springer, Berlin; Berardi, V.L., Zhang, G.P., The effect of misclassification costs on neural network classifiers (1999) Decis Sci, 30, pp. 659-682; Chen, S., Luo, M., He, F., A universal algorithm for sensorless collision detection of robot actuator faults (2018) Adv Mech Eng, 10, pp. 1-10; Cho, C., Kim, J., Lee, S., Song, J., Collision detection and reaction on 7 DOF service robot arm using residual observer (2012) J Mech Sci Technol, 26, pp. 1197-1203; Cho, C., Kim, J., Kim, Y., Song, J.-B., Kyung, J.-H., Collision detection algorithm to distinguish between intended contact and unexpected collision (2012) Adv Robot, 26, pp. 1825-1840; de Luca, A., Albu-Schaffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reaction with the DLR-III lightweight manipulator arm (2006) In, 2006, pp. 1623-1630. , https://doi.org/10.1109/iros.2006.282053, IEEE/RSJ international conference on intelligent robots and systems. 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Mechanisms and machine science, 67. , Aspragathos N, Koustoumpardis P, Moulianitis V, (eds), Springer, Cham; Smith, A.C., Hashtrudi-Zaad K (2005) Application of neural networks in inverse dynamics based contact force estimation Proceedings of 2005 IEEE Conference Control Application, IEEE, pp. 1021-1026. , https://doi.org/10.1109/cca.2005.1507264, Toronto, Canada; Vemuri, A.T., Polycarpou, M.M., Neural-network-based robust fault diagnosis in robotic systems (1997) IEEE Trans Neural Netw, 8, pp. 1410-1420; Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y., Suita, K., Human–robot contact in the safeguarding space (1997) IEEE/ASME Trans Mechatron, 2, pp. 230-236; Zhang, G.P., Neural networks for classification: a survey (2000) IEEE Trans Syst MAN Cybern C Appl Rev, 30, pp. 451-462","Sharkawy, A.-N.; Mechanical Engineering Department, Egypt; email: eng.abdelnassersharkawy@gmail.com",,,"Springer",,,,,14327643,,,,"English","Soft Comput.",Article,"Final","",Scopus,2-s2.0-85072043740
"Li J., Zuo S., Xu C., Zhang L., Dong M., Tao C., Ji R.","8322805400;57205027114;57188969826;55344237500;56081285600;23480909700;55903189500;","Influence of a Compatible Design on Physical Human-Robot Interaction Force: a Case Study of a Self-Adapting Lower-Limb Exoskeleton Mechanism",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","2",,"525","538",,5,"10.1007/s10846-019-01063-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069839130&doi=10.1007%2fs10846-019-01063-5&partnerID=40&md5=ac4d5e7666ddde97a26d9b861120a071","College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China","Li, J., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Zuo, S., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Xu, C., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Zhang, L., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Dong, M., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Tao, C., National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China; Ji, R., National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China","In the kinematic design of wearable exoskeletons, the issue of axis misalignments between the human and the exoskeleton joints should be well dealt with. Otherwise, large physical human-robot interaction (p-HRI) forces may occur at the human-robot interfaces, which makes the p-HRI uncomfortable or even unsafe. To cope with this issue, a kinematically compatible design approach of wearable exoskeletons has been investigated by researchers, and great development has been made in recent years. Moreover, the influence of such a design on the exoskeleton’s p-HRI performance should be evaluated to determine if the design is feasible. In this paper, a self-adapting lower-limb exoskeleton mechanism for three degrees of freedom gait training is proposed, and the mechanical structure of the exoskeleton mechanism is designed in detail. Then, based on the presented exoskeleton mechanism and the use of suitable force/torque sensors, a p-HRI force measurement system is developed. Subsequently, the p-HRI forces of the human-robot closed chain under the static and motion modes are detected, and the influence of the self-adapting design on the lower-limb exoskeleton mechanism’s p-HRI force feature is evaluated. The results indicate that additional human-robot connective joints could reduce the p-HRI force significantly, the compatible design of the exoskeleton mechanism is effective, and is thus applied to human lower-limb gait training. © 2019, Springer Nature B.V.","Axis misalignment; Compatible design; Lower-limb exoskeleton mechanism; Physical human-robot interaction force; Self-adapting","Alignment; Degrees of freedom (mechanics); Exoskeleton (Robotics); Joints (anatomy); Machine design; Man machine systems; Mechanisms; Wearable technology; Axis misalignments; Compatible design; Exoskeleton mechanism; Physical human-robot interactions; Self adapting; Human robot interaction",,,,,"KM201810005015; National Natural Science Foundation of China, NSFC: 51675008, 51705007; China Postdoctoral Science Foundation: 2018 T110017; Natural Science Foundation of Beijing Municipality: 17 L20019, 3171001","This work was supported by the National Natural Science Foundation of China under Grants No. 51675008 and No. 51705007, the Beijing Natural Science Foundation under Grants No. 3171001 and No. 17 L20019. Natural Science Foundation of Beijing Education Committee (No. KM201810005015) and China Postdoctoral Science Foundation (2018 T110017).",,,,,,,,,,"Hidler, J., Wisman, W., Neckel, N., Kinematic trajectories while walking within the Lokomat robotic gait-orthosis (2008) Clin. Biomech., 23 (10), pp. 1251-1259; Zanotto, D., Stegall, P., Agrawal, S.K., ALEX III: A novel robotic platform with 12 DOFs for human gait training (2013) IEEE International Conference on Robotics and Automation. Proceedings, pp. 3914-3919. , IEEE, Germany; Veneman, J.F., Kruidhof, R., Hekman, E.E.G., Ekkelenkamp, R., Asseldonk, E.H.F.V., Kooij, H.V.D., Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation (2007) IEEE Trans. Neural Syst. Rehabil. Eng., 15 (3), pp. 379-386; Prange, G.B., Jannink, M.J.A., Groothuis-Oudshoorn, C.G.M., Hermens, H.J., Ijzerman, M.J., Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke (2006) J. Rehabil. 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Eng., 18 (4), pp. 389-397; Wang, H., (2008) Anatomy of the human system, , Fudan University Press, Shanghai, China; Cenciarini, M., Dollar, A.M., Biomechanical considerations in the design of lower limb exoskeletons (2011) 2011 IEEE International Conference on Rehabilitation Robotics, , IEEE. Zurich; Cai, V.A.D., Bidaud, P., Hayward, V., Self-adjusting, isostatic exoskeleton for the human knee joint (2011) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 612-618. , IEEE, Boston; Celebi, B., Yalcin, M., Patoglu, V., AssistON- Knee: a self-aligning knee exoskeleton (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems; Radcliffe, C.W., Four-bar linkage prosthetic knee mechanisms: kinematics, alignment and prescription criteria (1994) Prosthetics Orthot. Int., 8 (18), pp. 159-173; Yamaguchi, G.T., Zajac, F.E., A planar model of the knee joint to characterize the knee extensor mechanism (1989) J. Biomech., 22 (1), pp. 1-10; Bapat, G., Sujatha, S., A method for optimal synthesis of a biomimetic four-bar linkage knee joint for a knee-ankle-foot orthosis, J (2017) Biomech, 32, pp. 20-28; Chen, G., Qi, P., Guo, Z., Yu, H., Mechanical design and evaluation of a compact portable knee–ankle–foot robot for gait rehabilitation (2016) Mech. & Mach. Theory, 103, pp. 51-64; Xu, L., Wang, D.H., Fu, Q., Yuan, G., Hu, L.Z., A novel four-bar linkage prosthetic knee based on magnetorheological effect: principle, structure, simulation and control (2016) Smart Mater. Struct., 25 (11), p. 115007","Zhang, L.; College of Mechanical Engineering and Applied Electronics Technology, China; email: zhangleiyu1988@126.com",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85069839130
"Zeller F., Smith D.H., Au Duong J., Mager A.","57099059200;57000688100;57209828231;57209822188;","Social Media in Human–Robot Interaction",2020,"International Journal of Social Robotics","12","2",,"389","402",,1,"10.1007/s12369-019-00573-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068872653&doi=10.1007%2fs12369-019-00573-4&partnerID=40&md5=a850e062f567fdc0dcdf18f8a2ad0af2","School of Professional Communication, Ryerson University, 350 Victoria Street, Toronto, ON, Canada; McMaster University, 1280 Main Street West, Hamilton, ON, Canada; Toronto, ON, Canada","Zeller, F., School of Professional Communication, Ryerson University, 350 Victoria Street, Toronto, ON, Canada; Smith, D.H., McMaster University, 1280 Main Street West, Hamilton, ON, Canada; Au Duong, J., School of Professional Communication, Ryerson University, 350 Victoria Street, Toronto, ON, Canada; Mager, A., Toronto, ON, Canada","The objective of this paper is to discuss and demonstrate in a case study the application of social media as a tool for acceptance, sociability and interaction in human–robot interaction (HRI). By adopting the concept of mediatization as discussed in media and communication studies, we aim to propose an extended usage of social media in the design of robots but also in collecting data on their acceptance and sociability. We aim to provide a starting point for ongoing studies into the extension of HRI evaluation frameworks, which provide additional ways to measure HRI based on a media and communication studies approach. Our discussion is supported by results of an HRI public engagement project, which included a social media strategy. The project was designed around a hitchhiking robot, called hitchBOT, which was dependent on the public’s engagement and support in its hitchhiking objectives. The robot’s social media strategies included accounts and profiles on Facebook, Twitter, Instagram, and a website. The aim was to use social media in order to engage not only with those people finding hitchBOT on the side of the road, but also a wider international audience. The robot’s social media accounts provided a tool to communicate hitchBOT’s origins, personality, goals, and travels. Our use of social media to communicate hitchBOT’s personality and to instill trust, interest and emotional engagement constituted a novel contribution to the design of social robots and to the scope and scale of evaluation in HRI. © 2019, Springer Nature B.V.","Human–robot interaction; Mediatization; Public engagement; Public understanding; Social media","Agricultural robots; Machine design; Social networking (online); Emotional engagements; Evaluation framework; Facebook; Public engagement; Robot interactions; Social media; Social robots",,,,,,,,,,,,,,,,"Hjarvard, S., Mediatisation of society. A theory of the media as agents of social and cultural change (2008) Nordicom, 29, pp. 105-134; Schulz, W., Reconstructing mediatization as an analytical concept (2016) Eur J Commun, 19, pp. 87-101; Weiss, A., Bernhaupt, R., Lankes, M., Tscheligi, M., The USUS evaluation framework for human–robot interaction (2012) AISB2009: Proceedings of the Symposium on New Frontiers in human–robot Interaction, pp. 158-165; Smith, D.H., Zeller, F., The death and lives of hitchBOT: the design and implementation of a hitchhiking robot (2017) Leonardo; Ma, X., Yang, X., Zhao, S., Robots in my contact list: Using social media platforms for human-robot interaction in domestic environment (2012) Proceedings of the 10Th Asia Pacific Conference on Computer Human Interaction, pp. 133-140. , ACM; Ma, X., Yang, X., Zhao, S., Using social media platforms for human–robot interaction in domestic environment (2014) Int J Hum Comput Interact, 30, pp. 627-642; Cramer, H., Büttner, S., Things that tweet, check-in and are befriended: Two explorations on robotics and social media (2011) Proceedings of the 6Th International Conference on human–robot Interaction, pp. 125-126. , ACM; Mavridis, N., On artificial agents within human social networks: Examples, open questions, and potentialities (2010) 2010 4Th IEEE International Conference on Digital Ecosystems and Technologies (DEST), pp. 685-690; Mavridis, N., Datta, C., Emami, S., FaceBots: Robots utilizing and publishing social information in Facebook (2009) 2009 4Th ACM/IEEE International Conference on human–robot Interaction (HRI), pp. 273-274; Finger, L., (2015) Do evil—the Business of Social Media Bots, , https://www.forbes.com/sites/lutzfinger/2015/02/17/do-evil-the-business-of-social-media-bots/#6a4f4589fb58, Forbes online magazine, Accessed 5 Nov 2017; Linden, A., (2013) Roli Rockt Den Roten Teppich, , http://www.spiegel.de/karriere/berufsstart/roboter-roli-bei-premiere-von-schweighoefers-schlussmacher-a-876458.html, Der Spiegel Online, Accessed 5 Nov 2017; Zeller, F., Smith, D.H., My kulturBOT 1.0 (2013) Proceedings of EVA—electronic Visualisations of the Arts, pp. 121-126. , London; Ford, M., (2015) Rise of the robots: technology and the threat of a jobless future, , Basic Books, New York; Vardi, M., (2016) When Machines Can Do Any Job, What Will Humans Do? 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CNBC, , https://www.cnbc.com/2017/08/11/elon-musk-issues-a-stark-warning-about-a-i-calls-it-a-bigger-threat-than-north-korea.html, Accessed 22 Sep 2017; Sainato, M., Stephen Hawking, Elon Musk, and Bill Gates warn about artificial intelligence (2015) The Observer, , http://observer.com/2015/08/stephen-hawking-elon-musk-and-bill-gates-warn-about-artificial-intelligence/, Accessed 5 Nov 2017; Zeller, F., (2005) Mensch–Roboter Interaktion: Eine sprachwissenschaftliche Perspektive, , Kassel University Press, Kassel; Yagoda, R.E., Gillan, D.J., You want me to trust a ROBOT? The development of a human–robot interaction trust scale (2012) Int J Soc Robot, 4, pp. 235-248; Sung, J., Grinter, R.E., Christensen, H.I., Domestic robot ecology (2010) Int J Soc Robot, 2, pp. 417-429; Sugiura, Y., Sakamoto, D., Withana, A., Inami, M., Igarashi, T., Cooking with robots: Designing a household system working in open environments (2010) Proceedings of the 2010 SIGCHI Conference on Human Factors in Computing Systems, pp. 2427-2430; Young, J.E., Hawkins, R., Sharlin, E., Igarashi, T., Toward acceptable domestic robots: applying insights from social psychology (2009) Int J Soc Robot, 1, pp. 95-108; Bedaf, S., Draper, H., Gelderblom, G.J., Sorell, T., Witte, L., Can a service robot which supports independent living of older people disobey a command? 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A concept with which to grasp media and societal change (2009) Mediatization concept, changes, consequences, pp. 19-38. , Lundby K, (ed), Peter Lang Inc., New York; Weingart, P., Die Stunde der Wahrheit? Zum Verhältnis der Wissenschaft zu Politik (2001) Wirtschaft Und Medien in Der Wissensgesellschaft, , Velbrück Verlag, Weilerswist; Rodder, S., Reassessing the concept of a medialization of science: a story from the “book of life (2009) Pub Underst Sci, 18, pp. 452-463; Schafer, M.S., From public understanding to public engagement: an empirical assessment of changes in science coverage (2009) Sci Commun, 30, pp. 475-505; Zeller, F., Wolling, J., Porten-Chee, P., Framing 0/1. Wie die Medien über die “Digitalisierung der Gesellschaft” berichten (2010) Medien und Kommunikationswissenschaft, 58, pp. 503-524; O’Reilly, T., (2005) What is Web 2.0. O’Reilly Network, , http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html, Accessed 5 Nov 2017; Taddicken, M., Schmidt, J.H., Entwicklung und Verbreitung sozialer Medien (2015) Handbuch Soziale Medien, pp. 1-20. , Taddicken M, Schmidt JH, (eds), Springer Fachmedien Wiesbaden, Wiesbaden; Pauwels, K., Ambler, T., Clark, B., Lapointe, P., Reibstein, D., Skiera, B., Wierenga, B., Wiesel, T., (2008) Dashboards and marketing: Why, what, how and what research is needed? MSI working paper, , Marketing Science Institute, report 08-203; Zeller, F., Analyzing social media data and other data sources: a methodological overview (2017) The Sage handbook of social media research methods, pp. 386-404. , Quan-Haase A, Sloan L, (eds), Sage, London; Scott, M., WordSmith tools version 6 (2012) Lexical Analysis Software; Pennebaker, J.W., Booth, R.J., Boyd, R.L., Francis, M.E., (2015) Linguistic Inquiry and Word Count: LIWC2015. Pennebaker Conglomerates, , Austin.www.LIWC.net; Bae, Y., Lee, H., Sentiment analysis of Twitter audiences: measuring the positive or negative influence of popular twitterers (2012) J Am Soc Inf Sci Technol, 63, pp. 2521-2535; Grimmer, J., Stewart, B.M., Text as data: the promise and pitfalls of automatic content analysis methods for political texts (2013) Polit Anal, 21, pp. 267-297; Kennedy, H., Perspectives on sentiment analysis (2012) J Broadcast Electron Media, 56, pp. 435-450; Young, L., Soroka, S., Affective news: the automated coding of sentiment in political texts (2012) Polit Commun, 29, pp. 205-231; Tausczik, Y.R., Pennebaker, J.W., The psychological meaning of words: LIWC and computerized text analysis methods (2010) J Lang Soc Psychol, 29, pp. 24-54; Bednarek, M., Caple, H., (2012) News discourse, , Continuum International Publishing Group, London, New York","Zeller, F.; School of Professional Communication, 350 Victoria Street, Canada; email: fzeller@ryerson.ca",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85068872653
"Ferrari F., Divan S., Guerrero C., Zenatti F., Guidolin R., Palopoli L., Fontanelli D.","57188842302;57192431452;57209571111;57192423373;57209575434;35318200500;23488529800;","Human–Robot Interaction Analysis for a Smart Walker for Elderly: The ACANTO Interactive Guidance System",2020,"International Journal of Social Robotics","12","2",,"479","492",,9,"10.1007/s12369-019-00572-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068156286&doi=10.1007%2fs12369-019-00572-5&partnerID=40&md5=0d0c1ec69f38001b3710736da6988fbd","Department of Engineering and Computer Science, University of Trento, Trento, Italy; Department of Industrial Engineering, University of Trento, Trento, Italy","Ferrari, F., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Divan, S., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Guerrero, C., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Zenatti, F., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Guidolin, R., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Palopoli, L., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Fontanelli, D., Department of Industrial Engineering, University of Trento, Trento, Italy","The present study aims to investigate the interaction between older adults and a robotic walker named FriWalk, which has the capability to act as a navigation support and to guide the user through indoor environments along a planned path. To this purpose, we developed a guidance system named Simulated Passivity, which leaves the responsibility of the locomotion to the user, both to increase the mobility of elder users and to enhance their perception of control over the robot. Moreover, the robotic walker can be integrated with a tablet and graphical user interface (GUI) which provides visual indications to the user on the path to follow. Since the FriWalk and Simulated Passivity were developed to suit the needs of users with different deficits, we conducted a human–robot interaction experiment, complemented with direct interviews of the participants. The goals of the present work were to observe the relation between elders (with and without visual impairments) and the robot in completing a path (with and without the support of the GUI), and to collect the impressions about of the older adult participants about the interaction. Our results show an overall positive impression of the FriWalk and an evident flexibility and adaptability of its guidance system across different categories of users (e.g., with or without visual impairments). In the paper, we discuss the implications of these findings on service social robotics. © 2019, Springer Nature B.V.","Assistive robotics; Human–robot interaction; Perception of robots; Robotic walker; Social service robotics","Agricultural robots; Graphical user interfaces; Remote control; Robotics; Visual servoing; Graphical user interfaces (GUI); Guidance system; Indoor environment; Navigation support; Robot interactions; Robotic walkers; Social robotics; Visual impairment; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 643644","We want to thank the referents of the following elder centres of Trento and province for their support to the ACANTO Project: Civica di Trento, APSP Levico Terme San Valentino, Casa Di Riposo A.P.S.P. S. Spirito?Fondazione Montel, APSP Clementino Vannetti (Rovereto), Centro Diurno ?Sempreverde??Mattarello?Cooperativa Sad, APSP Beato De Tschiderer (Trento), and Casa Santa Maria?Kaleidoscopio Scs (Vigolo Vattaro).",,,,,,,,,,"(2015) A Cyberphysical Social Network Using Robot Friends, , http://www.ict-acanto.eu/acanto, EU Project; Andreetto, M., Divan, S., Ferrari, F., Fontanelli, D., Palopoli, L., Zenatti, F., Simulating passivity for robotic walkers via authority-sharing (2018) IEEE Robot Autom Lett, 3 (2), pp. 1306-1313; Andreetto, M., Divan, S., Fontanelli, D., Palopoli, L., Path following with authority sharing between humans and passive robotic walkers equipped with low-cost actuators (2017) IEEE Robot Autom Lett, 2 (4), pp. 2271-2278; Bennett, C., Doub, T., Bragg, A., Luellen, J., van Regenmorter, C., Lockman, J., Reiserer, R., Data mining session-based patient reported outcomes (PROs) in a mental health setting: Toward data-driven clinical decision support and personalized treatment (2011) 2011 IEEE 1St International Conference on Healthcare Informatics, Imaging and Systems Biology. 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In: Designing for - and with - Vulnerable People, , Paris; Montemerlo, M., Pineau, J., Roy, N., Thrun, S., Verma, V., Experiences with a mobile robotic guide for the elderly (2002) AAAI/IAAI, 2002, pp. 587-592; (2015) World Population Ageing, , http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf, Accessed 12 July 2018; Nazemzadeh, P., Fontanelli, D., Macii, D., Palopoli, L., Indoor localization of mobile robots through QR code detection and dead reckoning data fusion (2017) IEEE/ASME Trans Mechatron, 22 (6), pp. 2588-2599; Nazemzadeh, P., Moro, F., Fontanelli, D., Macii, D., Palopoli, L., Indoor positioning of a robotic walking assistant for large public environments (2015) IEEE Trans Instrum Meas, 64 (11), pp. 2965-2976; Ni, D., Song, A., Tian, L., Xu, X., Chen, D., A walking assistant robotic system for the visually impaired based on computer vision and tactile perception (2015) Int J Soc Robot, 7 (5), pp. 617-628; Nielsen, J., (2013) Seniors as web users, 28. , Nielsen Norman Group, Fremont; Palopoli, L., Argyros, A., Birchbauer, J., Colombo, A., Fontanelli, D., Navigation assistance and guidance of older adults across complex public spaces: the DALi approach (2015) Intell Serv Robot, 8 (2), pp. 77-92; Peng, H., Song, G., You, J., Zhang, Y., Lian, J., An indoor navigation service robot system based on vibration tactile feedback (2017) Int J Soc Robot, 9 (3), pp. 331-341; Pinto, J.M., Wroblewski, K.E., Huisingh-Scheetz, M., Correia, C., Lopez, K.J., Chen, R.C., Kern, D.W., McClintock, M.K., Global sensory impairment predicts morbidity and mortality in older us adults (2017) J Am Geriatr Soc, 65 (12), pp. 2587-2595; Robinson, H., MacDonald, B., Broadbent, E., The role of healthcare robots for older people at home: a review (2014) Int J Soc Robot, 6 (4), pp. 575-591; Soetanto, D., Lapierre, L., Pascoal, A., Adaptive, non-singular path-following control of dynamic wheeled robots (2003) IEEE Conference on Decision and Control, 2, pp. 1765-1770. , IEEE, New York; Vaportzis, E., Giatsi Clausen, M., Gow, A.J., Older adults perceptions of technology and barriers to interacting with tablet computers: a focus group study (2017) Front Psychol, 8, p. 1687; Wachaja, A., Agarwal, P., Zink, M., Adame, M.R., Möller, K., Burgard, W., Navigating blind people with a smart walker (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6014-6019; Walsh, S.A., Conducting research with the elderly: ethical concerns for a vulnerable population (2009) South Online J Nurs Res, 9 (4), pp. 1-13; Wang, Y., Wang, S., A new directional-intent recognition method for walking training using an omnidirectional robot (2017) J Intell Robot Syst, 87 (2), pp. 231-246; Whelan, S., Murphy, K., Barrett, E., Krusche, C., Santorelli, A., Casey, D., Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: a literature review (2018) Int J Soc Robot, 10, pp. 1-26","Fontanelli, D.; Department of Industrial Engineering, Italy; email: daniele.fontanelli@unitn.it",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85068156286
"Park J.-Y., Kim J.-H.","57192667984;36484629700;","Online Incremental Classification Resonance Network and Its Application to Human-Robot Interaction",2020,"IEEE Transactions on Neural Networks and Learning Systems","31","5","8744499","1426","1436",,4,"10.1109/TNNLS.2019.2920158","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068105633&doi=10.1109%2fTNNLS.2019.2920158&partnerID=40&md5=0ec69ba5f4a2ac178f16c2d2c7dc516b","School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea","Park, J.-Y., School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Kim, J.-H., School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea","In human-robot interaction (HRI), classification is one of the most important problems, and it is essential particularly when the robot recognizes the surroundings and chooses a reaction based on a certain situation. Each interaction is different since new people appear or the environment changes, and the robot should be able to adapt to different situations during a brief interaction. Thus, it is imperative that the classification is performed incrementally in real time. In this sense, we propose an online incremental classification resonance network (OICRN) that enables incremental class learning in multi-class classification with high performance online. In OICRN, a scale-preserving projection process is introduced to use the raw input vectors online without a normalization process in advance. The integrated network of the convolutional neural network (CNN) for feature extraction and the OICRN for classification is applied to a robotic system that learns human identities through HRIs. To demonstrate the effectiveness of our network, experiments are carried out on benchmark data sets and on a humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory, KAIST. © 2012 IEEE.","Concept vectors; face identification; human-robot interaction (HRI); incremental class learning; online incremental classification resonance network (OICRN)","Anthropomorphic robots; Classification (of information); Intelligent robots; Man machine systems; Neural networks; Resonance; Convolutional neural network; Face identification; Human robot Interaction (HRI); incremental class learning; Multi-class classification; Normalization process; Resonance networks; Robot intelligences; Human robot interaction; artificial intelligence; classification; factual database; human; procedures; robotics; Artificial Intelligence; Automated Facial Recognition; Databases, Factual; Humans; Neural Networks, Computer; Robotics",,,,,"National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT: NRF-2017R1A2A1A17069837","Manuscript received December 13, 2017; revised May 31, 2018, November 6, 2018, and March 6, 2019; accepted May 24, 2019. Date of publication June 24, 2019; date of current version May 1, 2020. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. NRF-2017R1A2A1A17069837). (Corresponding author: Jong-Hwan Kim.) The authors are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea (e-mail: johkim@rit.kaist.ac.kr).",,,,,,,,,,"Cauwenberghs, G., Poggio, T., Incremental and decremental support vector machine learning (2001) Proc. Adv. Neural Inf. Process. Syst., pp. 409-415; Zhang, B.-F., Su, J.-S., Xu, X., A class-incremental learning method for multi-class support vector machines in text classification (2006) Proc. Int. Conf. Mach. Learn. Cybern., pp. 2581-2585; Yao, C., Zou, J., Luo, Y., Li, T., Bai, G., (2018) A Class-incremental Learning Method Based on One Class Support Vector Machine, , https://arxiv.org/abs/1803.00159, Mar; Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B., Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps (1992) IEEE Trans. Neural Netw., 3 (5), pp. 698-713. , Sep; Liang, N.-Y., Huang, G.-B., Saratchandran, P., Sundararajan, N., A fast and accurate online sequential learning algorithm for feedforward networks (2006) IEEE Trans. Neural Netw., 17 (6), pp. 1411-1423. , Nov; Zhao, Z., Chen, Z., Chen, Y., Wang, S., Wang, H., A class incremental extreme learning machine for activity recognition (2014) Cogn. Comput., 6 (3), pp. 423-431. , Sep; Er, M.J., Yalavarthi, V.K., Wang, N., Venkatesan, R., A novel incremental class learning technique for multi-class classification (2016) Proc. Int. Symp. Neural Netw., pp. 474-481. , Jul; Carpenter, G.A., Grossberg, S., Rosen, D.B., Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system (1991) Neural Netw., 4 (6), pp. 759-771; Park, G.-M., Choi, J.-W., Kim, J.-H., Developmental resonance network (2019) IEEE Trans. Neural Netw. Learn. Syst., 30 (4), pp. 1278-1284. , Apr; Dhillon, I.S., Modha, D.S., Concept decompositions for large sparse text data using clustering (2001) Mach. Learn., 42 (1), pp. 143-175; Hsu, C.-W., Lin, C.-J., A comparison of methods for multiclass support vector machines (2002) IEEE Trans. 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Massachusetts, , Amherst, MA, USA, Tech. Rep. 07-49; Best-Rowden, L., Han, H., Otto, C., Klare, B.F., Jain, A.K., Unconstrained face recognition: Identifying a person of interest from a media collection (2014) IEEE Trans. Inf. Forensics Security, 9 (12), pp. 2144-2157. , Dec; Taigman, Y., Yang, M., Ranzato, M., Wolf, L., DeepFace: Closing the gap to human-level performance in face verification (2014) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1701-1708. , Jun; Taigman, Y., Yang, M., Ranzato, M., Wolf, L., Web-scale training for face identification (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2746-2754. , Jun; Sun, Y., Wang, X., Tang, X., Deeply learned face representations are sparse, selective, and robust (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2892-2900. , Jun; Liu, X., Kan, M., Wu, W., Shan, S., Chen, X., VIPLFaceNet: An open source deep face recognition SDK (2017) Frontiers Comput. Sci., 11 (2), pp. 208-218; Parkhi, O.M., Vedaldi, A., Zisserman, A., Deep face recognition (2015) Proc. Brit. Mach. Vis. Conf. (BMVC), pp. 1-12; Wen, Y., Zhang, K., Li, Z., Qiao, Y., A discriminative feature learning approach for deep face recognition (2016) Proc. Eur. Conf. Comput. Vis. (ECCV)., pp. 499-515. , Berlin, Germany: Springer; Crosswhite, N., Byrne, J., Stauffer, C., Parkhi, O., Cao, Q., Zisserman, A., Template adaptation for face verification and identification (2018) Image Vis. Comput., 79, pp. 35-48. , Nov; Sohn, K., Liu, S., Zhong, G., Yu, X., Yang, M.-H., Chandraker, M., Unsupervised domain adaptation for face recognition in unlabeled videos (2017) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3210-3218. , Oct; Yang, J., Neural aggregation network for video face recognition (2017) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4362-4371. , Jul; Klare, B.F., Pushing the frontiers of unconstrained face detection and recognition: IARPA janus benchmark A (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1931-1939. , Jun","Kim, J.-H.; School of Electrical Engineering, South Korea; email: johkim@rit.kaist.ac.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,2162237X,,,"31247580","English","IEEE Trans. Neural Networks Learn. Sys.",Article,"Final","",Scopus,2-s2.0-85068105633
"Carros F., Meurer J., Löffler D., Unbehaun D., Matthies S., Koch I., Wieching R., Randall D., Hassenzahl M., Wulf V.","57216504675;55507036100;56096474900;57202049825;57219116227;57219112210;6505461480;7202208815;6602454385;55072850600;","Exploring Human-Robot Interaction with the Elderly: Results from a Ten-Week Case Study in a Care Home",2020,"Conference on Human Factors in Computing Systems - Proceedings",,,"3376402","","",,14,"10.1145/3313831.3376402","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091320815&doi=10.1145%2f3313831.3376402&partnerID=40&md5=f603751f9b097e0144951523b4e2fac2","University of Siegen, Siegen, Germany","Carros, F., University of Siegen, Siegen, Germany; Meurer, J., University of Siegen, Siegen, Germany; Löffler, D., University of Siegen, Siegen, Germany; Unbehaun, D., University of Siegen, Siegen, Germany; Matthies, S., University of Siegen, Siegen, Germany; Koch, I., University of Siegen, Siegen, Germany; Wieching, R., University of Siegen, Siegen, Germany; Randall, D., University of Siegen, Siegen, Germany; Hassenzahl, M., University of Siegen, Siegen, Germany; Wulf, V., University of Siegen, Siegen, Germany","Ageing societies and the associated pressure on the care systems are major drivers for new developments in socially assistive robotics. To understand better the real-world potential of robot-based assistance, we undertook a 10-week case study in a care home involving groups of residents, caregivers and managers as stakeholders. We identified both, enablers and barriers to the potential implementation of robot systems. The study employed the robot platform Pepper, which was deployed with a view to understanding better multi-domain interventions with a robot supporting physical activation, cognitive training and social facilitation. We employed the robot in a group setting in a care facility over the course of 10 weeks and 20 sessions, observing how stakeholders, including residents and caregivers, appropriated, adapted to, and perceived the robot. We also conducted interviews with 11 residents and caregivers. Our results indicate that the residents were positively engaged in the training sessions that were moderated by the robot. The study revealed that such humanoid robots can work in a care home but that there is a moderating person needed, that is in control of the robot. © 2020 ACM.","elderly care; ethics; social robots; user studies","Anthropomorphic robots; Educational robots; Human engineering; Assistive robotics; Cognitive training; Humanoid robot; Multi domains; Physical activation; Robot platform; Social facilitations; Training sessions; Human robot interaction",,,,,,,,,,,,,,,,"Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2), p. e018815. , https://doi.org/10.1136/bmjopen-2017-018815; Albornos-Muñoz, L., Moreno-Casbas, M.T., Sánchez-Pablo, C., Bays-Moneo, A., Fernández-Domínguez, J.C., Rich-Ruiz, M., Gea-Sánchez, M., Efficacy of the otago exercise programme to reduce falls in community-dwelling adults aged 65-80 years old when delivered as group or individual training (2018) Journal of Advanced Nursing, 74 (7), pp. 1700-1711. , https://doi.org/10.1111/jan.13583; Baisch, S., Kolling, T., Schall, A., Rühl, S., Selic, S., Kim, Z., Rossberg, H., Oswald, F., Acceptance of social robots by elder people: Does psychosocial functioning matter? 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Intelligent Robots and Autonomous Agents, , MIT Press, Cambridge, Mass; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: Review and future directions (2009) International Journal of Social Robotics, 1 (4), pp. 319-330. , https://doi.org/10.1007/s12369-009-0030-6; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8, p. 2. , https://doi.org/10.4017/gt.2009.08.02.002.00; Bussolo, M., Koettl, J., Sinnott, E., (2015) Golden Aging: Prospects for Healthy, Active, and Prosperous Aging in Europe and Central Asia, , The World Bank; Chi, E.H., A position paper on 'living laboratories': Rethinking ecological designs and experimentation in human-computer interaction (2009) New Trends, 5610, pp. 597-605. , https://doi.org/10.1007/978-3-642-02574-7-67, Julie A. Jacko, Ed. Lecture Notes in Computer Science,. 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ACM Hum.-Comput. Interact, 2, pp. 1-23. , https://doi.org/10.1145/3274444, CSCW; Unbehaun, D., Vaziri, D.D., Aal, K., Wieching, R., Tolmie, P., Wulf, V., Exploring the potential of exergames to affect the social and daily life of people with dementia and their caregivers (2018) CHI 2018. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-15. , https://doi.org/10.1145/3173574.3173636, April 21-26, 2018, Montreal, QC, Canada. ACM, New York, NY; Wada, K., Shibata, T., Living with seal robots-its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. Robot, 23 (5), pp. 972-980. , https://doi.org/10.1109/TRO.2007.906261",,,"ACM SIGCHI","Association for Computing Machinery","2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020","25 April 2020 through 30 April 2020",,160500,,9781450367080,,,"English","Conf Hum Fact Comput Syst Proc",Conference Paper,"Final","",Scopus,2-s2.0-85091320815
"Ting H.-Y., Hsu H.-K., Huang M.-B., Huang H.-P.","57196039825;56102370200;57208340682;7405609488;","Safety of Human-Robot Interaction: Concepts and Implementation Based on Robot-Related Standards [人機交互安全:基於機器人相關標準的概念與實現]",2020,"Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao","41","2",,"199","209",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090888734&partnerID=40&md5=7860d95ac179a5dd234212bebb05d18a","Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan","Ting, H.-Y., Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan; Hsu, H.-K., Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan; Huang, M.-B., Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan; Huang, H.-P., Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan","In this article, the core concepts of the safety standards of machinery and robots are introduced, and the pre-collision safety strategies that integrate operating speed adjustments and motion trajectory modifications in order to guarantee the safety of humans are explored. We propose a safety strategy based on risk assessment and speed reduction procedures to avoid collisions between robots and humans. The proposed strategy uses a Kinect sensor to estimate the distance between a human and the robot to slow the robot down or to calculate the virtual force in the risk space to further modify the motion of the robot. The strategy is validated by experiment results. Particularly, the proposed strategy can meet the requirements of the ISO 15066 guidelines for the robot in a human-robot collaboration. Additionally, the loop structure of safety function included in the proposed strategy can meet performance level e of ISO 13849. © 2020, Chinese Mechanical Engineering Society. All right reserved.","Human-robot collaboration; ISO 13849; ISO 15066; Physical human-robot interaction; Pre-collision safety strategy","Machinery; Risk assessment; Risk perception; Safety engineering; Collision safety; Human-robot collaboration; Motion trajectories; Operating speed; Performance level; Safety functions; Safety standard; Safety strategy; Social robots",,,,,,,,,,,,,,,,"Flacco, F., Kröger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) Proceeding of IEEE International Conference on Robotics and Automation, pp. 338-345; Golz, S., Osendorfer, C., Haddadin, S., Using tactile sensation for learning contact knowledge: Discriminate collision from physical interaction (2015) Proceeding of IEEE International Conference on Robotics and Automation, pp. 3788-3794; Goodrich, M. A., Schultz, A. C., Human-robot interaction: A survey (2008) Foundations and Trends® in Human-Computer Interaction, 1 (3), pp. 203-275; Huang, M.-B., Huang, H.-P., Innovative human-like dual robotic hand mechatronic design and its chess-playing experiment (2019) IEEE Access, 7 (1), pp. 7872-7888; (2010) Functional safety of electrical/electronic/programmable electronic safety-related systems - Part 1: General requirements, , IEC, IEC 61508-1; (2005) IEC 62061, , IEC, Safety of machinery Functional safety of safety-related electrical, electronic and programmable electronic control systems; Ikuta, K., Ishii, H., Nokata, M., Safety evaluation method of design and control for human-care robots (2003) The International Journal of Robotics Research, 22 (5), pp. 281-297; (2011) Robots and robotic devices - Safety requirements for industrial robots - Part 1: Robots, International Organization for Standardization, , ISO, ISO 10218-1; (2011) Robots and robotic devices - Safety requirements for industrial robots - Part 2: Robot systems and integration, International Organization for Standardization, , ISO, ISO 10218-2; (2010) Safety of machinery - General principles for design - Risk assessment and risk reduction, , ISO, ISO 12100; (2015) Safety of machinery - Safety-related parts of control systems - Part 1: General principles for design, , ISO, ISO 13849-1; Safety aspects - Guidelines for their inclusion in standards (2014) ISO/IEC Guide, 51. , ISO/IEC; (2016) Robots and robotic devices - Collaborative robots, International Organization for Standardization, , ISO/TS, ISO/TS 15066; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) Proceeding of IEEE International Conference on Robotics and Automation, pp. 500-505. , St. Louis, MO, USA; Krüger, J., Lien, T. K., Verl, A., Cooperation of human and machines in assembly lines (2009) CIRP Annals - Manufacturing Technology, 58 (2), pp. 628-646; Kulić, D., Croft, E., Pre-collision safety strategies for human-robot interaction (2007) Autonomous Robots, 22 (2), pp. 149-164; Lacevic, B., Rocco, P., Kinetostatic danger field - a novel safety assessment for human-robot interaction (2010) Proceeding of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2169-2174. , Taipei, Taiwan; Lasota, P. A., Fong, T., Shah, J. A., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5 (3), pp. 261-349; Lo, S.-Y., Cheng, C.-A., Huang, H.-P., Virtual impedance control for safe human-robot interaction (2016) Journal of Intelligent Robotic Systems, 82 (1), pp. 3-19; Moon, S., Rhim, S., Cho, Y.-J., Park, K.-H., Virk, G. S., Summary of recent standardization activities in the field of robotics (2013) Robotica, 31 (2), pp. 217-224; Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D. G., Carloni, R., Catalano, M., Wolf, S., Variable impedance actuators: A review (2013) Robotics and Autonomous Systems, 61 (12), pp. 1601-1614; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Wang, X. V., Kemény, Z., Váncza, J., Wang, L., Human-robot collaborative assembly in cyber-physical production: Classification framework and implementation (2017) CIRP Annals - Manufacturing Technology, 66 (1), pp. 5-8; Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y., Suita, K., Human-robot contact in the safeguarding space (1997) IEEE/ASME Transactions on Mechatronics, 2 (4), pp. 230-236","Huang, H.-P.; Department of Mechanical Engineering, No. 1, Sec. 4, Roosevelt Rd., Taiwan",,,"Chinese Mechanical Engineering Society",,,,,02579731,,,,"English","J Chin Soc Mech Eng Trans Chin Inst Eng Ser C",Article,"Final","",Scopus,2-s2.0-85090888734
"Filippini C., Perpetuini D., Cardone D., Chiarelli A.M., Merla A.","57205533040;57196390915;54788843200;55501906000;24169413000;","Thermal infrared imaging-based affective computing and its application to facilitate human robot interaction: A review",2020,"Applied Sciences (Switzerland)","10","8","2924","","",,18,"10.3390/APP10082924","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085551754&doi=10.3390%2fAPP10082924&partnerID=40&md5=60ff26a38ae1c9e534a5249309859208","Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Next2U s.r.l., Via dei Peligni 137, Pescara, 65127, Italy","Filippini, C., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy, Next2U s.r.l., Via dei Peligni 137, Pescara, 65127, Italy; Perpetuini, D., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Cardone, D., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Chiarelli, A.M., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Merla, A., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy, Next2U s.r.l., Via dei Peligni 137, Pescara, 65127, Italy","Over recent years, robots are increasingly being employed in several aspects of modern society. Among others, social robots have the potential to benefit education, healthcare, and tourism. To achieve this purpose, robots should be able to engag+e humans, recognize users' emotions, and to some extent properly react and ""behave"" in a natural interaction. Most robotics applications primarily use visual information for emotion recognition, which is often based on facial expressions. However, the display of emotional states through facial expression is inherently a voluntary controlled process that is typical of human-human interaction. In fact, humans have not yet learned to use this channel when communicating with a robotic technology. Hence, there is an urgent need to exploit emotion information channels not directly controlled by humans, such as those that can be ascribed to physiological modulations. Thermal infrared imaging-based affective computing has the potential to be the solution to such an issue. It is a validated technology that allows the non-obtrusive monitoring of physiological parameters and from which it might be possible to infer affective states. This review is aimed to outline the advantages and the current research challenges of thermal imaging-based affective computing for human-robot interaction. © 2020 by the authors.","Affective computing; Emotion recognition; Human-robot interaction; Social robots; Thermal IR imaging",,,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01_01120","This research was funded by PON FESR MIUR R&I 2014-2020-Asse II-ADAS, ARS01_00459 and PON MIUR SI-ROBOTICS, ARS01_01120.",,,,,,,,,,"Sung, J.-Y., Guo, L., Grinter, R.E., Christensen, H.I., ""My Roomba is Rambo"": Intimate home appliances (2007) Proceedings of the International Conference on Ubiquitous Computing, Innsbruck, Austria, pp. 145-162. , 16-19, September 2007; Springer: Berlin/Heidelberg, Germany; Dautenhahn, K., Methodology & themes of human-robot interaction: A growing research field (2007) Int. J. Adv. Robot. 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"Podpora M., Gardecki A., Beniak R., Klin B., Vicario J.L., Kawala-Sterniuk A.","23019670600;25651679200;23018119100;57203919049;24345524800;57215665281;","Human interaction smart subsystem—extending speech-based human-robot interaction systems with an implementation of external smart sensors",2020,"Sensors (Switzerland)","20","8","2376","","",,11,"10.3390/s20082376","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083844056&doi=10.3390%2fs20082376&partnerID=40&md5=327a24d05881af58f0507a0f8528aeb1","Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland; Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Wireless Information Networking Group, Escola d’Enginyeria—Universitat Autonoma de Barcelona, Bellaterra08193, Spain","Podpora, M., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland, Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Gardecki, A., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland, Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Beniak, R., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland, Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Klin, B., Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Vicario, J.L., Wireless Information Networking Group, Escola d’Enginyeria—Universitat Autonoma de Barcelona, Bellaterra08193, Spain; Kawala-Sterniuk, A., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland","This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot’s interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems—Bluetooth Human Identification Smart Subsystem—was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Cloud services; COVID-19; Humanoid robots; Pepper robot; Sensor networks; Smart infrastructure; Thermal imaging","Intelligent robots; Man machine systems; Mergers and acquisitions; Robotics; Cloud computing services; Information acquisitions; Interaction framework; Interaction parameters; Proposed architectures; Prototype implementations; Sensors and actuators; Smart infrastructures; Human robot interaction",,,,,"European Commission, EC; European Regional Development Fund, FEDER","Funding: Project Title: Industrial research and development works in Weegree Sp. z o.o.(LLC) Sp.K.(LLP) on the system of hybrid intelligence supporting smart specializations of the Opole region. Agreement number: RPOP.01.01.00-16-0072/16-00. Project co-financed by the European Union from the European Regional Development Fund within the framework of the Regional Operational Programme of the Opolskie region for the years 2014–2020: Priority Axis 01 Innovation in the Economy, Measure 1.1 Innovation in Enterprises.",,,,,,,,,,"Podpora, M., Gardecki, A., Kawala-Sterniuk, A., Humanoid Receptionist Connected to IoT Subsystems and Smart Infrastructure is Smarter than Expected (2019) Ifac-Papersonline, 52, pp. 347-352; Gardecki, A., Podpora, M., Kawala-Janik, A., Innovative Internet of Things-reinforced Human Recognition for Human-Machine Interaction Purposes (2018) Ifac-Papersonline, 51, pp. 138-142; Redondo, M.E.L., Comfortability Detection for Adaptive Human-Robot Interactions (2019) Proceedings of the 8Th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 35-39. , Cambridge, UK, 3–6 Setember; Belanche, D., Casalo, L.V., Flavian, C., Customer’s acceptance of humanoid robots in services: The moderating role of risk aversion (2020) Marketing and Smart Technologies, pp. 449-458. , Springer: Singapore; Cambridge Centre for Smart Infrastructure and Construction What is Smart Infrastructure?, , https://iotevent.eu/what-is-smart-infrastructure, accessed on 1 April 2020); Sahu, S.N., Moharana, M., Prusti, P.C., Chakrabarty, S., Khan, F., Pattanayak, S.K., Real-time data analytics in healthcare using the Internet of Things (2020) Real-Time Data Analytics for Large Scale Sensor Data, pp. 37-50. , Academic Press: Cambridge, MA, USA; Greco, A., Roberto, A., Saggese, A., Vento, M., Vigilante, V., Emotion analysis from faces for social robotics (2019) Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 358-364. , In, Bari, Italy, 6–9 October; Gobillot, N., Lesire, C., Doose, D., A design and analysis methodology for component-based real-time architectures of autonomous systems (2019) J. 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"Melinte D.O., Vladareanu L.","37020401600;55884695400;","Facial expressions recognition for human–robot interaction using deep convolutional neural networks with rectified adam optimizer",2020,"Sensors (Switzerland)","20","8","2393","","",,12,"10.3390/s20082393","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083819267&doi=10.3390%2fs20082393&partnerID=40&md5=59cf57f52130afcf605385c60d61542e","Department of Robotics and Mechatronics, Romanian Academy Institute of Solid Mechanics, Bucharest, 010141, Romania","Melinte, D.O., Department of Robotics and Mechatronics, Romanian Academy Institute of Solid Mechanics, Bucharest, 010141, Romania; Vladareanu, L., Department of Robotics and Mechatronics, Romanian Academy Institute of Solid Mechanics, Bucharest, 010141, Romania","The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one for the facial expression recognition (FER) in order to obtain real-time inference speed for the entire process. Two different models for FR are considered, one known to be very accurate, but has low inference speed (faster region-based convolutional neural network), and one that is not as accurate but has high inference speed (single shot detector convolutional neural network). For emotion recognition transfer learning and fine-tuning of three CNN models (VGG, Inception V3 and ResNet) has been used. The overall results show that single shot detector convolutional neural network (SSD CNN) and faster region-based convolutional neural network (Faster R-CNN) models for face detection share almost the same accuracy: 97.8% for Faster R-CNN on PASCAL visual object classes (PASCAL VOCs) evaluation metrics and 97.42% for SSD Inception. In terms of FER, ResNet obtained the highest training accuracy (90.14%), while the visual geometry group (VGG) network had 87% accuracy and Inception V3 reached 81%. The results show improvements over 10% when using two serialized CNN, instead of using only the FER CNN, while the recent optimization model, called rectified adaptive moment optimization (RAdam), lead to a better generalization and accuracy improvement of 3%-4% on each emotion recognition CNN. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Advanced intelligent control; Computer vision; Convolutional neural networks; Deep learning; Face recognition; Facial emotion recognition; NAO robot","Convolution; Deep neural networks; Face recognition; Human robot interaction; Speech recognition; Transfer learning; Accuracy Improvement; Emotion recognition; Evaluation metrics; Facial expression recognition; Facial expressions recognition; Optimization modeling; Real-time inference; Robot interactions; Convolutional neural networks; algorithm; facial expression; facial recognition; female; human; image processing; physiology; robotics; Algorithms; Deep Learning; Facial Expression; Facial Recognition; Female; Humans; Image Processing, Computer-Assisted; Neural Networks, Computer; Robotics",,,,,"H2020-MSCA-RISE-2016-73487, PN-III-P1-1.2-PCCDI2017-0637/33PCCDI/ 01.03.2018; 22 PCCDI /2018, KY201501009, PN-III-P1-1.2-PCCDI-2017-0086; National Institutes of Health, NIH: R01AG002017; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, UEFISCDI; Yanshan University, YSU","The paper was funded by: 1. UEFISCDI Multi-MonD2 Project, Multi-Agent Intelligent Systems Platform for Water Quality Monitoring on the Romanian Danube and Danube Delta, PN-III-P1-1.2-PCCDI2017-0637/33PCCDI/ 01.03.2018; 2. Romanian Ministry of Research and In-novation, CCCDI?UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0086/contract no. 22 PCCDI /2018, within PNCDI III; 3. Yanshan University: ?Joint Laboratory of Intelligent Rehabilitation Robot? project, KY201501009, Collaborative research agreement between Yanshan University, China and Romanian Academy by IMSAR, RO; 4. The European Commission Marie Sk?odowska-Curie SMOOTH project, Smart Robots for Fire-Fighting, H2020-MSCA-RISE-2016-73487. This work was supported: 1. UEFISCDI Multi-MonD2 Project, Multi-Agent Intelligent Systems Platform for Water Quality Monitoring on the Romanian Danube and Danube Delta, PN-III-P1-1.2-PCCDI2017-0637/ 33PCCDI/01.03.2018; 2. Romanian Ministry of Research and In-novation, CCCDI?UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0086/contract no. 22 PCCDI /2018, within PNCDI III; 3. Yanshan University: ?Joint Laboratory of Intelligent Rehabilitation Robot? project, KY201501009, Collaborative research agreement between Yanshan University, China and Romanian Academy by IMSAR, RO; 4. The European Commission Marie Sk?odowska-Curie SMOOTH project, Smart Robots for Fire-Fighting, H2020-MSCA-RISE-2016-73487. The authors gratefully acknowledge the support of the Robotics and Mechatronics Department, Institute of Solid Mechanics of the Romanian Academy.",,,,,,,,,,"Lopez-Rincon, A., Emotion recognition using facial expressions in children using the NAO Robot Proceedings of the International Conference on Electronics, Communications and Computers (CONIELECOMP, pp. 146-153. , ), Cholula, Mexico, 27 February–1 March 2019; IEEE:, Piscataway, NJ, USA; Faria, D.R., Vieira, M., Faria, F.C., (2017) Towards the Development of Affective Facial Expression Recognition for Human-Robot Interaction, pp. 300-304. , In Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, Island of Rhodes, Greece, 21–23 June; Zhang, Z., Luo, P., Loy, C.C., Tang, X., From facial expression recognition to interpersonal relation prediction (2018) Int. J. Comput. 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"Leichtmann B., Nitsch V.","57191978459;25929508000;","How much distance do humans keep toward robots? Literature review, meta-analysis, and theoretical considerations on personal space in human-robot interaction",2020,"Journal of Environmental Psychology","68",,"101386","","",,11,"10.1016/j.jenvp.2019.101386","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079555414&doi=10.1016%2fj.jenvp.2019.101386&partnerID=40&md5=7001fd5e16a7d43f7cb9e0b9c28ed45a","Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, 52062, Germany","Leichtmann, B., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, 52062, Germany; Nitsch, V., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, 52062, Germany","With the development of mobile robots, research on interpersonal distances grew by a new body of research introducing new opportunities to environmental psychology. In this review, an overview of the literature on interpersonal distances focusing on human-robot interaction is given. Findings on the most prominent factors are summarized and discussed with respect to findings from distancing in human-human interaction and theoretical considerations. Additionally, meta-analyses were performed to gain new insights based on data of k = 27 studies including a total of N = 1299 participants. However, results did not show any meaningful effects for gender, robot appearance or whether one is active or passive in the approach. Most studies lack a theoretical foundation and have methodological weaknesses, including low statistical power or questionable research practices, leading to a high false-positive rate or overestimated effect sizes. Solutions such as pre-registration, higher sample sizes, transparency and a more careful theoretical underpinning based on models of human spacing behavior are discussed. Psychologists are encouraged to support research on human-robot interaction in this respect. © 2020 Elsevier Ltd",,,,,,,"Bayerische Forschungsstiftung","This work was supported by the FORobotics research project in support of the Bavarian Research Foundation .",,,,,,,,,,"Admoni, H., Shah, J., Srinivasa, S., Editorial: Special issue on human-robot interaction (2017) The International Journal of Robotics Research, 36 (5-7), pp. 459-460; Aiello, J.R., Human spatial behavior (1987) Handbook of environmental psychology, pp. 389-504. , D. Stokols I. Altman Wiley New York; Appel, J., von der Pütten, A., Krämer, N.C., Gratch, J., Does humanity matter? 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"Hamed A., Masouleh M.T., Kalhor A.","57208001686;16550342100;24483441300;","Design & characterization of a bio-inspired 3-DOF Tactile/Force sensor and implementation on a 3-DOF decoupled parallel mechanism for human-robot interaction purposes",2020,"Mechatronics","66",,"102325","","",,6,"10.1016/j.mechatronics.2020.102325","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078134197&doi=10.1016%2fj.mechatronics.2020.102325&partnerID=40&md5=bc8b09d28ac254c7a194c0f07e53661a","School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran","Hamed, A., School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran; Masouleh, M.T., School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran; Kalhor, A., School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran","This paper presents the design and implementation of a Tactile/Force sensor which has been used on a 3-DOF decoupled parallel mechanism for Human-Robot Interaction purposes.The sensor, called HexaTactile, is a soft tactile sensor array based on six MEMS barometers, where each of them is covered by a silicone layer in the form of an incomplete pyramid. HexaTactile consists of six soft and highly sensitive tactile modules which are placed on six sides of a cube to allow simultaneous measurement of the force in the positive and negative directions along the x, y and z axes. Some of the advantages of this sensor can be regarded as its high precision, excellent linearity (coefficient of determination r2=0.99), low cost and low noise. The accuracy of the sensor is 0.01 N, within a range of 4 N and therefore HexaTactile can be suitably attached to a robot end-effector for human-robot interaction applications. Then, using the proposed force sensor some control scenarios, including fixed admittance control and active admittance control are applied for human-robot interaction purposes. 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No. 02CH37292), Vol. 1, pp. 644-650. , IEEE; Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC’07), pp. 446-451. , IEEE; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3903-3908. , IEEE; Campeau-Lecours, A., Otis, M., Belzile, P.-L., Gosselin, C., A time-domain vibration observer and controller for physical human-robot interaction (2016) Mechatronics, 36, pp. 45-53; Sharifzadeh, M., Masouleh, M.T., Kalhor, A., Design, construction & calibration of a novel human-robot interaction 3-dof force sensor (2015) 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), pp. 182-187. , IEEE; Sharifzadeh, M., Masouleh, M.T., Kalhor, A., On human–robot interaction of a 3-dof decoupled parallel mechanism based on the design and construction of a novel and low-cost 3-dof force sensor (2017) Meccanica, 52 (10), pp. 2471-2489; Gosselin, C., Kong, X., Cartesian parallel manipulators (2004) uS Patent 6,729,202 May 4; Hamed, A., Masouleh, M.T., Kalhor, A., Design & characterization of a bio-inspired 3-dof tactile/force sensor for human-robot interaction purposes (2018) 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM), pp. 488-493. , IEEE; Davison, B., , p. 53. , Techniques for robust touch sensing design. In: AN1334 Microchip Technology Inc","Masouleh, M.T.; School of Electrical and Computer Engineering, Iran; email: m.t.masouleh@ut.ac.ir",,,"Elsevier Ltd",,,,,09574158,,MECHE,,"English","Mechatronics",Article,"Final","",Scopus,2-s2.0-85078134197
"Huo W., Alouane M.A., Amirat Y., Mohammed S.","36925250000;57195559399;7003336093;7102894395;","Force Control of SEA-Based Exoskeletons for Multimode Human-Robot Interactions",2020,"IEEE Transactions on Robotics","36","2","8935391","570","577",,6,"10.1109/TRO.2019.2956341","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076853428&doi=10.1109%2fTRO.2019.2956341&partnerID=40&md5=65133081a479cda37706f1e68328979a","Department of Mechanical Engineering, Imperial College London, London, United Kingdom; LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France","Huo, W., Department of Mechanical Engineering, Imperial College London, London, United Kingdom; Alouane, M.A., LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France; Amirat, Y., LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France; Mohammed, S., LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France","In this article, a proxy-based force control method is proposed for three important human-robot interaction modes: zero-impedance mode, force assistive mode, and large force mode. A two-mass dynamic-model-based nonlinear disturbance observer is used to meet the zero-impedance output and accurate force tracking requirements with respect to disturbances from the wearer and environment. Additionally, significant force compliance can be achieved to guarantee the wearer's safety when the interaction torque is large. The proposed method is evaluated via experiments by comparison to the conventional proportional-integral-derivative and proxy-based sliding mode control methods. The results indicate that the proposed approach achieves better force tracking accuracy, robustness, and force compliance in three-mode human-robot interactions. © 2019 IEEE.","Exoskeletons; force control; nonlinear disturbance observer (NDO); series elastic actuator (SEA)","Exoskeleton (Robotics); Force control; Sliding mode control; Control methods; Force tracking; Nonlinear disturbance observer; Proxy-based sliding mode controls; Robot interactions; Series elastic actuators; Two-mass dynamic model; Zero impedance; Human robot interaction",,,,,,,,,,,,,,,,"Huo, W., Mohammed, S., Moreno, J.C., Amirat, Y., Lower limb wearable robots for assistance and rehabilitation: A state of the art (2016) IEEE Syst. J., 10 (3), pp. 1068-1081. , Sep; Tucker, M.R., Control strategies for active lower extremity prosthetics and orthotics: A review (2015) J. Neuroeng. Rehabil., 12 (1), pp. 1-29; Li, X., Pan, Y., Chen, G., Yu, H., Multi-modal control scheme for rehabilitation robotic exoskeletons (2017) Int. J. Robot. Res., 36 (5-7), pp. 759-777; Oh, S., Kong, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 71-80. , Feb; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100. , Oct; Kong, K., Bae, J., Tomizuka, M., A compact rotary series elastic actuator for human assistive systems (2012) IEEE/ASME Trans. Mechatronics, 17 (2), pp. 288-297. , Apr; Calanca, A., Fiorini, P., A rationale for acceleration feedback in force control of series elastic actuators (2018) IEEE Trans. Robot., 34 (1), pp. 48-61. , Feb; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 399-406; Rouse, E.J., Mooney, L.M., Martinez-Villalpando, E.C., Herr, H.M., Clutchable series-elastic actuator: Design of a robotic knee prosthesis for minimum energy consumption (2013) Proc. IEEE Int. Conf. Rehabil. Robot., pp. 1-6; Kaya, K.D., Çetin, L., Adaptive state feedback controller design for a rotary series elastic actuator (2017) Trans. Inst. Meas. Control, 39 (1), pp. 61-74; Losey, D.P., Erwin, A., McDonald, C.G., Sergi, F., O'Malley, M.K., A time-domain approach to control of series elastic actuators: Adaptive torque and passivity-based impedance control (2016) IEEE/ASME Trans. Mechatronics, 21 (4), pp. 2085-2096. , Aug; Farris, R.J., Quintero, H.A., Goldfarb, M., Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals (2011) IEEE Trans. Neural Syst. Rehabil. Eng., 19 (6), pp. 652-659. , Dec; Aoustin, Y., Formalskii, A., Walking of biped with passive exoskeleton: Evaluation of energy consumption (2018) Multibody Syst. Dyn., 43 (1), pp. 71-96; Huo, W., Mohammed, S., Amirat, Y., Kong, K., Fast gait mode detection and assistive torque control of an exoskeletal robotic orthosis for walking assistance (2018) IEEE Trans. Robot., 34 (4), pp. 1035-1052. , Aug; Ott, C., Albu-Schaffer, A., Kugi, A., Hirzinger, G., On the passivitybased impedance control of flexible joint robots (2008) IEEE Trans. Robot., 24 (2), pp. 416-429. , Apr; Oh, S., Woo, H., Kong, K., Frequency-shaped impedance control for safe human-robot interaction in reference tracking application (2014) IEEE/ASME Trans. Mechatronics, 19 (6), pp. 1907-1916. , Dec; Ju, M.-S., Lin, C.-C., Lin, D.-H., Hwang, I.-S., Chen, S.-M., A rehabilitation robot with force-position hybrid fuzzy controller: Hybrid fuzzy control of rehabilitation robot (2005) IEEE Trans. Neural Syst. Rehabil. Eng., 13 (3), pp. 349-358. , Sep; Simon, A.M., Gillespie, R.B., Ferris, D.P., Symmetry-based resistance as a novel means of lower limb rehabilitation (2007) J. Biomech., 40 (6), pp. 1286-1292; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Trans. Robot., 33 (1), pp. 169-182. , Feb; Kikuuwe, R., Yasukouchi, S., Fujimoto, H., Yamamoto, M., Proxybased sliding mode control: A safer extension of PID position control (2010) IEEE Trans. Robot., 26 (4), pp. 670-683. , Aug; Van Damme, M., Vanderborght, B., Verrelst, B., Van Ham, R., Daerden, F., Lefeber, D., Proxy-based sliding mode control of a planar pneumatic manipulator (2009) Int. J. Robot. Res., 28 (2), pp. 266-284; Kashiri, N., Lee, J., Tsagarakis, N.G., Van Damme, M., Vanderborght, B., Caldwell, D.G., Proxy-based position control of manipulators with passive compliant actuators: Stability analysis and experiments (2016) Robot. Auton. Syst., 75, pp. 398-408; Yeh, T.-J., Wu, F.-K., Modeling and robust control ofworm-gear driven systems (2009) Simul. Model. Pract. Theory, 17 (5), pp. 767-777; Huo, W., Mohammed, S., Amirat, Y., Impedance reduction control of a knee joint human-exoskeleton system (2019) IEEE Trans. Control Syst. Technol., 27 (6), pp. 2541-2556. , Nov; Chen, W.-H., Disturbance observer based control for nonlinear systems (2004) IEEE/ASME Trans. Mechatronics, 9 (4), pp. 706-710. , Dec; Mohammadi, A., Tavakoli, M., Marquez, H.J., Hashemzadeh, F., Nonlinear disturbance observer design for robotic manipulators (2013) Control Eng. Pract., 21 (3), pp. 253-267; Mallat, R., Human-exoskeleton system dynamics identification using affordable sensors (2018) Proc. IEEE Int. Conf. Robot. Autom., pp. 6759-6765","Huo, W.; Department of Mechanical Engineering, United Kingdom; email: w.huo@imperial.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85076853428
"Petrick R.P.A., Foster M.E.","36447706600;23091020100;","Knowledge engineering and planning for social human-robot interaction: A case study",2020,"Knowledge Engineering Tools and Techniques for AI Planning",,,,"261","277",,2,"10.1007/978-3-030-38561-3_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097144353&doi=10.1007%2f978-3-030-38561-3_14&partnerID=40&md5=b349e967e986b87ffe48000956289015","Department of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom; School of Computing Science, University of Glasgow, Glasgow, United Kingdom","Petrick, R.P.A., Department of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom; Foster, M.E., School of Computing Science, University of Glasgow, Glasgow, United Kingdom","The core task of automated planning is goal-directed action selection; this task is not unique to the planning community, but is also relevant to numerous other research areas within AI. One such area is interactive systems, where a fundamental component called the interaction manager selects actions in the context of conversing with humans using natural language. Although this has obvious parallels to automated planning, using a planner to address the interaction management task relies on appropriate engineering of the underlying planning domain and planning problem to capture the necessary dynamics of the world, the agents involved, their actions, and their knowledge. In this chapter, we describe work on using domain-independent automated planning for action section in social human-robot interaction, focusing on work from the JAMES (Joint Action for Multimodal Embodied Social Systems) robot bartender project. © Springer Nature Switzerland AG 2020.",,,,,,,,,,,,,,,,,,"(2020) Alexa Skills Kit Official Site, , https://developer.amazon.com/en-GB/alexa/alexa-skills-kit, accessed: 2020-02-09; Appelt, D., (1985) Planning English Sentences, , Cambridge University Press; Asher, N., Lascarides, A., (2003), Logics of Conversation. Cambridge University Press; Benotti, L., Accommodation through tacit sensing (2008) Proceedings of LONDIAL 2008, pp. 75-82. , London, United Kingdom; Brenner, M., Kruijff-Korbayova, I., A continual multiagent planning approach to situated dialogue (2008) Proceedings of LONDIAL 2008, pp. 67-74; Bui, T.H., (2006) Multimodal dialogue management - state of the art. Tech. Rep. 06-01, , University of Twente (UT), Enschede, The Netherlands; Cohen, P., Levesque, H., Rational interaction as the basis for communication (1990) Intentions in Communication, pp. 221-255. , MIT Press, Cambridge, MA; Fikes, R.E., Nilsson, N.J., STRIPS: A new approach to the application of theorem proving to problem solving (1971) Artificial Intelligence, 2, pp. 189-208; Foster, M.E., Petrick, R.P.A., Separating representation, reasoning, and implementation for interaction management. Lessons from automated planning (2017) Dialogues with Social Robots: Enablements, pp. 93-107. , https://doi.org/10.1007/978-981-10-2585-3_7, Analyses, and Evaluation, Springer Singapore, Singapore; Foster, M.E., Gaschler, A., Giuliani, M., Isard, A., Pateraki, M., Petrick, R.P.A., Two people walk into a bar: Dynamic multi-party social interaction with a robot agent (2012) Proceedings of ICMI 2012, pp. 3-10. , https://doi.org/10.1145/2388676.2388680; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9 (5), pp. 659-674. , https://doi.org/10.1007/s12369-017-0414-y; Fox, M., Long, D., Magazzeni, D., Explainable planning (2017) Proceedings of the IJCAI Workshop on Explainable AI; Giuliani, M., Petrick, R.P.A., Foster, M.E., Gaschler, A., Isard, A., Pateraki, M., Sigalas, M., Comparing task-based and socially intelligent behaviour in a robot bartender (2013) Proceedings of ICMI 2013, , https://doi.org/10.1145/2522848.2522869; (2020) Dialogflow, , https://dialogflow.com/, accessed: 2020-02-09; Hovy, E., (1988) Generating natural language under pragmatic constraints, , Lawrence Erlbaum Associates, Hillsdale, NJ, USA; (2019) ICAPS Competitions, , http://www.icaps-conference.org/index.php/Main/, Competitions, accessed: 2019-08-01; Isard, A., Matheson, C., Rhetorical structure for natural language generation in dialogue (2012) Proceedings of SemDial-2012 (SeineDial), pp. 161-162; Janarthanam, S., Hastie, H., Deshmukh, A., Aylett, R., Foster, M.E., A reusable interaction management module: Use case for empathic robotic tutoring (2015) Proceedings of goDIAL 2015, , Gothenburg, Sweden; Johnston, M., Bangalore, S., Vasireddy, G., Stent, A., Ehlen, P., Walker, M., Whittaker, S., Maloor, P., MATCH: An architecture for multimodal dialogue systems (2002) Proceedings of ACL 2002, pp. 376-383. , Philadelphia, Pennsylvania, USA; Jokinen, K., McTear, M., Spoken dialogue systems (2009) Synthesis Lectures on Human Language Technologies, 2 (1), pp. 1-151; Koller, A., Stone, M., Sentence generation as planning (2007) Proceedings of ACL 2007, pp. 336-343. , Prague, Czech Republic; Larsson, S., Traum, D.R., Information state and dialogue management in the TRINDI dialogue move engine toolkit (2000) Natural Language Engineering, 6 (3-4), pp. 323-340. , https://doi.org/10.1017/S1351324900002539; Lison, P., A hybrid approach to dialogue management based on probabilistic rules (2015) Computer Speech & Language, , https://doi.org/10.1016/j.csl.2015.01.001; Loth, S., Huth, K., de Ruiter, J.P., Automatic detection of service initiation signals used in bars (2013) Frontiers in Psychology, 4 (557). , https://doi.org/10.3389/fpsyg.2013.00557; Mann, W.C., Thompson, S.A., Rhetorical structure theory: Toward a functional theory of text organization (1988) Text, 8 (3), pp. 243-281; McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D., (1998) PDDL - The Planning Domain Definition Language, , (Version 1.2). Technical Report CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control; McTear, M., Callejas, Z., Griol, D., (2016) The Conversational Interface, , https://doi.org/10.1007/978-3-319-32967-3, Springer International Publishing; Olaso, J.M., Milhorat, P., Himmelsbach, J., Boudy, J., Chollet, G., Schlögl, S., Torres, M.I.T., A multi-lingual evaluation of the vAssist spoken dialog system: Comparing Disco and RavenClaw (2016) Proceedings of IWSDS 2016, , Saariselkä, Finland; Papaioannou, I., Dondrup, C., Lemon, O., Human-robot interaction requires more than slot filling - multi-threaded dialogue for collaborative tasks and social conversation (2018) Proceedings of the FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, pp. 61-64; Pateraki, M., Sigalas, M., Chliveros, G., Trahanias, P., Visual human-robot communication in social settings (2013) Proceedings of ICRA Workshop on Semantics, Identification and Control of Robot-Human-Environment Interaction; Peltason, J., Wrede, B., The curious robot as a case-study for comparing dialog systems (2011) AI Magazine, 32 (4), pp. 85-99. , https://doi.org/10.1609/aimag.v32i4.2382; Perrault, C.R., Allen, J.F., A plan-based analysis of indirect speech acts (1980) American Journal of Computational Linguistics, 6 (3-4), pp. 167-182; Petrick, R.P.A., Bacchus, F., A knowledge-based approach to planning with incomplete information and sensing (2002) Proceedings of AIPS 2002, pp. 212-221; Petrick, R.P.A., Bacchus, F., Extending the knowledge-based approach to planning with incomplete information and sensing (2004) Proceedings of ICAPS 2004, pp. 2-11; Petrick, R.P.A., Foster, M.E., Planning for social interaction in a robot bartender domain (2013) Proceedings of ICAPS 2013, , Rome, Italy; Rintanen, J., Complexity of planning with partial observability (2004) Proceedings of ICAPS 2004, pp. 345-354; Wang, Z., Lemon, O., (2013) A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information, , Proceedings of SIGDIAL 2013; White, M., Efficient realization of coordinate structures in Combinatory Categorial Grammar (2006) Research on Language and Computation, 4 (1), pp. 39-75; Young, R.M., Moore, J.D., (1994) DPOCL: A principled approach to discourse planning, pp. 13-20. , Proceedings of INLG 2004, Kennebunkport, Maine, USA","Petrick, R.P.A.; Department of Computer Science, United Kingdom; email: R.Petrick@hw.ac.uk",,,"Springer International Publishing",,,,,,9783030385613; 9783030385606,,,"English","Knowl. Eng. Tools and Techniques for AI Plan.",Book Chapter,"Final","",Scopus,2-s2.0-85097144353
"Rajavenkatanarayanan A., Kanal V., Kyrarini M., Makedon F.","57192370020;57192913571;56786567700;57207521629;","Cognitive performance assessment based on everyday activities for human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"398","400",,2,"10.1145/3371382.3378384","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083283410&doi=10.1145%2f3371382.3378384&partnerID=40&md5=52bce8cfeba8508e9d7b5c058a1208e0","Department of Computer Science, University of Texas at Arlington, United States","Rajavenkatanarayanan, A., Department of Computer Science, University of Texas at Arlington, United States; Kanal, V., Department of Computer Science, University of Texas at Arlington, United States; Kyrarini, M., Department of Computer Science, University of Texas at Arlington, United States; Makedon, F., Department of Computer Science, University of Texas at Arlington, United States","Human-Robot Interaction often involves a robot assisting or providing feedback to a human partner's performance or cooperating to complete a task. In such an interaction scenario, the robotic system requires to perceive the human teammate's cognitive state that might affect task performance. In this pilot study, the focus is on developing a framework that assesses the human's cognitive performance for human-robot synergetic task, such as an assembly task. Specifically, we explore the correlation between a person's quality of sleep and performance metric through a standard task for cognitive assessment, the N-back task. To validate our hypothesis, we conducted a study with 25 participants, and our results indicate that there is a moderate correlation between some stages of sleep cycle and performance. Additionally, we present a possible Human-Robot Interaction setup that could benefit from our results. © 2020 ACM.","Cognitive assessment; Human activity; Human-robot interaction; Wearable sensors","Agricultural robots; Man machine systems; Assembly tasks; Cognitive assessments; Cognitive performance; Cognitive state; Performance metrices; Pilot studies; Robotic systems; Task performance; Human robot interaction",,,,,"National Science Foundation, NSF; Directorate for Engineering, ENG: 1719031","This work was supported in part by National Science Foundation under award number 1719031.",,,,,,,,,,"Baxter, P., Lightbody, P., Hanheide, M., Robots providing cognitive assistance in shared workspaces (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 57-58; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954. , 2018; Busch, B., Toussaint, M., Lopes, M., Planning ergonomic sequences of actions in human-robot interaction (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1916-1923; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robotics and Computer-Integrated Manufacturing, 40, pp. 1-13. , 2016; Collins, S., Sabanovi, S., Fraune, M., Randall, N., Eldridge, L., Piatt, J.A., Bennett, C.C., Nagata, S., Sensing companions: Potential clinical uses of robot sensor data for home care of older adults with depression (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 89-90; D'Agostino, R.B., Belanger, A., D'Agostino, R.B., Jr., A suggestion for using powerful and informative tests of normality (1990) The American Statistician, 44 (4), pp. 316-321. , 1990; Georgilas, I., Dagnino, G., Dogramadzi, S., Safe human-robot interaction in medical robotics: A case study on robotic fracture surgery system (2017) Journal of Medical Robotics Research, 2 (3), p. 1740008. , 2017; Ferdinand Goldau, F., Kumar Shastha, T., Kyrarini, M., Gräser, A., Autonomous multi-sensory robotic assistant for a drinking task (2019) 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, pp. 210-216; Honn, K.A., Hinson, J.M., Whitney, P., Van Dongen, H., Cognitive flexibility: A distinct element of performance impairment due to sleep deprivation (2019) Accident Analysis & Prevention, 126, pp. 191-197. , 2019; Kane, M.J., Conway, A.R.A., Miura, T.K., Colflesh, G.J.H., Working memory, attention control, and the N-back task: A question of construct validity (2007) Journal of Experimental Psychology: Learning, Memory, and Cognition, 33 (3), p. 615. , 2007; Kosecki, D., (2018) REM, Light, Deep: How Much of Each Stage of Sleep Are You Getting, , https://blog.fitbit.com/sleepstages-explained/, Retrieved December 10 2019; Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L., Sierra, B., Human-robot collaboration in industrial applications: Safety, interaction and trust (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1729881417716010. , 2017; Shafti, A., Ataka, A., Urbistondo, L.B., Shiva, A., Wurdemann, H.A., Althoefer, K., Real-time robot-assisted ergonomics (2019) 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 1975-1981; Smith, M.E., McEvoy, L.K., Gevins, A., The impact of moderate sleep loss on neurophysiologic signals duringworking-memory task performance (2002) Sleep, 25 (7), pp. 56-66. , 2002; Tsiakas, K., Abellanoza, C., Abujelala, M., Papakostas, M., Makada, T., Makedon, F., Towards designing a socially assistive robot for adaptive and personalized cognitive training (2017) Proceedings of the Robots, 4",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083283410
"Krummheuer A.L., Rehm M., Rodil K.","7801324145;10039896700;52063699200;","Triadic human-robot interaction. distributed agency and memory in robot assisted interactions",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"317","319",,1,"10.1145/3371382.3378269","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083282765&doi=10.1145%2f3371382.3378269&partnerID=40&md5=f44bc122bd5c1ccd9dabc932826c5577","Faculty of Humanities, Aalborg University, Denmark; Technical Faculty of IT and Design, Aalborg University, Denmark","Krummheuer, A.L., Faculty of Humanities, Aalborg University, Denmark; Rehm, M., Technical Faculty of IT and Design, Aalborg University, Denmark; Rodil, K., Technical Faculty of IT and Design, Aalborg University, Denmark","We argue that the field of human-robot interaction needs a distributed and socially situated understanding of reminding and scheduling practices in the design of robots to meet the needs of people with cognitive disabilities. The results are based on an interaction analysis of video recorded workshop interactions during a co-creation process in which the participants tested a reminder-robot prototype that was designed for and with people with acquired brain injury. © 2020 ACM.","Brain injury; Ethnomethodology; Memory aid; Multimodal interaction analysis; Reminder robot","Agricultural robots; Machine design; Man machine systems; Acquired brain injuries; Co-creation; Cognitive disability; Distributed agencies; Interaction analysis; Robot prototypes; Human robot interaction",,,,,,,,,,,,,,,,"Amrhein, A., Cyra, K., Pitsch, K., Processes of reminding and requesting in supporting people with special needs: Human practices as basis for modeling a virtual assistant (2016) Proceedings of the 1st Workshop on Ethics in the Design of Intelligent Agents, 30, pp. 14-19. , http://ceur-ws.org/Vol-1668/paper3.pdf, The Hague, The Netherlands, August 2016; Barnes, S., Planning' talk and traumatic brain injury: An exploratory application of conversation analysis (2012) Journal of Interactional Research in Communication Disorders, 3 (2), pp. 115-140. , https://doi.org/, 2012; Brockmeier, J., From memory as archive to remembering as conversation (2017) Handbook of Culture and Memory, pp. 41-64. , BradyWagoner (Ed.). Oxford University Press, Oxford; Crabtree, A., Rouncefield, M., Tolmie, P., (2012) Doing Design Ethnography, , Springer, London; Frennert, S., Ostlund, B., Review: Seven matters of concern of social robots and older people (2014) International Journal of Social Robotics, 6 (2), pp. 299-310. , https://doi.org/10.1007/s12369-013-0225-8, April 2014; Gross, H., Schroeter, C., Mueller, S., Volkhardt, M., Einhorn, E., Bley, A., Martin, C., Merten, M., Progress in developing a socially assistive mobile home robot companion for the elderly with mild cognitive impairment (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2430-2437. , https://doi.org/10.1109/IROS.2011.6094770; Heath, C., Hindmarsh, J., Luff, P., (2010) Video in Qualitative Research : Analysing Social Interaction in Everyday Life, , Sage, London; Hughes, J., King, V., Rodden, T., Andersen, H., Moving out from the Control Room: Ethnography in System Design (1994) Proceedings of the 1994 ACM Conference on Computer Supported CooperativeWork (CSCW '94), pp. 429-439. , https://doi.org/10.1145/192844.193065, ACM, New York, NY, USA; Hydén, L., Cutting brussels sprouts: Collaboration involving persons with dementia (2014) Journal of Aging Studies, 29, pp. 115-123. , https://doi.org/10.1016/j.jaging.2014.02.004, April 2014; Jayawardena, C., Kuo, I.H., Unger, U., Igic, A., Wong, R., Watson, C.I., Stafford, R.Q., MacDonald, B.A., Deployment of a service robot to help older people (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5990-5995. , https://doi.org/10.1109/IROS.2010.5649910; Kopp, S., Brandt, M., Buschmeier, H., Cyra, K., Freigang, F., Krämer, N.C., Kummert, F., Yaghoubzadeh, R., Conversational assistants for elderly users-The importance of socially cooperative dialogue (2018) Proceedings of the AAMAS Workshop on Intelligent Conversation Agents in Home and Geriatric Care Applications Co-located with the Federated AI Meeting (FAIM 2018), 15, pp. 10-17. , http://ceur-ws.org/Vol-2338/paper1.pdf, Stockholm, Sweden, July2018; Krummheuer, A., Conversation analysis, video recordings, and humancomputer interchanges (2009) Video Interaction Analysis. Methods and Methodology, pp. 59-83. , Ulrike Tikvah Kissmann (Ed.). Peter Lang, Frankfurt am Main; Lina Krummheuer, A., Rehm, M., Lund, M.K.L., Nielsen, K.N., The chance for sociability-An institutional corridor as a place for learning for adolescents with brain injury (2019) ID&A Interaction Design & Architecture(s), 39, pp. 78-95. , 2019; Majlesi, A.R., Ekström, A., Baking together- The coordination of actions in activities involving people with dementia (2016) Journal of Aging Studies, 38, pp. 37-46. , https://doi.org/10.1016/j.jaging.2016.04.004, Aug. 2016; Matari, M., On relevance: Balancing theory and practice in hri (2018) ACM Trans. Hum.-Robot Interact, 7 (1), p. 2. , https://doi.org/10.1145/3209770, May 2018; Mataric, M., Tapus, A., Winstein, C., Eriksson, J., Advanced technologies in rehabilitation (2009) IOS Press, Chapter Socially Assistive Robotics for Stroke and Mild TBI Rehabilitation, pp. 249-262; Montemerlo, M., Pineau, J., Roy, N., Thrun, S., Verma, V., Experiences with a mobile robotic guide for the elderly (2002) Eighteenth National Conference on Artificial Intelligence. American Association for Artificial Intelligence, pp. 587-592; Rehm, M., Lina Krummheuer, A., Rodil, K., Developing a new brand of culturally-aware personal robots based on local cultural practices in the danish health care system (2018) Proceedings of the International Conference on Intelligent Robots and Systems (IROS). IEEE, United States, , https://doi.org/10.1109/IROS.2018.8594478; Rehm, M., Lina Krummheuer, A., Rodil, K., Nguyen, M., Thorlacius, B., From social practices to social robots: User-driven robot development in elder care (2016) Social Robotics. Springer, Germany, pp. 692-701. , https://doi.org/10.1007/978-3-319-47437-368; Rodil, K., Rehm, M., Lina Krummheuer, A., Co-designing social robots with cognitively impaired citizens (2018) Proceedings of the 10th Nordic Conference on Human-Computer Interaction (NordiCHI '18), pp. 686-690. , https://doi.org/10.1145/3240167.3240253, ACM, New York, NY, USA; Sander, A.M., Van Veldhoven, L.M., Rehabilitation of memory problems associated with traumatic brain injury (2014) Handbook on the Neuropsychology of Traumatic Brain Injury, pp. 173-190. , https://doi.org/10.1007/978-1-4939-0784-79, Mark Sherer and Angelle M. Sander (Eds.). Springer New York, New York, NY; Suchman, L., (2007) Human and Machine Reconfigurations : Plans and Situated Actions, , 2nd ed ). Cambridge University Press, Cambridge; Tekin, B.S., Reeves, S., Ways of spectating: Unravelling spectator participation in kinect play (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), pp. 1558-1570. , https://doi.org/10.1145/3025453.3025813, ACM, New York, NY, USA; Van Der Machiel, L.H.F., Reinkensmeyer, D.J., Guglielmelli, E., Rehabilitation and health care robotics (2016) Springer Handbook of Robotics, pp. 1685-1728. , https://doi.org/10.1007/978-3-319-32552-164, Bruno Siciliano and Oussama Khatib (Eds.). Springer International Publishing, Cham; Wilson, B.A., Memory rehabilitation (2009) Integrating Theory and Practice. The Guilford Press",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083282765
"Sarigul B., Saltik I., Hokelek B., Urgen B.A.","57216346581;57216334524;57216333548;56295243200;","Does the appearance of an agent affect how we perceive his/her voice? audio-visual predictive processes in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"430","432",,3,"10.1145/3371382.3378302","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083273453&doi=10.1145%2f3371382.3378302&partnerID=40&md5=ca5b1bbc5ad713431c45bedc8c52c162","Interdisciplinary Social Psychiatry Program, Ankara University, Turkey; Department of Psychology, Nuh Naci Yazgan University, Turkey; Department of Psychology, Bilkent University, Turkey; Department of Psychology and Interdisciplinary Neuroscience Program, National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Center, Bilkent University, Turkey","Sarigul, B., Interdisciplinary Social Psychiatry Program, Ankara University, Turkey, Department of Psychology, Nuh Naci Yazgan University, Turkey; Saltik, I., Department of Psychology, Nuh Naci Yazgan University, Turkey; Hokelek, B., Department of Psychology, Bilkent University, Turkey; Urgen, B.A., Department of Psychology and Interdisciplinary Neuroscience Program, National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Center, Bilkent University, Turkey","Robots increasingly become part of our lives. How we perceive and predict their behavior has been an important issue in HRI. To address this issue, we adapted a well-established prediction paradigm from cognitive science for HRI. Participants listened a greeting phrase that sounds either human-like or robotic. They indicated whether the voice belongs to a human or a robot as fast as possible with a key press. Each voice was preceded with a human or robot image (a human-like robot or a mechanical robot) to cue the participant about the upcoming voice. The image was either congruent or incongruent with the sound stimulus. Our findings show that people reacted faster to robotic sounds in congruent trials than incongruent trials, suggesting the role of predictive processes in robot perception. In sum, our study provides insights about how robots should be designed, and suggests that designing robots that do not violate our expectations may result in a more efficient interaction between humans and robots. © 2020 ACM.","Audio-visual mismatch; Cognitive sciences; Human perception; Humanoid robots; Prediction; Robot design; Robotic voice","Agricultural robots; Man machine systems; Robotics; Cognitive science; Efficient interaction; Human like; Human like robots; Predictive process; Robot images; Robot perception; Human robot interaction",,,,,,,,,,,,,,,,"Clark, A., Whatever next Predictive brains, situated agents, and the future of cognitive science (2013) Behavioral and Brain Sciences, 36 (3), pp. 181-204; Doehrmann, O., Naumer, M.J., Semantics and the multisensory brain: How meaning modulates processes of audio-visual integration (2008) Brain Research, 1242, pp. 136-150; Nass, C., (2015) Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship, , MIT Press, Cambridge, MA; Stern, S.E., Mullennix, J.W., Yaroslavsky, I., Persuasion and social perception of human vs (2006) Synthetic Voice Across Person As Source and Computer As Source Conditions. International Journal of Human-Computer Interaction, 64, pp. 43-52; Walters, M.L., Dyrdal, D.D., Koay, K.L., Dautenhahn, K., Te Boeckhorst, R., Human approach distances to a mechanical-looking robot with different voice styles (2008) Proceedings of RO-MAN, Munich, Germany; Zibrek, K., Kokkinara, E., Mcdonnell, R., The effect of realistic appearance of virtual characters in immersive environments-Does the character's personality play a role (2018) IEEE Transactions on Visualization and Computer Graphics, 24 (4), pp. 1681-1690; Mousas, C., Anastasiou, D., Spantidi, O., The effects of appearance and motion of virtual characters on emotional reactivity (2018) Computers in Human Behavior, 86, pp. 99-108; Mori, M., MacDorman, K.F., Kageki, N., The uncanny valley [from the field] (2012) IEEE Robotics & Automation Magazine, 19 (2), pp. 98-100; Urgen, B.A., Kutas, M., Saygin, A.P., Uncanny valley as a window into predictive processing in the social brain (2018) Neuropsychologia, 114, pp. 181-185; Mitchell, W.J., Szerszen, K.A., Lu, A.S., Schermerhorn, P.W., Scheutz, M., Macdorman, K.F., A mismatch in the human realism of face and voice produces an uncanny valley (2011) Iperception, 2 (1), pp. 10-12; Saygin, A.P., Chaminade, T., Ishiguro, H., Driver, J., Frith, C., The thing that should not be: Predictive coding and the uncanny valley in perceiving human and humanoid robot actions (2012) Social Cognitive Affective Neuroscience, 7, pp. 413-422; Urgen, B.A., Plank, M., Ishiguro, H., Poizner, H., Saygin, A.P., EEG theta and mu oscillations during perception of human and robot actions (2013) Frontiers in Neurorobotics, 7 (19); Urgen, B.A., Pehlivan, S., Saygin, A.P., Distinct representations in occipito-temporal, parietal, and premotor cortex during action perception revealed by fMRI and computational modeling (2019) Neuropsychologia, 127, pp. 35-47; De Lange, F.P., Heilbron, M., Kok, P., How do expectations shape perception (2018) Trends in Cognitive Sciences, 22 (9), pp. 764-779",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083273453
"Schwaninger I.","57215293552;","Robots in older people's living spaces: Designing for trust in situated human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"600","602",,,"10.1145/3371382.3377449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083260134&doi=10.1145%2f3371382.3377449&partnerID=40&md5=058a813be53663f805089a4d34ff8c89","HCI Group, TU Wien, Vienna, Austria","Schwaninger, I., HCI Group, TU Wien, Vienna, Austria","As robots are being designed to support older people in their living spaces, the use of robots in these contexts and particularly for AAL may come with issues of trust. While trust has gained increasing interest in HRI, we still have little understanding of trust when using robots in people's living spaces and taking into account their social practices. Drawing on literature, a methodological contribution and various (long-term) studies with older people and technology, the aim of this thesis is to define design guidelines for trust in situated human-robot interaction in older people's living spaces. © 2020 ACM.","Aal; Human-robot interaction; Living spaces; Situated hri; Trust","Agricultural robots; Machine design; Man machine systems; Living spaces; Methodological contributions; Older People; Situated human; Social practices; Social robots",,,,,"Technische Universitat Wien; Österreichische Forschungsförderungsgesellschaft, FFG: 856179","I greatly thank my supervisors Astrid Weiss and Geraldine Fitz-patrick for their input. This research is funded by the Trust Robots Doctoral College (TU Wien) and partly supported by the FFG (Austrian Research Promotion Agency - grant number 856179) .",,,,,,,,,,"Alves-Oliveira, P., Petisca, S., Correia, F., Maia, N., Paiva, A., Social robots for older adults: Framework of activities for aging in place with robots (2015) SpringerLink, pp. 11-20. , https://doi.org/10.1007/978-3-319-25554-52, Oct 2015; Ates, N., Aumayr, G., Drobics, M., Maria, F.K., Frauenberger, C., Garschall, M., Kofler, M., Willner, V., Assistive solutions in practice: Experiences from aal pilot regions in Austria (2017) Stud Health Technol Inform, 236, pp. 184-195. , https://www.ncbi.nlm.nih.gov/pubmed/28508795, 2017; Keith, C.B., Elicitation techniques: Getting people to talk about ideas they don't usually talk about (2015) Theory and Research in Social Education, 43 (2), pp. 179-205. , https://doi.org/10.1080/00933104.2015.1034392, May 2015; Billings, D.R., Schaefer, K.E., Chen, C.J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction-HRI '12, 109. , https://doi.org/10.1145/2157689.2157709, ACM Press, Boston, Massachusetts, USA; Broadbent, E., Garrett, J., Jepsen, N., Li Ogilvie, V., Seok Ahn, H., Robinson, H., Peri, K., Pillai, A., Using robots at home to support patients with chronic obstructive pulmonary disease: Pilot randomized controlled trial (2018) Journal of Medical Internet Research, 20 (2), p. e45. , 2018; Charalambous, G., Fletcher, S., Webb, P., The development of a scale to evaluate trust in industrial human-robot collaboration (2016) Int J of Soc Robotics, 8 (2), pp. 193-209. , https://doi.org/10.1007/s12369-015-0333-8, Apr 2016; Elizabeth Clarke, R., Briggs, J., Armstrong, A., MacDonald, A., Vines, J., Flynn, E., Socio-materiality of trust: Co-design with a resource limited community organisation (2019) CoDesign, , https://doi.org/10.1080/15710882.2019.1631349, Jul 2019; Corbett, E., Le Dantec, C.A., Going the distance: Trustwork for citizen participation (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18), 312, p. 13. , https://doi.org/10.1145/3173574.3173886, ACM, New York, NY, USA, Article; Crabtree, A., Tolmie, P., Knight, W., (2017) Repacking 'Privacy' for A Networked World, , https://dl.eusset.eu/handle/20.500.12015/2865, Springer, London 2017; Deindio, F., Dearco, A., Laura, A.R., (2007) Enriching Community Networks by Supporting Deliberation, , https://dl.eusset.eu/handle/20.500.12015/2683, Springer London, Dordrecht Amsterdam 2007; Fitzpatrick, G., Huldtgren, A., Malmborg, L., Harley, D., Ijsselsteijn, W., Design for agency, adaptivity and reciprocity: Reimagining aal and telecare agendas (2015) SpringerLink, pp. 305-338. , https://doi.org/10.1007/978-1-4471-6720-413, 2015; (2019) Population Ages 65 and above (% of Total Population) | Data, , https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS, (Oct 2019) IBRD / IDA The World Bank Group[Online; accessed 25. Oct. 2019]; Jung, M.F., DiFranzo, D., Stoll, B., Shen, S., Lawrence, A., Claure, H., (2018) Robot Assisted Tower Construction-A Resource Distribution Task to Study Human-Robot Collaboration and Interaction with Groups of People, , arXiv preprint (Dec 2018). ArXiv:1812.09548; Jung, M.F., Martelaro, N., Hinds, P.J., Using robots to moderate team conflict: The case of repairing violations (2015) ACM, , https://doi.org/10.1145/2696454.2696460; Kuutti, K., Bannon, L.J., The turn to practice in hci: Towards a research agenda (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14), pp. 3543-3552. , https://doi.org/10.1145/2556288.2557111, ACM, New York, NY, USA; Lampinen, A., Bellotti, V., Cheshire, C., Gray, M., CSCW and theSharing Economy: The Future of Platforms As Sites of Work Collaboration and Trust (2016) Proceedings of the 19th ACM Conference on Computer Supported CooperativeWork and Social Computing Companion (CSCW '16 Companion), pp. 491-497. , https://doi.org/10.1145/2818052.2855527, ACM, New York, NY, USA; Ting, K.L.H., Derras, M., Voilmy, D., Designing human-robot interaction for dependent elderlies: A Living Lab approach (2018) BCS Learning and Development Ltd. Proceedings of British HCI 2018, , https://doi.org/10.14236/ewic/HCI2018.142, Jan 2018; Lee, H.R., Sabanovi, S., Kwak, S.S., Collaborative map making: A reflexive method for understanding matters of concern in design research (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), pp. 5678-5689. , https://doi.org/10.1145/3025453.3025535, ACM, New York, NY, USA; Lee, J.D., Katrina, A.S., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80. , 2004; Lorenz, T., Weiss, A., Hirche, S., Synchrony and reciprocity: Key mechanisms for social companion robots in therapy and care (2015) International Journal of Social Robotics, 8 (1). , https://doi.org/10.1007/s12369-015-0325-8, Nov 2015; Martelaro, A., Nneji, C., Ju, A., Hinds, A., Tell me more: Designing HRI to encourage more trust, disclosure, and companionship (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), p. 577. , https://doi.org/10.1109/HRI.2016.7451864; Mayer, R.C., Davis, J.H., David Schoorman, F., An integrative model of organizational trust (1995) The Academy of Management Review, 20 (3), p. 709. , https://doi.org/10.2307/258792, July 1995; Pripfl, J., Kortner, T., Batko-Klein, D., Hebesberger, D., Vincze, M., Results of a real world trial with a mobile social service robot for older adults (2016) HRI '16 the Eleventh ACM/IEEE International Conference on Human Robot Interaction Christchurch (Mar 2016), pp. 497-498. , https://doi.org/10.1109/HRI.2016.7451824; Lionel, P.R., Jr., Monitoring and trust in virtual teams (2016) Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW '16), pp. 245-259. , https://doi.org/10.1145/2818048.2820076, ACM, New York, NY, USA; Rossi, A., Dautenhahn, K., Lee Koay, K., Saunders, J., Investigating human perceptions of trust in robots for safe hri in home environments (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI '17), pp. 375-376. , https://doi.org/10.1145/3029798.3034822, ACM, New York, NY, USA; Schaefer, K.E., Measuring trust in human robot interactions: Development of the (2016) Trust Perception Scale-HRI SpringerLink, pp. 191-218. , https://doi.org/10.1007/978-1-4899-7668-010, 2016; Schwaninger, I., Fitzpatrick, G., Weiss, A., Exploring trust in human-agent collaboration (2019) Proceedings of the 17th European Conference on Computer-Supported Cooperative Work: The International Venue on Practicecentred Computing An the Design of Cooperation Technologies-Exploratory Papers, , https://doi.org/10.18420/ecscw2019ep08, 2019; Sabanovi, S., Robots in society, society in robots (2010) Int J of Soc Robotics, 2 (4), pp. 439-450. , https://doi.org/10.1007/s12369-010-0066-7, Dec 2010; Sebo, S.S., Traeger, M., Jung, M., Scassellati, B., The ripple effects of vulnerability: The effects of a robot's vulnerable behavior on trust in human-robot teams (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 178-186. , https://doi.org/10.1145/3171221.3171275, ACM, New York, NY, USA; Tulli, S., Correia, F., Mascarenhas, S., Gomes, S., Melo, F.S., Paiva, A., Effects of agents' transparency on teamwork (2019) SpringerLink, pp. 22-37. , https://doi.org/10.1007/978-3-030-30391-42, May 2019; Ullman, D., Malle, B.F., What does it mean to trust a robot: Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 263-264. , https://doi.org/10.1145/3173386.3176991, ACM, New York, NY, USA; Van Straten, C.L., Peter, J., Kühne, R., De Jong, C., Barco, A., Technological and interpersonal trust in child-robot interaction: An exploratory study (2018) Proceedings of the 6th International Conference on Human-Agent Interaction-HAI '18, pp. 253-259. , https://doi.org/10.1145/3284432.3284440, ACM Press; Wulf, V., Rohde, M., Pipek, V., Stevens, G., Engaging with practices: Design case studies as a research framework in cscw (2011) Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work (CSCW '11), pp. 505-512. , https://doi.org/10.1145/1958824.1958902, ACM, New York, NY, USA; Zafari, S., Schwaninger, I., Hirschmanner, M., Schmidbauer, C., Weiss, A., Koeszegi, S.T., You are doing so great!'-The effect of a robot's interaction style on self-efficacy in hri (2019) Proceedings of the 28th IEEE International Conference on Robot and Human Interactive Communication (ROMAN)","Schwaninger, I.; HCI Group, Austria; email: isabel.schwaninger@tuwien.ac.at",,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083260134
"Sheppard E., Lohan K.S.","57212669634;35194251000;","Multimodal representation learning for human robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"445","446",,2,"10.1145/3371382.3378265","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083256051&doi=10.1145%2f3371382.3378265&partnerID=40&md5=f13852a3845f50816028eb94dfb44059","Edinburgh Centre for Robotics, Edinburgh, United Kingdom; Heriot-Watt University, Edinburgh, United Kingdom","Sheppard, E., Edinburgh Centre for Robotics, Edinburgh, United Kingdom; Lohan, K.S., Heriot-Watt University, Edinburgh, United Kingdom","We present a neural network based system capable of learning a multimodal representation of images and words. This representation allows for bidirectional grounding of the meaning of words and the visual attributes that they represent, such as colour, size and object name. We also present a new dataset captured specifically for this task. © 2020 ACM.","Datasets; Neural networks; Robotics; Symbol grounding; Unsupervised learning","Agricultural robots; Man machine systems; Multi-modal; Network based systems; Visual attributes; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC","This work was funded by the EPSRC.",,,,,,,,,,"Broz, F., Nehaniv, C.L., Belpaeme, T., Bisio, A., Dautenhahn, K., Fadiga, L., Ferrauto, T., Gigliotta, O., The italk project: A developmental robotics approach to the study of individual, social, and linguistic learning Topics in Cognitive Science, 6 (3), pp. 534-544. , 2014; Cangelosi, A., Belpaeme, T., Sandini, G., Metta, G., Fadiga, L., Sagerer, G., Rohlfing, K., Parisi, D., The italk project: Integration and transfer of action and language knowledge in robots (2008) Proceedings of Third ACM/IEEE International Conference on Human Robot Interaction (HRI 2008), 12, p. 15; Keller, I., Lohan, K.S., Analysis of illumination robustness in long-term object learning (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 240-245. , IEEE; Keller, I., Lohan, K.S., On the illumination influence for object learning on robot companions (2019) Frontiers in Robotics and AI,(in Press), pp. 1-17; Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., Distributed representations of words and phrases and their compositionality (2013) Advances in Neural Information Processing Systems, pp. 3111-3119; Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y., Multimodal deep learning (2011) Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689-696; Schillingmann, L., Wrede, B., Rohlfing, K., Towards a computational model of acoustic packaging (2009) Development and Learning. 2009. ICDL 2009. IEEE 8th International Conference on, pp. 1-6. , IEEE; Schillingmann, L., Wrede, B., Rohlfing, K.J., A computational model of acoustic packaging (2009) IEEE Transactions on Autonomous Mental Development, 1 (4), pp. 226-237; Sheppard, E., Lehmann, H., Rajendran, G., McKenna, P.E., Lemon, O., Lohan, K.S., Towards life long learning: Multimodal learning of mnist handwritten digits (2018) IEEE ICDL EPIROB 2018 Workshop on Life Long Learning; Silberer, C., Lapata, M., Learning grounded meaning representations with autoencoders (2014) Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1, pp. 721-732; Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting The Journal of Machine Learning Research, 15 (1), pp. 1929-1958. , 2014",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083256051
"Tan X.Z., Andrist S., Bohus D., Horvitz E.","57198744765;36717352400;14017613800;6701318945;","Now, over here: Leveraging extended atentional capabilities in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"468","470",,1,"10.1145/3371382.3378363","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083254566&doi=10.1145%2f3371382.3378363&partnerID=40&md5=bcbbfb89ad3f0b7df68468ce967b49a5","Microsoft Research, Redmond, WA, United States; Carnegie Mellon University, Pittsburgh, PA, United States","Tan, X.Z., Microsoft Research, Redmond, WA, United States, Carnegie Mellon University, Pittsburgh, PA, United States; Andrist, S., Microsoft Research, Redmond, WA, United States; Bohus, D., Microsoft Research, Redmond, WA, United States; Horvitz, E., Microsoft Research, Redmond, WA, United States","Competent collaboration between robots and people in the open world requires sensing and reasoning about transitions of people's attention to the robots themselves, as well as to other people and objects in the environment. We present challenges and opportunities with designing extended attentional capabilities for interactive systems, including the need to track, reason about, and manage the attentional foci of all actors. We describe work in progress to leverage such attentional capabilities for interaction management with a prototype situated robotic system. © 2020 ACM.","Interaction management; Situated interaction; Visual attention","Agricultural robots; Man machine systems; Interaction management; Interactive system; Open world; Robotic systems; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Admoni, H., Dragan, A., Srinivasa, S.S., Scassellati, B., Deliberate delays during robot-to-human handovers improve compliance with gaze communication (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction-HRI '14, pp. 49-56. , https://doi.org/10.1145/2559636.2559682, ACM Press, Bielefeld, Germany; Andrist, S., Gleicher, M., Mutlu, B., Looking coordinated: Bidirectional gaze mechanisms for collaborative interaction with virtual char-acters (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, pp. 2571-2582; Axelsson, N., Skantze, G., Modelling adaptive presentations in human-robot interaction using behaviour trees (2019) Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue. Association for Computational Linguistics, Stockholm, Sweden, pp. 345-352; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954. , 2018; Goodwin, C., Restarts, pauses, and the achievement of a state of mutual gaze at turn-beginning (1980) Sociological Inquiry, 50 (3-4), pp. 272-302. , 1980; Huang, C., Mutlu, B., Anticipatory robot control for efcient human-robot collaboration (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction. IEEE Press, pp. 83-90; Imai, M., Ono, T., Ishiguro, H., Physical relation and expression: Joint at-tention for human-robot interaction (2003) IEEE Transactions on Industrial Electronics, 50 (4), pp. 636-643. , https://doi.org/10.1109/TIE.2003.814769, Aug. 2003; Kidwell, M., Zimmerman, D.H., Joint attention as action (2007) Journal of Pragmatics, 39 (3), pp. 592-611. , 2007; Kontogiorgos, D., Pereira, A., Gustafson, J., Estimat-ing uncertainty in task-oriented dialogue (2019) 2019 International Conference on Multimodal Interaction, pp. 414-418; Kurylo, U., Wilson, J.R., Using human eye gaze patterns as indicators of need for assistance from a socially assistive robot (2019) International Conference on Social Robotics. Springer, pp. 200-210; Liu, P., Glas, D.F., Kanda, T., Ishiguro, H., Learning proactive behavior for interactive social robots (2018) Autonomous Robots, 42 (5), pp. 1067-1085. , 2018; Moon, A.J., Troniak, D.M., Gleeson, B., Pan, M.K.X.J., Zeng, M., Blumer, B.A., MacLean, K., Croft, E.A., Meet Me Where i'm Gazing: How Shared Attention Gaze Afects Human-Robot Handover Timing (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction-HRI '14, pp. 334-341. , https://doi.org/10.1145/2559636.2559656, ACM Press, Bielefeld, Germany; Schober, M.F., Spatial perspective-taking in conversation (1993) Cognition47, 1, pp. 1-24. , 1993; Stefanov, K., Salvi, G., Kontogiorgos, D., Kjellström, H., Beskow, J., Modeling of human visual attention in multiparty open-world dialogues (2019) ACM Transactions on Human-Robot Interaction, 8 (2), pp. 1-21. , https://doi.org/10.1145/3323231, June 2019; Zhi Tan, X., Reig, S., Carter, E.J., Steinfeld, A., From one to another: How robot-robot interaction afects users' perceptions following a transition between robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 114-122; Yu, Z., Bohus, D., Horvitz, E., Incremental coordination: Attention-centric speech production in a physically situated conversational agent (2015) Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, Prague, Czech Republic, pp. 402-406. , https://doi.org/10.18653/v1/W15-4652",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083254566
"Buschmeier H., Foster M.E., Gkatzia D.","35483237200;23091020100;56349875100;","Second workshop on natural language generation for human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"646","647",,,"10.1145/3371382.3374853","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083247050&doi=10.1145%2f3371382.3374853&partnerID=40&md5=68fc63bf306d0137f47978010a5361fe","Bielefeld University, Bielefeld, Germany; University of Glasgow, Glasgow, United Kingdom; Edinburgh Napier University, Edinburgh, United Kingdom","Buschmeier, H., Bielefeld University, Bielefeld, Germany; Foster, M.E., University of Glasgow, Glasgow, United Kingdom; Gkatzia, D., Edinburgh Napier University, Edinburgh, United Kingdom","This workshop is the second in a series bringing together the Natural Language Generation and Human-Robot Interaction communities to discuss topics of mutual interest with the goal of developing an HRI-inspired NLG shared task. The workshop website is urlhttps: //purl.org/nlg-hri-workshop/2020. © 2020 ACM.","Human-robot interaction; Natural language generation","Agricultural robots; Man machine systems; Natural language processing systems; Natural language generation; Human robot interaction",,,,,,,,,,,,,,,,"Ellen Foster, M., Natural language generation for social robotics: Opportunities and challenges (2019) Philosophical Transactions of the Royal Society B: Biological Sciences, 374 (1771), p. 20180027. , https://doi.org/10.1098/rstb.2018.0027, 2019; Ellen Foster, M., Buschmeier, H., Gkatzia, D., Proceedings of the workshop on nlg for human-robot interaction (2018) Association for Computational Linguistics, Tilburg, the Netherlands, , https://aclweb.org/anthology/volumes/W18-69/; Gatt, A., Krahmer, E., Survey of the state of the art in Natural Language Generation: Core tasks, applications and evaluation (2018) Journal of Artificial Intelligence Research, 61, pp. 65-170. , https://doi.org/10.1613/jair.5477, 2018; Striegnitz, K., Denis, A., Gargett, A., Garoufi, K., Koller, A., Theune, M., Report on the second second challenge on generating instructions in virtual environments (give-2 5) (2011) Proceedings of the 13th European Workshop on Natural Language Generation. Nancy, France, pp. 270-279",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083247050
"Robinson F.A., Bown O., Velonaki M.","57215966842;27867522200;24073920300;","Implicit communication through distributed sound design: Exploring a new modality in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"597","599",,3,"10.1145/3371382.3377431","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083232681&doi=10.1145%2f3371382.3377431&partnerID=40&md5=e624fc683e52dbaee0dc30bf823f5634","Creative Robotics Lab, University of New South Wales, Sydney, Australia; Interactive Media Lab, University of New South Wales, Sydney, Australia","Robinson, F.A., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Bown, O., Interactive Media Lab, University of New South Wales, Sydney, Australia; Velonaki, M., Creative Robotics Lab, University of New South Wales, Sydney, Australia","As robots find their way into homes, workplaces, and public spaces, rich and effective human-robot interaction will play an essential role in their success. While most sound-related research in the field of HRI focuses on speech and semantic-free utterances, the potential of sound as an implicit non-verbal channel of communication has only recently received attention and remains largely unexplored. This research will bring design approaches from the fields of sound design and spatial audio into the context of human-robot interaction to influence human perception of robot characteristics and refine non-verbal auditory communication. It will implement sound design systems into various physical robots and evaluate their effect through user studies. By developing design principles for the sonic augmentation of robots, we aim to provide the HRI community with new tools to enrich the way robots communicate with humans. © 2020 ACM.","Human-robot interaction; Sonic interaction design; Spatial audio","Agricultural robots; Audio acoustics; Machine design; Man machine systems; Semantics; Speech communication; Design approaches; Design Principles; Distributed sounds; Hri communities; Human perception; Implicit communications; Physical robots; Sound designs; Human robot interaction",,,,,,,,,,,,,,,,"Bethel, C.L., Murphy, R.R., Auditory and other non-verbal expressions of affect for robots (2006) AAAI Fall Symposium: Aurally Informed Performance, pp. 1-5; Bown, O., Loke, L., Ferguson, S., Reinhardt, D., (2015) Distributed Interactive Audio Devices: Creative Strategies and Audience Responses to Novel Musical Interaction Scenarios, p. 4. , 2015; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, Edmonton, Alta., Canada, pp. 708-713. , https://doi.org/10.1109/IROS.2005.1545011; Frid, E., Bresin, R., Alexanderson, S., Perception of mechanical sounds inherent to expressive gestures of a nao robot-implications for movement sonification of humanoids (2018) Sound and Music Computing; Heerink, M., Krose, B., Evers, V., Wielinga, B., Measuring acceptance of an assistive social robot: A suggested toolkit (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, Toyama, Japan, pp. 528-533. , https://doi.org/10.1109/ROMAN.2009.5326320; Inoue, K., Wada, K., Ito, Y., Effective application of Paro: Seal type robots for disabled people in according to ideas of occupational therapists (2008) International Conference on Computers for Handicapped Persons. Springer, pp. 1321-1324; Kulezic-Wilson, D., Sound Design is the New Score (2008) Music, Sound, and the Moving Image, 2 (2), pp. 127-131. , https://doi.org/10.3828/msmi.2.2.5, Dec. 2008; Moore, D., Dahl, T., Varela, P., Ju, W., Naes, T., Berget, I., Unintended consonances: Methods to understand robot motor sound perception (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems-CHI '19, pp. 1-12. , https://doi.org/10.1145/3290605.3300730, ACM Press, Glasgow, Scotland Uk; Moore, D., Tennent, H., Martelaro, N., Ju, W., Making noise intentional: A study of servo sound perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17, pp. 12-21. , https://doi.org/10.1145/2909824.3020238, ACM Press, Vienna, Austria; Robinson, F., Spindler, C., Böhm, V., Oña, E., Gestural control in electronic music performance: Sound design based on the 'striking' and 'bowing' movement metaphors (2015) Proceedings of the Audio Mostly 2015 on Interaction with Sound-AM '15, pp. 1-6. , https://doi.org/10.1145/2814895.2814901, ACM Press, Thessaloniki, Greece; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN). IEEE, Lisbon, pp. 928-935. , https://doi.org/10.1109/ROMAN.2017.8172414; Thiessen, R., Rea, D.J., Garcha, D.S., Cheng, C., Young, J.E., Infrasound for hri: A robot using low-frequency vibrations to impact how people perceive its actions (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 11-18; Trovato, G., Do, M., Terlemez, O., Mandery, C., Ishii, H., Bianchi-Berthouze, N., Asfour, T., Takanishi, A., Is hugging a robot weird Investigating the influence of robot appearance on users' perception of hugging (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). IEEE, pp. 318-323; Trovato, G., Paredes, R., Balvin, J., Cuellar, F., Baek Thomsen, N., Bech, S., Tan, Z., The sound or silence: Investigating the influence of robot noise on proxemics (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, Nanjing, pp. 713-718. , https://doi.org/10.1109/ROMAN.2018.8525795; Tünnermann, R., Hammerschmidt, J., Hermann, T., Blended sonification-Sonification for casual information interaction (2013) Georgia Institute of Technology; Van Egmond, R., The experience of product sounds (2008) Product Experience. Elsevier, pp. 69-89; Wolford, J., Gabaldon, B., Rivas, J., Min, B., Condition-based robot audio techniques (2019) Google Patents; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32 (1), pp. 63-85. , https://doi.org/10.1080/10447318.2015.1093856, Jan. 2016",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083232681
"Yu C., Tapus A.","57212384467;23010519200;","Multimodal emotion recognition with thermal and rgb-d cameras for human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"532","534",,1,"10.1145/3371382.3378342","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083232127&doi=10.1145%2f3371382.3378342&partnerID=40&md5=af648dde6595526570aa65b19787a8e1","Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Blvd des Maréchaux, Palaiseau, 91120, France","Yu, C., Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Blvd des Maréchaux, Palaiseau, 91120, France; Tapus, A., Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Blvd des Maréchaux, Palaiseau, 91120, France","Human emotion detection is an important aspect in social robotics and in human-robot interaction (HRI). In this paper, we propose a vision-based multimodal emotion recognition method based on gait data and facial thermal images designed for social robots. Our method can detect four human emotional states (i.e., neutral, happiness, anger, and sadness). We gathered data from 25 participants in order to build-up an emotion database for training and testing our classification models. We implemented and tested several approaches such as Convolutional Neural Network (CNN), Hidden Markov Model (HMM), Support Vector Machine (SVM), and Random Forest (RF). These were trained and tested in order to compare the emotion recognition ability and to find the best approach. We designed a hybrid model with both the gait and the thermal data and the accuracy of our system shows an improvement of 10% over the other models based on our emotion database. This is a promising approach to be explored in a real-time human-robot interaction scenario. © 2020 ACM.","Gait; Human-robot interaction; Multimodal emotion recognition; Thermal face","Agricultural robots; Classification (of information); Convolutional neural networks; Decision trees; Hidden Markov models; Man machine systems; Speech recognition; Support vector machines; Classification models; Emotion recognition; Emotional state; Human robot Interaction (HRI); Multimodal emotion recognition; Social robotics; Thermal images; Training and testing; Social robots",,,,,,,,,,,,,,,,"Brody, L.R., On understanding gender differences in the expression of emotion (1993) Human Feelings: Explorations in Affect Development and Meaning, pp. 87-121. , 1993; Castellano, G., Villalba, S., Camurri, A., Recognising human emotions from body movement and gesture dynamics (2007) Affective Computing and Intelligent Interaction, pp. 71-82. , 2007; Chaplin, T.M., Gender and emotion expression: A developmental contextual perspective (2015) Emotion Review, 7 (1), pp. 14-21. , 2015; Deng, Y., Yang, M., Zhou, R., A new standardized emotional film database for Asian culture (2017) Frontiers in Psychology, 8, p. 1941. , 2017); King, D.E., Dlib-ml: A machine learning toolkit (2009) Journal of Machine Learning Research, 10, pp. 1755-1758. , Jul 2009; Nakanishi, R., Imai-Matsumura, K., Facial skin temperature decreases in infants with joyful expression (2008) Infant Behavior and Development, 31 (1), pp. 137-144. , 2008; Salazar-López, E., Domínguez, E., Ramos, V.J., De La Fuente, J., Meins, A., Iborra, O., Gálvez, G., Gómez-Milán, E., The mental and subjective skin: Emotion, empathy, feelings and thermography (2015) Consciousness and Cognition, 34, pp. 149-162. , 2015; Schaefer, A., Nils, F., Sanchez, X., Philippot, P., Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers (2010) Cognition and Emotion, 24 (7), pp. 1153-1172. , 2010; Tanawongsuwan, R., Bobick, A., Gait recognition from timenormalized joint-angle trajectories in the walking plane (2001) Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR, 2. , IEEE, II-II 2001; Uhrig, M.K., Trautmann, N., Baumgärtner, U., Treede, R., Henrich, F., Hiller, W., Marschall, S., Emotion elicitation: A comparison of pictures and films (2016) Frontiers in Psychology, 7, p. 180. , 2016; Zhang, Y., Zhang, L., Hossain, M., Adaptive 3D facial action intensity estimation and emotion recognition (2015) Expert Systems with Applications, 42 (3), pp. 1446-1464. , 2015",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083232127
"Wykowska A., Perez-Osorio J., Kopp S.","15924284200;55978227200;35722438500;","Social cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"665","666",,,"10.1145/3371382.3374851","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083229049&doi=10.1145%2f3371382.3374851&partnerID=40&md5=d0db09d7ba208616810d8c134d323fc4","Social Cognition in Human-Robot Interaction Unit-S4HRI, Istituto Italiano di Tecnologia, Genova, Italy; CITEC-Cognitive Interaction Technology, Social Cognitive Systems Group Faculty of Technology Bielefeld University, Germany","Wykowska, A., Social Cognition in Human-Robot Interaction Unit-S4HRI, Istituto Italiano di Tecnologia, Genova, Italy; Perez-Osorio, J., Social Cognition in Human-Robot Interaction Unit-S4HRI, Istituto Italiano di Tecnologia, Genova, Italy; Kopp, S., CITEC-Cognitive Interaction Technology, Social Cognitive Systems Group Faculty of Technology Bielefeld University, Germany","This workshop aims at bringing together various disciplines to address the relationship between mentalizing or adopting the intentional stance towards robots and social attunement in human-robot interaction. The question will be tackled from the empirical, theoretical, computational, and philosophical approaches as well as potential applications in clinical domains. We invite speakers from the areas of cognitive and social neuroscience, psychology, computational modeling, cognitive science, human-robot interaction, robotics, and philosophy of mind, who address the question of conditions and consequences of attributing mental states to robots. We will also discuss individual differences in attitudes regarding robots, including variability in the likelihood of adopting the intentional stance towards artificial agents. The concluding discussion will focus on how empirical results influence the implementation of behavior in robots, and which application contexts should promote robot design that elicits mentalizing. In the discussion, we will also address ethical aspects related to evoking socio-cognitive mechanisms (including mentalizing) towards robots. © 2020 ACM.","Computational models; Human-robot interaction; Intentional stance; Mentalizing; Mind reading; Theory of mind","Agricultural robots; Computational neuroscience; Ethical aspects; Machine design; Man machine systems; Application contexts; Artificial agents; Cognitive science; Computational model; Individual Differences; Philosophical approaches; Social cognition; Socio-cognitive; Social robots",,,,,,,,,,,,,,,,"Premack, D., Wodruff, G., Does the chimpanzee have a theory of mind (1978) Behavioral & Brain Sciences, 1, pp. 515-526; Kulms, P., Kopp, S., More human-likeness, more trust: The effect of anthropomorphism on self-reported and behavioral trust in continued and interdependent human-agent cooperation (2019) Proceedings of Mensch und Computer 2019, pp. 31-42. , ACM; Wykowska, A., Chaminade, T., Cheng, G., Embodied artificial agents for understanding human social cognition (2016) Philosophical Transaction of the Royal Society B, 371, p. 20150375. , 1693; Perez-Osorio, J., Wykowska, A., (2019) Adopting the Intentional Stance Towards Humanoid Robots, , In: Laumond JP., Danblon E., Pieters C. (eds) Wording Robotics. Springer Tracts in Advanced Robotics, vol 130. Springer, Cham",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083229049
"Honig S., Bartal A., Oron-Gilad T.","57201589559;35088013100;16042913400;","Using customers' online reviews to identify and classify human robot interaction failures in domestic robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"251","253",,,"10.1145/3371382.3378323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083228190&doi=10.1145%2f3371382.3378323&partnerID=40&md5=78dcc02adf2a64e809b74e33cc5ee7f3","Ben-Gurion University of the Negev, Be'er Sheva, Israel","Honig, S., Ben-Gurion University of the Negev, Be'er Sheva, Israel; Bartal, A., Ben-Gurion University of the Negev, Be'er Sheva, Israel; Oron-Gilad, T., Ben-Gurion University of the Negev, Be'er Sheva, Israel","Little information is available regarding which types of failures robots experience in domestic settings. To further the community's knowledge, we manually classified 3062 customer reviews of robotic vacuum cleaners on Amazon.com. The resulting database was analyzed and used to create a Natural Language Processing (NLP) model capable of predicting whether a review contains a description of a failure or not. The current work describes the initial analysis and model development process as well as preliminary findings. © 2020 ACM.","Human-robot interaction; Social network analysis; User satisfaction; User-centered","Agricultural robots; Man machine systems; Natural language processing systems; Amazon.com; Analysis and modeling; Customer review; Domestic robots; NAtural language processing; Online reviews; Robotic vacuum cleaners; Human robot interaction",,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","The first author is supported by The Helmsley Charitable Trust through the Agricultural, Biological, Cognitive Robotics Initiative and the Marcus Endowment Fund, and by Ben-Gurion University through the High-tech, Bio-tech and Chemo-tech Scholarship.",,,,,,,,,,"Anderson, E.T., Simester, D.I., Reviews without a purchase: Low ratings, loyal customers, and deception (2014) Journal of Marketing Research, 51 (3), pp. 249-269. , https://doi.org/10.1509/jmr.13.0209arXivhttps://doi.org/10.1509/jmr.13.0209, 2014; Brooks, D.J., (2017) A Human-centric Approach Toautonomous Robot Failures, , Ph.D. Dissertation. University of Massachusetts Lowell; Carlson, J., Murphy, R.R., How ugvs physically fail in the field (2005) Trans. Rob, 21 (3), pp. 423-437. , https://doi.org/10.1109/TRO.2004.838027, June 2005; Cuevas, C.A.G., Rodriguez, R.A.C., Complications in robotic urologic surgery (1 ed ) (2018) Springer International Publishing, Chapter, 3, pp. 23-28. , https://doi.org/10.1007/978-3-319-62277-4; Devlin, J., Chang, M., Lee, K., Toutanova, K., (2018) Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding, , arXiv preprint arXiv:1810.04805 2018; Friedman, B., Kahn, P.H., Jr., Hagman, J., Hardware companions: What online aibo discussion forums reveal about the humanrobotic relationship (2003) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '03), pp. 273-280. , https://doi.org/10.1145/642611.642660, ACM, New York, NY, USA; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Frontiers in Psychology, 9, p. 861. , https://doi.org/10.3389/fpsyg.2018.00861, 2018; Mubin, O., Khan, A., Obaid, M., Naorobot: Exploring nao discourse on twitter (2016) Proceedings of the 28th Australian Conference on Computer-Human Interaction (OzCHI '16), pp. 155-159. , https://doi.org/10.1145/3010915.3011002, ACM, New York, NY, USA; Rajih, E., Tholomier, C., Cormier, B., Samouëlian, V., Warkus, T., Liberman, M., Widmer, H., Meskawi, M., Error reporting from the da Vinci surgical system in robotic surgery: A Canadian multispecialty experience at a single academic centre (2017) Canadian Urological Association Journal, 11 (5), p. E197. , 2017; Short, E., Hart, J., Vu, M., Scassellati, B., No fair!! An interaction with a cheating robot (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 219-226. , https://doi.org/10.1109/HRI.2010.5453193; Steinbauer, G., A survey about faults of robots used in robocup (2013) RoboCup 2012: Robot Soccer World Cup XVI, pp. 344-355. , Xiaoping Chen, Peter Stone, Luis Enrique Sucar, and Tijn van der Zant (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg; Zorn, K.C., Gofrit, O.N., Orvieto, M.A., Mikhail, A.A., Matthew Galocy, R., Shalhav, A.L., Zagaja, G.P., Da Vinci robot error and failure rates: Single institution experience on a single three-arm robot unit of more than 700 consecutive robot-assisted laparoscopic radical prostatectomies (2007) Journal of Endourology, 21 (11), pp. 1341-1344. , 2007",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083228190
[No author name available],[No author id available],"HRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",681,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083225577&partnerID=40&md5=3ac39d64493c61bfb258fe3ced60830d",,"","The proceedings contain 228 papers. The topics discussed include: arts + health: new approaches to arts and robots in health care; robonomics: the study of robot-human peer-to-peer financial transactions and agreements; dog sit! domestic dogs (canis familiaris) follow a robot’s sit commands; the confucian matador: three defenses against the mechanical bull; adapting to a robot: adapting gardening and the garden to fit a robot lawn mower; four years in review: statistical practices of Likert scales in human-robot interaction studies; would you turn off a robot because it confronts you with your own mortality?; voice puppetry: towards conversational HRI WoZ experiments with synthesized voices; an application of HRI in low-cost digital manufacturing; and demonstration of a social robot for control of remote autonomous systems.",,,,,,,,,,,,,,,,,,,,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85083225577
"Panteris M., Manschitz S., Calinon S.","57216331414;55936997600;6507248533;","Learning, generating and adaptingwave gestures for expressive human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"386","388",,,"10.1145/3371382.3378286","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083221696&doi=10.1145%2f3371382.3378286&partnerID=40&md5=9f15862c1090edd1c0a13e9e922b63c3","MINES ParisTech, Paris, France; Honda Research Institute Europe, Offenbach/Main, Germany; Idiap Research Institute, Martigny, Switzerland","Panteris, M., MINES ParisTech, Paris, France; Manschitz, S., Honda Research Institute Europe, Offenbach/Main, Germany; Calinon, S., Idiap Research Institute, Martigny, Switzerland","This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective non-verbal communication through a probabilistic formulation using joint angle data from human demonstrations. This is achieved by learning and modulating the overall expression characteristics of the gesture (e.g., arm posture, waving frequency and amplitude) in the frequency domain. The method was evaluated on simulated robot experiments involving a robot with a manipulator of 6 degrees of freedom. The results show that the method provides efficient encoding and modulation of rhythmic movements and ensures variability in their execution. © 2020 ACM.","Imitation learning; Movement primitives; Social robots","Agricultural robots; Degrees of freedom (mechanics); Frequency domain analysis; Man machine systems; Manipulators; Modulation; Stochastic systems; Efficient encoding; Frequency domains; Human demonstrations; Imitation learning; Non-verbal communications; Probabilistic formulation; Rhythmic movements; Stochastic generation; Human robot interaction",,,,,,"This work was supported by Honda Research Institute Europe.",,,,,,,,,,"Calinon, S., Mixture models for the analysis, edition, and synthesis of continuous time series (2019) Mixture Models and Applications, pp. 39-57. , https://doi.org/10.1007/978-3-030-23876-63, N. Bouguila and W. Fan (Eds.). Springer; Paraschos, A., Daniel, C., Peters, J., Neumann, G., Using probabilistic movement primitives in robotics (2018) Autonomous Robots, 42 (3), pp. 529-551. , https://doi.org/10.1007/s10514-017-9648-7, Mar 2018; Stergiou, N., Harbourne, R.T., Cavanaugh, J.T., Optimal movement variability: A new theoretical perspective for neurologic physical therapy (2006) Journal of Neurologic Physical Therapy, 30 (3), pp. 120-129. , https://doi.org/10.1097/01.NPT.0000281949.48193.d9, September 2006",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083221696
"He J., Van Maris A., Caleb-Solly P.","57216344754;57193757921;16244225300;","Investigating the effectiveness of different interaction modalities for spatial human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"239","241",,,"10.1145/3371382.3378273","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083219014&doi=10.1145%2f3371382.3378273&partnerID=40&md5=2ca101680a19d01f2a5df99d244864d1","Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","He, J., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Van Maris, A., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Caleb-Solly, P., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","With the increasing use of social robots in real environments, one of the areas of research requiring more attention is the study of human-robot interaction (HRI) when a person and robot are moving close to each other. Understanding effective ways to design how a robot should communicate its intention during dynamic movement is based on what people's expectations are and how they interpret different cues from the robot. Building on the existing literature, we tested a range of non-verbal cues such as eye contact, gaze and head nodding as part of the robot's behaviour during close proximate passing. The research aimed to investigate the effects of these cues, as well as their combination with body posture, on the efficiency of passing and the quality of HRI. Our results show that the combination of eye contact and the robot turning sideways is the most effective and appropriate compared to other modalities. © 2020 ACM.","Body posture; Eye contact; Gaze; Human robot interaction modalities; Movement intention; Nod","Agricultural robots; Behavioral research; Man machine systems; Social robots; Body postures; Dynamic movements; Eye contact; Head nodding; Human robot Interaction (HRI); Real environments; Robot turnings; Machine design",,,,,"Horizon 2020 Framework Programme, H2020: 721619","AvM was funded by EC MSCA-ITN grant agreetment No 721619 (SOCRATES project).",,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63. , 2017; Boucher, J., Pattacini, U., Lelong, A., Bailly, G., Elisei, F., Fagel, S., Dominey, P.F., Ventre-Dominey, J., I reach faster when i see you look: Gaze effects in human-human and human-robot face-to-face cooperation (2012) Frontiers in Neurorobotics, 6, p. 3. , 2012; Hegel, F., Gieselmann, S., Peters, A., Holthaus, P., Wrede, B., Towards a typology of meaningful signals and cues in social robotics (2011) 2011 RO-MAN. IEEE, pp. 72-78; Knapp, M.L., Hall, J.A., Horgan, T.G., Nonverbal communication in human interaction (2013) Cengage Learning; Liu, C., Ishi, C.T., Ishiguro, H., Hagita, N., Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 285-292; Moon, A., Daniel, M.T., Gleeson, B., Pan, M., Zheng, M., Blumer, B.A., MacLean, K., Croft, E.A., Meet me where i'm gazing: How shared attention gaze affects human-robot handover timing (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction. ACM, pp. 334-341; Petersen, S., Houston, S., Qin, H., Tague, C., Studley, J., The utilization of robotic pets in dementia care (2017) Journal of Alzheimer's Disease, 55 (2), pp. 569-574. , 2017; Pinillos, R., Marcos, S., Feliz, R., Zalama, E., Gómez-García-Bermejo, J., Long-term assessment of a service robot in a hotel environment (2016) Robotics and Autonomous Systems, 79, pp. 40-57. , 2016; https://www.softbankrobotics.com/emea/en/pepper, SoftBank Robotics. [n.d.]. Pepper the humanoid and programmable robot: SoftBank Robotics; Schneider, B., Pea, R., Real-time mutual gaze perception enhances collaborative learning and collaboration quality (2013) International Journal of Computer-supported Collaborative Learning, 8 (4), pp. 375-397. , 2013; Short, E., Swift-Spong, K., Greczek, J., Ramachandran, A., Litoiu, A., Corina Grigore, E., Feil-Seifer, D., Huang, S., How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 924-929; Warta, S.F., Newton, O.B., Song, J., Best, A., Fiore, S.M., Effects of social cues on social signals in human-robot interaction during a hallway navigation task (2018) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62, pp. 1128-1132. , SAGE Publications Sage CA: Los Angeles, CA",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083219014
"Stoeva D., Gelautz M.","57216345097;8610583800;","Body language in affective human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"606","608",,5,"10.1145/3371382.3377432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083213455&doi=10.1145%2f3371382.3377432&partnerID=40&md5=a7010604b56175763410769fc8a38fe6","Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Stoeva, D., Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria; Gelautz, M., Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Social human-robot interaction is concerned with exploring the ways in which social interaction can be achieved between a human and a sociable robot. Affect has an important role in interaction as it helps interactants coordinate and indicate the success of the communication. Designing socially intelligent robots requires competence in communication, which includes exchanges of both verbal and non-verbal cues. This project will focus on non-verbal communication, more specifically body movements, postures and gestures as means of conveying socially affective information. Using the affective grounding perspective, which conceptualizes emotion as a coordination mechanism, together with honest signals as a measurement of the dynamics of the interaction, and the robot Pepper, we aim to develop a system that would be able to communicate affect, with the goal to enhance affective human-robot interaction. © 2020 ACM.","Affective computing; Body language; Human-robot interaction; Robotics; Social signals","Agricultural robots; Intelligent robots; Man machine systems; Body language; Body movements; Coordination mechanisms; Non-verbal communications; Postures and gestures; Sociable robots; Social human-robot interactions; Social interactions; Social robots",,,,,,,,,,,,,,,,"Argyle, M., (1969) Social Interaction: Process and Products, , Routledge, New York; Argyle, M., (1975) Bodily Communication, , Methuen & Co Ltd, London; Atkinson, A.P., Dittrich, W.H., Gemmell, A.J., Young, A.W., Emotion perception from dynamic and static body expressions in point-light and full-light displays (2004) Perception, 33 (6), pp. 717-746. , https://doi.org/10.1068/p5096, 2004; Bailenson, J.N., Yee, N., Digital chameleons: Automatic assimilation of nonverbal gestures in immersive virtual environments (2005) Psychological Science, 16 (10), pp. 814-819. , https://doi.org/10.1111/j.1467-9280.2005.01619.x, 2005; Beck, A., Cañamero, L., Hiolle, A., Damiano, L., Cosi, P., Tesser, F., Sommavilla, G., Interpretation of emotional body language displayed by a humanoid robot: A case study with children (2013) International Journal of Social Robotics, 5 (3), pp. 325-334. , https://doi.org/10.1007/s12369-013-0193-z, 2013; Camurri, A., Lagerlöf, I., Volpe, G., Recognizing emotion from dance movement: Comparison of spectator recognition and automated techniques (2003) International Journal of Human-Computer Studies, 59 (1-2), pp. 213-225. , https://doi.org/10.1016/S1071-5819(03)00050-8, 2003; Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y., (2018) OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields, , arXiv:1812.08008 [cs] 2018 arXiv:cs/1812. 08008; Choi, M., Kornfield, R., Takayama, L., Mutlu, B., Movement matters: Effects of motion and mimicry on perception of similarity and closeness in robot-mediated communication (2017) 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), pp. 325-335. , https://doi.org/10.1145/3025453.3025734, ACM, Denver, Colorado, USA; Chu, V., Bullard, K., Thomaz, A.L., Multimodal real-time contingency detection for hri (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3327-3332. , https://doi.org/10.1109/IROS.2014.6943025; Cohen, I., Looije, R., Neerincx, M., Child's perception of robot's emotions: Effects of platform, context and experience (2014) International Journal of Social Robotics, 6 (4), pp. 507-518. , https://doi.org/10.1007/s12369-014-0230-6, 2014; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139. , https://doi.org/10.1023/B:JONB.0000023655.25550.be, 2004; Delaherche, E., Chetouani, M., Mahdhaoui, A., Saint-Georges, C., Viaux, S., Cohen, D., Interpersonal synchrony: A survey of evaluation methods across disciplines (2012) IEEE Transactions on Affective Computing, 3 (3), pp. 349-365. , https://doi.org/10.1109/T-AFFC.2012.12, 2012; Ekman, P., An argument for basic emotions (1992) Cognition and Emotion, 6 (3-4), pp. 169-200. , https://doi.org/10.1080/02699939208411068, 1992; Suphi Erden, M., Emotional postures for the humanoid-robot nao (2013) International Journal of Social Robotics, 5 (4), pp. 441-456. , https://doi.org/10.1007/s12369-013-0200-4, 2013; Fischer, K., Jung, M.F., Jensen, L.C., Der Wieschen, A.M.V., Emotion expression in hri-When and why (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 29-38. , https://doi.org/10.1109/HRI.2019.8673078; Fuente, L.A., Ierardi, H., Pilling, M., Crook, N.T., Influence of upper body pose mirroring in human-robot interaction (2015) Social Robotics, Adriana Tapus, Elisabeth André, pp. 214-223. , Jean-Claude Martin, Fraņois Ferland, and Mehdi Ammi (Eds.). Springer International Publishing; Hale, J., Antonia, F., De Hamilton, C., Testing the relationship between mimicry, trust and rapport in virtual reality conversations (2016) Scientific Reports, 6, p. 35295. , https://doi.org/10.1038/srep35295, 2016; Häring, M., Bee, N., André, E., Creation and evaluation of emotion expression with body movement, sound and eye color for humanoid robots (2011) 2011 RO-MAN, pp. 204-209. , https://doi.org/10.1109/ROMAN.2011.6005263; Khursheed Hasnain, S., Gaussier, P., Mostafaoui, G., Synchrony as a way to choose an interacting partner (2012) 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp. 1-6. , https://doi.org/10.1109/DevLrn.2012.6400882; Hasnain, S.K., Mostafaoui, G., Gaussier, P., A synchrony-based perspective for partner selection and attentional mechanism in human-robot interaction (2012) Paladyn, 3 (3), pp. 156-171. , https://doi.org/10.2478/s13230-013-0111-y, 2012; Hasumoto, R., Nakadai, K., Imai, M., Reactive chameleon: A method to mimic conversation partner's body sway for a robot (2019) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-019-00557-4, 2019; Malte, F.J., Affective grounding in human-robot interaction (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17, pp. 263-273. , https://doi.org/10.1145/2909824.3020224; Knight, H., Simmons, R., Laban head-motions convey robot state: A call for robot body language (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Stockholm, Sweden, pp. 2881-2888. , https://doi.org/10.1109/ICRA.2016.7487451; Kofinas, N., Orfanoudakis, E., Lagoudakis, M.G., Complete analytical forward and inverse kinematics for the nao humanoid robot (2015) Journal of Intelligent & Robotic Systems, 77 (2), pp. 251-264. , https://doi.org/10.1007/s10846-013-0015-4, 2015; Kozima, H., Nakagawa, C., Yasuda, Y., Interactive robots for communication-care: A case-study in autism therapy (2005) ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005, pp. 341-346. , https://doi.org/10.1109/ROMAN.2005.1513802; Lee, J.J., Sha, F., Breazeal, C., A Bayesian theory of mind approach to nonverbal communication (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 487-496. , https://doi.org/10.1109/HRI.2019.8673023; Li, J., Ju, W., Nass, C., Observer perception of dominance and mirroring behavior in human-robot relationships (2015) Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI '15), , https://doi.org/10.1145/2696454.2696459, ACM, Portland, Oregon, USA; Löffler, D., Schmidt, N., Tscharn, R., Multimodal expression of artificial emotion in social robots using color, motion and sound (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction-HRI '18, pp. 334-343. , https://doi.org/10.1145/3171221.3171261, ACM Press, Chicago, IL, USA; McColl, D., Nejat, G., Recognizing emotional body language displayed by a human-like social robot (2014) International Journal of Social Robotics, 6 (2), pp. 261-280. , https://doi.org/10.1007/s12369-013-0226-7, 2014; Miwa, H., Itoh, K., Matsumoto, M., Zecca, M., Takanobu, H., Rocella, S., Carrozza, M.C., Takanishi, A., Effective emotional expressions with expression humanoid robot we-4rii: Integration of humanoid robot hand rch-1 (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat No. 04CH37566), 3, pp. 2203-2208. , https://doi.org/10.1109/IROS.2004.1389736; (2008) Honest Signals: How They Shape Our World, , Alex (Sandy) Pentland The MIT Press; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375-382. , https://doi.org/10.1109/HRI.2010.5453163; James, A.R., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), pp. 1161-1178. , https://doi.org/10.1037/h0077714, 1980; Saerbeck, M., Bartneck, C., Perception of affect elicited by robot motion (2010) Fifth ACM/IEEE International Conference on Human-Robot Interaction-HRI '10, pp. 53-60. , https://doi.org/10.1145/1734454.1734473; Senecal, S., Cuel, L., Aristidou, A., Magnenat-Thalmann, N., Continuous body emotion recognition system during theater performances: Continuous body emotion recognition (2016) Computer Animation and Virtual Worlds, 27 (3-4), pp. 311-320. , https://doi.org/10.1002/cav.1714, 2016; Simmons, R., Knight, H., Keep on dancing: Effects of expressive motion mimicry (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), , https://doi.org/10.1109/ROMAN.2017.8172382; Stoeva, D., (2018) Robot-Assisted Game Incorporating Emotional Postures for Children with ASD, , Master Thesis. Heriot Watt University, Edinburgh, UK; Stolzenwald, J., Bremner, P., Gesture mimicry in social human-robot interaction (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 430-436. , https://doi.org/10.1109/ROMAN.2017.8172338; Tsiourti, C., Weiss, A., Wac, K., Vincze, M., Designing emotionally expressive robots: A comparative study on the perception of communication modalities (2017) 2017 5th International Conference on Human Agent Interaction (HAI '17), pp. 213-222. , https://doi.org/10.1145/3125739.3125744, ACM, Bielefeld, Germany; Tuyen, V.N.T., Jeong, S., Chong, N.Y., Emotional bodily expressions for culturally competent robots through long term human-robot interaction (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2008-2013. , https://doi.org/10.1109/IROS.2018.8593974; Vicaria, I.M., Dickens, L., Meta-analyses of the intra-and interpersonal outcomes of interpersonal coordination (2016) Journal of Nonverbal Behavior, 40 (4), pp. 335-361. , https://doi.org/10.1007/s10919-016-0238-8, 2016; Zecca, M., Mizoguchi, Y., Endo, K., Iida, F., Kawabata, Y., Endo, N., Itoh, K., Takanishi, A., Whole body emotion expressions for kobian humanoid robot-Preliminary experiments with different emotional patterns (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 381-386. , https://doi.org/10.1109/ROMAN.2009.5326184",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083213455
"Ali W., Williams A.B.","57194724682;7405838909;","Evaluating the effectiveness of nonverbal communication in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"99","100",,1,"10.1145/3371382.3378354","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083202633&doi=10.1145%2f3371382.3378354&partnerID=40&md5=7894b7665040a9c6a82a3abfe66389b3","University of Kansas, Lawrence, KS, United States","Ali, W., University of Kansas, Lawrence, KS, United States; Williams, A.B., University of Kansas, Lawrence, KS, United States","We evaluate the effectiveness of the type of directions given by a robot in a joint-activity task. The joint-activity task we focus on involves a humanoid robot giving directions to a human subject in exploring an unknown environment and finding objects hidden in certain locations inside the environment. The experiment is conducted in the form of a between-group study with 8 participants in which one group is provided directions by the robot verbally and the other group is given nonverbal directions in the form of hand gestures and gaze direction. Our findings show that the type of directions given by the robot do have a noticeable impact on the performance of the human subjects in the experiment. Specifically, the group which is given nonverbal directions by the robot is on average 70% quicker in finding all the hidden objects than the group which is given verbal directions. We evaluate the hypothesis from prior research that the reason for this discrepancy is correlated with the reduced perceived mental workload of the activity by the human subjects when the robot provides nonverbal directions, with the help of post-experiment survey responses provided by the participants of our study groups. © 2020 ACM.","Guide robot; Joint-activity; Kinesics; Nonverbal cues","Agricultural robots; Anthropomorphic robots; Man machine systems; Surveys; Gaze direction; Hidden objects; Human subjects; Humanoid robot; Joint activity; Mental workload; Non-verbal communications; Unknown environments; Human robot interaction",,,,,,,,,,,,,,,,"http://doc.aldebaran.com/2-4/software/choregraphe/index.html, [n. d.]. Choregraphe Suite; https://www.softbankrobotics.com/emea/en/nao, [n. d.]. Nao: The Humanoid and Programmable Robot; Huang, C., Mutlu, B., Modeling and evaluating narrative gestures for humanlike robots (2013) Robotics: Science and Systems; Iio, T., Shiomi, M., Shinozawa, K., Akimoto, T., Shimohara, K., Hagita, N., Entrainment of pointing gestures by robot motion (2010) Proceedings of the Second International Conference on Social Robotics (ICSR'10). Springer-Verlag, pp. 372-381. , http://dl.acm.org/citation.cfmid=1948708.1948755; Lohse, M., Rothuis, R., Gallego-Pérez, J., Karreman, D.E., Evers, V., Robot gestures make difficult tasks easier: The impact of gestures on perceived workload and task performance (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). ACM, pp. 1459-1466. , https://doi.org/10.1145/2556288.2557274; Sales, J., Martí, J.V., Marín, R., Cervera, E., Sanz, P.J., CompaRob: The shopping cart assistance robot (2016) International Journal of Distributed Sensor Networks, 12 (2), p. 4781280. , 2016; Urdiales, C., Peula, J.M., Barrué, C., Pérez, E.J., Sánchez-Tato, I., Del Toro, J.C., Cortés, U., Caltagirone, C., A new collaborative-shared control strategy for continuous elder/robot assisted navigation (2008) Gerontechnology, 7 (2), p. 229. , 2008",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083202633
"Ishiguro H., Kawahara T., Nakamura Y.","26643316500;7202414284;55871593000;","Autonomous dialogue technologies in symbiotic human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"650","651",,1,"10.1145/3371382.3374855","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083201002&doi=10.1145%2f3371382.3374855&partnerID=40&md5=428a68251aaf3e43857b164062c0c99d","Osaka University, Osaka and Kyoto, Japan; Kyoto University, Kyoto, Japan; Osaka University, Toyonaka, Osaka, Japan","Ishiguro, H., Osaka University, Osaka and Kyoto, Japan; Kawahara, T., Kyoto University, Kyoto, Japan; Nakamura, Y., Osaka University, Toyonaka, Osaka, Japan","The aim of this workshop is to discuss autonomous dialogue technologies in ""Symbiotic Human-Robot Interaction."" In order to inspire participants and encourage the discussion, we will introduce our research activities and have invited talks by experts in human-robot interaction and dialogue systems. We also publicly invite poster presentations to share state-of-the-art technologies in this research area. Finally, we clarify the key challenges required to develop companions living together with us through the panel discussion. © 2020 ACM.","Android; Multi-modal dialogue system","Agricultural robots; Man machine systems; Speech processing; Dialogue systems; Invited talk; Panel discussions; Poster presentations; Research activities; State-of-the-art technology; Human robot interaction",,,,,"JPM-JER1401","This work was supported by JST ERATO Grant Number JPM-JER1401, Japan.",,,,,,,,,,"Inoue, K., Lala, D., Takanashi, K., Kawahara, T., Engagement recognition by a latent character model based on multimodal listener behaviors in spoken dialogue (2018) APSIPA Transactions on Signal and Information Processing, 7, p. e9. , 2018; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A Communication robot in a shopping mall (2010) IEEE Trans. Rob, 26 (5), pp. 897-913. , Oct. 2010; Kobayashi, T., Fujie, S., Conversational robots: An approach to conversation protocol issues that utilizes the paralinguistic information available in a robot-human setting (2013) Acoustical Science and Technology, 34 (2), pp. 64-72. , https://doi.org/10.1250/ast.34.64, 2013; Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R., (2019) ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, , (Sept. 2019). ArXiv:cs.CL/1909.11942; Leviathan, Y., Matias, Y., Google Duplex: An AI System for Accomplishing Real-World Tasks over the Phone, , https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html, Accessed: 2019-12-19; MacDorman, K.F., Ishiguro, H., The uncanny advantage of using androids in cognitive and social science research (2006) Interact. Stud, 7 (3), pp. 297-337. , Jan. 2006; Sakai, K., Inago, A., Higashinaka, R., Yoshikawa, Y., Ishiguro, H., Tomita, J., Creating large-scale argumentation structures for dialogue systems Proceedings of the Eleventh International Conference on Language Resources and Evaluation. European Language Resources Association, pp. L18-1627; Shiomi, M., Shatani, K., Minato, T., Ishiguro, H., How should a robot react before people's touch: Modeling a pre-touch reaction distance for a robot's face (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3773-3780. , Oct. 2018",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083201002
"Suddrey G., Robinson N.","57031893500;23767534700;","A software system for human-robot interaction to collect research data: A HTML/javascript service on the pepper robot",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"459","461",,2,"10.1145/3371382.3378287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083183771&doi=10.1145%2f3371382.3378287&partnerID=40&md5=1e7d88b9a9a2658f5e823a0252900ea9","ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia","Suddrey, G., ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia; Robinson, N., ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia","A key area in human-robot interaction research is the use of a robot to collect participant research data. However, traditional website-based data collection methods do not intergrate with the robot providing the interaction. This leaves a clear disconnect between the static delivery of a digital questionnaire and a lack context-relevant behaviours from the robot. In this paper, we present a HTML/Javascript software system to create a direct link between digital data collection and the robot that delivers it. In doing so, this system can be used to create more dynamic data collection sessions using the robot's speech, movement or presence to support the delivery of questionnaires. This system can also be used to create interactive sessions to support experimentation. We present two proposed use-case scenarios built with the system with the Pepper humanoid robot in mental health and well-being services. We present this software system to help speed up development time for user studies, lower the entry barrier for non-technical researchers who want to use social robots for data collection, and to create more systematic data collection methods for robots. Future work of the software includes increasing the repertoire of questionnaire items available to allow for more sophisticated data collection. © 2020 ACM.","Human-robot interaction; Interface design; Robotics; Survey toolkit","Agricultural robots; Anthropomorphic robots; Computer software; HTML; Man machine systems; Social robots; Surveys; Data collection; Data collection method; Development time; Entry barriers; Humanoid robot; Interactive session; Software systems; Use case scenario; Data acquisition",,,,,"Department of Science, Information Technology and Innovation, Queensland Government, DSITI","These authors were supported by a Research Grant from the State of Queensland acting through the Department of Science, Information Technology and Innovation. Professor Daniel Johnson and Dr Jo Mitchell advised on the content in the ’Peppers Well-being Technique’ application.",,,,,,,,,,"Boumans, R., Van Meulen, F., Hindriks, K., Neerincx, M., Rikkert, O.M.G.M., Robot for health data acquisition among older adults: A pilot randomised controlled cross-over trial (2019) BMJ Quality & Safety, , 2019 bmjqs-2018; Lovibond, S.H., Lovibond, P.F., Manual for the depression anxiety stress scales (1996) Psychology Foundation of Australia; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35. , https://doi.org/10.1016/j.robot.2014.09.031, 2015; Niemelä, M., Heikkilä, P., Lammi, H., A Social service robot in a shopping mall: Expectations of the management, retailers and consumers (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI '17), pp. 227-228. , https://doi.org/10.1145/3029798.3038301, ACM, New York, NY, USA; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Choregraphe: A graphical tool for humanoid robot programming (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 46-51. , https://doi.org/10.1109/ROMAN.2009.5326209; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608. , https://doi.org/10.1007/s12369-019-00523-0, 01 Aug 2019; Tanaka, F., Isshiki, K., Takahashi, F., Uekusa, M., Sei, R., Hayashi, K., Pepper learns together with children: Development of an educational application (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 270-275. , https://doi.org/10.1109/HUMANOIDS.2015.7363546",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083183771
"Williams T., Szafir D., Chakraborti T., Soh Khim O., Rosen E., Booth S., Groechel T.","7404172627;50362161600;41261182500;57216331095;57195424425;57194724923;57215285175;","Virtual, augmented, and mixed reality for human-robot interaction (VAM-HRI)",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"663","664",,2,"10.1145/3371382.3374850","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083172037&doi=10.1145%2f3371382.3374850&partnerID=40&md5=7108f9d9081fca7ea0e227354acf6f02","Colorado School of Mines, Golden, CO, United States; University of Colorado Boulder, Boulder, CO, United States; IBM Research AI, Cambridge, MA, United States; National University of Singapore, Singapore, Singapore; Brown University, Providence, RI, United States; Massachusetts Institute of Technology, Cambridge, MA, United States; University of Southern California, Los Angeles, CA, United States","Williams, T., Colorado School of Mines, Golden, CO, United States; Szafir, D., University of Colorado Boulder, Boulder, CO, United States; Chakraborti, T., IBM Research AI, Cambridge, MA, United States; Soh Khim, O., National University of Singapore, Singapore, Singapore; Rosen, E., Brown University, Providence, RI, United States; Booth, S., Massachusetts Institute of Technology, Cambridge, MA, United States; Groechel, T., University of Southern California, Los Angeles, CA, United States","The 3rd International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interactions (VAM-HRI) will bring together HRI, Robotics, and Mixed Reality researchers to address challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of augmented reality interfaces that mediate communication between humans and robots, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. VAM-HRI 2020 will follow on the success of VAM-HRI 2018 and 2019, and advance the cause of this nascent research community. © 2020 ACM.","Augmented reality; Human-robot interaction; Mixed reality; Virtual reality","Agricultural robots; Augmented reality; Machine design; Man machine systems; Mixed reality; Virtual addresses; Design practice; Interactive robot; International workshops; Reality interface; Research communities; Virtual agent; Human robot interaction",,,,,,,,,,,,,,,,"Teja Chadalavada, R., Andreasson, H., Krug, R., Lilienthal, A.J., That's on my mind! robot to human intention communication through on-board projection on shared floor space (2015) ECMR; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., (2017) Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots, , CoRR, abs/1703.08930; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., Projection-aware task planning and execution for human-in-the-loop operation of robots (2018) IROS; Christensen, H.I., Batzinger, T., Bekris, K., Bohringer, K., Bordogna, J., Bradski, G., Brock, O., Eastman, R., A roadmap for us robotics: From internet to robotics (2009) CCC and CRA; Diaz, C., Walker, M., Szafir, D.A., Szafir, D., Designing for depth perceptions in augmented reality (2017) Proc. ISMAR; Gadre, S.Y., Rosen, E., Chien, G., Phillips, E., Tellex, S., Konidaris, G., End-user robot programming using mixed reality (2019) Proc. ICRA; Hedayati, H., Walker, M., Szafir, D., Improving collocated robot teleoperation with augmented reality (2018) Proc. HRI; Huang, B., Bayazit, D., Ullman, D., Gopalan, N., Tellex, S., Flight, camera, action! using natural language and mixed reality to control a drone (2019) ICRA; Leutert, F., Herrmann, C., Schilling, K., A spatial augmented reality system for intuitive display of robotic data (2013) Proc. HRI; Rosen, E., Whitney, D., Phillips, E., Chien, G., Tompkin, J., Konidaris, G., Tellex, S., Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays (2019) IJRR; Sato, S., Sakane, S., A human-robot interface using an interactive hand pointer that projects a mark in the real work space (2000) Proc. ICRA; Shen, J., Jin, J., Gans, N., A multi-view camera-projector system for object detection and robot-human feedback (2013) Proc. ICRA; Szafir, D., Mutlu, B., Fong, T., Communication of intent in assistive free flyers (2014) Proceedings of HRI. ACM; Szafir, D., Mutlu, B., Fong, T., Communicating directionality in flying robots (2015) Proceedings of HRI. ACM; Turk, M., Fragoso, V., Computer vision for mobile augmented reality (2015) Mobile Cloud Visual Media Computing; Vogel, C., (2016) Projection-based Interaction and Safeguarding, , https://goo.gl/61zfnE, FourByThree; Walker, M., Hedayati, H., Lee, J., Szafir, D., Communicating robot motion intent with augmented reality (2018) Proc. HRI; Watanabe, A., Ikeda, T., Morales, Y., Shinozawa, K., Miyashita, T., Hagita, N., Communicating robotic navigational intentions (2015) IROS, pp. 5763-5769. , IEEE; Whitney, D., Rosen, E., Ullman, D., Phillips, E., Tellex, S., Ros reality: A virtual reality framework using consumer-grade hardware for ros-enabled robots (2018) IROS. IEEE; Williams, T., Briggs, G., Oosterveld, B., Scheutz, M., Going beyond literal command-based instructions: Extending robotic natural language interaction capabilities (2015) Proceedings of AAAI; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., The 1st international workshop on virtual, augmented, and mixed reality for humanrobot interaction (2018) AI Magazine; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., Virtual, augmented, and mixed reality for human-robot interaction (2018) Comp. HRI; Zhang, Y., Sreedharan, S., Kulkarni, A., Chakraborti, T., Hankui Zhuo, H., Kambhampati, S., Plan explicability and predictability for robot task planning (2017) ICRA. IEEE; Zhu, D., Veloso, M., Virtually adapted reality and algorithm visualization for autonomous robots (2016) RoboCup Symposium",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083172037
"Honig S., Oron-Gilad T.","57201589559;16042913400;","Comparing laboratory user studies and video-enhancedweb surveys for eliciting user gestures in human-robot interactions",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"248","250",,3,"10.1145/3371382.3378325","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083166516&doi=10.1145%2f3371382.3378325&partnerID=40&md5=26512668011217823a7ee1e7fb2d47ae","Ben-Gurion University of the Negev, Be'er Sheva, Israel","Honig, S., Ben-Gurion University of the Negev, Be'er Sheva, Israel; Oron-Gilad, T., Ben-Gurion University of the Negev, Be'er Sheva, Israel","Laboratory studies are time consuming and costly. We aimed to examine whether the gestures users naturally select to communicate commands to a mobile robot during a laboratory user study can be comparable to those selected during an online video-enhanced survey. 64 participants were divided into two experimental groups according to the interaction methodology. In both conditions, participants instructed the robot to perform eight different tasks using only upper body gestures. For 7 of the 8 tasks we did not find evidence that that the physical gestures by which the participants chose to communicate with the robot depended on the interaction methodology. Our investigation, while still preliminary, may suggest that video enhanced surveys can be used for human robot interaction design and evaluation, especially in the preliminary stages of defining users' existing mental models and expectations. © 2020 ACM.","Human-robot communication; Laboratory studies; Online surveys; Scenario-based evaluation; User gestures; Video surveys","Agricultural robots; Machine design; Man machine systems; Surveys; Experimental groups; Laboratory studies; Mental model; Online video; Upper bodies; User study; Human robot interaction",,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","The first author is supported by The Helmsley Charitable Trust through the Agricultural, Biological, Cognitive Robotics Initiative and the Marcus Endowment Fund, and by Ben-Gurion University through the High-tech, Bio-tech and Chemo-tech Scholarship.",,,,,,,,,,"Fuchs, M., Funke, F., Video Web Survey. Results of an experimental comparison with a text-based Web survey. in Challenges of a changing world (2007) Proceedings of the Fifth International Conference of the Association for Survey Computing. Association for Survey Computing Berkeley, pp. 63-80; David Kidd, C., (2003) Sociable Robots: The Role of Presence and Task in Humanrobot Interaction, , Ph.D. Dissertation. Massachusetts Institute of Technology; Peshkova, E., Hitz, M., Ahlström, D., Exploring user-defined gestures and voice commands to control an unmanned aerial vehicle (2017) Intelligent Technologies for Interactive Entertainment, pp. 47-62. , Ronald Poppe, John-Jules Meyer, Remco Veltkamp, and Mehdi Dastani (Eds.). Springer International Publishing, Cham; Powers, A., Kiesler, S., The advisor robot: Tracing people's mental model from a robot's physical attributes (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction (HRI '06), pp. 218-225. , https://doi.org/10.1145/1121241.1121280, ACM, New York, NY, USA; Ratchford, M., Barnhart, M., Development and validation of the technology adoption propensity (TAP) index (2012) Journal of Business Research, 65 (8), pp. 1209-1215. , https://doi.org/10.1016/j.jbusres.2011.07.001, 2012; Riek, L.D., Rabinowitch, T., Bremner, P., Pipe, A.G., Fraser, M., Robinson, P., Cooperative gestures: Effective signaling for humanoid robots (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 61-68. , https://doi.org/10.1109/HRI.2010.5453266; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., How the timing and magnitude of robot errors influence peoples' trust of robots in an emergency scenario (2017) Social Robotics, Abderrahmane Kheddar, Eiichi Yoshida, Shuzhi Sam Ge, Kenji Suzuki, John-john Cabibihan, pp. 42-52. , friederike eyssel, and hongsheng he (eds.). springer international publishing, cham; Saez-Pons, J., Lehmann, H., Syrdal, D.S., Dautenhahn, K., Development of the sociability of non-anthropomorphic robot home companions (2014) 4th International Conference on Development and Learning and on Epigenetic Robotics, pp. 111-116. , https://doi.org/10.1109/DEVLRN.2014.6982964; Sverre Syrdal, D., Dautenhahn, K., Lee Koay, K., Walters, M.L., The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study (2009) Adaptive and Emergent Behaviour and Complex Systems, , 2009; Sverre Syrdal, D., Otero, N., Dautenhahn, K., Video prototyping in human-robot interaction: Results from a qualitative study (2008) Proceedings of the 15th European Conference on Cognitive Ergonomics: The Ergonomics of Cool Interaction (ECCE '08), 29 (8). , https://doi.org/10.1145/1473018.1473055, ACM, New York, NY, USA, Article; Weiss, A., Wurhofer, D., Bernhaupt, R., Altmaninger, M., Tscheligi, M., A methodological adaptation for heuristic evaluation of HRI (2010) 19th International Symposium in Robot and Human Interactive Communication, pp. 1-6. , https://doi.org/10.1109/ROMAN.2010.5598735; Woods, S.N., Walters, M.L., Koay, K.L., Dautenhahn, K., Methodological issues in hri: A comparison of live and video-based methods in robot to human approach direction trials (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 51-58. , https://doi.org/10.1109/ROMAN.2006.314394; Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., Park, T., Methodological issues in scenario-based evaluation of human-robot interaction (2015) International Journal of Social Robotics, 7 (2), pp. 279-291. , 2015",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083166516
"Law T.","57212389268;","Measuring relational trust in human-robot interactions",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"579","581",,3,"10.1145/3371382.3377435","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083161577&doi=10.1145%2f3371382.3377435&partnerID=40&md5=4a5bc90aaabc93a13647203a16db44e3","Tufts University, Medford, MA, United States","Law, T., Tufts University, Medford, MA, United States","Trust is an integral part of almost any human-robot interaction (HRI). The most technologically advanced robot can sit unused if a human interactant does not trust it. Conversely, a robot that is overly trusted may be assumed to be more advanced than it truly is, resulting in over-reliance on an imperfect system [8]. One of the most widely used definitions for trust in HRI is that trust is ""the attitude that an agent will help achieve an individual's goals in a situation characterized by uncertainty and vulnerability."" [6]. As robots initially emerged into society in factory roles, their goals were clear and their performance was concretely measurable with metrics such as time to completion, number of errors in a given behavior, and behavior consistency over time. The humans who worked with them, therefore, could base their trust in the robots on how effectively they achieved these clearly defined goals. © 2020 ACM.","Human-robot interaction; Human-robot trust; Relational trust; Trust","Agricultural robots; Man machine systems; Behavior consistency; Human robot Interaction (HRI); Imperfect systems; Integral part; Over reliance; Human robot interaction",,,,,"National Science Foundation, NSF: IIS-1316809; Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0465","This project was funded in part by the National Science Foundation grant IIS-1316809 and the AFOSR grant FA9550-18-1-0465.",,,,,,,,,,"Caruso, D.R., Salovey, P., (2004) The Emotionally Intelligent Manager: How to Develop and Use the Four Key Emotional Skills of Leadership, , John Wiley & Sons; Clarke, N., Emotional intelligence and its relationship to transformational leadership and key project manager competences (2010) Project Management Journal, 41 (2), pp. 5-20. , 2010; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527. , 2011; Law, T., Chita-Tegmark, M., Scheutz, M., Forthcoming. The Interplay between Emotional Intelligence, Trust, and Gender in Human-robot Interaction. International Journal of Social Robotics (Forthcoming); Law, T., Scheutz, M., Forthcoming. Trust: Recent Concepts and Evaluations in Human-robot Interaction. in Trust in Human-Robot Interaction: Research and Applications, , Chang Nam and Joe Lyons (Eds.). Elsevier, San Diego, CA, Chapter 2; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80. , 2004; Mayer, R.C., Davis, J.H., The effect of the performance appraisal system on trust for management: A field quasi-experiment (1999) Journal of Applied Psychology, 84 (1), p. 123. , 1999; Parasuraman, R., Riley, V., Humans and automation: Use, misuse, disuse, abuse (1997) Human Factors, 39 (2), pp. 230-253. , 1997; Salovey, P., Mayer, J.D., Emotional intelligence (1990) Imagination, Cognition and Personality, 9 (3), pp. 185-211. , 1990; Ullman, D., Malle, B.F., What does it mean to trust a robot: Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 263-264","Law, T.; Tufts UniversityUnited States; email: theresa.law@tufts.edu",,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083161577
"Schrum M.L., Johnson M., Ghuy M., Gombolay M.C.","57208165285;57216333404;57215827463;55806238000;","Four years in review: Statistical practices of likert scales in human-robot interaction studies",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"43","52",,5,"10.1145/3371382.3380739","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083160218&doi=10.1145%2f3371382.3380739&partnerID=40&md5=6cbdd405770972856b18074d018da56e","Georgia Institute of Technology, Atlanta, GA, United States","Schrum, M.L., Georgia Institute of Technology, Atlanta, GA, United States; Johnson, M., Georgia Institute of Technology, Atlanta, GA, United States; Ghuy, M., Georgia Institute of Technology, Atlanta, GA, United States; Gombolay, M.C., Georgia Institute of Technology, Atlanta, GA, United States","As robots become more prevalent, the importance of the field of human-robot interaction (HRI) grows accordingly. As such, we should endeavor to employ the best statistical practices. Likert scales are commonly used metrics in HRI to measure perceptions and attitudes. Due to misinformation or honest mistakes, most HRI researchers do not adopt best practices when analyzing Likert data. We conduct a review of psychometric literature to determine the current standard for Likert scale design and analysis. Next, we conduct a survey of four years of the International Conference on Human-Robot Interaction (2016 through 2019) and report on incorrect statistical practices and design of Likert scales. During these years, only 3 of the 110 papers applied proper statistical testing to correctly-designed Likert scales. Our analysis suggests there are areas for meaningful improvement in the design and testing of Likert scales. Lastly, we provide recommendations to improve the accuracy of conclusions drawn from Likert data. © 2020 ACM.","Likert scales; Metrics for hri; Statistical practices","Agricultural robots; Machine design; Man machine systems; Best practices; Design and analysis; Human robot Interaction (HRI); Likert data; Likert scale; Statistical testing; Human robot interaction",,,,,"National Science Foundation, NSF: 1545287; Georgia Institute of Technology, Georgia Tech","We thank Ankit Shah for his statistical insights and support. This work was supported by institute funding at the Georgia Institute of Technology and NSF ARMS Fellowship under Grant #1545287.",,,,,,,,,,"Allen, I.E., Seaman, C.A., (2007) Likert Scales and Data Analyses; Asún, R.A., Rdz-Navarro, K., Alvarado, J.M., Developing multidimensional likert scales using item factor analysis: The case of fourpoint items (2016) Sociological Methods and Research, 45 (1), pp. 109-133. , https://doi.org/10.1177/0049124114566716, 2016; Austin, P.C., Mamdani, M.M., Juurlink, D.N., Hux, J.E., Testing multiple statistical hypotheses resulted in spurious associations: A study of astrological signs and health (2006) Journal of Clinical Epidemiology, 59 (9), pp. 964-969. , https://doi.org/10.1016/j.jclinepi.2006.01.012, 2006; Balasubramanian, N., Likert technique of attitude scale construction in nursing research (2012) Cultural Anthropology Methods, 2. , 2012; Bendig, A.W., (1953) The Reliability of Self-Ratings As A Function of the Amount of Verbal Anchoring and of the Number of Categories on the Scale, 37 (1), pp. 38-41. , 1953; Bergkvist, L., Rossiter, J.R., (2007) The Predictive Validity of Multiple-Item Versus Single-Item Measures of the Same Constructs, p. 2437. , 2007; Bishop, P.A., Herron, R.L., Use and misuse of the likert item responses and other ordinal measures (2015) International Journal of Exercise Science, 8 (3), pp. 297-302. , http://www.ncbi.nlm.nih.gov/pubmed/27182418{%}0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgiartid=PMC4833473, 2015; Blair, C.R., A reaction to consequences of failure to meet assumptions underlying the fixed effects analysis of variance and covariance (1981) Review of Educational Research, 51 (4), pp. 499-507. , 1981; Brooke, J., SUS: A quick and dirty usability scale (1996) Usability Evaluation in Industry. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083160218
"Dobrosovestnova A., Hannibal G.","57199645448;56512012200;","Teachers' disappointment: Theoretical perspective on the inclusion of ambivalent emotions in human-robot interactions in education",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"471","480",,,"10.1145/3319502.3374816","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082021189&doi=10.1145%2f3319502.3374816&partnerID=40&md5=de151492edf988bc9f14487cf171c721","Austrian Institute for Artificial Intelligence (OFAI), Vienna, Austria; Human Computer Interaction, Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Dobrosovestnova, A., Austrian Institute for Artificial Intelligence (OFAI), Vienna, Austria; Hannibal, G., Human Computer Interaction, Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Following affective turn in cognitive science, recent decades have witnessed an increasing interest toward the role of emotions in education. Ample evidence suggests that learners and teachers experience a variety of emotions, ranging from joy and pride to anger and frustration. However, when it comes to the design of affective behavior in robotic systems for education purposes, the emphasis has been predominantly on communication of positive emotions. While we recognize that positive emotions are fundamental to successful learning, in this paper we wish to make the case for the consideration of ambivalent emotions for the design of social robots for tutoring. To ground this proposal, we focus on the emotion of teachers' disappointment. First, we discuss under which conditions communicated teachers' disappointment, while it may be experienced as emotionally ambivalent by teachers and students, functions as an affiliating pedagogical strategy. We proceed to sketch out the methodological suggestions we consider relevant for future studies of communicated disappointment in human-robot interactions within learning contexts. We conclude with critical reflections about the ethics of responsible designs of such studies. © 2020 Association for Computing Machinery.","Complex emotions; Disappointment; Human-robot interaction; Learning; Robotic tutors; Socio-cognitive paradigm; Teaching","Human robot interaction; Man machine systems; Robotics; Social robots; Teaching; Affective behaviors; Complex emotions; Critical reflections; Disappointment; Learning; Pedagogical strategies; Positive emotions; Socio-cognitive; Machine design",,,,,,,,,,,,,,,,"Alves-Oliveira, P., Sequeira, P., Paiva, A., The role that an educational robot plays (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 817-822. , IEEE; Arroyo, I., Beal, C., Murray, T., Walles, R., Woolf, B.P., Web-based intelligent multimedia tutoring for high stakes achievement tests (2004) International Conference on Intelligent Tutoring Systems, pp. 468-477. , Springer; James, R., Averill, A constructivist view of emotion (1980) Theories of Emotion, pp. 305-339. , Elsevier; Bandura, A., (1986) Social Foundations of Thought and Action, , Englewood Cliffs, NJ 19861986); Susann Becker, E., Goetz, T., Morger, V., Ranellucci, J., The importance of teachers' emotions and instructional behavior for their students' emotions-an experience sampling analysis (2014) Teaching and Teacher Education, 43, pp. 15-26. , 2014; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. 5954. , 2018; Beran, T.N., Ramirez-Serrano, A., Kuzyk, R., Fior, M., Nugent, S., Understanding how children understand robots: Perceived animism in child-robot interaction (2011) International Journal of Human-Computer Studies, 69 (7-8), pp. 539-550. , 2011; Brogaard Bertel, L., Hannibal, G., Tema 2: The nao robot as a persuasive educational and entertainment robot (peer)-a case study on children's articulation, categorization and interaction with a social robot for learning (2016) Tidsskriftet Læring Og Medier (LOM), 8 (14). , 2016; Brogaard Bertel, L., Malig Rasmussen, D., Christiansen, E., Robots for real: Developing a participatory design framework for implementing educational robots in real-world learning environments (2013) IFIP Conference on Human-Computer Interaction, pp. 437-444. , Springer; Björling, E.A., Rose, E., Participatory research principles in humancentered design: Engaging teens in the co-design of a social robot (2019) Multimodal Technologies and Interaction, 3 (1), p. 8. , 2019; Breazeal, C., (2002) Designing Sociable Robots, , MIT Press, Cambridge, MA, USA; Breazeal, C., Toward sociable robots (2003) Robotics and Autonomous Systems, 42 (3), pp. 167-175. , http://doi.org/10.1016/S0921-8890(02)00373-1, 2003; Brown, B., (2015) Daring Greatly: How the Courage to Be Vulnerable Transforms the Way We Live, Love, Parent, and Lead, , Penguin; Butler, J., (2002) Gender Trouble, , routledge; Chase, C.C., Chin, D.B., Oppezzo, M.A., Schwartz, D.L., Teachable agents and the protégé effect: Increasing the effort towards learning (2009) Journal of Science Education and Technology, 18 (4), pp. 334-352; Cho, E., Lee, K., Cherniak, S., Eun Jung, S., Heterogeneous associations of second-graders' learning in robotics class (2017) Technology, Knowledge and Learning, 22 (3), pp. 465-483. , 2017; Coeckelbergh, M., Are emotional robots deceptive (2011) IEEE Transactions on Affective Computing, 3 (4), pp. 388-393. , 2011; Coeckelbergh, M., (2013) Human Being@ Risk: Enhancement, Technology, and the Evaluation of Vulnerability Transformations., 12. , Springer Science & Business Media; Coeckelbergh, M., Care robots and the future of ict-mediated elderly care: A response to doom scenarios (2016) AI & Society, 31 (4), pp. 455-462. , 2016; Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., Paiva, A., Just follow the suit! 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082021189
"Zanatto D., Patacchiola M., Goslin J., Thill S., Cangelosi A.","55463260500;56537094300;55915556900;36624312800;6701387796;","Do humans imitate robots? An investigation of strategic social learning in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"449","457",,5,"10.1145/3319502.3374776","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082011166&doi=10.1145%2f3319502.3374776&partnerID=40&md5=a0ab5afdadcc7aa152619a46fd591147","School of Computer Science, University of Plymouth, Plymouth, United Kingdom; School of Psychology, University of Plymouth, Plymouth, United Kingdom; Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; Department of Computer Science, University of Manchester, Manchester, United Kingdom; Department of Computer Science, University of Bristol, Bristol, United Kingdom; School of Informatics, University of Edinburgh, Edinburgh, The chameleon effect: The perception-behavior link and social interaction, United Kingdom","Zanatto, D., School of Computer Science, University of Plymouth, Plymouth, United Kingdom, Department of Computer Science, University of Bristol, Bristol, United Kingdom; Patacchiola, M., School of Computer Science, University of Plymouth, Plymouth, United Kingdom, School of Informatics, University of Edinburgh, Edinburgh, The chameleon effect: The perception-behavior link and social interaction, United Kingdom; Goslin, J., School of Psychology, University of Plymouth, Plymouth, United Kingdom; Thill, S., Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; Cangelosi, A., Department of Computer Science, University of Manchester, Manchester, United Kingdom","Theories on social learning indicate that imitative choices are usually performed whenever copying the others' behaviour has no additional cost. Here, we extended such investigations of social learning to Human-Robot Interaction (HRI). Participants played the Economic Investment Game with a robot banker while observing another robot player also investing in the robot banker. By manipulating the robot banker payoff, three conditions of unfairness were created: (1) unfair payoff for the participants, (2) unfair payoff for the robot player and (3) unfair payoff for both. Results showed that when the payoff was low for the participants and high for the robot player, participants invested more money in the robot banker than when both parties received a low return. Also, for this specific condition, participants' investments increased further with a more interactive robot player (defined as demonstrating increased attention, congruent movements and speech) This suggests that social and cognitive human competencies can be used and transposed to non-human agents. Further, imitation can potentially be extended to HRI, with interactivity likely having a key role in increasing this effect. © 2020 Association for Computing Machinery.","Human-Robot Interaction; Imitation; Investment Game; Social Learning; Strategy","Human robot interaction; Investments; Man machine systems; Additional costs; Human competencies; Human robot Interaction (HRI); Imitation; Interactive robot; Interactivity; Social learning; Strategy; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR; Air Force Materiel Command, AFMC; U.S. Air Force, USAF: FA9550-19-1-7002","This material is based upon work supported by the Air Force Office of Scientific Research, Air Force Materiel Command, USAF under Award No, FA9550-19-1-7002 (THRIVE++).",,,,,,,,,,"Ali, S., Moroso, T., Breazeal, C., Can children learn creativity from a social robot (2019) Proceedings of the 2019 on Creativity and Cognition, pp. 359-368. , ACM; Bandura, A., (1962) Social Learning Through Imitation, , Univer. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85082011166
"Natarajan M., Gombolay M.","57215830081;55806238000;","Effects of anthropomorphism and accountability on trust in human robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"33","42",,21,"10.1145/3319502.3374839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082003650&doi=10.1145%2f3319502.3374839&partnerID=40&md5=ea79419a97d8ba6db2f7852c216535f4","Georgia Institute of Technology, Atlanta, GA, United States","Natarajan, M., Georgia Institute of Technology, Atlanta, GA, United States; Gombolay, M., Georgia Institute of Technology, Atlanta, GA, United States","This paper examines how people's trust and dependence on robot teammates providing decision support varies as a function of different attributes of the robot, such as perceived anthropomorphism, type of support provided by the robot, and its physical presence.We conduct a mixed-design user study with multiple robots to investigate trust, inappropriate reliance, and compliance measures in the context of a time-constrained game.We also examine how the effect of human accountability addresses errors due to over-compliance in the context of human robot interaction (HRI). This study is novel as it involves examining multiple attributes at once, thus enabling us to perform multi-way comparisons between different attributes on trust and compliance with the agent. Results from the 4x4x2x2 study show that behavior and anthropomorphism of the agent are the most significant factors in predicting the trust and compliance with the robot. Furthermore, adding a coalition-building preface, where the agent provides context to why it might make errors while giving advice, leads to an increase in trust for specific behaviors of the agent. © 2020 Association for Computing Machinery.","Accountability; Coalition-building; Compliance; HRI; Inappropriate Reliance; Trust","Decision support systems; Machine design; Man machine systems; Accountability; Compliance; Decision supports; Human robot Interaction (HRI); Inappropriate Reliance; Multiple attributes; Multiple robot; Trust; Human robot interaction",,,,,"Georgia Institute of Technology, Georgia Tech","We would like to thank Woradorn Kamolopornwijit for his support in designing and developing the game leveraged for this user study. This work was sponsored by institutional funding from the Georgia Institute of Technology.",,,,,,,,,,"Bainbridge, W., Hart, J., Kim, E., Scassellati, B., The benefits of interactions with physically present robots over video-displayed agents (2011) International Journal of Social Robotics, 3 (10), pp. 41-52. , http://doi.org/10.1007/s12369-010-0082-7, 2011; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2008) International Journal of Social Robotics, 1 (1), pp. 71-81. , http://doi.org/10.1007/s12369-008-0001-3, 2008; Benitez, J., Wyman, A., Carpinella, C., Stroessner, S., (2017) The Authority of Appearance: How Robot Features Influence Trait Inferences and Evaluative Responses, , http://doi.org/10.1109/ROMAN.2017.8172333; De Visser, E., Parasuraman, R., Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload (2011) Journal of Cognitive Engineering and Decision Making, 5 (6), pp. 209-231. , http://doi.org/10.1177/1555343411410160, 2011; 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082003650
[No author name available],[No author id available],"HRI 2020 - Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",642,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081996091&partnerID=40&md5=ff995bb8833b1bf3474e28d612bb8050",,"","The proceedings contain 66 papers. The topics discussed include: taxonomy of trust-relevant failures and mitigation strategies; why should we gender? the effect of robot gendering and occupational stereotypes on human trust and perceived competency; examining profiles for robotic risk assessment: does a robot's approach to risk affect user trust?; effects of anthropomorphism and accountability on trust in human robot interaction; when humans aren't optimal: robots that collaborate with risk-aware humans; behavioral responses to robot conversational failures; working with a social robot in school: a long-term real-world unsupervised deployment; the persistence of first impressions: the effect of repeated interactions on the perception of a social robot; and in storage, yet on display: an empirical investigation of robots' value as social signals.",,,,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85081996091
"Coyne A.K., Murtagh A., McGinn C.","57215828426;57208161184;36651232500;","Using the Geneva emotion wheel to measure perceived affect in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"491","498",,4,"10.1145/3319502.3374834","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081988316&doi=10.1145%2f3319502.3374834&partnerID=40&md5=e5c713f805098e48a5ff312ad233653e","Trinity College, Dublin, Ireland; Akara Robotics, Dublin, Ireland","Coyne, A.K., Trinity College, Dublin, Ireland; Murtagh, A., Akara Robotics, Dublin, Ireland; McGinn, C., Trinity College, Dublin, Ireland","The ability to clearly communicate a wide range of emotional states is considered a desirable trait for social robots. This research proposes that the Geneva Emotion Wheel (GEW), a self-report instrument for measuring emotional reactions, has strong potential for use as a tool for evaluating the expression of affective content by robots. Factors that make the GEW advantageous over existing evaluation methods include: Ease of administration, reduction in the importance of word labels, and coverage of 'no emotion' states. Statistical analyses of the GEW are proposed, isolating quantitative metrics of emotion distinctness. An experiment requiring participants to rate the perceived emotion of a social robot was conducted, employing the proposed methods. Analysis using the GEW revealed significant differences in the reliability of different expressions to clearly convey emotional states. The GEW provided a repeatable, systematic framework for estimating perceived affect of robot expression. Thus, the results suggest the GEW offers a powerful tool for design purposes as well as analysis. To support future research using the GEW, the software used for the analysis has been packaged and made available as an open-source resource to the community. © 2020 Association for Computing Machinery.","Affect measurement; Emotion; Geneva emotion wheel; Human-robot interaction; Stevie robot","Human robot interaction; Man machine systems; Open source software; Open systems; Reliability analysis; Social robots; Wheels; Emotion; Emotional reactions; Emotional state; Evaluation methods; Open sources; Quantitative metrics; Systematic framework; Behavioral research",,,,,,,,,,,,,,,,"Álvarez, M., Galán, R., Matiá, F., Rodríguez-Losada, D., Jiménez, A., An emotional model for a guide robot (2010) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40 (5), pp. 982-992. , 2010; Bänziger, T., Scherer, K.R., Introducing the geneva multimodal emotion portrayal (gemep) corpus (2010) Blueprint for Affective Computing: A Sourcebook, pp. 271-294. , 2010; Bazo, D., Vaidyanathan, R., Lentz, A., Melhuish, C., Design and testing of a hybrid expressive face for a humanoid robot (2010) Intelligent Robots and Systems (IROS) 2010 IEEE/RSJ International Conference on, pp. 5317-5322. , IEEE; Beck, A., Hiolle, A., Mazel, A., Canãmero, L., Interpretation of emotional body language displayed by robots (2010) Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments, pp. 37-42. , ACM; Becker-Asano, C., Ishiguro, H., Laughter in social robotics-no laughing matter (2009) Intl. Workshop on Social Intelligence Design, pp. 287-300; Berns, K., Hirth, J., Control of facial expressions of the humanoid robot head roman (2006) Intelligent Robots and Systems 2006IEEE/RSJ International Conference on, pp. 3119-3124. , IEEE; Bradley, M.M., Lang, P.J., Measuring emotion: The selfassessment manikin and the semantic differential (1994) Journal of Behavior Therapy and Experimental Psychiatry, 25 (1), pp. 49-59. , 1994; Breazeal, C., Scassellati, B., How to build robots that make friends and influence people (1999) Intelligent Robots and Systems, 1999. IROS'99. Proceedings. 1999 IEEE/RSJ International Conference on, 2, pp. 858-863. , IEEE; Canãmero, L., Aylett, R., (2008) Animating Expressive Characters for Social Interaction., 74. , John Benjamins Publishing; Canãmero, L., Fredslund, J., I showyou howi like you-can you read it in my face [robotics] (2001) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31 (5), pp. 454-459. , 2001; Chen, T.L., King, C., Thomaz, A.L., Kemp, C.C., Touched by a robot: An investigation of subjective responses to robot-initiated touch (2011) 2011 6th ACM/ IEEE International Conference on Human-Robot Interaction (HRI), pp. 457-464. , IEEE; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139. , 2004; De Gelder, B., Why bodies twelve reasons for including bodily expressions in affective neuroscience (2009) Philosophical Transactions of the Royal Society B: Biological Sciences, 364 (1535), pp. 3475-3484. , 2009; De Gelder, B., Van Den Stock, J., The bodily expressive action stimulus test (beast). Construction and validation of a stimulus basis for measuring perception of whole body expression of emotions (2011) Frontiers in Psychology, 2. , 2011; Dewan, S., Agarwal, S., Singh, N., Laban movement analysis to classify emotions from motion (2018) Tenth International Conference on Machine Vision (ICMV 2017). International Society for Optics and Photonics, pp. 10696-106962Q; Brian, R., Duffy, Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 177-190. , 2003; Fussell, S.R., Kiesler, S., Setlock, L.D., Yew, V., How people anthropomorphize robots (2008) 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 145-152. , IEEE; Gendall, P., Hoek, J., Gendall, K., Evaluating the emotional impact of warning images on young adult smokers and susceptible non-smokers (2018) Journal of Health Communication, 23 (3), pp. 291-298. , mar 2018; Gockley, R., Simmons, R., Forlizzi, J., Modeling affect in socially interactive robots (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 558-563. , IEEE; Grandjean, D., Sander, D., Scherer, K.R., Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization (2008) Consciousness and Cognition, 17 (2), pp. 484-495. , 2008; Gunes, H., Pantic, M., Automatic, dimensional and continuous emotion recognition (2010) International Journal of Synthetic Emotions (IJSE), 1 (1), pp. 68-99. , 2010; Gurtman, M.B., Balakrishnan, J.D., Circular measurement redux: The analysis and interpretation of interpersonal circle profiles (1998) Clinical Psychology: Science and Practice, 5 (3), pp. 344-360. , 1998; Gurtman, M.B., Pincus, A.L., The circumplex model: Methods and research applications (2003) American Cancer Society, pp. 407-428. , http://doi.org/10.1002/0471264385.wei0216, Chapter 16; Hagelbäck, J., Hilborn, O., Jeri, P., Johansson, S.J., Lindley, C.A., Svensson, J., Wen, W., Psychophysiological interaction and empathic cognition for human-robot cooperative work (psyintec) (2014) Gearing Up and Accelerating Cross-fertilization between Academic and Industrial Robotics Research in Europe, pp. 283-299. , Springer; Hu, Y., Hoffman, G., Using skin texture change to design emotion expression in social robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 2-10. , IEEE; Kishi, T., Otani, T., Endo, N., Kryczka, P., Hashimoto, K., Nakata, K., Takanishi, A., Development of expressive robotic head for bipedal humanoid robot (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4584-4589. , IEEE; Korovina, O., Casati, F., Baez, M., Berestneva, O., Nielek, R., Investigating crowdsourcing as a method to collect emotion labels for images (2018) Conference on Human Factors in Computing Systems-Proceedings, pp. 1-6. , 2018-April, ACM Press, New York, New York, USA; Kühnlenz, K., Sosnowski, S., Buss, M., Impact of animal-like features on emotion expression of robot head eddie (2010) Advanced Robotics, 24 (8-9), pp. 1239-1255. , 2010; Kulic, D., Croft, E.A., Affective state estimation for human-robot interaction (2007) IEEE Transactions on Robotics, 23 (5), pp. 991-1000. , 2007; Latta, C., Alvarado, N., Adams, S.S., Burbeck, S., An expressive system for endowing robots or animated characters with affective facial displays (2002) The 2002 Conference of the British Society for Artificial Intelligence and the Simulation of Behavior; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Modelling empathic behaviour in a robotic game companion for children: An ethnographic study in real-world settings (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 367-374. , ACM; Varichand Mardia, K., (1972) Statistics of Directional Data, , Academic Press; Marg, E., Descartes' error: Emotion, reason, and the human brain (1995) Optometry and Vision Science, 72 (11), pp. 847-848. , 1995; McGinn, C., Why do robots need a head the role of social interfaces on service robots (2019) International Journal of Social Robotics, pp. 1-15. , 2019; McGinn, C., Bourke, E., Murtagh, A., Donovan, C., Lynch, P., Cullinan, M.F., Kelly, K., Meet stevie: A socially assistive robot developed through application of a 'design-thinking'approach (2019) Journal of Intelligent & Robotic Systems, pp. 1-20. , 2019; McGinn, C., Kelly, K., Using the geneva emotion wheel to classify the expression of emotion on robots (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 191-192. , ACM; Mehrabian, A., (1971) Silent Messages, 8. , Wadsworth Belmont, CA; Nixon, M., Pasquier, P., Seif El-Nasr, M., Delsartmap: Applying delsarte's aesthetic system to virtual agents (2010) International Conference on Intelligent Virtual Agents, pp. 139-145. , Springer; Ragot, M., Martin, N., Em, S., Pallamin, N., Diverrez, J., Emotion recognition using physiological signals: Laboratory vs wearable sensors (2017) International Conference on Applied Human Factors and Ergonomics, pp. 15-22. , Springer; James, A., Russell, A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), p. 1161. , 1980; Russell, J.A., Weiss, A., Mendelsohn, G.A., Affect grid: A single-item scale of pleasure and arousal (1989) Journal of Personality and Social Psychology, 57 (3), p. 493. , 1989; Sacharin, V., Schlegel, K., Scherer, K.R., (2012) Geneva Emotion Wheel Rating Study, , Center for Person, Kommunikation, Aalborg University, NCCR Affective Sciences. Aalborg University, Aalborg (2012); Saerbeck, M., Bartneck, C., Perception of affect elicited by robot motion (2010) Human-Robot Interaction (HRI) 2010 5th ACM/IEEE International Conference on, pp. 53-60. , IEEE; Klaus, R., Scherer, What are emotions and how can they be measured (2005) Social Science Information, 44 (4), pp. 695-729. , 2005; Scherer, K.R., Shuman, V., Fontaine, J.R., Soriano, C., The grid meets the wheel: Assessing emotional feeling via self-report (2013) Components of Emotional Meaning: A Sourcebook, pp. 281-298. , 2013; Shayganfar, M., Rich, C., Sidner, C.L., A design methodology for expressing emotion on robot faces (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4577-4583. , IEEE; Shinozawa, K., Reeves, B., Wise, K., Lim, S., Maldonado, H., Naya, F., Robots as new media: A cross-cultural examination of social and cognitive responses to robotic and on-screen agents (2003) Proceedings of Annual Conference of Internation Communication Association, pp. 998-1002; Song, S., Yamada, S., Expressing emotions through color, sound, and vibration with an appearance-constrained social robot (2017) Proceedings of the 2017 ACM/ IEEE International Conference on Human-Robot Interaction, pp. 2-11. , ACM; Swangnetr, M., Kaber, D.B., Emotional state classification in patient-robot interaction using wavelet analysis and statistics-based feature selection (2012) IEEE Transactions on Human-Machine Systems, 43 (1), pp. 63-75. , 2012; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 314-321. , IEEE; Thomas, F., Johnston, O., Thomas, F., (1995) The Illusion of Life: Disney Animation, , Hyperion New York; Trovato, G., Kishi, T., Endo, N., Hashimoto, K., Takanishi, A., Development of facial expressions generator for emotion expressive humanoid robot (2012) 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pp. 303-308. , IEEE; Tschöpe, N., Elias Reiser, J., Oehl, M., Exploring the uncanny valley effect in social robotics (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 307-308. , ACM; Wallbott, H.G., Bodily expression of emotion (1998) European Journal of Social Psychology, 28 (6), pp. 879-896. , 1998; Watson, D., Clark, L.A., Tellegen, A., Development and validation of brief measures of positive and negative affect: The panas scales (1988) Journal of Personality and Social Psychology, 54 (6), p. 1063. , 1988; Yaffe, P., The 7% rule: Fact, fiction, or misunderstanding (2011) Ubiquity, , 2011, October 2011, 1",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85081988316
"Zhang K., Yu Z., Zhang D., Wang Z., Guo B.","57207105166;16178409600;57192814397;56086598300;55265626800;","RaCon: A gesture recognition approach via Doppler radar for intelligent human-robot interaction",2020,"2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",,,"9156109","","",,1,"10.1109/PerComWorkshops48775.2020.9156109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092003912&doi=10.1109%2fPerComWorkshops48775.2020.9156109&partnerID=40&md5=0e7470d998edfea6c36344a3aba1462f","School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","Zhang, K., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Yu, Z., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Zhang, D., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Wang, Z., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Guo, B., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","As an important entrance for human-robot interaction, the hand gesture recognition based on wireless sensor has received great attention in recent years. By recognizing fine-grained arm movements, remotely deployed collaborative robot could work more accurately to satisfy human demands. Existing approaches mostly use wearable sensors or wireless devices to recognize human movement, which is with strict position requirements. In this paper, we propose a robust gesture recognition method based on double Doppler radars. Specifically, we use two Doppler radars to collect two sources of doppler signal of a gesture. Then 6 types of gestures with different angles between people and the radar were classified by employing an improved dynamic time warping (DTW) algorithm. Furthermore, we demonstrate the practicability of the proposed method by developing a cooperative robot control system and the average recognition accuracy is 96%. © 2020 IEEE.","Doppler radar; Gesture recognition; Human-robot interaction; Signal synthesis analysis; Wireless sensing","Doppler radar; Intelligent robots; Man machine systems; Social robots; Ubiquitous computing; Wearable sensors; Collaborative robots; Cooperative robot control; Doppler signals; Dynamic time warping algorithms; Hand-gesture recognition; Human movements; Recognition accuracy; Wireless devices; Gesture recognition",,,,,,,,,,,,,,,,"Jacob, M., Li, Y.-T., Akingba, G., Wachs, J.P., Gestonurse: A robotic surgical nurse for handling surgical instruments in the operating room (2012) Journal of Robotic Surgery, 6 (1), pp. 53-63; Park, T., Lee, J., Hwang, I., Yoo, C., Nachman, L., Song, J., E-gesture: A collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices (2011) Proceedings of the 9th Acm Conference on Embedded Networked Sensor Systems, pp. 260-273; Fang, B., Lane, N.D., Zhang, M., Boran, A., Kawsar, F., Bodyscan: Enabling radio-based sensing on wearable devices for contactless ac-tivity and vital sign monitoring (2016) Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, pp. 97-110; Gupta, S., Morris, D., Patel, S., Tan, D., Soundwave: Using the doppler effect to sense gestures (2012) Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 1911-1914; Ketabdar, H., Moghadam, P., Naderi, B., Roshandel, M., Magnetic signatures in air for mobile devices (2012) Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services Companion, pp. 185-188; Agrawal, S., Constandache, I., Gaonkar, S., Roy Choudhury, R., Caves, K., DeRuyter, F., Using mobile phones to write in air (2011) Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 15-28; Abdelnasser, H., Youssef, M., Harras, K.A., Wigest: A ubiquitous wifi-based gesture recognition system (2015) 2015 Ieee Conference on Computer Communications INFOCOM, pp. 1472-1480; Li, H., Yang, W., Wang, J., Xu, Y., Huang, L., Wifinger: Talk to your smart devices with finger-grained gesture (2016) Proceedings of the 2016 Acm International Joint Conference on Pervasive and Ubiquitous Computing, pp. 250-261; Al-Qaness, M., Li, F., Wiger: Wifi-based gesture recognition system (2016) Isprs International Journal of Geo-Information, 5 (6), p. 92; Peng, Z., Li, C., Muñoz-Ferreras, J.-M., Gómez-Garćia, R., An fmcw radar sensor for human gesture recognition in the presence of multiple targets (2017) 2017 First Ieee MTT-S International Microwave Bio Conference IMBIOC, pp. 1-3; Chetty, K., Chen, Q., Ritchie, M., Woodbridge, K., A low-cost through-the-wall fmcw radar for stand-off operation and activity de-tection (2017) Radar Sensor Technology Xxi 10188 1018808, International Society for Optics and Photonics; Khan, M.A.A.H., Kukkapalli, R., Waradpande, P., Kulandaivel, S., Banerjee, N., Roy, N., Robucci, R., Ram: Radar-based activity monitor (2016) Ieee Infocom 2016-The 35th Annual Ieee International Conference on Computer Communications, pp. 1-9; Li, Z., Robucci, R., Banerjee, N., Patel, C., Tongue-n-cheek: Non-contact tongue gesture recognition (2015) Proceedings of the 14th International Conference on Information Processing in Sensor Networks, pp. 95-105; Dekker, B., Jacobs, S., Kossen, A., Kruithof, M., Huizing, A., Geurts, M., Gesture recognition with a low power fmcw radar and a deep convolutional neural network (2017) 2017 European Radar Conference EURAD, pp. 163-166; Kim, Y., Toomajian, B., Hand gesture recognition using micro-doppler signatures with convolutional neural network (2016) Ieee Access, 4, pp. 7125-7130; Lou, X., Yu, Z., Wang, Z., Zhang, K., Guo, B., Gesture-radar: Enabling natural human-computer interactions with radar-based adaptive and robust arm gesture recognition (2018) 2018 Ieee International Conference on Systems, Man, and Cybernetics (SMC, pp. 4291-4297",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020","23 March 2020 through 27 March 2020",,162492,,9781728147161,,,"English","IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PerCom Workshops",Conference Paper,"Final","",Scopus,2-s2.0-85092003912
"Basori A.H.","24765987500;","NAO-Teach: Helping kids to learn societal and theoretical knowledge with friendly human-robot interaction",2020,"Indonesian Journal of Electrical Engineering and Computer Science","17","3",,"1657","1664",,,"10.11591/IJEECS.V17.I3.PP1657-1664","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088970663&doi=10.11591%2fIJEECS.V17.I3.PP1657-1664&partnerID=40&md5=733e43f2a390de4e1e746515d2ebb377","Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Saudi Arabia","Basori, A.H., Faculty of Computing and Information Technology Rabigh, King Abdulaziz University, Saudi Arabia","Robotic technology has affected the education field, and even early education involves robot to attract Kids. Technical education is the notion of giving students knowledge of robots and technology. The main contribution of our research is to provide an interactive way of learning for kids through play and fun method. Two approaches proposed here: first, we provide an interactive game by touching robots body parts to teach kids how they were practising their motoric nerve and the listening to the instruction. In this game, kids asked to find some robot parts such as right hand, or left hand, where it equipped with a tactile sensor. The game difficulty can be increased by setting up the time limit for the answer and make kids touch the body parts of the robot very fast. The second learning method is practising number counting and pronunciation with NAO Robots. The robots will do computer vision processing to analyse and pronounce the kids handwriting with an artificial neural network. The result of implementation has obtained more than 75% success rate on recognition part with loss es than 0.6. The system received strong appreciation from kids and their parent, while this research believed able to attract kids to study in interactive and fun ways. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.","Character recognition; Child education; Edutainment; Intelligence; Robotics",,,,,,"Department of Information Technology, Ministry of Communications and Information Technology, DIT; King Abdulaziz University, KAU","This work was supported by the Department of Information Technology, Faculty of Computing and Information technology Rabigh, King Abdulaziz University, Rabigh, Makkah Saudi Arabia. The authors, therefore, gratefully acknowledge the FCITR technical support.","This work was supported by the Department of Information Technology, Faculty of Computing and Information technology Rabigh, King Abdulaziz University, Rabigh, Makkah Saudi Arabia. The authors, therefore, gratefully acknowledge the FCITR technical support",,,,,,,,,"(2018) World Robotics Survey:Service robots are conquering the world, , https://ifr.org/news/world-robotics-survey-service-robots-are-conquering-the-world-/, IFR. (25 October). Available; Han, J., Comparative study on the educational use of home robots for children (2008) Journal of Information Processing Systems, 4, pp. 159-168; Lees, D., LePage, P., Robots in education: the current state of the art (1996) Journal of Educational Technology Systems, 24, pp. 299-320; Benitti, F. B. V., Exploring the educational potential of robotics in schools: a systematic review (2012) Computers and Education, 58, pp. 978-988; Qidwai, U., Humanoid Robot as a Teacher?s Assistant: Helping Children with Autism to Learn Social and Academic Skills (2019) Journal of Intelligent & Robotic Systems, 1; Han, J., Kim, D., r-Learning services for elementary school students with a teaching assistant robot (2009) The 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 255-256. , La Jolla, CA; Han, J. H., Physical learning activities with a teaching assistant robot in elementary school music class (2009) Journal of Convergence Information Technology, 5, pp. 1406-1410; Highfield, K., (2008) Early mathematics learning through exploration with programable toys, , http://hdl.handle.net/1959.14/322176, (1-11-2019). Available; Jamet, F., Learning by Teaching with Humanoid Robot: A New Powerful Experimental Tool to Improve Children?s Learning Ability (2018) Journal of Robotics, 2018, pp. 1-11; Okita, S. Y., Learning together: ASIMO developing an interactive learning partnership with children (2009) The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1125-1130; Balch, T., Designing personal robots for education: hardware, software, and curriculum (2008) IEEE Pervasive Computing, 7, pp. 5-9; Mubin, O., Improving speech recognition with the robot interaction language (2012) Disruptive Science and Technology, 1, pp. 79-88; Belanche, D., Service robot implementation: a theoretical framework and research agenda (2020) The Service Industries Journal, 40, pp. 203-225. , 2020/03/11; Jawaid, I., Robotic system education for young children by collaborative project based learning (2020) Comput Appl Eng Educ, 28, pp. 178-192; Saerbeck, M., Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor (2010) CHI Conference on Human Factors in Computing Systems, pp. 1613-1622. , Atlanta Georgia USA; Mitnik, R., An autonomous educational mobile robot mediator (2008) Autonomous Robots, 25, pp. 367-382. , 2008/11/01; Chang, C.-W., Exploring the Possibility of Using Humanoid Robots as Instructional Tools for Teaching a Second Language in Primary School (2010) Educational Technology & Society, 13, pp. 13-24; Abdullasim, N., Velocity Perception: Collision Handling Technique for Agent Avoidance Behavior (2013) Telkomnika, 11; Albaqami, N. N., Augmenting Pilgrim Experience and Safety with Geo-location Way finding and Mobile Augmented Reality (2018) International Journal of Computer Science and Network Security, 8, pp. 23-32; AlJahdali, H. M. A., Basori, A. H., Emotional Contagion Driven of Parent-Child's Agents in Crowd during Panic Situation (2019) International Journal of Computer Network and Information Security, 19, pp. 261-266; Basori, A. H., Emotion Walking for Humanoid Avatars Using Brain Signals (2013) International Journal of Advanced Robotic Systems, 10, p. 29. , 2013/01/01; Basori, A. H., AlJahdali, H. M. A., Performance Driven-biped Control for Animated Human Model with Motion Synthesis Data (2018) Journal of Information Systems Engineering and Business Intelligence, 4; (2019) Nao Sensor, , https://www.alisys.net/en/robot-nao, Alysis. (November). Available; (2019) NAO Actuator and Sensor, , http://doc.aldebaran.com/2-1/family/nao_dcm/actuator_sensor_names.html, Aldebaran. (November). Available; Nielsen, M. A., (2015) Neural networks and deep learning, , Determination press USA; LeCun, Y., (1998) The MNIST database of handwritten digits, , http://yann.lecun.com/exdb/mnist, (Available","Basori, A.H.; Department of Information Technology, Saudi Arabia; email: abasori@kau.edu.sa",,,"Institute of Advanced Engineering and Science",,,,,25024752,,,,"English","Indones. J. Electrical Eng. Comput. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85088970663
"Ramos A., Hashtrudi-Zaad K.","57216857142;6701420299;","Estimation of Upper-Limb Energy Absorption Capabilities for Stable Human-Robot Interactions",2020,"IEEE Haptics Symposium, HAPTICS","2020-March",,"9086305","115","120",,1,"10.1109/HAPTICS45997.2020.ras.HAP20.20.95bee409","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085045366&doi=10.1109%2fHAPTICS45997.2020.ras.HAP20.20.95bee409&partnerID=40&md5=b545c1f2d8b8cc9de417c871f9a77924","Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada","Ramos, A., Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada; Hashtrudi-Zaad, K., Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada","Human-robot interactions are becoming more and more prevalent in various aspects of life, enhancing humans' mobility, accessibility and health. However, safety measures need to be addressed when applying robotic-generated forces that put human users at risk. One way to improve safety and performance in robotic tasks is to include physiological information, such as damping properties of the arm, in the control system to help regulate the energy that is delivered to the user. In this work, we estimated the energy absorbing capabilities of the human arm, based on the metric Excess of Passivity (EOP), originally defined in [1]. We used data from healthy subjects to generate models that fit different levels of safety and stability. Variability in subjects' EOP was a major finding in this study. For demanding applications such as robotic rehabilitation therapy, we suggest using a linear model with two EOP points. Such points are the mean values of EOP estimations at relaxed and rigid levels of hand-grasp forces. Two standard deviations were subtracted from each EOP point to consider the variability due to the neuromuscular changes in the human arm. © 2020 IEEE.","Control systems; excess of passivity; haptic interfaces; human-robot interaction; rehabilitation robotics; stability; teleoperation; upper-limb; variability","Energy absorption; Man machine systems; Risk assessment; Robotics; Safety engineering; Damping property; Energy absorbing capability; Energy absorption capability; Healthy subjects; Physiological informations; Robotic rehabilitation; Safety and stabilities; Standard deviation; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Consejo Nacional de Ciencia y Tecnología, CONACYT: 439840","This paper was first submitted on October 25th, 2019, then it was accepted on December 30th, 2019, and it was revised and resubmitted as final version on January 27th, 2020. This research was supported by Consejo Nacional de Ciencia y Tecnología (CONACYT) México, under the ‘Becas al Extranjero’ program, grant number 439840, and Natural Sciences and Engineering Research Council of Canada (NSERC).",,,,,,,,,,"Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A passivity-based approach for stable patient-robot interaction in haptics-enabled rehabilitation systems: Modulated time-domain passivity control (2017) IEEE Transactions on Control Systems Technology, 25 (3), pp. 991-1006; Coles, T.R., Meglan, D., John, N.W., The role of haptics in medical training simulators: A survey of the state of the art (2011) IEEE Transactions on Haptics, 4 (1), pp. 51-66; Hung, V.M., Mihai, V., Ion, I., Paraschiv, N., Dragana, C., Improvements of force tracking performances for a minimal surgery training system using haptic and robot devices (2017) IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, , Bucharest, Romania; Wu, A.R., Dzeladini, F., Brug, T.J.H., Federica Tamburella, N.L.T., Asseldonk, E.H.F.V., Kooij, H.V.D., Ijspeert, A.J., An adaptive neuromuscular controller for assistive lower-limb exoskeletons: A preliminary study on subjects with spinal cord injury (2017) Frontiers in Neurorobotics, 11 (30); Dehem, S., Montedoro, V., Brouwers, I., Edwards, M.G., Detrembleur, C., Stoquart, G., Renders, A., Lejeune, T., Validation of a robot serious game assessment protocol for upper limb motor impairment in children with cerebral palsy (2019) NeuroRehabilitation, 45 (2), pp. 137-149; Dyck, M., Jazayeri, A., Tavakoli, M., Is the human operator in a teleoperation system passive? (2013) IEEE World Haptics Conference, , Daejeon, South Korea; Li, H., Zhang, L., Kawashima, K., Operator dynamics for stability condition in haptic and teleoperation system: A survey (2018) International Journal of Medical Robotics and Computer Assisted Surgery, 14 (2); Colgate, J.E., Schenkel, G., Passivity of a class of sampled-data systems: Application to haptic interfaces (1997) Field Robotics, 14, pp. 37-47; Yin, S., Koti, A., Haddadi, A., Hashtrudi-Zaad, K., Uncoupled stability analysis of haptic simulation systems for various kinematic sampled data and discretization methods (2014) IEEE Haptics Symposium, , Houston, USA; Cleveland, D.M., (2017) Digital Control Techniques for High Fidelity Haptic Simulation Systems, , Master's Dissertation, Queen's University; Hashtrudi-Zaad, K., Salcudean, S.E., Analysis of control architectures for teleoperation systems with impedance-admittance master and slave manipulators (2001) The International Journal of Robotics Research, 20 (6), pp. 419-445; Colgate, J.E., Robust impedance shaping telemanipulation (1993) IEEE Transactions on Robotics and Automation, 9 (4), pp. 374-384; Burdet, E., Tee, K.P., Mareels, I., Milner, T.E., Chew, C.M., Franklin, D.W., Osu, R., Kawato, M., Stability and motor adaptation in human arm movements (2006) Biological Cybernetics, 94 (1), pp. 20-32; Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A grasp-based passivity signature for haptics-enabled human-robot interaction: Application to design of a new safety mechanism for robotic rehabilitation (2017) The International Journal of Robotics Research, 36 (5-7), pp. 778-799; Emerson, I., Potgieter, J., Xu, W., Control implementation for an integrated robotic and virtual mirror therapy system for stroke rehabilitation (2016) IEEE International Workshop on Advanced Motion Control, , Auckland New Zealand",,,"Facebook;Immersion;Reality Labs","IEEE Computer Society","26th IEEE Haptics Symposium, HAPTICS 2020","28 March 2020 through 31 March 2020",,159714,23247347,9781728102344,,,"English","IEEE Haptics Symp., HAPTICS",Conference Paper,"Final","",Scopus,2-s2.0-85085045366
"Akbari A., Diab M., Rosell J.","56574458000;57196709757;38562674300;","Contingent task and motion planning under uncertainty for human-robot interactions",2020,"Applied Sciences (Switzerland)","10","5","1665","","",,5,"10.3390/app10051665","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081908186&doi=10.3390%2fapp10051665&partnerID=40&md5=bdb4f0a375bb000c5730e0cf30a5cbd7","Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain","Akbari, A., Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain; Diab, M., Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain; Rosell, J., Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain","Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human-robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment. © 2020 by the authors.","Manipulation planning; Perception; Robot-human interactions; Task and motion planning",,,,,,"DPI2016-80077-R","This work was partially supported by the Spanish Government through the project DPI2016-80077-R. Mohammed Diab is supported by the Spanish Government through the grant 2017.",,,,,,,,,,"Hoffmann, J., Brafman, R., Contingent planning via heuristic forward search with implicit belief states (2005) In Proceedings of the International Conference on Automated Planning and Scheduling, , Monterey, CA, USA, 5-10 June; Stilman, M., Schamburek, J.U., Kuffner, J., Asfour, T., Manipulation planning among movable obstacles (2007) Proceedings of the 2007 IEEE International Conference on Robotics and Automation, pp. 3327-3332. , Roma, Italy, 10-14 April; Stilman, M., Kuffner, J., Planning among movable obstacles with artificial constraints (2008) Int. J. Robot. 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Mag, 19, pp. 72-82. , [CrossRef]; Kuffner, J.J., LaValle, S.M., RRT-connect: An efficient approach to single-query path planning (2000) In Proceedings of the International Conference on Robotics and Automation, 2, pp. 995-1001. , San Francisco, CA, USA, 24-28 April 2; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source Robot Operating System (2009) In Proceedings of the International Conference on Robotics and Automation Workshop on Open Source Software, 3, p. 5. , Kobe, Japan, 12-17 May","Akbari, A.; Institute of Industrial and Control Engineering, Spain; email: ali.akbari101@gmail.com",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85081908186
"Brinck I., Balkenius C.","6506619590;6602268390;","Mutual Recognition in Human-Robot Interaction: a Deflationary Account",2020,"Philosophy and Technology","33","1",,"53","70",,16,"10.1007/s13347-018-0339-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081269799&doi=10.1007%2fs13347-018-0339-x&partnerID=40&md5=39398e2fd2f35349fd56f4fe32b97aa1","Theoretical Philosophy and LUCS Robotics Lab, Department of Philosophy and Cognitive Science, Lund University, Lund, Sweden; LUCS Robotics Lab, Department of Philosophy and Cognitive Science, Lund University, Lund, Sweden","Brinck, I., Theoretical Philosophy and LUCS Robotics Lab, Department of Philosophy and Cognitive Science, Lund University, Lund, Sweden; Balkenius, C., LUCS Robotics Lab, Department of Philosophy and Cognitive Science, Lund University, Lund, Sweden","Mutually adaptive interaction involves the robot as a partner as opposed to a tool, and requires that the robot is susceptible to similar environmental cues and behavior patterns as humans are. Recognition, or the acknowledgement of the other as individual, is fundamental to mutually adaptive interaction between humans. We discuss what recognition involves and its behavioral manifestations, and describe the benefits of implementing it in HRI. © 2018, The Author(s).","Entrainment; Identification; Joint action; Recognition",,,,,,"Lunds Universitet",,,,,,,,,,,"Agravante, D.J., Cherubini, A., Bussy, A., Kheddar, A., Human-humanoid joint haptic table carrying task with height stabilization using vision (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 4609-4614; Becchio, C., Sartori, L., Castiello, U., Toward you: the social side of actions (2010) Current Directions in Psychological Science, 19, pp. 183-188; Bicchieri, C., (2006) The grammar of society: the nature and dynamics of social norms, , Cambridge University Press, New York; Brandom, R.B., The structure of desire and recognition: self-consciousness and self-constitution (2007) Philosophy & Social Criticism, 33 (1), pp. 127-150; Bratman, M., Shared cooperative activity (1992) Philosophical Review, 101 (2), pp. 327-341; Breazeal, C., (2002) Designing sociable robots, , MIT Press, Cambridge Mass; Breazeal, C., Buchsbaum, D., Gray, J., Gatenby, D., Blumberg, B., Learning from and about others: Towards using imitation to bootstrap the social understanding of others by robots (2005) Artificial Life, 11, pp. 1-32; Brinck, I., The role of intersubjectivity in the development of intentional communication (2008) The shared mind: perspectives on intersubjectivity, pp. 115-140. , Zlatev J, Racine T, Sinha C, Itkonen E, (eds), John Benjamins, Amsterdam; Brinck, I., Developing an understanding of social norms and games: emotional engagement, nonverbal agreement, and conversation (2014) Theory & Psychology, 24 (6), pp. 737-754; Brinck, I., Balkenius, C., Johansson, B., Making place for social norms in the design of human-robot interaction (2016) What Social Robots Can and Should Do: Proceedings of Robophilosophy 2016/TRANSOR 2016, pp. 303-312. , W. 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"Coronado E., Venture G.","57188864138;15623574000;","Towards IoT-aided human–robot interaction using NEP and ROS: A platform-independent, accessible and distributed approach",2020,"Sensors (Switzerland)","20","5","1500","","",,5,"10.3390/s20051500","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081206205&doi=10.3390%2fs20051500&partnerID=40&md5=a6f419d40b010bb13f2198c4d68d3da1","Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan","Coronado, E., Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan; Venture, G., Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan","This article presents the novel Python, C# and JavaScript libraries of Node Primitives (NEP), a high-level, open, distributed, and component-based framework designed to enable easy development of cross-platform software architectures. NEP is built on top of low-level, high-performance and robust sockets libraries (ZeroMQ and Nanomsg) and robot middlewares (ROS 1 and ROS 2). This enables platform-independent development of Human–Robot Interaction (HRI) software architectures. We show minimal code examples for enabling Publish/Subscribe communication between Internet of Things (IoT) and Robotics modules. Two user cases performed outside laboratories are briefly described in order to prove the technological feasibility of NEP for developing real-world applications. The first user case briefly shows the potential of using NEP for enabling the creation of End-User Development (EUD) interfaces for IoT-aided Human–Robot Interaction. The second user case briefly describes a software architecture integrating state-of-art sensory devices, deep learning perceptual modules, and a ROS-based humanoid robot to enable IoT-aided HRI in a public space. Finally, a comparative study showed better latency results of NEP over a popular state-of-art tool (ROS using rosbridge) for connecting different nodes executed in local-host and local area network (LAN). © 2019 by the authors. Licensee MDPI, Basel, Switzerland.","Artificial intelligence and robotics; Computer interaction; Human; Human; Internet of robot things; Robot interaction","Anthropomorphic robots; Arts computing; Computer software; Deep learning; High level languages; Human computer interaction; Human robot interaction; Intelligent robots; Libraries; Local area networks; Network architecture; Robotics; Component-based framework; Computer interaction; End user development(EUD); Human; Internet of Things (IOT); Local area networks (LAN); Robot interactions; Technological feasibility; Internet of things; adult; artificial intelligence; child; computer interface; human; procedures; robotics; software; Adult; Artificial Intelligence; Child; Humans; Internet of Things; Robotics; Software; User-Computer Interface",,,,,"Japan Society for the Promotion of Science, KAKEN: 19K12167",,,,,,,,,,,"Coronado, E., Indurkhya, X., Venture, G., Robots Meet Children, Development of Semi-Autonomous Control Systems for Children-Robot Interaction in the Wild Proceedings of the 2019 IEEE 4Th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 360-365. , Toyonaka, Japan, 3–5 July 2019; Coronado, E., Mastrogiovanni, F., Venture, G., Design of a Human-Centered Robot Framework for End-User Programming and Applications (2019) ROMANSY 22–Robot Design, Dynamics and Control, pp. 450-457. , Springer: Cham, Switzerland; Simoens, P., Dragone, M., Saffiotti, A., The Internet of Robotic Things: A review of concept, added value and applications (2018) Int. 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Ser, 664; Mirabito, L., ZDAQ, a light data acquisition framework based on ZeroMQ (2019) J. Instrum., 14; Babaians, E., Tamiz, M., Sarti, Y., Mogoei, A., Mehrabi, E., ROS2Unity3D; High-Performance Plugin to Interface ROS with Unity3d engine Proceedings of the 2018 9Th Conference on Artificial Intelligence and Robotics and 2Nd Asia-Pacific International Symposium, pp. 59-64. , Kish Island, Iran, 10 December 2018; (2019), https://netmarketshare.com/operating-system-market-share.aspx?id=platformsDesktopVersions, accessed on 28 December; (2019), https://rfc.zeromq.org/spec/15/, accessed on 28 December; Kuffner, J., Cloud-enabled robots in (2010) Ieee-Ras International Conference on Humanoid Robots, , Piscataway, NJ: IEEE; Kehoe, B., Patil, S., Abbeel, P., Goldberg, K., A survey of research on cloud robotics and automation (2015) IEEE Trans. Autom. Sci. Eng., 12, pp. 398-409; Mohanarajah, G., Hunziker, D., D’Andrea, R., Waibel, M., Rapyuta: A cloud robotics platform (2014) IEEE Trans. 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Dyn. Syst. Meas. Contr., 138; Json.Net, , https://www.newtonsoft.com/json, Available online, (accessed on 28 December 2019); Zubrycki, I., Kolesiński, M., Granosik, G., (2017) In Graphical Programming Interface for Enabling Non-Technical Professionals to Program Robots and Internet-Of-Things Devices, pp. 620-631. , Springer: Cadiz, Spain; Fischer, G., (2013) End-User Development: From Creating Technologies to Transforming Cultures, pp. 217-222. , Springer: Berlin, Heidelberg; Fischer, G., End user development and meta-design: Foundations for cultures of participation (2012) End-User Computing, Development, and Software Engineering: New Challenges, pp. 202-226. , IGI Global: Berlin, Germany; Lourens, T., Examples How to Use Tivipe, , http://www.tivipe.com/TVPeducation/TVPuse.pdf, Available online:, accessed on 28 December 2019; Barakova, E.I., Gillesen, J.C., Huskens, B.E., Lourens, T., End-user programming architecture facilitates the uptake of robots in social therapies (2013) Rob. 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"van Wynsberghe A., Comes T.","25655523600;36998241600;","Drones in humanitarian contexts, robot ethics, and the human–robot interaction",2020,"Ethics and Information Technology","22","1",,"43","53",,8,"10.1007/s10676-019-09514-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076533010&doi=10.1007%2fs10676-019-09514-1&partnerID=40&md5=6e8a280954b5d8376d287e422f374922","Technical University of Delft, Jaffalaan 5, Delft, 2628 BX, Netherlands","van Wynsberghe, A., Technical University of Delft, Jaffalaan 5, Delft, 2628 BX, Netherlands; Comes, T., Technical University of Delft, Jaffalaan 5, Delft, 2628 BX, Netherlands","There are two dominant trends in the humanitarian care of 2019: the ‘technologizing of care’ and the centrality of the humanitarian principles. The concern, however, is that these two trends may conflict with one another. Faced with the growing use of drones in the humanitarian space there is need for ethical reflection to understand if this technology undermines humanitarian care. In the humanitarian space, few agree over the value of drone deployment; one school of thought believes drones can provide a utility serving those in need while another believes the large scale deployment of drones will exacerbate the already prevalent issues facing humanitarian aid providers. We suggest in this paper that the strength of the humanitarian principles approach to answer questions of aid provision can be complimented by a technology-facing approach, namely that of robot ethics. We have shown that for humanitarian actors we ought to be concerned with the risks of a loss of contextualization and de-skilling. For the beneficiary, we raise three concerns associated with the threat to the principle of humanity for this group: a loss of dignity by reducing human-to-human interactions; a threat to dignity through a lack of informational transparency; and, a threat to dignity by failing to account for the physiological and behavioral impacts of the drone on human actors. Although we acknowledge the obstacles (and dangers) associated with understanding the physiological and behavioral impacts we insist that the moral acceptability and desirability of drones in humanitarian contexts is dependent on the findings from such studies and that tailored ethical guidelines for drone deployment in humanitarian action be created to reflect the results of such studies. © 2019, The Author(s).","Dignity; Drones; Humanitarian ethics; Robot ethics; Robots","Drones; Facings; Philosophical aspects; Physiology; Robots; Contextualization; Dignity; Human actor; Human-to-human interactions; Humanitarian ethics; Large-scale deployment; Robot ethics; Robot interactions; Human robot interaction",,,,,"Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO: 275-20-054","This work was inspired by a workshop of the Foundation for Responsible Robotics in The Hague, March 2017, on the topic of drones in humanitarian contexts. We are grateful for the opinions and insights shared at this time by all participants and for the additional reflections from in particular Scott Robbins, Andrew Billo, Denise Soesilo, and Duuk Baten. This research is supported by the Netherlands Organization for Scientific Research (NWO), Project Number 275-20-054.",,,,,,,,,,"Alvarez-de-los-Mozos, E., Renteria, A., Collaborative robots in e-waste management (2017) Procedia Manufacturing, 11, pp. 55-62; Anderson, M., Anderson, S.L., Robot be good: A call for ethical autonomous machines (2010) Scientific American, 303 (4), pp. 15-24; Arkin, R., (2009) Governing lethal behavior in autonomous robots, , CRC Press, Boca Raton; Bagshaw, S., (2012) OCHA on Message: Humanitarian Principles, p. 2. , http://www.unocha.org/sites/dms/Documents/OOM-humanitarianprinciples_eng_June12.pdf, Retrieved from, Accessed 13 Oct 2019; Bouvier, P., (2012) Humanitarian Care and Small Things in Dehumanised Places. 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Technol.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85076533010
"Soh H., Xie Y., Chen M., Hsu D.","56001754300;57208167074;57190131729;34569904000;","Multi-task trust transfer for human–robot interaction",2020,"International Journal of Robotics Research","39","2-3",,"233","249",,12,"10.1177/0278364919866905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071515968&doi=10.1177%2f0278364919866905&partnerID=40&md5=8c34395066b37d8542136c148db1620c","Department of Computer Science, School of Computing, National University of Singapore, Singapore","Soh, H., Department of Computer Science, School of Computing, National University of Singapore, Singapore; Xie, Y., Department of Computer Science, School of Computing, National University of Singapore, Singapore; Chen, M., Department of Computer Science, School of Computing, National University of Singapore, Singapore; Hsu, D., Department of Computer Science, School of Computing, National University of Singapore, Singapore","Trust is essential in shaping human interactions with one another and with robots. In this article we investigate how human trust in robot capabilities transfers across multiple tasks. We present a human-subject study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. The findings expand our understanding of trust and provide new predictive models of trust evolution and transfer via latent task representations: a rational Bayes model, a data-driven neural network model, and a hybrid model that combines the two. Experiments show that the proposed models outperform prevailing models when predicting trust over unseen tasks and users. These results suggest that (i) task-dependent functional trust models capture human trust in robot capabilities more accurately and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-mediated robot decision-making for fluent human–robot interaction in multi-task settings. © The Author(s) 2019.","human-centered robotics; Human–robot interaction; trust","Bayesian networks; Decision making; Virtual reality; Human centered robotics; Human interactions; Neural network model; Predictive models; Robot interactions; Task representation; trust; Virtual reality simulations; Human robot interaction",,,,,"MOE2016-T2-2-068; National University of Singapore, NUS","Thank you to Indu Prasad for her help with the data analysis. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by a NUS Office of the Deputy President (Research and Technology) Startup Grant and in part by MoE AcRF (Tier 2 grant number MOE2016-T2-2-068).",,,,,,,,,,"Billings, D.R., Schaefer, K.E., Chen, J.Y.C., Hancock, P.A., Human–robot interaction: Developing trust in robots (2012) HRI ’12, pp. 5-8. , In; Bowers, J.S., Davis, C.J., Bayesian just-so stories in psychology and neuroscience (2012) Psychological Bulletin, 138 (3), p. 389; Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S., Generating sentences from a continuous space (2016) CoNLL 2016, p. 10. , In:, p; Castelfranchi, C., Falcone, R., (2010) Trust Theory: A Socio-Cognitive and Computational Model, , 1st ed, New York, John Wiley & Sons; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., (2017) The role of trust in decision-making for human robot collaboration, , Workshop on Human-Centered Robotics, RSS, In; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) HRI’18, pp. 307-315. , New York, ACM Press, In; Cho, K., van Merrienboer, B., Gulcehre, C., Learning phrase representations using RNN encoder–decoder for statistical machine translation (2014) EMNLP, pp. 1724-1734. , In; Csató, L., Opper, M., Sparse on-line Gaussian processes (2002) Neural Computation, 14 (3), pp. 641-668; Desai, M., Kaniarasu, P., Medvedev, M., Steinfeld, A., Yanco, H., Impact of robot failures and feedback on real-time trust (2013) HRI ’13, pp. 251-258. , In; Desai, M., (2012) Modeling Trust to Improve Human–Robot Interaction, , University of Massachussetts, Lowell, MA, PhD thesis; Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J., Path planning for autonomous vehicles in unknown semi-structured environments (2010) The International Journal of Robotics Research, 29 (5), pp. 485-501; Gombolay, M., Yang, X.J., Hayes, B., Robotic assistance in the coordination of patient care (2018) The International Journal of Robotics Research, 37 (10), pp. 1300-1316; Griffiths, T.L., Lucas, C.G., Williams, J.J., Kalish, M.L., Modeling human function learning with Gaussian processes (2009) Neural Information Processing Systems, 21, pp. 553-560; Hall, R., Trusting your assistant (1996) Proceedings of the 11th Knowledge-Based Software Engineering Conference, pp. 42-51. , Los Alamitos, CA, IEEE Computer Society Press, In; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human–robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Hoffman, R.R., Johnson, M., Bradshaw, J.M., Trust in automation (2013) IEEE Intelligent Systems, 13, pp. 1541-1672; Huang, S.H., Bhatia, K., Abbeel, P., Dragan, A.D., (2018) Establishing (appropriate) trust via critical states, , Workshop on Explainable Robotic Systems, HRI, In; Jian, J.Y., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) International Journal of Cognitive Ergonomics, 4 (1), pp. 53-71; Jozefowicz, R., Zaremba, W., Sutskever, I., An empirical exploration of recurrent network architectures (2015) ICML, pp. 2342-2350. , In; Kingma, D.P., Ba, J., (2014) Adam: A method for stochastic optimization; Lee, J., Moray, N., Trust, control strategies and allocation of function in human–machine systems (1992) Ergonomics, 35 (10), pp. 1243-1270; Lee, J.D., Moray, N., Trust, self-confidence, and operators’ adaptation to automation (1994) International Journal of Human–Computer Studies, 40 (1), pp. 153-184; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., (2013) Distributed representations of words and phrases and their compositionality, 2, pp. 3111-3119. , Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS’13), Lake Tahoe, NV, 5–10 December 2013, In; Muir, B.M., (1989) Operators Trust in and Percentage of Time Spent Using the Automatic Controllers in a Supervisory Process Control Task, , Toronto, ON, University of Toronto Press, :, 0315510145; Muir, B.M., Trust in automation: Part I. 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"Zong C., Ji Z., Yu H.","55413844400;8555087100;36164668500;","Dynamic stability analysis of a tracked mobile robot based on human–robot interaction",2020,"Assembly Automation","40","1",,"143","154",,3,"10.1108/AA-10-2018-0156","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063905572&doi=10.1108%2fAA-10-2018-0156&partnerID=40&md5=b45937b43db93257eec1c0ab4897f49a","Institute of Complexity Science, College of Automation, Qingdao University, Qingdao, China; College of Automation, Qingdao University, Qingdao, China","Zong, C., Institute of Complexity Science, College of Automation, Qingdao University, Qingdao, China; Ji, Z., Institute of Complexity Science, College of Automation, Qingdao University, Qingdao, China; Yu, H., College of Automation, Qingdao University, Qingdao, China","Purpose: This paper aims to provide a theoretical principle for the stability control of robot climbing stairs, autonomously based on human–robot interaction. Through this research, tracked mobile robots with human-robot interaction will be extensively used in rescue in disaster, exploration on planetary, fighting in battle, and searching for survivors in collapsed buildings. Design/methodology/approach: This paper introduces the tracked mobile robot, based on human–robot interaction, and its six moving postures. The dynamic process of climbing stairs is analyzed, and the dynamic model of the robot is proposed. The dynamic stability criterion is derived when the tracked mobile robot contacts the stairs steps in one, two and more points. A further conduction of simulation on the relationship of the traction force and bearing force vs the velocity and acceleration in the three cases was carried out. Findings: This paper explains that the tracked mobile robot, based on human–robot interaction, can stably climb stairs so long as the velocity and acceleration satisfy the dynamic stability criterion as noted above. In addition, the experiment tests the correctness of dynamic stability analysis when the tracked mobile robot contacts the stair steps in one, two or more points. Originality/value: This paper provides the mechanical structure and working principle of the tracked mobile robot based on human–robot interaction and proposes an identification method of dynamic stability criterion when the robot contacts the stairs steps in one, two and more points. © 2019, Emerald Publishing Limited.","Climbing stairs; Dynamic model; Dynamic stability; Human–robot interaction; Tracked mobile robot","Dynamic models; Machine design; Mobile robots; Stability; Stability criteria; Stairs; Collapsed buildings; Design/methodology/approach; Dynamic stability analysis; Identification method; Mechanical structures; Robot interactions; Stability control; Tracked mobile robots; Human robot interaction",,,,,"JQ201419; 201702038; National Natural Science Foundation of China, NSFC: 61374062, 61603288, 61873136","This work was supported by the National Natural Science Foundation of China (Nos. 61873136, 61374062, 61603288), Science Foundation of Shandong Province for Distinguished Young Scholars (No. JQ201419), the Special Funds of Shandong Province for Post-doctoral Innovation Projects under Grant 201702038, Post-doctoral Application Research Project of Qingdao.",,,,,,,,,,"Ben-Tzvi, P., Ito, S., Goldenberg, A., (2007) Autonomous stair climbing with reconfigurable tracked mobile robot, pp. 1-6. , IEEE International Workshop on Robotic and Sensors Environments, Ottawa; 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Yu, J.Z., Sun, F.H., Xu, D., Tan, M., Embedded vision guided 3-D tracking control for robotic fish (2016) IEEE Transactions on Industrial Electronics, 63 (1), pp. 355-363; Yu, J.Z., Tan, M., Chen, J., Zhang, J.W., A survey on CPG-inspired control models and system implementation (2014) IEEE Transactions on Neural Networks and Learning Systems, 25 (3), pp. 441-456; Yu, J.Z., Wang, C., Xie, G.M., Coordination of multiple robotic fish with applications to underwater robot competition (2016) IEEE Transactions on Industrial Electronics, 63 (2), pp. 1280-1288; Zong, C., Gao, X.Y.Y., Kinematics analysis for obstacle-surmounting capability of a joint double-tracked robot (2016) Journal of Beijing Institute of Technology, 25 (2), pp. 202-210; Zong, C., Jiang, S., Guo, W., Obstacle-surmounting capability analysis of a joint double-tracked robot (2014) Proceedings of IEEE International Conference on Mechatronics and Automation (ICMA) 2014, pp. 723-728. , Tianjin, China","Ji, Z.; Institute of Complexity Science, China; email: jizhijian@pku.org.cn",,,"Emerald Group Holdings Ltd.",,,,,01445154,,ASAUD,,"English","Assem. Autom.",Article,"Final","",Scopus,2-s2.0-85063905572
"Fitter N.T., Mohan M., Kuchenbecker K.J., Johnson M.J.","55921515500;55933547900;8349364500;57203731750;","Exercising with Baxter: Preliminary support for assistive social-physical human-robot interaction",2020,"Journal of NeuroEngineering and Rehabilitation","17","1","19","","",,,"10.1186/s12984-020-0642-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079606323&doi=10.1186%2fs12984-020-0642-5&partnerID=40&md5=418ed98a282929c962669cfb3a50e7da","Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR, United States; Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States; Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany; Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States","Fitter, N.T., Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR, United States, Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States; Mohan, M., Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany, Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States; Kuchenbecker, K.J., Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States, Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany; Johnson, M.J., Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States, Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA, United States","Background: The worldwide population of older adults will soon exceed the capacity of assisted living facilities. Accordingly, we aim to understand whether appropriately designed robots could help older adults stay active at home. Methods: Building on related literature as well as guidance from experts in game design, rehabilitation, and physical and occupational therapy, we developed eight human-robot exercise games for the Baxter Research Robot, six of which involve physical human-robot contact. After extensive iteration, these games were tested in an exploratory user study including 20 younger adult and 20 older adult users. Results: Only socially and physically interactive games fell in the highest ranges for pleasantness, enjoyment, engagement, cognitive challenge, and energy level. Our games successfully spanned three different physical, cognitive, and temporal challenge levels. User trust and confidence in Baxter increased significantly between pre- and post-study assessments. Older adults experienced higher exercise, energy, and engagement levels than younger adults, and women rated the robot more highly than men on several survey questions. Conclusions: The results indicate that social-physical exercise with a robot is more pleasant, enjoyable, engaging, cognitively challenging, and energetic than similar interactions that lack physical touch. In addition to this main finding, researchers working in similar areas can build on our design practices, our open-source resources, and the age-group and gender differences that we found. © 2020 The Author(s).","Exercise games; Personal robots; Physical human-robot interaction; Socially assistive robotics","adult; age; Article; calmness; cognition; controlled study; dominance behavior; emotion; energy; enjoyment; exercise; exercise intensity; exploratory research; female; human; human experiment; male; normal human; pain intensity; perception; personal experience; pleasantness; priority journal; recreational game; robotics; safety; sex difference; social interaction; social participation; task performance; trust; aged; clinical trial; exercise; human relation; middle aged; motivation; psychology; questionnaire; young adult; Aged; Exercise; Female; Humans; Interpersonal Relations; Male; Middle Aged; Motivation; Robotics; Surveys and Questionnaires; Young Adult",,,,,"National Science Foundation, NSF: 0966142",,,,,,,,,,,"Coughlin, J.F., Pope, J.E., Leedle, B.R., Old age, new technology, and future innovations in disease management and home health care (2006) Home Health Care Manag Pract, 18 (3), pp. 196-207; (2015) World Report on Ageing and Health, , World Health Organization World Health Organization Geneva; Dawe, D., Moore-Orr, R., Low-intensity, range-of-motion exercise: Invaluable nursing care for elderly patients (1995) J Adv Nurs, 21 (4), pp. 675-681; Bauman, A., Merom, D., Bull, F.C., Buchner, D.M., Fiatarone Singh, M.A., Updating the evidence for physical activity: Summative reviews of the epidemiological evidence, prevalence, and interventions to promote ""active aging (2016) Gerontologist, 56 (SUPPL_2), pp. 268-280; De Souto Barreto, P., Morley, J.E., Chodzko-Zajko, W., Pitkala, K.H., Weening-Djiksterhuis, E., Rodriguez-Manas, L., Barbagallo, M., Landi, F., Recommendations on physical activity and exercise for older adults living in long-term care facilities: A taskforce report (2016) J Am Med Dir Assoc, 17 (5), pp. 381-392; Brackenridge, J., Bradnam, L.V., Lennon, S., Costi, J.J., Hobbs, D.A., A review of rehabilitation devices to promote upper limb function following stroke (2016) Neurosci Biomed Eng, 4 (1), pp. 25-42; Fasola, J., Matarić, M.J., Using socially assistive human-robot interaction to motivate physical exercise for older adults (2012) Proc IEEE, 100 (8), pp. 2512-2526; Kashi, S., Levy-Tzedek, S., Smooth leader or sharp follower? playing the mirror game with a robot (2018) Restor Neurol Neurosci, 36 (2), pp. 147-159; Fitter, N.T., Kuchenbecker, K.J., Equipping the Baxter robot with human-inspired hand-clapping skills (2016) Proc. 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Rehabil.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079606323
"El Hafi L., Isobe S., Tabuchi Y., Katsumata Y., Nakamura H., Fukui T., Matsuo T., Garcia Ricardez G.A., Yamamoto M., Taniguchi A., Hagiwara Y., Taniguchi T.","57188836029;57197875878;57212147622;57208401562;57212144620;57212140619;26424278200;55779197600;7405302568;57189575562;26648361300;35585520600;","System for augmented human–robot interaction through mixed reality and robot training by non-experts in customer service environments",2020,"Advanced Robotics","34","3-4",,"157","172",,12,"10.1080/01691864.2019.1694068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076035415&doi=10.1080%2f01691864.2019.1694068&partnerID=40&md5=b64f705dbfe5ac2ee7bf3ef70c016206","College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan; Business Innovation Division, Panasonic Corporation, Osaka, Japan","El Hafi, L., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Isobe, S., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Tabuchi, Y., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Katsumata, Y., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Nakamura, H., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Fukui, T., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Matsuo, T., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Garcia Ricardez, G.A., Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan; Yamamoto, M., Business Innovation Division, Panasonic Corporation, Osaka, Japan; Taniguchi, A., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Hagiwara, Y., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Taniguchi, T., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan","Human–robot interaction during general service tasks in home or retail environment has been proven challenging, partly because (1) robots lack high-level context-based cognition and (2) humans cannot intuit the perception state of robots as they can for other humans. To solve these two problems, we present a complete robot system that has been given the highest evaluation score at the Customer Interaction Task of the Future Convenience Store Challenge at the World Robot Summit 2018, which implements several key technologies: (1) a hierarchical spatial concepts formation for general robot task planning and (2) a mixed reality interface to enable users to intuitively visualize the current state of the robot perception and naturally interact with it. The results obtained during the competition indicate that the proposed system allows both non-expert operators and end users to achieve human–robot interactions in customer service environments. Furthermore, we describe a detailed scenario including employee operation and customer interaction which serves as a set of requirements for service robots and a road map for development. The system integration and task scenario described in this paper should be helpful for groups facing customer interaction challenges and looking for a successfully deployed base to build on. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","human–robot interaction; mixed reality interface; Service robotics competition; spatial concepts formation; unsupervised multimodal learning","Interface states; Mixed reality; Personnel training; Robot learning; Robot programming; Sales; Mixed reality interfaces; Multi-modal learning; Robot interactions; Service robotics; Spatial concepts; Human robot interaction",,,,,"Australian Institute of Physics, AIP; New Energy and Industrial Technology Development Organization, NEDO; Japan Science and Technology Agency, JST; Ministry of Economy, Trade and Industry, METI; Core Research for Evolutional Science and Technology, CREST: JPMJCR15E3","We gratefully acknowledge the Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO) of Japan for organizing WRS and providing us with the necessary financial support to participate. This project was also partially funded by the Japan Science and Technology Agency (JST) Core Research for Evolutionary Science and Technology (CREST) research programme (grant number JPMJCR15E3), including the Center for Advanced Intelligence Project (AIP) challenge programme. We thank all the members of Team NAIST-RITS-Panasonic for their valuable contributions and high commitment during the FCSC at WRS 2018.",,,,,,,,,,"Kishida, N., Nishiura, H., Demographic supply-demand imbalance in industrial structure in the super-aged nation Japan (2018) Theor Biol Med Model, 15 (1), p. 19; Wada, K., New robot technology challenge for convenience store. 2017 IEEE/SICE International Symposium on System Integration (SII 2017); 2017 Dec; Taipei. 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"Robinson F.A.","57215966842;","Audio cells: A spatial audio prototyping environment for human-robot interaction",2020,"TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction",,,,"955","960",,1,"10.1145/3374920.3374999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082449173&doi=10.1145%2f3374920.3374999&partnerID=40&md5=e702fbd5e88172bb1ed5ec3b413ae665","Creative Robotics Lab and Interactive Media Lab, University of New South Wales, Sydney, NSW, Australia","Robinson, F.A., Creative Robotics Lab and Interactive Media Lab, University of New South Wales, Sydney, NSW, Australia","Rich and elaborate communication will play an essential part in the success of social robotics. The role of non-verbal sound as a communication channel has received relatively little attention in human-robot interaction research so far. Audio Cells is a prototyping environment for spatial sonic interaction design. It is part of my ongoing work on the sonification of artificial bodies. Through a combination of modular loudspeakers and sensors, it allows the exploration of a range of spatial interactive audio effects. By bringing methods from the fields of sound design and spatial audio into the context of human-robot interaction, my research aims to enrich and refine the ways robotic agents and responsive structures communicate with humans. © 2020 Association for Computing Machinery.","Human-robot interaction; Sonic interaction design; Spatial audio","Audio acoustics; Loudspeakers; Machine design; Man machine systems; Robotics; Interactive audio; Robotic agents; Social robotics; Sonic interaction design; Sonifications; Sound designs; Spatial audio; Human robot interaction",,,,,,,,,,,,,,,,"Frid, E., Bresin, R., Alexanderson, S., Perception of mechanical sounds inherent to expressive gestures of a nao robot-implications for Movement Sonification of Humanoids (2018) Sound and Music Computing; Kulezic-Wilson, D., (2008) Sound Design Is the New Score. 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Int. Conf. Tangible, Embed., Embodied Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082449173
"Jia J., Zhang M.","57193810489;7601556980;","Research Progress and Development Trend of the Safety of Human-robot Interaction Technology [人机安全交互技术研究进展及发展趋势]",2020,"Jixie Gongcheng Xuebao/Journal of Mechanical Engineering","56","3",,"16","30",,3,"10.3901/JME.2020.03.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082297264&doi=10.3901%2fJME.2020.03.016&partnerID=40&md5=4f19213957dffe0b15e65e9939f8f675","School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China; State Key Laboratory of Robotics and System (Harbin Institute of Technology), Harbin, 150006, China","Jia, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China, State Key Laboratory of Robotics and System (Harbin Institute of Technology), Harbin, 150006, China; Zhang, M., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China","Human-Robot integration is the focus of future life, smart factories, and so on. It bears the burden of building a new industrial system and measuring the level of national scientific and technological innovation. Focusing on the key technologies of human-computer safety interaction, the current research progress is summarized around the structural design, compliance control, environment perception, behavioral learning and decision-making of the communicator systematically sorted out the hot and difficult issues. The realization of human-computer security interaction at this stage is summarized. The prospects for future development are pointed out. The purpose of this research is to promote the understanding of key technologies in common areas and promote the comprehensive development of related disciplines. © 2020 Journal of Mechanical Engineering.","Collision detection; Compliance control; Human-robot interaction; Intention recognition; Motion control; Safety","Accident prevention; Compliance control; Decision making; Human computer interaction; Man machine systems; Motion control; Security of data; Structural design; Collision detection; Development trends; Environment perceptions; Industrial systems; Intention recognition; Key technologies; Scientific and technological innovations; Security interactions; Human robot interaction",,,,,,,,,,,,,,,,"Sheikholeslami, S., Moon, A.J., Croft, E.A., Cooperative gestures for industry: Exploring the efficacy of robot hand configurations in expression of instructional gestures for human-robot interaction (2017) The International Journal of Robotics Research, 36 (5-7), pp. 699-720; Portugal, D., Alvito, P., Christodoulou, E., A study on the deployment of a service robot in an elderly care center (2019) International Journal of Social Robotics, 11 (2), pp. 317-341; De Stefano, M., Mishra, H., Balachandran, R., Multi-rate tracking control for a space robot on a controlled satellite: A passivity-based strategy (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 1319-1326; Wang, T., Tao, Y., Research status and industrialization development strategy of Chinese industrial robot (2014) Journal of Mechanical Engineering, 50 (9), pp. 1-13. , 王田苗, 陶永. 我国工业机器人技术现状与产业化发展战略[J]. 机械工程学报, 2014, 50(9): 1-13; Wang, G., Chen, D., Chen, K., The current research status and development strategy on biomimetic robot (2015) Journal of Mechanical Engineering, 51 (13), pp. 27-44. , 王国彪, 陈殿生, 陈科位, 等. 仿生机器人研究现状与发展趋势[J]. 机械工程学报, 2015, 51(13): 27-44; Gao, F., Guo, W., Thinking of the development strategy of robots in China (2016) Journal of Mechanical Engineering, 52 (7), pp. 1-5. , 高峰, 郭为忠. 中国机器人的发展战略思考[J]. 机械工程学报, 2016, 52(7): 1-5; Kebria, P.M., Al-Wais, S., Abdi, H., Kinematic and dynamic modelling of UR5 manipulator (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4229-4234. , 9-12 Oct. 2016, Budapest, Hungary. 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IEEE; Creswell, A., White, T., Dumoulin, V., Generative adversarial networks: An overview (2018) IEEE Signal Processing Magazine, 35 (1), pp. 53-65; Dai, J., (2014) Geometrical Foundation s and Screw Algebra for Mechanisms and Robotics, , 戴建生. 机构学与机器人学的几何基础与旋量代数[M]. 北京: 高等教育出版社, 2014. Beijing: Higher Education Press; Wei, Y., Xu, Q., An overview of micro-force sensing techniques (2015) Sensors and Actuators A: Physical, 234, pp. 359-374","Zhang, M.; School of Mechanical Engineering, China; email: zhangml@hebut.edu.cn",,,"Editorial Office of Chinese Journal of Mechanical Engineering",,,,,05776686,,CHHKA,,"Chinese","Jixie Gongcheng Xuebao",Review,"Final","All Open Access, Bronze",Scopus,2-s2.0-85082297264
"She Y., Su H.-J., Meng D., Lai C.","56545216500;8379272100;55827505800;57192718439;","Design and modeling of a continuously tunable stiffness arm for safe physical human-robot interaction",2020,"Journal of Mechanisms and Robotics","12","1","011006","","",,7,"10.1115/1.4044840","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106493202&doi=10.1115%2f1.4044840&partnerID=40&md5=3ca9154d3cbe48218beaf922601e76bc","Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, China; Department of Mechanical Engineering, Texas A and M University, College Station, TX  77840, United States","She, Y., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Meng, D., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States, Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, China; Lai, C., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States, Department of Mechanical Engineering, Texas A and M University, College Station, TX  77840, United States","To reduce injury in physical human-robot interactions (pHRIs), a common practice is to introduce compliance to joints or arm of a robotic manipulator. In this paper, we present a robotic arm made of parallel guided beams whose stiffness can be continuously tuned by morphing the shape of the cross section through two four-bar linkages actuated by servo motors. An analytical lateral stiffness model is derived based on the pseudo-rigid-body model and validated by experiments. A physical prototype of a three-armed manipulator is built. Extensive stiffness and impact tests are conducted, and the results show that the stiffness of the robotic arm can be changed up to 3.6 times at a morphing angle of 37 deg. At an impact velocity of 2.2 m/s, the peak acceleration has a decrease of 19.4% and a 28.57% reduction of head injury criteria (HIC) when the arm is tuned from the high stiffness mode to the low stiffness mode. These preliminary results demonstrate the feasibility to reduce impact injury by introducing compliance into the robotic link and that the compliant link solution could be an alternative approach for addressing safety concerns of physical human-robot interactions. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.","Compliant mechanisms; mechanism design; physical human-robot interactions; robot design; tunable stiffness arm","Agricultural robots; Industrial manipulators; Machine design; Man machine systems; Manipulators; Robotic arms; Robotics; Stiffness; Design and modeling; Head injury criterion (HIC); Impact velocities; Peak acceleration; Physical human-robot interactions; Pseudo-rigid body models; Robotic manipulators; Tunable stiffness; Human robot interaction",,,,,"National Science Foundation, NSF: 1637656, CMMI-1637656","This material is based upon work supported by National Science Foundation (Grant No. CMMI-1637656; Funder ID: 10.13039/ 501100008982). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"Colgate, E., Wannasuphoprasit, W., Peshkin, M. 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"Das A., Deb R., Deb S.","57217280522;57217279901;55003599100;","Designing an Adaptive Body Art Maker with Computer Numerical Control-A paradigm of human robot interaction",2020,"Proceedings of the 5th International Conference on Inventive Computation Technologies, ICICT 2020",,,"9112577","","",,,"10.1109/ICICT48043.2020.9112577","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086998710&doi=10.1109%2fICICT48043.2020.9112577&partnerID=40&md5=1a0cc7495c92720972b14fbaf8d524b3","National Institute of Technology, Agartala CSE Dept, Tripura, India","Das, A., National Institute of Technology, Agartala CSE Dept, Tripura, India; Deb, R., National Institute of Technology, Agartala CSE Dept, Tripura, India; Deb, S., National Institute of Technology, Agartala CSE Dept, Tripura, India","There is a definite rise in the art forms of twenty first century. Body Arts has gained acceptance in the world of art. Despite the availability and creation of realistic software applications for digital painting, the development of adaptive body art system still has a long way to go to be recognized as a visual form of art and has lot to be explored. The essence of this paper is to present a design that can cultivate thought and helps to idealize a techno-tasteful value that can imitate the human way of perception from viewing and drawing based on human modeling of Body Art printing. The plan of action in this paper comprises an optical feedback system based on camera input and an open source software framework for agent processing. Movements of the agent can be dynamically defined through an intelligent interface and are performed by the agent in a responsive manner. © 2020 IEEE.","Body Art; Body Art Open- loop System; CNC; CNC machine; Multi-layered Adaptive Interface","Application programs; Arts computing; Computer control systems; Computer programming; Human computer interaction; Open source software; Open systems; Software agents; Computer numerical control; Digital painting; Human Model; Intelligent interface; Software applications; Visual forms; Human robot interaction",,,,,,,,,,,,,,,,"Adaptive Human-Computer Interfaces A. F. Norcio and J. Stanley Human Computer Interaction Laboratory Tech. 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IJCA Special issue on Imaging and Biomedical Applications (CASCT); Andrei, T., Nae, I., (2010) Practical Applications Performed Bya St Epper Mot or CNC Router; Pabolu, V.K., Srinivas, K.N.H., Design and implementation of a three dimensional cnc machine (2010) International Journal on Computer Science and Engineering, 2 (8). , Andhra Pradesh, India; Landau, I.D., (1979) Adaptive Control the Model Reference Approach, , Marcel DekkerInc., New York and Basel; Bhandari, S., Regina, B., 3d printing and its applications International Journal of Computer Science and Information Technology Research; Lipson, H., (2013) The New World of 3D Printing, , Fabricated Indianapolis, Indiana: John Wiley & Sons; Ramesh, R., Mannan, M.A., Poo, A.N., Tracking and contour error control in cnc servo systems (2005) Int. J. Mach. Tools Manuf., 45 (3), pp. 301-326. , Mar; Astrom, K.J., (2008) Adaptive Control, , 2nd ed., Dover ed. Mineola, N. Y: Dover Publications; Misik, S., Cela, A., Bradac, Z., Optimal predictive control-A brief review of t heory and practice Int Ernational Federation of Automatic Control; Greblicki, W., Nonparamet ric ident ification of wiener systems by ort hogonal series (1994) IEEE Transactions on AC, 39 (10), pp. 2077-2086; Tajimas Sencer, B., (2016) Kinematic Corner Smoothening for High Speed Machine Tools, , Corvallis, OR; Pessoles, X., Landon, Y., Rubio, W., (2010) Kinematic Modeling of A 3-axis NC Machine Tool in Linear and Circular Interpolation, , Toulouse, France; Duan, M., Okwudire, C., (2016) Minimum-Time Cornering for CNC Machines Using Optimal Control Method with NURBS Parameterization, , London, U. K. : Springer-Verlag; Yutkowitz, S.J., Chester, W., (2005) Apparatus and Method for Smooth Cornering Ina Motion Control System, , United States, Siemens Energy & Automation, Inc. Alpharet ta, GA (US Patent 6922606); Jianxin, G., Qiang, Z., Xiao-Shan, G., Tracking Error Reduction in CNC Machine, , (August 2013) The Editorial Office of JSSC & Springer-Verlag Berlin; Hussain, J., Al-Hassan, A., (2018) Model-Based Adaptive User Int Erface Based on ConText and User Experience Evaluation; Gootjes, D., (2017) Applying Feedback Control to Improve 3D Printing Quality; Sencer, B., (2013) T Raject Ory Generation for CNC Machine Tools; Benjamin McKittrick, W., (2014) Closed-loop Control of A 3D Printer Gantry, , by",,,,"Institute of Electrical and Electronics Engineers Inc.","5th International Conference on Inventive Computation Technologies, ICICT 2020","26 February 2020 through 28 February 2020",,161044,,9781728146850,,,"English","Proc. Int. Conf. Inventive Comput. Technol., ICICT",Conference Paper,"Final","",Scopus,2-s2.0-85086998710
"Cremer S., Das S.K., Wijayasinghe I.B., Popa D.O., Lewis F.L.","56383673500;56383202800;36679469200;7102861346;24729085600;","Model-Free Online Neuroadaptive Controller with Intent Estimation for Physical Human-Robot Interaction",2020,"IEEE Transactions on Robotics","36","1","8884133","240","253",,5,"10.1109/TRO.2019.2946721","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079664872&doi=10.1109%2fTRO.2019.2946721&partnerID=40&md5=d54fda20330cb8392fd17c0cee575bc5","Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX  76019, United States; Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States","Cremer, S., Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX  76019, United States; Das, S.K., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States; Wijayasinghe, I.B., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States; Popa, D.O., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States; Lewis, F.L., Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX  76019, United States","With the rise of collaborative robots, the need for safe, reliable, and efficient physical human-robot interaction (pHRI) has grown. High-performance pHRI requires robust and stable controllers suitable for multiple degrees of freedom (DoF) and highly nonlinear robots. In this article, we describe a cascade-loop pHRI controller, which relies on human force and pose measurements and can adapt to varying robot dynamics online. It can also adapt to different users and simplifies the interaction by making the robot behave according to a prescribed dynamic model. In our controller formulation, two neural networks (NNs) in the 'outer-loop' predict human motion intent and estimate a reference trajectory for the robot that the 'inner-loop' controller follows. The inner-loop imposes a prescribed error dynamics (PED) with the help of a model-free neuroadaptive controller (NAC), which uses a NN to feedback linearize the robot dynamics. Lyapunov stability analysis gives weight tuning laws that guarantee that the error signals are bounded and the desired reference trajectory is achieved. Our control scheme was implemented on a Personal Robot 2 robot and validated through an exploratory experimental study in point-to-point collaborative motion. Results indicate fast convergence of our controller, and the resulting tracking error, motion jerk, and human control effort are comparable with other methods that require prior training, knowledge, and calibration. © 2004-2012 IEEE.","Human intent estimation; neuroadaptive control; physical human-robot interaction (pHRI)","Controllers; Degrees of freedom (mechanics); Dynamics; Errors; Man machine systems; Motion tracking; Navigation; Lyapunov stability analysis; Multiple degrees of freedom; Neural networks (NNS); Neuro-adaptive control; Neuro-adaptive controllers; Physical human-robot interactions; Physical humanrobot interaction (phri); Reference trajectories; Human robot interaction",,,,,"1643989; National Science Foundation, NSF: 1208623","Manuscript received September 22, 2018; accepted October 5, 2019. Date of publication October 28, 2019; date of current version February 4, 2020. This work was supported by U.S. National Science Foundation Grant 1208623 and Grant 1643989. This article was recommended for publication by Associate Editor Prof. R. Carloni and Editor Prof. A. Billard upon evaluation of the reviewers’ comments. (Corresponding author: Sven Cremer.) S. Cremer and F. L. Lewis are with the Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019 USA (e-mail: sven.cremer@mavs.uta.edu; lewis@uta.edu).",,,,,,,,,,"Erden, M.S., Marić, B., Assisting manual welding with robot (2011) Robot. And Comput.-Integr. Manuf., 27 (4), pp. 818-828; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput.-Integr. Manuf., 40, pp. 1-13; Safavi, A., Zadeh, M.H., Model-based haptic guidance in surgical skill improvement (2015) Proc. IEEE Int. Conf. Syst., Man, Cybern., pp. 1104-1109. , Oct; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. Syst., 57 (5), pp. 469-483; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100. , Oct; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. Theory, 43 (3), pp. 253-270. , Mar; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robot. Auton. Syst., 58 (10), pp. 1159-1176; Shin, D., Sardellitti, I., Park, Y.-L., Khatib, O., Cutkosky, M., Design and control of a bio-inspired human-friendly robot (2009) Experimental Robotics Springer Tracts in Advanced Robotics, 54, pp. 43-52. , O. Khatib, V. Kumar, and G. Pappas, Eds. Berlin, Germany: Springer; Pervez, A., Ryu, J., Safe physical human robot interaction-past, present and future (2008) J. Mech. Sci. Technol., 22 (3), pp. 469-483. , May; Lewis, F.L., Dawson, D.M., Abdallah, C.T., (2004) Robot Manipulator Control: Theory and Practice, p. 638. , Boca Raton, FL, USA: CRC Press; Gribovskaya, E., Kheddar, A., Billard, A., Motion learning and adaptive impedance for robot control during physical interaction with humans (2011) Proc. IEEE Int. Conf. Robot. Autom., pp. 4326-4332. , May; Suzuki, S., Furuta, K., Adaptive impedance control to enhance human skill on a haptic interface system (2012) J. Control Sci. Eng., 2012, pp. 1-10; Li, Y., Sam Ge, S., Impedance learning for robots interacting with unknownenvironments (2014) IEEE Trans.Control Syst. Technol., 22 (4), pp. 1422-1432. , Jul; Wang, Z., Peer, A., Buss, M., An HMM approach to realistic haptic human-robot interaction (2009) Proc. 3rd Joint EuroHaptics Conf. Symp. Haptic Interfaces Virtual Environ. Teleoperator Syst., pp. 374-379. , World Haptics, Mar; Futamure, S., Bonnet, V., Dumas, R., Kulic, D., Venture, G., Dynamically consistent inverse kinematics framework using optimizations for human motion analysis (2016) Proc. IEEE-RAS 16th Int. Conf. Humanoid Robots, pp. 436-441. , Nov; Medina, J.R., Endo, S., Hirche, S., Impedance-based Gaussian processes for predicting human behavior during physical interaction (2016) Proc. IEEE Int. Conf. Robot. Autom., pp. 3055-3061. , May; Franklin, D.W., CNS learns stable, accurate, and efficient movements using a simple algorithm (2008) J. Neurosci., 28 (44), pp. 11165-11173; Ragazzini, J., Engineering aspects of the human being as a servomechanism (1948) Proc.Meet. Am. Psychological Assoc., pp. 219-314; Phatak, A., Weinert, H., Segall, I., Day, C.N., Identification of a modified optimal control model for the human operator (1976) Automatica, 12 (1), pp. 31-41; Chen, F.-C., Khalil, H., Adaptive control of nonlinear systems using neural networks (1992) Int. J. Control, 55 (6), pp. 1299-1317; Lewis, F.L., Liu, K., Yesildirek, A., Neural net robot controller with guaranteed tracking performance (1995) IEEE Trans. Neural Netw., 6 (3), pp. 703-715. , Jan; Ge, S.S., Lee, T.H., Harris, C.J., (1998) Adaptive Neural Network Control of Robotic Manipulators World Scientific Series in Robotics and Intelligent Systems, 19 (4). , Singapore: World Scientific, Dec; Lewis, F.L., Jagannathan, S., Yesildirak, A., (1998) Neural Network Control of Robot Manipulators and Nonlinear Systems, , Boca Raton, FL, USA: CRC Press; Ranatunga, I., Cremer, S., Lewis, F.L., Popa, D.O., Neuroadaptive control for safe robots in human environments: A case study (2015) Proc. IEEE Int. Conf. Autom. Sci. Eng., pp. 322-327; Ranatunga, I., Cremer, S., Popa, D.O., Lewis, F.L., Intent aware adaptive admittance control for physical Human-Robot Interaction (2015) Proc. IEEE Int. Conf. Robot. Autom., pp. 5635-5640; Ranatunga, I., Lewis, F.L., Popa, D.O., Tousif, S.M., Adaptive admittance control for human-robot interaction using model reference design and adaptive inverse filtering (2017) IEEE Trans. Control Syst. Technol., 25 (1), pp. 278-285. , Jan; Hogan, N., Sternad, D., Sensitivity of smoothness measures to movement duration, amplitude, and arrests (2009) J. Motor Behav., 41 (6), pp. 529-534. , Nov; McRuer, D.T., Krendel, E.S., (1974) Mathematical Models of Human Pilot Behavior, , Advisory Group for Aerospace Research and Development, Tech. Rep. AGARD-AG-188, Neuilly-sure-Seine, France; ANSI/RIA R15.06-2012 American National Standard for industrial robots and robot systems-safety requirements (2012) Robot. Ind. Assoc., , R. I. Association et al., Ann Arbor, MI, USA; Li, Y., Sam Ge, S., Yang, C., Learning impedance control for physical robot-environment interaction (2012) Int. J. Control, 85 (2), pp. 182-193. , Feb; Quigley, M., ROS: An open-source robot operating system (2009) Proc. ICRA Workshop Open Source Softw., pp. 1-5; Guennebaud, G., (2010) Eigen v3, , http://eigen.tuxfamily.org, [Online]; Cremer, S., Ranatunga, I., Popa, D.O., (2017) SkinLearn, , https://bitbucket.org/nextgensystems/skinlearn, [Online]; Breazeal, C., Scassellati, B., Challenges in building robots that imitate people (2002) Imitation in Animals and Artifacts., pp. 363-390. , Cambridge, MA, USA: MIT Press","Cremer, S.; Department of Electrical Engineering, United States; email: sven.cremer@mavs.uta.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85079664872
"Li X.","56811045500;","Human–robot interaction based on gesture and movement recognition",2020,"Signal Processing: Image Communication","81",,"115686","","",,13,"10.1016/j.image.2019.115686","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075029019&doi=10.1016%2fj.image.2019.115686&partnerID=40&md5=c39e461f8c2f5d8b14aca6a4bfc03203","State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning  110819, China","Li, X., State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning  110819, China","Human–robot interaction (HRI) has become a research hotspot in computer vision and robotics due to its wide application in human–computer interaction (HCI) domain. Based on the explored algorithms of gesture recognition and limb movement recognition in somatosensory interaction, an HRI model of a robotic arm is proposed for robot arm manipulation. More specifically, 3D SSD architecture is used for the location and identification of gesture and arm movement. Then, DTW template matching algorithm is adopted to trace the dynamic gestures. The interactive scenarios and interactive modes are designed for experiment and implementation. Virtual interactive experimental results have demonstrated the usefulness of our method. © 2019","3D SSD; DTW; Gesture recognition; Human–robot interaction; Movement recognition","Computer vision; Gesture recognition; Human computer interaction; Human robot interaction; Robotic arms; Robotics; Template matching; 3D SSD; Arm movements; Computer interaction; Interactive mode; Limb movements; Movement recognition; Robot interactions; Template-matching algorithms; Motion estimation",,,,,"National Natural Science Foundation of China, NSFC: 61503070, 61873054; National Natural Science Foundation of China-Yunnan Joint Fund, NSFC-Yunnan Joint Fund; Fundamental Research Funds for the Central Universities: N170804006","The authors acknowledge the National Natural Science Foundation of China (Grant: 61873054 and 61503070 ). Fundamental Research Funds for the Central Universities, China ( N170804006 ) Xing Li was born in 1982. She received the Dr. Eng.degree in Power electronics and power transmission from Northeast University. She is currently an assistant Professor with the State Key Laboratory of Synthetical Automation for Process Industries, Northeast University, Liaoning, China. A number of research results has gained the support of the National Natural Science Fund and Liaoning Natural Science Fund. She is also the member of the Big Data Committee and Process Control Committee for the Chinese Association of Automation. She is current research interests include adaptive/robust control, motion control, Intelligent robot system, etc.",,,,,,,,,,"Malima, A.K., Özgür, E., Çetin, M., A fast algorithm for vision-based hand gesture recognition for robot control (2006); Sánchez-Nielsen, E., Antón-Canalís, L., Hernández-Tejera, M., Hand gesture recognition for human-machine interaction (2004); Wang, C.C., Wang, K.C., Hand posture recognition using adaboost with sift for human robot interaction (2007) Recent Progress in Robotics: Viable Robotic Service to Human, pp. 317-329. , Springer Berlin, Heidelberg; Tang, M., Recognizing Hand Gestures with Microsoft's Kinect (2011), Department of Electrical Engineering of Stanford University: [sn] Palo Alto; Ramey, A., González-Pacheco, V., Salichs, M.A., Integration of a low-cost RGB-D sensor in a social robot for gesture recognition (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 229-230. , IEEE; Van den Bergh, M., Carton, D., De Nijs, R., Mitsou, N., Landsiedel, C., Kuehnlenz, K., Buss, ….M., Real-time 3D hand gesture interaction with a robot for understanding directions from humans (2011) 2011 Ro-Man, pp. 357-362. , IEEE; Burger, B., Ferrané, I., Lerasle, F., Multimodal interaction abilities for a robot companion (2008) International Conference on Computer Vision Systems, pp. 549-558. , Springer Berlin, Heidelberg; Gast, J., Bannat, A., Rehrl, T., Wallhoff, F., Rigoll, G., Wendt, C., Farber, ….B., Real-time framework for multimodal human–robot interaction (2009) 2009 2nd Conference on Human System Interactions, pp. 276-283. , IEEE; Kahn, P.H., Freier, N.G., Kanda, T., Ishiguro, H., Ruckert, J.H., Severson, R.L., Kane, S.K., Design patterns for sociality in human–robot interaction (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, pp. 97-104. , ACM; Chen, Q., Georganas, N.D., Petriu, E.M., Hand gesture recognition using haar-like features and a stochastic context-free grammar (2008) IEEE Trans. Instrum. Meas., 57 (8), pp. 1562-1571; Rautaray, S.S., Agrawal, A., A novel human computer interface based on hand gesture recognition using computer vision techniques (2010) Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, pp. 292-296. , ACM; Thangali, A., Nash, J.P., Sclaroff, S., Neidle, C., Exploiting phonological constraints for handshape inference in ASL video (2011) CVPR 2011, pp. 521-528. , IEEE; Cooper, H., Ong, E.J., Pugeault, N., Bowden, R., Sign language recognition using sub-units (2012) J. Mach. Learn. Res., 13 (Jul), pp. 2205-2231; Wang, X., Xia, M., Cai, H., Gao, Y., Cattani, C., Hidden-markov-models-based dynamic hand gesture recognition (2012) Math. Probl. Eng., 2012; Zou, Y., Chen, W., Wu, X., Liu, Z., Indoor localization and 3D scene reconstruction for mobile robots using the Microsoft Kinect sensor (2012) IEEE 10th International Conference on Industrial Informatics, pp. 1182-1187. , IEEE; Biswas, J., Veloso, M., Depth camera based indoor mobile robot localization and navigation (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 1697-1702. , IEEE; Song, W., Guo, X., Jiang, F., Yang, S., Jiang, G., Shi, Y., Teleoperation humanoid robot control system based on kinect sensor (2012) 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, Vol. 2, pp. 264-267. , IEEE; Hartmann, J., Forouher, D., Litza, M., Kluessendorff, J.H., Maehle, E., Real-time visual slam using fastslam and the microsoft kinect camera (2012) ROBOTIK 2012; 7th German Conference on Robotics, pp. 1-6. , VDE; Siradjuddin, I., Behera, L., McGinnity, T.M., Coleman, S., A position based visual tracking system for a 7 DOF robot manipulator using a kinect camera (2012) The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. , IEEE; Schröder, M., Elbrechter, C., Maycock, J., Haschke, R., Botsch, M., Ritter, H., Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands (2012) 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pp. 262-269. , IEEE; Jambhale, S.S., Khaparde, A., Gesture recognition using DTW & piecewise DTW (2014) 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1-5. , IEEE; Wang, X., Xia, M., Cai, H., Gao, Y., Cattani, C., Hidden-markov-models-based dynamic hand gesture recognition (2012) Math. Probl. Eng., 2012; Elmezain, M., Al-Hamadi, A., Michaelis, B., Real-time capable system for hand gesture recognition using hidden markov models in stereo color image sequences (2008); Escalera, S., Gonzàlez, J., Baró, X., Reyes, M., Lopes, O., Guyon, I., Athistos, V., H.J., E., Multi-modal gesture recognition challenge 2013: Dataset and Results (2013) ICMI",,,,"Elsevier B.V.",,,,,09235965,,SPICE,,"English","Signal Process Image Commun",Article,"Final","",Scopus,2-s2.0-85075029019
"Zhang S., Zanchettin A.M., Villa R., Dai S.","57200408462;27268100300;57209134121;7203008798;","Real-time trajectory planning based on joint-decoupled optimization in human-robot interaction",2020,"Mechanism and Machine Theory","144",,"103664","","",,8,"10.1016/j.mechmachtheory.2019.103664","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073533220&doi=10.1016%2fj.mechmachtheory.2019.103664&partnerID=40&md5=5cd89224378d1e2dcf795ea0939e165d","State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy","Zhang, S., State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China; Zanchettin, A.M., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy; Villa, R., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy; Dai, S., State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","In order to perform safe and natural interactions with humans, robots are required to adjust their motions quickly according to human behaviors. Performing the complex calculation and updating the trajectories in real-time is a particular challenge. In this paper, we present a real-time optimization-based trajectory planning method for serial robots. We encode the trajectory planning problem into a series of optimization problems. To solve the high-dimensional complex non-linear optimization problems in real-time, we provide a joint-decoupling method that transforms the original joint-coupled optimization problem into multiple joint-independent optimization problems, with much lower computational complexity. We implement and validate our method in a specific human-robot interaction case. Experimental results show that the computational feasibility and efficiency of optimization solution were greatly improved by the joint-decoupling transformation. Smooth, safe, and rapid motion of the robot was generated in real-time, establishing a basis for safe and reactive human-robot interactions. © 2019","Human-robot interaction; Machine learning; Non-linear optimization; Real-time trajectory planning; Serial robot","Behavioral research; Computational efficiency; Learning systems; Man machine systems; Mathematical transformations; Nonlinear programming; Robot programming; Trajectories; Computational feasibility; Decoupling transformation; Non-linear optimization; Non-linear optimization problems; Real-time optimization; Real-time trajectories; Serial robots; Trajectory planning method; Human robot interaction",,,,,,,,,,,,,,,,"Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput. Integr. Manuf., 40, pp. 1-13; Liu, H., Wang, L., Human motion prediction for human-robot collaboration (2017) J. Manuf. 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"Chadalavada R.T., Andreasson H., Schindler M., Palm R., Lilienthal A.J.","57191629740;7003771590;57204823354;14623351700;15623407200;","Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human–robot interaction",2020,"Robotics and Computer-Integrated Manufacturing","61",,"101830","","",,11,"10.1016/j.rcim.2019.101830","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070732550&doi=10.1016%2fj.rcim.2019.101830&partnerID=40&md5=882db10d1a31eefd95bd593c80f1d448","AASS MRO Lab, Örebro University, Fakultetsgatan 1, Örebro, 70281, Sweden; Faculty of Human Sciences, University of Cologne, Germany","Chadalavada, R.T., AASS MRO Lab, Örebro University, Fakultetsgatan 1, Örebro, 70281, Sweden; Andreasson, H., AASS MRO Lab, Örebro University, Fakultetsgatan 1, Örebro, 70281, Sweden; Schindler, M., Faculty of Human Sciences, University of Cologne, Germany; Palm, R., AASS MRO Lab, Örebro University, Fakultetsgatan 1, Örebro, 70281, Sweden; Lilienthal, A.J., AASS MRO Lab, Örebro University, Fakultetsgatan 1, Örebro, 70281, Sweden","Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot's intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people's motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance. © 2019","Eye-tracking; Human–robot interaction (HRI); Intention communication; Intention recognition; Logistics; Mobile robots; Obstacle avoidance; Safety; Spatial augmented reality; Stimulated recall interview","Accident prevention; Augmented reality; Collision avoidance; Efficiency; Eye tracking; Glass; Human robot interaction; Logistics; Materials handling equipment; Mobile robots; Motion planning; Navigation; Trajectories; Autonomous Mobile Robot; Bi-directional communication; Industrial environments; Intention recognition; Robot interactions; Safety and efficiencies; Spatial augmented realities; Stimulated recall interview; Robot programming",,,,,,,,,,,,,,,,"Mutlu, B., Yamaoka, F., Kanda, T., Ishiguro, H., Hagita, N., Nonverbal leakage in robots: communication of intentions through seemingly unintentional behavior (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, HRI ’09, pp. 69-76. , ACM New York, NY, USA; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708-713; Bartneck, C., Croft, E., Kulic, D., Measuring the anthropomorphism, animacy, likeability, perceived intelligence and perceived safety of robots (2008) Metrics for HRI Workshop, Technical Report, 471, pp. 37-44. , Citeseer; Raineri, M., Perri, S., Bianco, C.G.L., Safety and efficiency management in LGV operated warehouses (2019) Rob. 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Manuf., 45, pp. 168-179","Chadalavada, R.T.; AASS MRO Lab, Fakultetsgatan 1, Sweden; email: ravi.chadalavada@oru.se",,,"Elsevier Ltd",,,,,07365845,,RCIME,,"English","Rob Comput Integr Manuf",Article,"Final","",Scopus,2-s2.0-85070732550
"Chen L., Wu M., Zhou M., Liu Z., She J., Hirota K.","55774663200;35231682600;57191199141;56101506800;7102576674;7402829702;","Dynamic Emotion Understanding in Human-Robot Interaction Based on Two-Layer Fuzzy SVR-TS Model",2020,"IEEE Transactions on Systems, Man, and Cybernetics: Systems","50","2","8071157","490","501",,28,"10.1109/TSMC.2017.2756447","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032278026&doi=10.1109%2fTSMC.2017.2756447&partnerID=40&md5=0c7c371937561eff0ad2f9d16fc158c0","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; School of Engineering, Tokyo University of Technology, Tokyo, 192-0982, Japan; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Zhou, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Liu, Z., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; She, J., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, School of Engineering, Tokyo University of Technology, Tokyo, 192-0982, Japan; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Two-layer fuzzy support vector regression-Takagi-Sugeno (TLFSVR-TS) model is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression, and emotional intention understanding is obtained mainly based on human emotions and identification information. It aims to make robots capable of recognizing and understanding human emotions, in such a way that make HRI run smoothly. TLFSVR-TS considers about the priori knowledge inferred from human personal preference to reduce the uncertainty of various people, and multiple support vector regression (SVR) corresponding to different genders/provinces/ages of human to guarantee the local learning ability. Preliminary application experiments are performed in the developing emotional social robot system, where 30 volunteers experience the scenario of 'drinking in the bar.' Results show that the proposal receives higher understanding accuracy than that of TLFSVR, kernel fuzzy c -means clustering is fused with SVR, and SVR. © 2013 IEEE.","Dynamic recognition; emotion understanding; human-robot interaction (HRI); two-layer fuzzy support vector regression-Takagi-Sugeno (TLFSVR-TS) model","Face recognition; Gold; Heuristic algorithms; Interactive computer systems; Man machine systems; Psychology computing; Real time systems; Regression analysis; Robots; Support vector machines; Vectors; Dynamic recognition; Emotion recognition; Emotion understanding; Fuzzy support vector regressions; Human robot Interaction (HRI); Human robot interaction",,,,,"2015CFA010; National Natural Science Foundation of China, NSFC: 61210011, 61403422, 61473313, 61603356; Natural Science Foundation of Hubei Province; China University of Geosciences, Wuhan, CUG: 201548; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","Manuscript received July 7, 2017; accepted September 6, 2017. Date of publication October 18, 2017; date of current version January 15, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61603356, Grant 61210011, Grant 61473313, and Grant 61403422, in part by the 111 Project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, under Grant 201548. This paper was recommended by Associate Editor P. Chen. (Corresponding author: Min Wu.) L. Chen, M. Wu, M. Zhou, and Z. Liu are with the School of Automation, China University of Geosciences, Wuhan 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China (e-mail: chenluefeng@cug.edu.cn; wumin@cug.edu.cn).","This work was supported in part by the National Natural Science Foundation of China under Grant 61603356, Grant 61210011, Grant 61473313, and Grant 61403422, in part by the 111 Project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, under Grant 201548.",,,,,,,,,"Tawari, A., Trivedi, M.M., Face expression recognition by cross modal data association (2013) IEEE Trans. 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Energy, 7 (1), pp. 241-249. , Jan; Cheng, W.-Y., Juang, C.-F., A fuzzy model with online incremental SVM and margin-selective gradient descent learning for classification problems (2014) IEEE Trans. Fuzzy Syst., 22 (2), pp. 324-337. , Apr; Juang, C.-F., Huang, R.-B., Cheng, W.-Y., An interval type-2 fuzzy-neural network with support-vector regression for noisy regression problems (2010) IEEE Trans. Fuzzy Syst., 18 (4), pp. 686-699. , Aug; Beyhan, S., Affine TS fuzzy model-based estimation and control of Hindmarsh-Rose neuronal model (2017) IEEE Trans. Syst., Man, Cybern., Syst., 47 (8), pp. 2342-2350. , Feb; Meda-Campana, J.A., Gomez-Mancilla, J.C., Castillo-Toledo, B., Exact output regulation for nonlinear systems described by Takagi-Sugeno fuzzy models (2012) IEEE Trans. Fuzzy Syst., 20 (2), pp. 235-247. , Apr; Robles, R., Bernal, M., Comments on 'exact output regulation for nonlinear systems described by Takagi-Sugeno fuzzy models, ""' (2015) IEEE Trans. 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Cybern., 44 (4), pp. 516-525. , Apr; Yang, X., Zhang, G., Lu, J., Ma, J., A kernel fuzzy c-means clusteringbased fuzzy support vector machine algorithm for classification problems with outliers or noises (2011) IEEE Trans. Fuzzy Syst., 19 (1), pp. 105-115. , Feb; Bottou, L., Vapnik, V., Local learning algorithms (1992) Neural Comput., 4 (6), pp. 888-900; Lin, K.-P., A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (2014) IEEE Trans. Fuzzy Syst., 22 (5), pp. 1074-1087. , Oct; Nguyen, D.D., Ngo, L.T., Multiple kernel interval type-2 fuzzy c-means clustering (2013) Proc. IEEE Int. Conf. Fuzzy Syst, pp. 1-8. , Hyderabad, India; Dong, W.M., Wong, F.S., Fuzzy weighted averages and implementation of the extension principle (1987) Fuzzy Sets Syst., 21 (2), pp. 183-199; Chen, L.-F., Adapting multi-robot behavior to communication atmosphere in humans-robots interaction using fuzzy production rule based friend-Q learning (2013) J. Adv. Comput. Intell. Intell. Informat., 17 (2), pp. 291-301; Chen, L.-F., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction (2014) J. Multimodal User Interfaces, 8 (3), pp. 289-303; Tanaka, F., Pepper learns together with children: Development of an educational application (2015) Proc. IEEE RAS 15th Int. Conf. Humanoid Robots, pp. 270-275. , Seoul, South Korea; Bicho, E., The power of prediction: Robots that read intentions (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 5458-5459. , Vilamoura, Portugal","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682216,,,,"English","IEEE Trans. Syst. Man Cybern. Syst.",Article,"Final","",Scopus,2-s2.0-85032278026
"Niemelä M., Ahtinen A., Turunen M.","23397605400;56091305500;8680277100;","Social human-robot interaction in the wild: A workshop proposal for academic mindtrek 2020",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"168","169",,,"10.1145/3377290.3377320","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080870105&doi=10.1145%2f3377290.3377320&partnerID=40&md5=a76d0001803ed5604fcf4cd48ad95dbb","VTT Technical Research Centre of Finland, Tampere, Finland; Human-Centered Technology, Tampere University, Tampere, Finland","Niemelä, M., VTT Technical Research Centre of Finland, Tampere, Finland; Ahtinen, A., Human-Centered Technology, Tampere University, Tampere, Finland; Turunen, M., VTT Technical Research Centre of Finland, Tampere, Finland","This workshop will collect experts and stakeholders from all fields of human-robot interaction: both social and industrial applications and uses of robotics are of interest as far as they have human in the loop. The workshop will present recent and fully new research work in social HRI, including first results of a 3.5 month field trial and mixed-method study of a social robot Pepper in a shopping mall in Finland. © 2020 Authors.","collaborative robots; human-robot interaction; social robots","Human robot interaction; Man machine systems; Collaborative robots; Field trial; Finland; Human-in-the-loop; Mixed method; Social hri; Social human-robot interactions; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 688147; Academy of Finland; Strategic Research Council, SRC: 292980, 314180","The workshop is supported by the project MultiModal Mall Entertainment Robot (http://www.mummer-project.eu) that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688147, and the project Robots and the Future of Welfare Services (http://roseproject.aalto.fi/en/) that is funded by the Academy of Finland, the Strategic Research Council (decision numbers: 292980 and 314180).",,,,,,,,,,"De Graaf, A.M.M., Ben Allouch, S., Klamer, T., Sharing a life with harvey: Exploring the acceptance of and relationship-building with a social robot (2015) Computers in Human Behavior 43 (February), pp. 1-14. , https://doi.org/10.1016/j.chb.2014.10.030; Vandemeulebroucke, T., De Casterlé, B.D., Gastmans, C., (2017) How Do Older Adults Experience and Perceive Socially Assistive Robots in Aged Care: A Systematic Review of Qualitative Evidence, pp. 1-19. , https://doi.org/10.1080/13607863.2017.1286455, Aging & Mental Health 7863 (February; Ellen Foster, M., Alami, R., Gestranius, O., Lemon, O., Niemelä, M., Jean-Marc Odobez, J.-M., Kumar, A., Pandey, A.K., The mummer project: Engaging human-robot interaction in real-world public spaces (2016) Lecture Notes in Computer Science, 9979, pp. 753-763. , https://doi.org/10.1007/978-3-319-47437-3_74, edited by A. Agah, JJ. Cabibihan, A. Howard, M. Salichs, and H. He, 9979 LNAI, Springer, Cham; Niemelä, M., Heikkilä, P., Lammi, H., Oksman, V., A social robot in a shopping mall: Studies on acceptance and stakeholder expectations (in publication) (2019) Book Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction, Edited by Oliver Korn, , Springer; Kaipainen, K., Ahtinen, A., Hiltunen, A., Nice surprise, more present than a machine: Experiences evoked by a social robot for guidance and edutainment at a city service point (2018) Proceedings of the 22nd International Academic Mindtrek Conference (Mindtrek '18) ACM, New York, NY, USA, pp. 163-171. , https://doi.org/10.1145/3275116.3275137",,,,"ICST","23rd International Academic Mindtrek Conference, AcademicMindtrek 2020","29 January 2020 through 30 January 2020",,157484,21531633,9781450377744,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85080870105
"Meng J., Feng Z., Xu T.","56198200400;7403443516;56683649700;","A method of fusing gesture and speech for human-robot interaction",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"265","269",,2,"10.1145/3379247.3379261","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082172399&doi=10.1145%2f3379247.3379261&partnerID=40&md5=d64c7e21ac9bd9d5d2260c19eff14801","Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China; Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China","Meng, J., Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China; Feng, Z., Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China; Xu, T., Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China","Aiming at the problem of using the unimodal model in human-robot interaction, in order to improve the operator's ability to control and interact with the robot and thus realize the natural and friendly interaction between human and robot, we propose a novel multimodal fusion architecture based on gesture and speech. Firstly, the convolutional neural network and Baidu API are used to recognize gesture and speech respectively. Secondly, the result of gesture prediction probability and the result of speech forward matching are normalized. Finally, the proposed multimodal fusion algorithm is used to fuse the two results, and the output results are filled into the intention slot. The operator's intention is determined by judging the fill integrity of intents the intention slot. The experimental results show that the proposed multimodal fusion architecture is superior to the unimodal model in recognition accuracy and interaction efficiency. © 2020 ACM International Conference Proceeding Series. All rights reserved.","Human-robot interaction; Intention understanding; Multimodal fusion","Convolutional neural networks; Man machine systems; Network architecture; Friendly interaction; Intention understanding; Interaction efficiency; Multi-modal fusion; Prediction probabilities; Recognition accuracy; Unimodal; Human robot interaction",,,,,,,,,,,,,,,,"Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2018) International Journal of Industrial Ergonomics, 68, pp. 355-367. , https://doi.org/10.1016/j.ergon.2017.02.004, (Nov. 2018); Narain, B.R., Priscilla, D., Gesture command recognition system for human machine interaction Australian Journal of Basic and Applied Sciences., 10 (5), pp. 41-45. , (Feb. 2016); Ahuja, M.K., Singh, A., Static vision based hand gesture recognition using principal component analysis (2015) 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE). IEEE, pp. 402-406. , Amritsar, India; Xu, D., Wu, X., Chen, Y.L., Xu, Y., Online dynamic gesture recognition for human robot interaction Journal of Intelligent & Robotic Systems, 77, pp. 583-596. , (Apr. 2015); Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., Static hand gesture recognition with parallel CNNs for space human-robot interaction (2017) International Conference on Intelligent Robotics and Applications, pp. 462-473. , Springer, Cham; Mi, J., Tang, S., Deng, Z., Goerner, M., Zhang, J., Object affordance based multimodal fusion for natural Human-Robot interaction Cognitive Systems Research, 54, pp. 128-137. , (May. 2019); Liu, H., Fang, T., Zhou, T.Y., Wang, Y.Q., Wang, L.H., Deep learning-based multimodal control interface for human-robot collaboration Procedia CIRP., 72, pp. 3-8. , (May. 2018); Schmidt, A., Deleforge, A., Kellermann, W., Ego-noise reduction using a motor data-guided multichannel dictionary (2016) IEEE/RSJ International Conference on Intelligent Robots & Systems, , IEEE, Daejeon, South Korea; Ye, S., Realization of speech intelligence control of artificial intelligence Technology Information., 17 (7), pp. 25-27; Trick, S., Koert, D., Peters, J., Rothkopf, C., (2019) Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction, , arXiv preprint arXiv; Rodriguez, S., Pérez, K., Quintero, C., López, J., Rojas, E., Calderón, J., Identification of multimodal human-robot interaction using combined kernels (2016) Innovations in Bio-Inspired Computing and Applications, pp. 263-273. , Springer, Cham; Liu, H., Fang, T., Zhou, T., Wang, L., Towards robust human-robot collaborative manufacturing: Multimodal fusion IEEE Access, 6, pp. 74762-74771; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, C.A., (2016) SSD: Single Shot MultiBox Detector. in European Conference on Computer Vision, pp. 21-37. , Springer, Cham",,,,"ICST","6th International Conference on Computing and Data Engineering, ICCDE 2020 and 2020 International Conference on Artificial Intelligence in Electronics Engineering, AIEE 2020","4 January 2020 through 6 January 2020",,158230,21531633,9781450376730,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85082172399
"Andronas D., Apostolopoulos G., Fourtakas N., Makris S.","57209693252;7004169705;57235211800;12241673400;","Multi-modal interfaces for natural Human-Robot Interaction",2020,"Procedia Manufacturing","54",,,"197","202",,1,"10.1016/j.promfg.2021.07.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113713445&doi=10.1016%2fj.promfg.2021.07.030&partnerID=40&md5=d1386940dc9c90c30627181d3ea59610","Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece","Andronas, D., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece; Apostolopoulos, G., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece; Fourtakas, N., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece; Makris, S., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece","Recent years, manufacturing aims on increasing flexibility while maintaining productivity for satisfying emerging market needs for higher product customization. Human Robot Collaboration (HRC) is able to bring about this balance by combining the benefits of manual assembly and robotic automation. When introducing a hybrid concept, safety and human acceptance are of vital importance for achieving implementation. Fenceless coexistence may lead to discomfort of operators especially in cases where close Human Robot Interaction (HRI) occurs. This work aims at designing and implementing a natural Human-System and System-Human interaction framework that enables seamless interaction between operators and their ""robot colleagues"". This natural interaction will strengthen hybrid implementation through increased: a) operator's and system's awareness, b) operator's trust to the system, and through the decrease of: a) human errors and b) safety incidents. The overall architecture of the proposed system makes it scalable, flexible, and applicable in different collaborative scenarios by enabling the connectivity of multiple interfaces with customizable environments according to operator's needs. The performance of the system is evaluated on a scenario originating from the automotive industry proving that an intuitive interaction framework can increase acceptance and performance of both robots and operators. © 2021 The Author(s).","Augmented Reality; Human Robot Collaboration; Human Robot Interaction; Interface; Robotic cells","Competition; Industrial robots; Man machine systems; Robotic assembly; Sustainable development; Human robot Interaction (HRI); Human-robot collaboration; Hybrid implementation; Intuitive interaction; Multi-modal interfaces; Multiple interfaces; Natural interactions; Product customization; Social robots",,,,,"European Commission, EC: 820807","This work has been partially funded by the EC research project “SHEWARK OR - Safe and effective HumAn-Robot coopEration toWards a better cOmpetiveness on current automation lacK manufacturing processes” ( Grant Agreement: 820807).",,,,,,,,,,"Chryssolouris, G., (2006) Manufacturing Systems: Theory and Practice, , Springer; Makris, S., (2021) Cooperating Robots for Flexible Manufacturing, , Springer International Publishing Cham; Khojasteh, Y., (2017) Production Managementâ€»: Advanced Tools, Models, and Applications for Pull Systems; Paletta, L., Pszeida, M., Ganster, H., Fuhrmann, F., Weiss, W., Ladstatter, S., Dini, A., Reiterer, B., (2019) Gaze-based Human Factors Measurements for the Evaluation of Intuitive Human-Robot Collaboration in Real-time., , IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. Institute of Electrical and Electronics Engineers Inc; Heyer, C., Human-robot interaction and future industrial robotics applications. (2010) IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, Iros 2010-Conference Proceedings.; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29, pp. 916-931; Kardos, C., Kemény, Z., Kovács, A., Pataki, B.E., Váncza, J., Context-dependent multimodal communication in human-robot collaboration (2018) Procedia Cirp, , Elsevier B.V; Rossi, S., Leone, E., Fiore, M., Finzi, A., Cutugno, F., An extensible architecture for robust multimodal human-robot communication. (2013) Ieee International Conference on Intelligent Robots and Systems.; Abich, J., Daniel, Barber, J., Barber, D.J., The impact of human-robot multimodal communication on mental workload, usability preference, and expectations of robot behavior (2017) J Multimodal User Interfaces, 11, pp. 211-225; Ong, S.K., Yuan, M.L., Nee, A.Y.C., Augmented reality applications in manufacturing: A survey (2008) International Journal of Production Research, 46, pp. 2707-2742; Chang, C.-C., New Approach for Static Gesture Recognition (2006) J. Inf. Sci. Eng., 22, pp. 1047-1057; Pires, J.N., Robot-by-voice: Experiments on commanding an industrial robot using the human voice (2005) Industrial Robot, 32, pp. 505-511; Nee, A.Y.C., Ong, S.K., Virtual and augmented reality applications in manufacturing. (2013) Ifac Proceedings Volumes (IFAC-PapersOnline). Ifac Secretariat; Murar, M., Brad, S., Monitoring and controlling of smart equipments using Android compatible devices towards IoT applications and services in manufacturing industry. (2014) Proceedings of 2014 Ieee International Conference on Automation, Quality and Testing, Robotics, Aqtr 2014., , IEEE Computer Society; Kühnlenz, B., Erhart, M., Kainert, M., Wang, Z.Q., Wilm, J., Kühnlenz, K., Impact of trajectory profiles on user stress in close human-robot interaction (2018) At-Automatisierungstechnik, 66, pp. 483-491; Andronas, D., Argyrou, A., Fourtakas, K., Paraskevopoulos, P., Makris, S., Design of Human Robot Collaboration workstations-Two automotive case studies (2020) Procedia Manufacturing, , Elsevier B.V; Brooke, J., (1995) SUS-A Quick and Dirty Usability Scale","Makris, S.; Laboratory for Manufacturing Systems and Automation, Greece; email: makris@lms.mech.upatras.gr",,,"Elsevier B.V.","10th CIRP Sponsored Conference on Digital Enterprise Technologies - Digital Technologies as Enablers of Industrial Competitiveness and Sustainability, DET 2020","12 October 2020 through 14 October 2020",,146288,23519789,,,,"English","Procedia Manuf.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113713445
"Song Z., Yin Z., Yuan Z., Zhang C., Chi W., Ling Y., Zhang S.","57210363378;57212483138;7401477128;57216944490;55340588700;56030248400;57216951195;","Attention-oriented action recognition for real-time human-robot interaction",2020,"Proceedings - International Conference on Pattern Recognition",,,"9412346","7087","7094",,,"10.1109/ICPR48806.2021.9412346","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110458942&doi=10.1109%2fICPR48806.2021.9412346&partnerID=40&md5=9c1233ef822d3fef9db33e80f431c108","Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Tencent RoboticsX, Shenzhen, China","Song, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Yin, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Yuan, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Zhang, C., Tencent RoboticsX, Shenzhen, China; Chi, W., Tencent RoboticsX, Shenzhen, China; Ling, Y., Tencent RoboticsX, Shenzhen, China; Zhang, S., Tencent RoboticsX, Shenzhen, China","Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction. In this paper, we deeply explore the characteristics of the action recognition task in interaction scenes and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. Specifically, a Pre-Attention network is employed to roughly focus on the interactor in the scene at low resolution firstly and then perform fine-grained pose estimation at high resolution. The other compact CNN receives the extracted skeleton sequence as input for action recognition, utilizing attention-like mechanisms to capture local spatial-temporal patterns and global semantic information effectively. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenes. Experimental results on our dataset and high efficiency (112 fps at 640 × 480 RGBD) on the mobile computing platform (Nvidia Jetson AGX Xavier) demonstrate excellent applicability of our method on action recognition in real-time human-robot interaction. © 2020 IEEE",,"Man machine systems; Pattern recognition; Semantics; Action recognition; High resolution; High-efficiency; Multi-level networks; Pose estimation; Real time interactions; Semantic information; Spatial temporals; Social robots",,,,,"201902, 201903; National Natural Science Foundation of China, NSFC: 61573280, 61976170, 91648121; National Key Research and Development Program of China, NKRDPC: 2016YFB1001001","This work was supported by the National Key R&D Program of China (No.2016YFB1001001), the National Natural Science Foundation of China (No.61573280, No.91648121, No.61976170), and Tencent Robotics X Lab Rhino-Bird Joint Research Program (No.201902, No.201903).",,,,,,,,,,"Ranasinghe, S., Al Machot, F., Mayr, H., A review on applications of activity recognition systems with regard to performance and evaluation (2016) International Journal of Distributed Sensor Networks, 12; Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Li, F.-F., Large-scale video classification with convolutional neural networks (2014) CVPR; Carreira, J., Zisserman, A., Quo vadis, action recognition? A new model and the kinetics dataset (2017) CVPR; Heilbron, F., Escorcia, V., Ghanem, B., Niebles, J.C., ActivityNet: A large-scale video benchmark for human activity understanding (2015) CVPR; Idrees, H., Zamir, A., Jiang, Y.-G., Gorban, A., Laptev, I., Sukthankar, R., Shah, M., The THUMOS challenge on action recognition for videos”in the wild (2016) Computer Vision and Image Understanding, 155; Chunhui, L., Yueyu, H., Yanghao, L., Sijie, S., Jiaying, L., PKU-MMD: A large scale benchmark for continuous multi-modal human action understanding (2017) ACM Multimedia Workshops; Shahroudy, A., Liu, J., Ng, T.-T., Wang, G., NTU Rgb+d: A large scale dataset for 3d human activity analysis (2016) CVPR; Elizabeth, D.S., Deep learning for human action recognition - Survey (2018) International Journal of Computer Science and Engineering, 6, pp. 323-328; Tran, D., Wang, H., Torresani, L., Feiszli, M., Video classification with channel-separated convolutional networks (2019) ICCV; Xu, H., Das, A., Saenko, K., Two-stream region convolutional 3d network for temporal activity detection (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (10), pp. 2319-2332; Liu, Y., Ma, L., Zhang, Y., Liu, W., Chang, S.-F., Multi-granularity generator for temporal action proposal (2019) CVPR; Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., Tuytelaars, T., Online action detection (2016) ECCV; Geest, R., Tuytelaars, T., Modeling temporal structure with lstm for online action detection (2018) WACV; Hoai, M., de la Torre, F., (2012) Max-Margin Early Event Detectors; Presti, L.L., la Cascia, M., 3D skeleton-based human action classification: A survey (2015) Pattern Recognition, 53; Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation (2016) ECCV; Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y., Convolutional pose machines (2016) CVPR; Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A., Real-time human pose recognition in parts from single depth images (2011) CVPR; Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B., Deepcut: Joint subset partition and labeling for multi person pose estimation (2016) CVPR; 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Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N., View adaptive neural networks for high performance skeleton-based human action recognition (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (8), pp. 1963-1978; Li, B., Chen, H., Yucheng, C., Dai, Y., He, M., Skeleton boxes: Solving skeleton based action detection with a single deep convolutional neural network (2017) ICMEW; Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F., A new representation of skeleton sequences for 3d action recognition (2017) CVPR; Caetano, C., Schwartz, W., (2019) Skeleton Image Representation for 3d Action Recognition Based on Tree Structure and Reference Joints, , arXiv preprint; Yan, S., Xiong, Y., Lin, D., Spatial temporal graph convolutional networks for skeleton-based action recognition (2018) AAAI; Shi, L., Zhang, Y., Cheng, J., Lu, H., Skeleton-based action recognition with directed graph neural networks (2019) CVPR; Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., (2019) Symbiotic Graph Neural Networks for 3d Skeleton-Based Human Action Recognition and Motion Prediction, , arXiv preprint; Gao, X., Hu, W., Tang, J., Liu, J., Guo, Z., Optimized skeleton-based action recognition via sparsified graph regression (2019) ACM; Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., Tang, X., Residual attention network for image classification (2017) CVPR; Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C., Microsoft coco: Common objects in context (2014) ECCV; Kingma, D., Ba, J., ADaM: A method for stochastic optimization (2014) ICLR","Song, Z.; Institute of Artificial Intelligence and Robotics, China
Yin, Z.; Institute of Artificial Intelligence and Robotics, China
Yuan, Z.; Institute of Artificial Intelligence and Robotics, China",,,"Institute of Electrical and Electronics Engineers Inc.","25th International Conference on Pattern Recognition, ICPR 2020","10 January 2021 through 15 January 2021",,169954,10514651,9781728188089,PICRE,,"English","Proc. Int. Conf. Pattern Recognit.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85110458942
"Steijn W.M.P., Van Oosterhout J., Willemsen J., Jansen A.","54416395400;57224214463;57224210844;57215215220;","Modelling human-robot interaction to optimize their safety, sustainability and efficiency: Identifying relevant characteristics",2020,"30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020",,,,"2571","2577",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110377688&partnerID=40&md5=c3e9e9dcd716a016d44101a8bc5951dd","Work Health and Technology, TNO, Netherlands; Sustainable Productivity and Employability, TNO, Netherlands; ZonMw, Netherlands","Steijn, W.M.P., Work Health and Technology, TNO, Netherlands; Van Oosterhout, J., Sustainable Productivity and Employability, TNO, Netherlands; Willemsen, J., Work Health and Technology, TNO, Netherlands; Jansen, A., ZonMw, Netherlands","Recent developments have introduced so-called cobots or cooperative robots, which place the machines on the work floor next to employees. A new challenge arises to keep these human robot interactions, safe and healthy, while also maintaining the sustainability and efficiency of the interaction. Here, we present a model, which can be a valuable tool to assist in future studies to systematically increase our understanding of Human-robot interaction (HRI). The model is based on the input-mediation-output (IMOI) model, which is often used in teamwork studies. Furthermore, it distinguishes between hardware, software, and mindware characteristics, to help researchers formulate more specific hypotheses for data collection in use cases. The purpose of this model is to help categorize various characteristics of humans, robots and the environment that can affect the outcome of the HRI. Ultimately, data gathered with the help of this model could help formulate design requirements for the development of future cobot applications and their implementation on the workfloor. This way we work towards inherently safe, healthy, sustainable and efficient HRI. Copyright © ESREL2020-PSAM15 Organizers.","Cobots; Efficient; Human-robot interaction; Safe; Sustainable","Efficiency; End effectors; Man machine systems; Safety engineering; Sustainable development; Cooperative robots; Data collection; Human robot Interaction (HRI); Social robots",,,,,,"This research has been made possible thanks to the support of the Ministry of Social Affairs and Employment regarding the Occupational Safety Research Program 2018. Furthermore, we would like to express our appreciation to Johan van der Vorm for his contributions to this project.",,,,,,,,,,"Adams, D.A., Nelson, R.R., Todd, P.A., Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication (1992) MIS Quarterly, 16 (2); Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., Romano, V.A., Modelling Workforce Aging in Job Rotation Problems (2015) IFAC-PapersOnLine, 48 (3), pp. 604-609; Bortot, D., Born, M., Bengler, K., Directly or on detours? How should industrial robots approximate humans? (2013) the 2013 8th ACM/IEEE International Conference on Human- Robot Interaction (HRI), , Paper presented at; Cazes, S., Hijzen, A., Saint-Martin, A., (2015) Measuring and assessing job quality: the OECD job quality framework, , Paper presented at the OECD Social, Employment and Migration; Chen, J. Y. 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ROSE 03; Stolt, A., Linderoth, M., Robertsson, A., Johansson, R., Force controlled robotic assembly without a force sensor (2012) the Robotics and Automation (ICRA), 2012 IEEE International Conference on, , Paper presented at; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) the 2013 IEEE International Conference on Robotics and Automation, , Paper presented at; Wang, L., Jamieson, G.A., Hollands, J.G., The Effects of Design Features on Users' Trust in and Reliance on a Combat Identification System (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 375-379; Werkhoven, P., Kester, L., Neerincx, M., Telling autonomous systems what to do (2018) the Proceedings of the 36th European Conference on Cognitive Ergonomics - ECCE'18, , Paper presented at; Xie, B., Salvendy, G., Review and reappraisal of modelling and predicting mental workload in single- and multi-task environments (2000) Work & Stress, 14 (1), pp. 74-99; You, S., Robbert, L., Teaming up with robots: An IMOI (Inputs-Mediators-Outputs-Inputs) framework of human-robot teamwork (2017) International journal of Robotic Engineering, 2, p. 003",,"Baraldi P.Di Maio F.Zio E.",,"Research Publishing Services","30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020","1 November 2020 through 5 November 2020",,169730,,9789811485930,,,"English","Eur. Saf. Reliab. Conf., ESREL Probab. Saf. Assess. Manag. Conf., PSAM",Conference Paper,"Final","",Scopus,2-s2.0-85110377688
"Mohammed S.N., Hassan A.K.A.","57213186106;55293743800;","A Survey on Emotion Recognition for Human Robot Interaction",2020,"Journal of Computing and Information Technology","28","2",,"125","146",,1,"10.20532/cit.2020.1004841","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108645378&doi=10.20532%2fcit.2020.1004841&partnerID=40&md5=5c0186ba32c6757a708d86c0ae14849a","Department of Computer Science, University of Technology, Baghdad, Iraq; Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq","Mohammed, S.N., Department of Computer Science, University of Technology, Baghdad, Iraq, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq; Hassan, A.K.A., Department of Computer Science, University of Technology, Baghdad, Iraq","With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined. ACM CCS (2012) Classification: Human-centered computing → Human computer interaction (HCI) → HCI theory, concepts and models Human-centered computing → Human computer interaction (HCI) → Interaction techniques © 2020. All Rights Reserved.","affective computing; emotional HRI challenges; social robotics","Computation theory; Human computer interaction; Learning systems; Speech recognition; Surveys; Artificial intelligent; Emotion recognition; Human computer interaction (HCI); Human robot Interaction (HRI); Human-centered computing; Interaction techniques; Literature reviews; Machine learning techniques; Social robots",,,,,,,,,,,,,,,,"Ahmad, M. 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G., Human–Robot Facial Expression Reciprocal Interaction Platform: Case Studies on Children with Autism (2018) International Journal of Social Robotics, 10 (2), pp. 179-198. , [5]; Salvador, M. J., An Emotion Recognition Comparative Study of Autistic and Typically- Developing Children Using the Zeno Robot (2015) Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pp. 6128-6133. , http://dx.doi.org/10.1109/ICRA.2015.7140059, [6] in; Calvo, R. 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Hassan, A.K.A.; Department of Computer Science, Iraq; email: 110018@uotechnology.edu.iq",,,"University of Zagreb, Faculty of Political Sciences",,,,,13301136,,,,"English","J. Compt. Inf. Technol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85108645378
"You S., Robert L.P., Jr.","37057989500;8247734500;","Human-robot interactions",2020,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"532","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108178243&partnerID=40&md5=0320969c735b3330b54ce620ddb8bb60","HEC Paris, France; University of Michigan, United States","You, S., HEC Paris, France; Robert, L.P., Jr., University of Michigan, United States",[No abstract available],,,,,,,,,,,,,,,,,,,,"Bui T.X.","AIS;Automation Anywhere;et al.;IBM;Pacific Research Institute for Informatics Systems and Management (PRIISM);University of Hawai'i at Manoa, Shidler College of Business","IEEE Computer Society","53rd Annual Hawaii International Conference on System Sciences, HICSS 2020","7 January 2020 through 10 January 2020",,169536,15301605,9780998133133,,,"English","Proc. Annu. Hawaii Int. Conf. Syst. Sci.",Editorial,"Final","",Scopus,2-s2.0-85108178243
"Baumgartl H., Buettner R.","57204587967;24831738000;","Development of a highly precise place recognition module for effective human-robot interactions in changing lighting and viewpoint conditions",2020,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"563","572",,32,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108152985&partnerID=40&md5=9cc247e80f48083c6d092a4e82fb958e","Aalen University, Germany","Baumgartl, H., Aalen University, Germany; Buettner, R., Aalen University, Germany","We present a highly precise and robust module for indoor place recognition, extending the work by Lemaignan et al. and Robert Jr. by giving the robot the ability to recognize its environment context. We developed a full end-to-end convolutional neural network architecture, using a pre-trained deep convolutional neural network and the explicit inductive bias transfer learning strategy. Experimental results based on the York University and Rzeszów University dataset show excellent performance values (over 94.75 and 97.95 percent accuracy) and a high level of robustness over changes in camera viewpoint and lighting conditions, outperforming current benchmarks. Furthermore, our architecture is 82.46 percent smaller than the current benchmark, making our module suitable for embedding into mobile robots and easily adoptable to other datasets without the need for heavy adjustments. © 2020 IEEE Computer Society. All rights reserved.",,"Benchmarking; Convolution; Convolutional neural networks; Deep neural networks; Lighting; Network architecture; Transfer learning; End to end; Inductive bias; Lighting conditions; Performance value; Place recognition; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 13FH176PX8, 13FH4E03IA, 13FH4E07IA","We would like to thank the reviewers, who provided very helpful comments on the refinement of the HICSS paper. 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(2012) EC-Web 2012 Proc., 123, pp. 149-162. , of LNBIP; Buettner, R., Asking both the user's brain and its owner using subjective and objective psychophysiological neurois instruments (2017) ICIS 2017 Proceedings; Buettner, R., Getting a job via career-oriented social networking markets: The weakness of too many ties (2017) Electronic Markets, 27 (4), pp. 371-385; Buettner, R., Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality-based product recommender framework (2017) Electronic Markets, 27 (3), pp. 247-265; Buettner, R., Hirschmiller, M., Schlosser, K., Roessle, M., Fernandes, M., Timm, I.J., High-performance exclusion of schizophrenia using a novel machine learning method on EEG data (2019) IEEE Healthcom 2019 Proc., , IEEE, press; Buettner, R., Fuhrmann, J., Kolb, L., Towards high-performance differentiation between narcolepsy and idiopathic hypersomnia in 10 minute EEG recordings using a novel machine learning approach (2019) IEEE Healthcom 2019 Proc., , IEEE, press; Buettner, R., Beil, D., Scholtz, S., Djemai, A., Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute EEG recordings HICSS-53 Proc., 2020. , IEEE, press; Buettner, R., Grimmeisen, A., Gotschlich, A., High-performance Diagnosis of Sleep Disorders: A novel, accurate and fast machine learning approach using electroencephalographic data HICSS-53 Proc., 2020. , IEEE, press; Rieg, T., Frick, J., Hitzler, M., Buettner, R., High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method (2019) HICSS-52 Proc, pp. 3769-3777; Buettner, R., Rieg, T., Frick, J., Machine Learning based Diagnosis of Diseases Using the Unfolded EEG Spectra: Towards an intelligent software sensor (2019) Information Systems and Neuroscience, 32. , of LNISO, Springer, press; Buettner, R., Rieg, T., Frick, J., High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands (2019) ICIS 2019 Proc., AIS, , press; Buettner, R., Bachus, L., Konzmann, L., Prohaska, S., Asking both the user's heart and its owner: Empirical evidence for substance dualism (2018) Information Systems and Neuroscience, 29, pp. 251-257. , of LNISO,. Springer; Buettner, R., Schunter, M., Efficient machine learning based detection of heart disease (2019) IEEE Healthcom 2019 Proc., , IEEE, press; Eckhardt, A., Maier, C., Buettner, R., The influence of pressure to perform and experience on changing perceptions and user performance: A multi-method experimental analysis (2012) ICIS 2012 Proc; Buettner, R., Cognitive workload of humans using artificial intelligence systems: Towards objective measurement applying eye-tracking technology (2013) KI 2013 Proc., 8077, pp. 37-48. , of LNAI; Eckhardt, A., Maier, C., Hsieh, J.P.-A., Chuk, T., Chan, A.B., Hsiao, A.B., Buettner, R., Objective measures of IS usage behavior under conditions of experience and pressure using eye fixation data (2013) ICIS'13 Proc.; Buettner, R., Social inclusion in eParticipation and eGovernment solutions: A systematic laboratory-experimental approach using objective psychophysiological measures (2013) EGOV/ePart 2013 Proc, P-221, pp. 260-261. , of LNI,. GI; Buettner, R., Daxenberger, B., Eckhardt, A., Maier, C., Cognitive workload induced by information systems: Introducing an Objective Way of Measuring based on Pupillary Diameter Responses (2013) Pre-ICIS HCI/MIS 2013 Proc, , Paper 20; Buettner, R., Sauer, S., Maier, C., Eckhardt, A., Towards ex ante Prediction of User Performance: A novel NeuroIS Methodology based on Real-Time Measurement of Mental Effort (2015) HICSS-48 Proc, pp. 533-542; Buettner, R., Robust user identification based on facial action units unaffected by users' emotions (2018) HICSS-51 Proc, pp. 265-273; Buettner, R., Daxenberger, B., Woesle, C., User acceptance in different electronic negotiation systems - A comparative approach (2013) ICEBE 2013 Proc, pp. 1-8; Buettner, R., Towards a new personal information technology acceptance model: Conceptualization and empirical evidence from a bring your own device dataset (2015) AMCIS'15 Proc.; Buettner, R., Analyzing the problem of employee internal social network site avoidance: Are users resistant due to their privacy concerns? (2015) HICSS-48 Proc, pp. 1819-1828; Buettner, R., Getting a Job via Career-oriented Social Networking Sites: The weakness of ties (2016) HICSS-49 Proc, pp. 2156-2165; Meixner, F., Buettner, R., Trust as an integral part for success of cloud computing (2012) ICIW 2012 Proc, pp. 207-214; Buettner, R., The state of the art in automated negotiation models of the behavior and information perspective (2006) ITSSA, 1 (4), pp. 351-356; Buettner, R., Electronic negotiations of the transactional costs perspective (2007) IADIS'07 WWW/Internet Proc, 2, pp. 99-105; Buettner, R., Imperfect information in electronic negotiations: An empirical study (2007) IADIS'07 WWW/Internet Proc, 2, pp. 116-121; Landes, J., Buettner, R., Job Allocation in a Temporary Employment Agency via Multi-dimensional Price VCG Auctions Using a Multi-agent System (2011) MICAI 2011 Proc, pp. 182-187; Buettner, R., Landes, J., Web Service-based Applications for Electronic Labor Markets: A Multi-dimensional Price VCG Auction with Individual Utilities (2012) ICIW 2012 Proc, pp. 168-177; Buettner, R., A systematic literature review of crowdsourcing research from a human resource management perspective (2015) HICSS-48 Proc, pp. 4609-4618; Timm, I., Reuter, L., Berndt, J.O., Ulfert, A.-S., Ellwart, T., Antoni, C., Analyzing the Effects of Role Configuration in Logistics Processes using Multiagent-Based Simulation: An interdisciplinary approach (2019) HICSS-52 Proc, pp. 5476-5485; Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A., Places: A 10 million image database for scene recognition (2018) IEEE Trans. Pattern Anal. Mach. Intell., 40 (6), pp. 1452-1464",,"Bui T.X.","AIS;Automation Anywhere;et al.;IBM;Pacific Research Institute for Informatics Systems and Management (PRIISM);University of Hawai'i at Manoa, Shidler College of Business","IEEE Computer Society","53rd Annual Hawaii International Conference on System Sciences, HICSS 2020","7 January 2020 through 10 January 2020",,169536,15301605,9780998133133,,,"English","Proc. Annu. Hawaii Int. Conf. Syst. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85108152985
"Jessup S.A., Gibson A.M., Capiola A., Alarcon G.M., Borders M.","57194107331;57209638343;55155617300;35101277300;57105476900;","Investigating the effect of trust manipulations on affect over time in human-human versus Human-Robot interactions",2020,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"553","562",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108142666&partnerID=40&md5=76992295d735b03e3efd5e0579e5d39d","Air Force Research Laboratory, Wright-Patterson AFB, OH, United States; Consortium of Universities, Dayton, OH, United States; Wright State University, Dayton, OH, United States","Jessup, S.A., Air Force Research Laboratory, Wright-Patterson AFB, OH, United States; Gibson, A.M., Consortium of Universities, Dayton, OH, United States; Capiola, A., Air Force Research Laboratory, Wright-Patterson AFB, OH, United States; Alarcon, G.M., Air Force Research Laboratory, Wright-Patterson AFB, OH, United States; Borders, M., Wright State University, Dayton, OH, United States","The current study explored the influence of trust and distrust behaviors on affect over time. We examined the differences in affect when participants (N=97) were paired with a human or a robot while playing a modified version of the investor game. Results indicated that there were no differences in affect between partner types when the partner performed a trustful behavior. When the partner performed a distrustful behavior, positive affect was higher for human partners than for robot partners. When robot partners performed a distrustful behavior, negative affect had a steeper incline compared to human partners. These findings suggest that people are more sensitive to distrust behaviors that are performed by a robot over a human. © 2020 IEEE Computer Society. All rights reserved.",,"Systems science; Negative affects; Positive affects; Robot Partners; Social robots",,,,,,,,,,,,,,,,"Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, pp. 50-80. , SAGE Publications, U.S.A; You, S., Robert, L.P., Emotional attachment, performance, and viability in teams collaborating with embodied physical action (EPA) robots (2018) Journal of the Association for Information Systems, pp. 377-407. , Association for Information Systems, U.S.A; You, S., Robert, L.P., Trusting robots in teams: Examining the impacts of trusting robots on team performance and satisfaction (2019) Proceedings of the 52th Hawaii International Conference on System Sciences, pp. 244-253. , Bui, T.X. and Sprague, R.H. Eds, U.S.A; Rosenthal-Von der Pütten, A.M., Krämer, N.C., Hoffmann, L., Sobieraj, S., Eimler, S.C., An experimental study on emotional reactions towards a robot (2013) International Journal of Social Robotics, pp. 17-34. , Springer, U.S.A; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Academy of Management Review, pp. 709-734. , Academy of Management, U.S.A; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) Journal of Social Issues, pp. 81-103. , Wiley-Blackwell, U.S.A; Madhavan, P., Wiegmann, D.A., Similarities and Differences between Human-human and Human-automation Trust: An integrative review (2007) Theoretical Issues in Ergonomics Science, pp. 277-301. , Taylor & Francis, United Kingdom; Cummings, M., Automation bias in intelligent time critical decision support systems (2004) 1st Intelligent Systems Technical Conference, pp. 1-6. , American Institute of Aeronautics and Astronautics, U.S.A; Merritt, S.M., Unnerstall, J.L., Lee, D., Huber, K., Measuring individual differences in the perfect automation schema (2015) Human Factors, pp. 740-753. , SAGE Publications, U.S.A; Salovey, P., Mayer, J.D., Emotional intelligence (1990) Imagination, Cognition, and Personality, pp. 185-211. , SAGE Publications, U.S.A; Watson, D., Clark, L.A., Tellegen, A., Development and validation of brief measures of positive and negative affect: The Panas scales (1988) Journal of Personality and Social Psychology, pp. 1063-1070. , American Psychological Association, U.S.A; Cacioppo, J.T., Berntson, G.G., The affect system: Architecture and operating characteristics (1999) Current Directions in Psychological Science, pp. 133-137. , SAGE Publications, U.S.A; Siddike, A.K., Kohda, Y., Towards a framework of trust determinants in people and cognitive assistants interactions (2018) Proceedings of the 51st Hawaii International Conference on System Sciences; Dunn, J.R., Schweitzer, M.E., Feeling and believing: The influence of emotion on trust (2005) Journal of Personality and Social Psychology, pp. 736-748. , American Psychological Association, U.S.A; Stokes, C.K., Lyons, J.B., Littlejohn, K., Natarian, J., Case, E., Speranza, N., Accounting for the human in cyberspace: Effects of mood on trust in automation (2010) International Symposium on Collaborative Technologies and Systems, pp. 180-187. , Institute of Electrical and Electronics, Unites States; Lount, R.B., Jr., The impact of positive mood on trust in interpersonal and intergroup interactions (2010) Journal of Personality and Social Psychology, pp. 420-433. , American Psychological Association, U.S.A; Camerer, C.F., (2003) Behavioral Game Theory: Experiments in Strategic Interaction, , Princeton University Press, U.S.A; Hewig, J., Kretschmer, N., Trippe, R.H., Hecht, H., Coles, M.G., Holroyd, C.B., Miltner, W.H., Why humans deviate from rational choice (2011) Psychophysiology, pp. 507-514. , Wiley- Blackwell, U.S.A; Pillutla, M.M., Murnighan, J.K., Unfairness, anger, and spite: Emotional rejections of ultimatum offers (1996) Organizational Behavior and Human Decision Processes, pp. 208-224. , Elsevier, Netherlands; Van't Wout, M., Kahn, R.S., Sanfey, A.G., Aleman, A., Affective state and decision-making in the ultimatum game (2006) Experimental Brain Research, pp. 564-568. , Springer, U.S.A; Alarcon, G.M., Lyons, J.B., Christensen, J.C., Klosterman, S.L., Bowers, M.A., Ryan, T.J., Jessup, S.A., Wynne, K.T., The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads (2018) Behavioral Research Methods, pp. 1906-1920. , Springer, U.S.A; Berg, J., Dickhaut, J., McCabe, K., Trust, reciprocity, and social history (1995) Games and Economic Behavior, pp. 122-142. , Elsevier, U.S.A; Bliese, P.D., Ployhart, R.E., Growth modeling using random coefficient models: Model building, testing, and illustrations (2002) Organizational Research Methods, pp. 362-387. , SAGE Publications, U.S.A; Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., NLME linear and nonlinear mixed effects models (2013) R Package Version, p. 111. , 1-108; (2019) R: A Language and Environment for Statistical Computing, , R Foundation for Statistical Computing, Austria; Duffy, B.R., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, pp. 177-190. , Elsevier, Netherlands; Sears, D.O., The Person-positivity Bias (1983) Journal of Personality and Social Psychology, pp. 233-250. , American Psychological Association, U.S.A; Zajonc, R.B., Mere exposure: A gateway to the subliminal (2001) Current Directions in Psychological Science, pp. 224-228. , Elsevier, Netherlands; Parasuraman, R., Riley, V., Humans and automation: Use, misuse, disuse, abuse (1997) Human Factors, pp. 230-253. , SAGE Publications, U.S.A; Curran, P.J., Obeidat, K., Losardo, D., Twelve frequently asked questions about growth curve modeling (2010) Journal of Cognition and Development, pp. 121-136. , Taylor & Francis, United Kingdom; Casler, K., Bickel, L., Hackett, E., Separate but equal? A comparison of participants and data gathered via Amazon's mturk, social media, and face-to-face behavioral testing (2013) Computers in Human Behavior, pp. 2156-2160. , Elsevier, Netherlands; Angie, A.D., Connelly, S., Waples, E.P., Kligyte, V., The influence of discrete emotions on judgement and decision-making: A meta-analytic review (2011) Cognition & Emotion, pp. 1393-1422. , Routledge, United Kingdom; Storbeck, J., Negative affect promotes encoding of and memory for details at the expense of the GIST: Affect, encoding, and false memories (2013) Cognition & Emotion, pp. 800-819. , Routledge, United Kingdom; Isen, A.M., Shalker, T.E., Clark, M., Karp, L., Affect, accessibility of material in memory, and behavior: A cognitive loop? (1978) Journal of Personality and Social Psychology, pp. 1-12. , American Psychological Association, U.S.A",,"Bui T.X.","AIS;Automation Anywhere;et al.;IBM;Pacific Research Institute for Informatics Systems and Management (PRIISM);University of Hawai'i at Manoa, Shidler College of Business","IEEE Computer Society","53rd Annual Hawaii International Conference on System Sciences, HICSS 2020","7 January 2020 through 10 January 2020",,169536,15301605,9780998133133,,,"English","Proc. Annu. Hawaii Int. Conf. Syst. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85108142666
"Zhang V., Friedman N., Goedicke D., Rivkin D., Jenkin M., Liu X., Dudek G.","57224643885;57215417038;57202043646;57224625727;7005126033;57206746907;7006413576;","The Answer Is Blowing in theWind: Directed Air Flow for Socially-acceptable Human-Robot Interaction",2020,"ROBOVIS 2020 - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems",,,,"106","113",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108060138&partnerID=40&md5=b6caf2d67c7b1ad046f96d5936bd2d44","Samsung Ai Center, Montreal, Canada; Information Science, Cornell Tech, New York, United States; York University, Eecs, Toronto, Canada; McGill University, Computer Science, Montreal, Canada","Zhang, V., Samsung Ai Center, Montreal, Canada; Friedman, N., Samsung Ai Center, Montreal, Canada, Information Science, Cornell Tech, New York, United States; Goedicke, D., Samsung Ai Center, Montreal, Canada, Information Science, Cornell Tech, New York, United States; Rivkin, D., Samsung Ai Center, Montreal, Canada; Jenkin, M., Samsung Ai Center, Montreal, Canada, York University, Eecs, Toronto, Canada; Liu, X., Samsung Ai Center, Montreal, Canada, McGill University, Computer Science, Montreal, Canada; Dudek, G., Samsung Ai Center, Montreal, Canada, McGill University, Computer Science, Montreal, Canada","A key problem for a robot moving within a social environment is the need to capture the attention of other people using the space. In most use cases, this capture of attention needs to be accomplished in a socially acceptable manner without loud noises or physical contact. Although there are many communication mechanisms that might be used to signal the need for a person's attention, one particular modality that has received little interest from the robotics community is the use of controlled air as a haptic signal. Recent work has demonstrated that controlled air can provide a useful signal in the social robot domain, but what is the best mechanism to provide this signal? Here, we evaluate a number of different mechanisms that can provide this attention-seeking communication. We demonstrate that many different simple haptic air delivery systems can be effective and show that air on and air off haptic events have very similar time courses using these delivery systems. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.","Air-driven haptic cues; Human-robot interaction","Agricultural robots; Computer vision; Intelligent systems; Robotics; Air delivery systems; Communication mechanisms; Community IS; Delivery systems; Different mechanisms; Physical contacts; Social environment; Time course; Social robots",,,,,,,,,,,,,,,,"Burgoon, J. K., Johnson, M. L., Koch, P. T., The nature and measurement of interpersonal dominance (1998) Communications Monographs, 65 (4), pp. 308-335; Cattafesta, L., Bahr, C., Mathew, J., (2010) Fundamentals of Wind-Tunnel Design, , Wiley, Chichester, UK; Dakopolos, A. J., Jahromi, L. B., Differences in sensory responses among children with autism spectrum disorder and typical development: Links to joint attention and social competence (2019) Infant and Child Development, 28 (1), p. e2117. , e2117 ICD-17-0123.R1; Friedman, N., Goedicke, D., Jenkin, M., Degutyte, Z., Zhang, V., Astell, A., Dudek, G., Exploring modalities to ask, ""excuse me, could i get through? (2020) Proc. Workshop on Integrating Multidisciplinary Approaches to Advance Physical Human-Robot Interaction, , Paris, France. Held in conjunction with IEEE ICRA; Friedman, N., Goedicke, D., Zhang, V., Jenkin, M., Degutyte, Z., Astell, A., Liu, S., Dudek, G., Out of my way! exploring different modalities for robots to ask people to move out of the way (2020) Workshop on Active Vision and Perception in Human(-Robot) Collaboration, Held Online. Held in conjunction with IEEE Int. Conf. on Robot and Human Interactive Communication; Gupta, S., Morris, D., Patel, S., Tan, D., Air-Wave: Non-contact haptic feedback using air vortex rings (2013) ACM Joint Conference on Pervasive and Ubiquitous Computing, pp. 419-428. , Zurich, Switzerland; Hall, E. T., (1966) The Hidden Dimension, 609. , Doubleday, Garden City, NY; Hasegawa, K., Qiu, L., Noda, A., Inoue, S., Shinoda, H., Electrically steerable ultrasound-driven long narrow air flow (2017) Applied Physics Letters, 111, p. 06104; Hoshi, T., Iwamoto, T., Shinoda, H., Noncontact tactile sensation synthesized by ultrasound transducers (2009) 3rd Joint Eurohaptics Conference and Symposium on Haptic Interfaces, pp. 256-260. , Salt Lake City, UT; Kant, K., Zucker, S., Toward efficient trajectory planning: The path-velocity decomposition (1986) International Journal of Robotic Research-IJRR, 5, pp. 72-89; Long, B., Seah, S. A., Carter, T., Subramanian, S., Rendering volumetric haptic shapes in midair using ultrasound (2014) ACM SIGGRAPH Asia, , Shenzhen, China; Morel, T., Comprehensive design of axisymmetric wind tunnel contractions (1975) Journal of Fluids Engineering, Trans. ASME, 97 (2), p. 255; Mowafi, O., Khamiodhi, M., Abouelsaadat, W., Airdisplay: Experimenting with air flow as a communication medium (2015) INTERACT, pp. 316-323. , Bamberg, Germany; Sodhi, R., Poupyrev, I., Glisson, M., Israr, A., AIREAL: Interactive tactile experiences in free air (2013) ACM SIGGRAPH, , Anaheim, CA; Suzuki, Y., Kobayashi, M., Air jet driven force feedback in virtual reality (2005) IEEE Computer Graphics and Applications, pp. 44-47. , Jan/Feb; Tolley, D., Nguyen, T., Tang, A., Ranasinghe, N., Kawauchi, K., Yen, C.-C., Windywall: Exploring create wind simulation (2019) TEI 2019: International Conference on Tangible and Embedded Interaction, pp. 635-644. , Tempe, AZ; Tsalamlal, M. Y., Issart, P., Ouarti, N., Ammi, M., Hair: Haptic feedback with a mobile air jet (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2699-2706. , Hong Kong, China. IEEE; Verlinden, J., Mulder, F. A., Vergeest, J. S. M., de Jonge, B., Krutly, D., Nagy, Z. E., Logeman, B. J., Schouten, P., Enhancement of presence in a virtual sailing environment through localized wind simulation (2013) Procedia Engineering, 60, pp. 435-441; Wogollter, M. S., Attention switch and maintenance (2006) Handbook of Warnings, pp. 245-265. , Mahwah, N., editor, pages Lawrence Erlbaum Associates",,"Galambos P.Madani K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","2020 International Conference on Robotics, Computer Vision and Intelligent Systems, ROBOVIS 2020","4 November 2020 through 6 November 2020",,165042,,9789897584794,,,"English","ROBOVIS - Proc. Int. Conf. Robot., Comput. Vis. Intell. Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85108060138
"Ma Q., Ma T., Lu C., Cheng B., Xie S., Gong L., Fu Z., Liu C.","57225932820;57212009944;57224535946;57224537571;57222182583;36637488200;7403346420;7409795747;","A Cloud-based Quadruped Service Robot with Multi-Scene Adaptability and various forms of Human-Robot Interaction",2020,"IFAC-PapersOnLine","53","5",,"134","139",,,"10.1016/j.ifacol.2021.04.092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107847432&doi=10.1016%2fj.ifacol.2021.04.092&partnerID=40&md5=98fffd28e8e1d5f7cf9f72b66b21e610","Shanghai Jiao Tong University, Shanghai CO, 200240, China","Ma, Q., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Ma, T., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Lu, C., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Cheng, B., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Xie, S., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Gong, L., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Fu, Z., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Liu, C., Shanghai Jiao Tong University, Shanghai CO, 200240, China","With the development of CPS technology, more robots are endowed with intelligence to interact and communicate with humans such as service robot AIBO and AMWELL. These intelligent pets show great human-machine interactive ability by voice and face recognition, storytelling. However, they are limited only in family scenes and fail to provide cross-scene services. Therefore, we design a quadruped service robot called Cloud Sphinx-whose motion, voice, and vision system are driven by artificial intelligent models in the cloud. Our work pushes the interaction between CPS and humans. First, an open-source 12-DoF robotic cat is rapidly prototyped with a 3D printing technique, and its versatile gestures and gaits are coordinated by advanced servo control algorithms. Second, by merging motion control, NLP, image processing deep learning and cloud computing, Cloud Sphinx can provide services as emotion classification, face recognition, and garbage classification through human-robot interaction models. Third, an RaaS (Robot as a service) architecture is developed to fulfill the incrementally learning from the human-robot interaction processes. Sufficient simulations and on-board tests verify the human-robot interaction functionality and cross-scene service performances. Applications that we designed show the A.I. performance of service robots can be effectively enhanced by model training during the human-robot interactions and knowledge accumulation in cloud database. © 2020 Elsevier B.V.. All rights reserved.","cloud architecture Cloud Sphinx; machine learning; man-machine interaction; quadruped robot","3D printers; Deep learning; Educational robots; Face recognition; Intelligent robots; Machine design; Man machine systems; Mobile robots; Visual servoing; Artificial intelligent; Emotion classification; Human-Machine Interactive; Knowledge accumulation; Model training; Service performance; Service robots; Vision systems; Social robots",,,,,"18391901000; National Natural Science Foundation of China, NSFC: 51775333; National Key Research and Development Program of China, NKRDPC: 2018YFB1305104","This work was supported by National Natural Science Foundation of China (Grant No.51775333), the Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.18391901000) and National Key Research and Development Project (2018YFB1305104).",,,,,,,,,,"Aravind, M.A., Dinesh, N.S., Application of EMPC for precise position control of DC-motor system with Backlash (2020) Control Engineering Practice, 100, p. 104422; Cheng, X., Lu, J., Feng, J., Yuan, B., Zhou, J., Scene recognition with objectness (2018) Pattern Recognition, 74, pp. 474-487; Du, Z., Robot Cloud: Bridging the power of robotics and cloud computing (2017) Future Generation Computer Systems, 74, pp. 337-348; Yang, D., Ma, L., Liao, F., An intelligent voice interaction system based on raspberry Pi (2019) 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 237-240; Gan, M.G., Zheng, C.Y., An adaptive sliding mode observer over wide speed range for sensorless control of a brushless DC motor (2018) Control Engineering Practice, 77, pp. 52-62; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the Ieee Conference on Com; Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., Densely connected convolutional networks (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 4700-4708; Hu, L., Follow me Robot-Mind: Cloud brain based personalized robot service with migration (2020) Future Generation Computer Systems, 107, pp. 324-332; Sentiment analysis algorithms and applications: A survey (2014) Ain Shams Engineering Journal, 5 (4), pp. 1093-1113; Garciá-López, M.C., Gorrostieta-Hurtado, Kinematic analysis for trajectory generation in one leg of a hexapod robot (2012) Procedia Technology, 3, pp. 342-350; Merle, P., Mobile cloud robotics as a service with OCCIware (2017) 2017 Ieee International Congress on Internet of Things; Patil Harshali, P., Mohammad, A., CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media (2020) Big Data, 8 (2), pp. 107-124; Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement[J], , arXiv preprint arXiv:1804.02767; Sheba, J.K., Phuc, L.T., Salman, A.A., Development of rehabilitative multimodal interactive pet robot for elderly residents (2018) Procedia Computer Science, 133, pp. 401-408; Yi, S., Reliable gait planning and control for miniaturized quadruped robot pet (2010) Mechatronics, 20 (4), pp. 485-495; Ye, W., Gao, J., Chen, H., A voice control platform of mobile robot through ROS (2019) 38th Chinese Control Conference (CCC), pp. 4338-4341; Trot Gait Design and CPG Method for a Quadruped Robot (2014) Journal of Bionic Engineering, 11 (1), pp. 18-25","Gong, L.; Shanghai Jiao Tong UniversityChina; email: gongliang_mi@sjtu.edu.cn",,,"Elsevier B.V.","3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020","3 December 2020 through 5 December 2020",,145888,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107847432
"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","Visual Intention Classification by Deep Learning for Gaze-based Human-Robot Interaction",2020,"IFAC-PapersOnLine","53","5",,"750","755",,,"10.1016/j.ifacol.2021.04.168","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107846337&doi=10.1016%2fj.ifacol.2021.04.168&partnerID=40&md5=46cd8486b12b4282cf39b89f042d74b4","Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium","Shi, L., Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Copot, C., Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Vanlanduit, S., Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium","In this work, we propose a deep learning model to classify a human's visual intention in gaze-based Human-Robot Interaction(HRI). We consider a scenario in which a human wears a pair of eye tracking glasses and can select an object by gaze and a robotic manipulator picks up the object. A neural network is trained as a binary classifier to classify if a human is looking at an object. The network architecture is based on Fully Convolutional Net(FCN), Convolutional Block Attention Modules(CBAM) and Residual Blocks. We evaluate our model with two experiments. In one experiment we test the performance in the scenario where only a single object exists and the other one multiple objects exist. The results show that our proposed network is accurate and it can generalize well. The F1 score on the single object is 0.971 and 0.962 on multiple objects. © 2020 Elsevier B.V.. All rights reserved.","deep learning; gaze intention; HRI","Convolution; Convolutional neural networks; Deep learning; Eye tracking; Industrial manipulators; Man machine systems; Manipulators; Network architecture; Object tracking; Binary classifiers; F1 scores; Human robot Interaction (HRI); Learning models; Multiple objects; Robotic manipulators; Single object; Human robot interaction",,,,,,,,,,,,,,,,"Anantrasirichai, N., Gilchrist, I.D., Bull, D.R., Fixation identification for low-sample-rate mobile eye trackers (2016) 2016 Ieee International Conference on Image Processing (ICIP), pp. 3126-3130; Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G., (2015) The Ucr Time Series Classification Archive, , www.cs.ucr.edu/eamonn/timeseriesdata, 2015). URL; Cui, Z., Chen, W., Chen, Y., (2016) Multi-scale Con-volutional Neural Networks for Time Series Classification, , arXiv preprint arXiv:1603.06995; Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A., Deep neural network ensembles for time series classification (2019) 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-6; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 770-778; Hessels, R.S., Niehorster, D.C., Kemner, C., Hooge, I.T., Noise-robust fixation detection in eye movement data: Identification by two-means clustering (i2mc) (2017) Behavior Research Methods, 49 (5), pp. 1802-1823; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Hoppe, S., Bulling, A., (2016) End-to-end Eye Movement Detection Using Convolutional Neural Networks, , arXiv preprint arXiv:1609.02452; Hu, J., Shen, L., Sun, G., Squeeze-and-excitation networks (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 7132-7141; Hu, Y., Li, J., Huang, Y., Gao, X., Channel-wise and spatial feature modulation network for single image super-resolution (2019) Ieee Transactions on Circuits and Systems for Video Technology; Ioffe, S., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, , arXiv preprint arXiv:1502.03167; Karim, F., Majumdar, S., Darabi, H., Chen, S., Lstm fully convolutional networks for time series classification (2017) Ieee Access, 6, pp. 1662-1669; Karim, F., Majumdar, S., Darabi, H., Harford, S., Multivariate lstm-fcns for time series classification (2019) Neural Networks, 116, pp. 237-245; Kassner, M., Patera, W., Bulling, A., Pupil: An open source platform for pervasive eye tracking and mobile gaze-based interaction (2014) Adjunct Proceedings of the 2014 Acm International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '14 Adjunct, pp. 1151-1160. , http://doi.acm.org/10.1145/2638728.2641695, ACM, New York, NY, USA. URL; Le Guennec, A., Malinowski, S., Tavenard, R., (2016) Data Augmentation for Time Series Classification Using Convolutional Neural Networks; Long, J., Shelhamer, E., Darrell, T., Fully convolutional networks for semantic segmentation (2015) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 3431-3440; Nair, V., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807-814; Redmon, J., Farhadi, A., (2016) Yolo9000: Better, Faster, Stronger, , arXiv preprint arXiv:1612.08242; Roy, A.G., Navab, N., Wachinger, C., Recalibrating fully convolutional networks with spatial and channel squeeze and excitation blocks (2019) Ieee Transactions on Medical Imaging, 38 (2), pp. 540-549; Salvucci, D.D., Goldberg, J.H., Identifying fixations and saccades in eye-tracking protocols (2000) Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp. 71-78; Serra, J., Pascual, S., Karatzoglou, A., Towards a universal neural network encoder for time series (2018) Ccia, pp. 120-129; Shi, L., Copot, C., Vanlanduit, S., (2019) What Are You Looking At? Detecting Human Intention in Gaze Based Human-robot Interaction, , arXiv preprint arXiv:1909.07953; Wang, Z., Yan, W., Oates, T., Time series classification from scratch with deep neural networks: A strong baseline (2017) 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578-1585; Woo, S., Park, J., Lee, J.Y., So Kweon, I., Cbam: Convolutional block attention module (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 3-19; Zemblys, R., Niehorster, D.C., Holmqvist, K., Gazenet: End-to-end eye-movement event detection with deep neural networks (2019) Behavior Research Methods, 51 (2), pp. 840-864; Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Chawla, N.V., A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data (2019) Proceedings of the Aaai Conference on Artificial Intelligence, 33, pp. 1409-1416; Zhao, B., Lu, H., Chen, S., Liu, J., Wu, D., Convolutional neural networks for time series classification (2017) Journal of Systems Engineering and Electronics, 28 (1), pp. 162-169; Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L., Time series classification using multi-channels deep convolutional neural networks (2014) International Conference on Web-Age Information Management, pp. 298-310. , Springer",,,,"Elsevier B.V.","3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020","3 December 2020 through 5 December 2020",,145888,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107846337
"Ghosh A., Soto D.A.P., Veres S.M., Rossiter A.","56729623500;57196546622;35579593700;7103035939;","Human robot interaction for future remote manipulations in industry 4.0",2020,"IFAC-PapersOnLine","53",,,"10223","10228",,2,"10.1016/j.ifacol.2020.12.2752","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107780953&doi=10.1016%2fj.ifacol.2020.12.2752&partnerID=40&md5=269a1536446a404bf8e3799e0d33142a","The University of Sheffield, Sheffield, S1 3JD, United Kingdom","Ghosh, A., The University of Sheffield, Sheffield, S1 3JD, United Kingdom; Soto, D.A.P., The University of Sheffield, Sheffield, S1 3JD, United Kingdom; Veres, S.M., The University of Sheffield, Sheffield, S1 3JD, United Kingdom; Rossiter, A., The University of Sheffield, Sheffield, S1 3JD, United Kingdom","In the nuclear industry it is still common to rely on tele-operated robots. Tele-operation however can be strenuous and demanding on operating personnel and productivity can be low without advanced HRI interfaces. Today, the world is moving towards Industry 4.0. With that vision, this paper introduces the concept of Remotely Instructed Robots (RIRs), which are reliable yet rely on human intelligence. RIRs can accept high and low level instructions from the operator and execute tasks based on operators' descriptions and at a variety of complexity levels. The paper outlines an agent model of RIRs and furthermore, presents how it could be implemented inside nuclear gloveboxes to achieve novel human robot interaction. Copyright © 2020 The Authors.","Digital twin; Industry4.0; Remotely Instructed robots; Safe Human-robot Interaction","Industrial robots; Industry 4.0; Man machine systems; Nuclear industry; Agent model; Complexity levels; Gloveboxes; Human intelligence; Operating personnel; Remote manipulation; Teleoperated robots; Social robots",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R026084/1","This work was supported by EPSRC Grant No. EP/R026084/1, Robotics and Artificial Intelligence for Nuclear (RAIN), UK.",,,,,,,,,,"Aitken, J.M., Veres, S.M., Shaukat, A., Gao, Y., Cucco, E., Dennis, L.A., Fisher, M., Mort, P.E., Autonomous nuclear waste management (2018) IEEE Intelligent Systems, 33 (6), pp. 47-55; Allspaw, J., Roche, J., Lemiesz, N., Yannuzzi, M., Yanco, H.A., Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality- (2018) Proceedings of the 1st International Workshop on Virtual, Augmented, and Mixed Reality for HRI (VAMHRI).; Baccarini, M., Maravita, A., (2013) Beyond the Boundaries of the Hand: Plasticity of Body-space Interactions following Tool-use.; Cancedda, L., Cannavò, A., Garofalo, G., Lamberti, F., Montuschi, P., Paravati, G., Mixed realitybased user interaction feedback for a hand-controlled interface targeted to robot teleoperation (2017) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 447-463. , Springer; Davis, J., Edgar, T., Graybill, R., Korambath, P., Schott, B., Swink, D., Wang, J., Wetzel, J., Smart manufacturing (2015) Annual Review of Chemical and Biomolecular Engineering, 6, pp. 141-160; Ghosh, A., Veres, S.M., Paredes-Soto, D., Clarke, J.E., Rossiter, J.A., Intuitive programming with remotely instructed robots inside future gloveboxes (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 209-211; Heyer, C., Human-robot interaction and future industrial robotics applications (2010) Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pp. 4749-4754. , IEEE; Hokayem, P.F., Spong, M.W., Bilateral teleoperation: An historical survey (2006) Automatica, 42 (12), pp. 2035-2057; Hu, H., Li, J., Xie, Z., Wang, B., Liu, H., Hirzinger, G., A robot arm/hand teleoperation system with telepresence and shared control (2005) Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics., pp. 1312-1317. , IEEE; Jang, I., Carrasco, J., Weightman, A., Lennox, B., Intuitive bare-hand teleoperation of a robotic manipulator using virtual reality and leap motion (2019) Annual Conference towards Autonomous Robotic Systems, pp. 283-294. , Springer; Kahol, K., Leyba, M.J., Deka, M., Deka, V., Mayes, S., Smith, M., Ferrara, J.J., Panchanathan, S., Effect of fatigue on psychomotor and cognitive skills (2008) The American Journal of Surgery, 195 (2), pp. 195-204; King, R., Hamilton, D., Augmented virtualised reality-applications and benefits in remote handling for fusion (2009) Fusion Engineering and Design, 84 (2-6), pp. 1055-1057; Lii, N.Y., Chen, Z., Pleintinger, B., Borst, C.H., Hirzinger, G., Schiele, A., Toward understanding the effects of visual-and force-feedback on robotic hand grasping performance for space teleoperation (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3745-3752. , IEEE; Lincoln, N.K., Veres, S.M., Natural language programming of complex robotic bdi agents (2013) Journal of Intelligent & Robotic Systems, 71 (2), pp. 211-230; Nur, N.M., Dawal, S.Z.M., Dahari, M., Sanusi, J., Muscle activity, time to fatigue, and maximum task duration at different levels of production standard time (2015) Journal of Physical Therapy Science, 27 (7), pp. 2323-2326; Poupyrev, I., Ichikawa, T., Manipulating objects in virtual worlds: Categorization and empirical evaluation of interaction techniques (1999) Journal of Visual Languages & Computing, 10 (1), pp. 19-35; Rademaker, R.L., Wu, D.A., Bloem, I.M., Sack, A.T., Intensive tool-practice and skillfulness facilitate the extension of body representations in humans (2014) Neuropsychologia, 56, pp. 196-203; Rakita, D., Mutlu, B., Gleicher, M., (2018) An Autonomous Dynamic Camera Method for e Ective Remote Teleoperation.; Rao, A.S., Agentspeak (l): Bdi agents speak out in a logical computable language (1996) European Workshop on Modelling Autonomous Agents in A Multi-Agent World, pp. 42-55. , Springer; Rollow, T., Type a accident investigation of the march 16, 2000 plutonium-238 multiple intake event at the plutonium facility, los alamos national laboratory, new mexico, united states department of energy, office of oversight (2000) Safety and Health.; Seed, A., Byrne, R., Animal tool-use (2010) Current Biology, 20 (23), pp. R1032-R1039; Talha, M., Ghalamzan, E.A.M., Takahashi, C., Kuo, J., Ingamells, W., Stolkin, R., Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning (2016) 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 166-173; Talha, M., Ghalamzan, E., Takahashi, C., Kuo, J., Ingamells, W., Stolkin, R., Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning (2016) 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 166-173. , IEEE; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) The International Journal of Advanced Manufacturing Technology, 94 (9-12), pp. 3563-3576; Thoben, K.D., Wiesner, S., Wuest, T., Industrie 4.0"" and smart manufacturing - A review of research issues and application examples (2017) International Journal of Automation Technology, 11 (1), pp. 4-16; Wang, L., Törngren, M., Onori, M., Current status and advancement of cyber-physical systems in manufacturing (2015) Journal of Manufacturing Systems, 37, pp. 517-527; Whitney, D., Rosen, E., Phillips, E., Konidaris, G., Tellex, S., Comparing robot grasping teleoperation across desktop and virtual reality with ros reality (2017) Proceedings of the International Symposium on Robotics Research.; Wooldridge, M., (2009) An Introduction to Multiagent Systems, , John Wiley & Sons",,,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85107780953
"Dos Santos C.W., Filho N.L.D., Espíndola D.B., Botelho S.S.C.","57224515682;36164021300;34879715800;7003517099;","Situational awareness oriented interfaces on human-robot interaction for industrial welding processes",2020,"IFAC-PapersOnLine","53",,,"10168","10173",,,"10.1016/j.ifacol.2020.12.2744","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107743975&doi=10.1016%2fj.ifacol.2020.12.2744&partnerID=40&md5=74bfec1a939c1d99e11d5a76bec45646","Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil","Dos Santos, C.W., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil; Filho, N.L.D., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil; Espíndola, D.B., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil; Botelho, S.S.C., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil","Safety, efficiency and effectiveness are important characteristics for human-robot interaction in a factory. So, workers and operators are exposed to challenges of interacting with these systems, in particular with welding robots. Considering the complexity of the industrial context and of the welding process itself, an important factor for the operator is ""to understand what is happening""or to obtain situational awareness (SA). The SA increases decision-making capacity, reduces errors, and adds features to improve the human-robot interface (HRI). In order to integrate SA into interfaces for human-robot interaction, this work proposes the mapping of important aspects of SA for the welding process, based on a literature review and a case study to identify SA on interfaces for HRI. Finally, comments which aspects should be included in situational awareness oriented interfaces in order to provide more intelligent interfaces. Copyright © 2020 The Authors.","Human-Robot Interaction; Intelligent Interface; Situational Awareness","Decision making; Industrial robots; Man machine systems; Robot applications; Welding; Exposed to; Human-robot interfaces; Industrial context; Intelligent interface; Literature reviews; Situational awareness; Welding process; Welding robots; Human robot interaction",,,,,,,,,,,,,,,,"Endsley, M.R., (2011) Designing for Situation Awareness: An Approach to User-Centred Design, 2nd Edition., , CRC Press, Boca Raton; Argall, B.D., Aude, G.B., A survey of tactile human-robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176; Kim, Y.C., Kwon, W.T., Yoon, J.C., Kim, J.C., Designing emotional and interactive behaviors for an entertainment robot (2009) International Conference on Human-Computer Interaction., pp. 321-330. , Springer, Berlin, Heidelberg; Siciliano, B., Khatib, O., (2007) Springer Handbook of Robotics, , Springer-Verlag New York; De Oliveira, A.C.M., (2016) Model-Based Design of User Interfaces to Support Situation Awareness in Maintenance, , Ph.D. Dissertation. Federal University of São Carlos, São Carlos; Garcia, E., Jimenez, M.A., De Santos, P.G., Armada, M., The evolution of robotics research (2007) IEEE Robotics & Automation Magazine, 14 (1), pp. 90-103; Scholtz, J., Evaluation methods for human-system performance of intelligent systems (2002) Proceedings of the 2002 Performance Metrics for Intelligent Systems Workshop, pp. 1-6. , Gaithersburg; Scholtz, J., Theory and evaluation of human robot interactions (2003) Proceedings of the 36th Annual Hawaii International Conference on System Sciences, pp. 1-10. , IEEE, Big Island - USA; Campana, J.R., Quaresma, M., The importance of specific usability guidelines for robot user interfaces (2017) Design, User Experience, and Usability: Designing Pleasurable Experiences. Lecture Notes in Computer Science, p. 10289. , Marcus A., Wang W. (eds), Springer, Cham; Moniz, A., Krings, B.J., Robots working with humans or humans working with robots? Searching for social dimensions in new human-robot interaction in industry (2016) Societies, 6 (3), p. 23; Bdiwi, M., Pfeifer, M., Sterzing, A., A new strategy for ensuring human safety during various levels of interaction with industrial robots (2017) CIRP Annals, 66 (1), pp. 453-456; Najmaei, N., Kermani, M.R., Prediction-based reactive control strategy for human-robot interactions (2010) The International Conferece on Robotics and Automation (ICRA), pp. 3434-3439. , IEEE, Anchorage, USA; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) The International Conference on Robotics and Automation (ICRA), pp. 197-204. , IEEE, Karlsruhe, Germany; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human- robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, (55), pp. 248-266; (1991) Welding Process. 8th Edition, , Welding Handbook., American Welding Society, Miami; Chen, S.B., Qiu, T., Lin, T., Wu, L., Tian, J.S., Lv, W.X., (2004) Intelligent Technologies for Robotic Welding. In: Lecture Notes in Control and Information Science., pp. 123-143. , Springer, Berlin; Perzanowski, D., Schultz, A.C., Adams, W., Building a multimodal human-robot interface (2001) IEEE Intelligent Systems, 16 (1), pp. 16-21; Endsley, M.R., Measurement of situation awareness in dynamic systems (1995) Human Factors, 37 (1), pp. 65-84; Endsley, M.R., Robertson, M.M., Situation awareness in aircraft maintenance teams (2000) International Journal of Industrial Ergonomics, (26), pp. 301-325; De Oliveira, A.C.M., Van Volkenburg, C.A., Jardine, A.K., De Araujo, R.B., The electronic maintenance situational awareness interface (2015) Reliability and Maintainability Symposium (RAMS), pp. 1-6. , IEEE, Palm Harbor; Parsons, K., (2014) Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance. 3rd Edition., , CRC Press, Boca Raton; Endsley, M.R., (2016) Designing for Situation Awareness: An Approach to User-centered Design. 2nd Edition., , CRC Press, Boca Raton; Steffens, C., Silva Filho, S., Quaresma, B., Rozante, M., Botelho, Silvia, S.C., Rosa, V., Huttner, V., A vmb system for intelligent robotic welding (2015) Simpósio Brasileiro de Automação Inteligente - SBAI, pp. 1-6. , Proceedings of SBAI, Natal; Schott, M., (2018) Interação Humano-robô em Processos de Soldagem: Uma Proposta de Desenvolvimento e Avaliação de Interfaces Para Controle de Um Robô Baseado em Engenharia Semiótica, , Sc.M. Dissertation. Federal University of Rio Grande, Rio Grande","Dos Santos, C.W.; Center of Computational Science, Brazil; email: carolinewaschburger@gmail.com.br
Filho, N.L.D.; Center of Computational Science, Brazil; email: dmtnldf@furg.br
Espíndola, D.B.; Center of Computational Science, Brazil; email: danubiaespindola@furg.br
Botelho, S.S.C.; Center of Computational Science, Brazil; email: silviacb@furg.br",,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107743975
"Steijn W.M.P., van Oosterhout J., Willemsen J., Jansen A.","54416395400;57224214463;57224210844;57215215220;","Modelling human-robot interaction to optimize their safety, sustainability and efficiency: Identifying relevant characteristics.",2020,"Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference",,,,"2571","2577",,,"10.3850/978-981-14-8593-0_4059-cd","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107317250&doi=10.3850%2f978-981-14-8593-0_4059-cd&partnerID=40&md5=77fe99f932482e359d417e20ef773eca","Work Health and Technology, TNO, Netherlands; Sustainable Productivity and Employability, TNO, Netherlands; ZonMw, Netherlands","Steijn, W.M.P., Work Health and Technology, TNO, Netherlands; van Oosterhout, J., Sustainable Productivity and Employability, TNO, Netherlands; Willemsen, J., Work Health and Technology, TNO, Netherlands; Jansen, A., ZonMw, Netherlands","Recent developments have introduced so-called cobots or cooperative robots, which place the machines on the work floor next to employees. A new challenge arises to keep these human robot interactions, safe and healthy, while also maintaining the sustainability and efficiency of the interaction. Here, we present a model, which can be a valuable tool to assist in future studies to systematically increase our understanding of Human-robot interaction (HRI). The model is based on the input-mediation-output (IMOI) model, which is often used in teamwork studies. Furthermore, it distinguishes between hardware, software, and mindware characteristics, to help researchers formulate more specific hypotheses for data collection in use cases. The purpose of this model is to help categorize various characteristics of humans, robots and the environment that can affect the outcome of the HRI. Ultimately, data gathered with the help of this model could help formulate design requirements for the development of future cobot applications and their implementation on the workfloor. This way we work towards inherently safe, healthy, sustainable and efficient HRI. © ESREL 2020-PSAM15 Organizers. Published by Research Publishing, Singapore.","Cobots; Efficient; Human-robot interaction; Safe; Sustainable","Efficiency; End effectors; Man machine systems; Safety engineering; Sustainable development; Cooperative robots; Data collection; Human robot Interaction (HRI); Social robots",,,,,,"This research has been made possible thanks to the support of the Ministry of Social Affairs and Employment regarding the Occupational Safety Research Program 2018. Furthermore, we would like to express our appreciation to Johan van der Vorm for his contributions to this project.",,,,,,,,,,"Adams, D.A., Nelson, R.R., Todd, P.A., Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication (1992) MIS Quarterly, 16 (2); Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., Romano, V.A., Modelling Workforce Aging in Job Rotation Problems (2015) Ifac-Papersonline, 48 (3), pp. 604-609; Bortot, D., Born, M., Bengler, K., Directly or on detours? How should industrial robots approximate humans? (2013) Paper Presented at the 2013 8Th ACM/IEEE International Conference on Humanrobot Interaction (HRI); Cazes, S., Hijzen, A., Saint-Martin, A., (2015) Measuring and Assessing Job Quality: The OECD Job Quality Framework, , Paper presented at the OECD Social, Employment and Migration; Chen, J.Y.C., Barnes, M.J., Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014) IEEE Transactions on Humanmachine Systems, 44 (1), pp. 13-29; de Santis, A., Siciliano, B., de Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Dohi, M., Okada, K., Maeda, I., Fujitani, S., Fujita, T., Proposal of Collaboration Safety in a Coexistence Environment of Human and Robots (2018) Paper Presented at the 2018 IEEE International Conference on Robotics and Automation (ICRA); Douwes, M., Huysmans, M.A., Kraan, K.O., de Looze, M.P., (2017) Robotisering En De Gevolgen Voor Arbeidsbelasting En Het Arbeidsdeskundig Vak. 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Med., 52 (3), pp. 121-128; Goodrich, M.A., Yi, D., (2013) Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach, , Paper presented at the International conference on virtual, augmented and mixed reality; Green, S.A., Billinghurst, M., Chen, X., Chase, J.G., Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design (2008) International Journal of Advanced Robotic Systems, 5 (1); Ilgen, D.R., Hollenbeck, J.R., Johnson, M., Jundt, D., Teams in organizations: From input-processoutput models to IMOI models (2005) Annu Rev Psychol, 56, pp. 517-543; Johnson, M., Bradshaw, J.M., Feltovich, P.J., Jonker, C.M., van Riemsdijk, M.B., Sierhuis, M., Coactive Design: Designing Support for Interdependence in Joint Activity (2014) Journal of Humanrobot Interaction, 3 (1); Lasota, P.A., Rossano, G.F., Shah, J.A., Toward safe close-proximity human-robot interaction with standard industrial robots (2014) Paper Presented at the 2014 IEEE International Conference on Automation Science and Engineering (CASE); Legris, P., Ingham, J., Collerette, P., Why do people use information technology? 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D6171; Nachreiner, F., Nickel, P., Meyer, I., Human factors in process control systems: The design of human–machine interfaces (2006) Safety Science, 44 (1), pp. 5-26; Niemela, R., Rautio, S., Hannula, M., Reijula, K., Work environment effects on labor productivity: An intervention study in a storage building (2002) Am J Ind Med, 42 (4), pp. 328-335; Ogorodnikova, O., Human Weaknesses and strengths in collaboration with robots (2008) Periodica Polytechnica Mechanical Engineering, 52 (1); Ososky, S.S.T.F.J., Hancock, P.A., Chen, J.Y.C., (2014) Determinants of System Transparancy and Its Influence on Trust in and Reliance on Unmanned Robotic Systems. Paper Presented at the The Internation Society for Optical Engineering; Pervez, A., Ryu, J., Safe physical human robot interaction-past, present and future (2008) Journal of Mechanical Science and Technology, 22 (3), pp. 469-483; Rahkonen, P., Ettala, M., Loikkanen, I., Working conditions and hygiene at sanitary landfills in Finland (1987) Ann. Occup. Hyg., 31 (4A), pp. 505-513. , , Vol; Reason, J., Human error: Models and management (2000) Bmj, 320 (7237), pp. 768-770; Rosen, M.A., Dietz, A.S., Yang, T., Priebe, C.E., Pronovost, P.J., An integrative framework for sensor-based measurement of teamwork in healthcare (2015) J am Med Inform Assoc, 22 (1), pp. 11-18; Salas, E., Dickinson, T.L., Converse, S.A., Tannenbaum, S.I., Toward an understanding of team performance and training (1992) Teams: Their Training and Performance, pp. 3-29. , R. W. Swezey and E. Salas (Eds.), (pp. , Westport, CT, US: Ablex Publishing; Sanders, T.L., Macarthur, K., Volante, W., Hancock, G., Macgillivray, T., Shugars, W., Hancock, P.A., Trust and Prior Experience in Human-Robot Interaction (2017) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61 (1), pp. 1809-1813; Scheutz, M., Deloach, S.A., Adams, J.A., A Framework for Developing and Using Shared Mental Models in Human-Agent Teams (2017) Journal of Cognitive Engineering and Decision Making, 11 (3), pp. 203-224; Selkowitz, A., Lakhmani, S., Chen, J.Y.C., Boyce, M., The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent (2016) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), pp. 806-810; Siegel, M., The sense-think-act paradigm revisited. Paper presented at the 1st International Workshop on Robotic Sensing, 2003 (2003) ROSE', p. 03; Stolt, A., Linderoth, M., Robertsson, A., Johansson, R., Force controlled robotic assembly without a force sensor. Paper presented at the Robotics and Automation (ICRA) (2012) 2012 IEEE International Conference On; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) 2013 IEEE International Conference on Robotics and Automation, , Paper presented at the; Wang, L., Jamieson, G.A., Hollands, J.G., The Effects of Design Features on Users' Trust in and Reliance on a Combat Identification System (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 375-379; Werkhoven, P., Kester, L., Neerincx, M., Telling autonomous systems what to do (2018) Proceedings of the 36Th European Conference on Cognitive Ergonomics-Ecce'18, , Paper presented at the; Xie, B., Salvendy, G., Review and reappraisal of modelling and predicting mental workload in single-and multi-task environments (2000) Work & Stress, 14 (1), pp. 74-99; You, S., Robbert, L., Teaming up with robots: An IMOI (Inputs-Mediators-Outputs-Inputs) framework of human-robot teamwork (2017) International Journal of Robotic Engineering, 2, p. 003",,"Baraldi P.Di Maio F.Zio E.",,"Research Publishing, Singapore","30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020","1 November 2020 through 5 November 2020",,259839,,9789811485930,,,"English","Proc. Eur. Saf. Reliab. Conf. Probab. Saf. Assess. Manag. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85107317250
"Sheikholeslami S., Lee G., Hart J.W., Srinivasa S., Croft E.A.","57117573300;57208850335;25031145200;6602084313;7003817722;","A Study of Reaching Motions for Collaborative Human-Robot Interaction",2020,"Springer Proceedings in Advanced Robotics","11",,,"584","594",,,"10.1007/978-3-030-33950-0_50","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107057338&doi=10.1007%2f978-3-030-33950-0_50&partnerID=40&md5=cc2c540946041eb6bc5b06563f933066","Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States; Faculty of Computer Science, University of Texas at Austin, Austin, TX, United States; Faculty of Engineering, Monash University, Clayton, Australia","Sheikholeslami, S., Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada; Lee, G., Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States; Hart, J.W., Faculty of Computer Science, University of Texas at Austin, Austin, TX, United States; Srinivasa, S., Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States; Croft, E.A., Faculty of Engineering, Monash University, Clayton, Australia","In human-human interactions, individuals naturally achieve fluency by anticipating the partner’s actions. This predictive ability is largely lacking in collaborative robots, leading to inefficient human-robot interactions. Fluent meshing in human-robot collaboration requires the robot to make its intentions clear to its human collaborator. We propose a unified generative model of human reaching motions that allows the robot to (a) infer human intent, and then (b) plan its motion to be legible, or intent-expressive. We conducted a study on human reaching motion and constructed an elliptical motion model that is shown to yield a good fit to empirical data. In future studies, we plan to confirm the effectiveness of this model in predicting human intent and conveying robot intent for achieving fluency in human-robot handovers. © 2020, Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Tomasello, M., Carpenter, M., Call, J., Behne, T., Moll, H., (2005) Behav. Brain Sci., 28 (5), pp. 675-691; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., (2015) In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (ACM), Pp, pp. 51-58; Cakmak, M., Srinivasa, S.S., Lee, M.K., Kiesler, S., Forlizzi, J., (2011) In: Proceedings of the 6Th International Conference on Human-Robot Interaction. ACM, New York, NY, USA,, HRI 2011, Pp, pp. 489-496; Beetz, M., Stulp, F., Esden-Tempski, P., Fedrizzi, A., Klank, U., Kresse, I., Maldonado, A., Ruiz, F., (2009) Autonom. Robots, 28 (1), p. 21; Dragan, A.D., Lee, K.C.T., Srinivasa, S.S., (2013) In: 2013 8Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp, pp. 301-308; Hoffman, G., Breazeal, C., (2007) IEEE Trans. Robot., 23 (5), p. 952; Lasseter, J.: In: ACM Siggraph Computer Graphics, vol. 21, pp. 35–44. ACM (1987); Flash, T., Hogan, N., (1985) J. Neurosci., 5 (7), p. 1688; Argall, B.D., Chernova, S., Veloso, M., Browning, B., (2009) Robot. Auton. Syst., 57 (5), p. 469; Abbeel, P., Ng, A.Y., In: Proceedings of the Twenty-First International Conference ON Machine Learning, p. 1 (2004) ACM; Dragan, A.D., Gordon, G.J., Srinivasa, S.S., In: Robotics Research, pp. 309–326 (2017) Springer; Sciutti, A., Sandini, G., (2017) IEEE Trans. Neural Syst. Rehabil. Eng., 25 (12), p. 2295; Flash, T., Hogans, N., (1985) J. Neurosci., 5, p. 1688; Harris, C., Wolpert, D.M., Signal-dependent noise determines motor planning (1998) Nature, 394 (6695), p. 780; Wu, G., van der Helm, F.C., (2005) J. Biomech., 38 (5), p. 981; Todorov, E., Jordan, M., (2002) Nat. Neurosci., 5, p. 1226; Morasso, P., (1981) Exp. Brain Res., 42 (2), p. 223; Collewijn, H., Erkelens, C.J., Steinman, R.M., (1988) J. Physiol., 404 (1), p. 157; Nurunnabi, A., Belton, D., West, G., (2012) ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science, 1-3, pp. 269-274. , vol., pp; Hartley, R.I., Zisserman, A., (2004) Multiple View Geometry in Computer Vision, , 2nd edn. Cambridge University Press, Cambridge, ). ISBN: 0521540518; Ayoub, A.B., (1993) Math. Mag., 66 (5), p. 322","Croft, E.A.; Faculty of Engineering, Australia; email: elizabeth.croft@monash.edu",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107057338
"Stuhlenmiller F., Velasco-Guillen R.J., Rinderknecht S., Beckerle P.","57015622100;57219802929;6603609535;36964076200;","Fault-Tolerant Physical Human-Robot Interaction via Stiffness Adaptation of Elastic Actuators",2020,"Springer Proceedings in Advanced Robotics","12",,,"73","87",,,"10.1007/978-3-030-42026-0_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107041920&doi=10.1007%2f978-3-030-42026-0_6&partnerID=40&md5=dcfb173cda710e4125ec2f00232ff712","Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany; Elastic Lightweight Robotics Group, Robotics Research Institute, Technical University Dortmund, Dortmund, Germany","Stuhlenmiller, F., Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany; Velasco-Guillen, R.J., Elastic Lightweight Robotics Group, Robotics Research Institute, Technical University Dortmund, Dortmund, Germany; Rinderknecht, S., Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany; Beckerle, P., Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany, Elastic Lightweight Robotics Group, Robotics Research Institute, Technical University Dortmund, Dortmund, Germany","Elastic actuators are popular in human-robot interaction as they can improve human safety and efficiency. Yet, such actuators are more complex than rigid ones and might be subject to additional technical faults, e.g., stiffness changes. This paper extends previous studies on stiffness-fault-tolerant physical human-robot interaction (pHRI) through control adaptation, introducing new methods for stiffness estimation and fault evaluation. Kalman filters with different measurement signals and system models estimating the actual stiffness value of the elastic element are compared. Faults are evaluated by analyzing the structural durability and compensated by adapting an impedance controller to provide a desired interaction stiffness. Experiments with a series elastic actuator underline the feasibility of the evaluation and compensation methods for attaining safe and reliable pHRI. Results show that stiffness estimation during pHRI is possible when the actuator friction and interaction torque is either negligible or well known, or when the torque at the spring is measured. © 2020, Springer Nature Switzerland AG.","Elastic actuator; Fault evaluation; Fault-tolerant control; Physical human-robot interaction",,,,,,"Deutsche Forschungsgemeinschaft, DFG: BE 5729/1","Acknowledgment. This work was supported by a Deutsche Forschungsgemeinschaft (DFG) Research Grant (no. BE 5729/1).",,,,,,,,,,"Haddadin, S., Albu-Schaeffer, A., de Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , , pp; Haddadin, S., Albu-Schäffer, A., Hirzinger, G., Safe physical human-robot interaction: Measurements, analysis and new insights (2007) ISRR, Vol. 66, pp. 395-407. , , pp. , Springer, Heidelberg; Lens, T., von Stryk, O., Investigation of safety in human-robot-interaction for a series elastic, tendon-driven robot driven robot arm (2012) IEEE/RSJ International Conference on Intelligent Robots and Systems; Vanderborght, B., van Ham, R., Lefeber, D., Sugar, T.G., Hollander, K.W., Comparison of mechanical design and energy consumption of adaptable, passive-compliant actuators (2009) Int. J. Robot. Res., 28 (1), pp. 90-103; Verstraten, T., Beckerle, P., Furnémont, R., Mathijssen, G., Vanderborght, B., Lefeber, D., Series and parallel elastic actuation: Impact of natural dynamics on power and energy consumption (2016) Mech. Mach. Theory, 102, pp. 232-246; Beckerle, P., Practical relevance of faults, diagnosis methods, and tolerance measures in elastically actuated robots (2016) Control Eng. Pract., 50, pp. 95-100; Filippini, R., Sen, S., Bicchi, A., Toward soft robots you can depend on (2008) IEEE Robot. Autom. Mag., 15 (3), pp. 31-41; Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D., Carloni, R., Catalano, M., Wolf, S., Variable impedance actuators: A review (2013) Robot. Auton. Syst., 61 (12), pp. 1601-1614; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) IEEE/RSJ International Conference on Intelligent Robots and Systems; Schiavi, R., Grioli, G., Sen, S., Bicchi, A., VSA-II: A novel prototype of variable stiffness actuator for safe and performing robots interacting with humans (2008) IEEE International Conference on Robotics and Automation; Isermann, R., (2006) Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, , Springer, Heidelberg; Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., (2010) Diagnosis and Fault-Tolerant Control, , Springer, Heidelberg; Stuhlenmiller, F., Perner, G., Rinderknecht, S., Beckerle, P., (2019) A Stiffness-Fault-Tolerant Control Strategy for Reliable Physical Human-Robot Interaction. In: Human Friendly Robotics, pp. 3-14. , pp. , Springer, Heidelberg; Lendermann, M., Singh, B.R.P., Stuhlenmiller, F., Beckerle, P., Rinderknecht, S., Manivannan, P.V., Comparison of passivity based impedance controllers without torque-feedback for variable stiffness actuators (2015) IEEE/ASME International Conference on Advanced Intelligent Mechatronics; Schuy, J., Beckerle, P., Wojtusch, J., Rinderknecht, S., von Stryk, O., Conception and evaluation of a novel variable torsion stiffness for biomechanical applications (2012) IEEE/RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 713-718. , , pp; Spong, M.W., Adaptive control of flexible joint manipulators (1989) Syst. Control Lett., 13, pp. 15-21; Andersson, S., Söderberg, A., Björklund, S., Friction models for sliding dry, boundary and mixed lubricated contacts (2007) Tribol. Int., 40 (4), pp. 580-587; Lemaitre, J., Dufailly, J., Damage measurements (1987) Eng. Fract. Mech., 28 (5), pp. 643-661; Flacco, F., de Luca, A., Residual-based stiffness estimation in robots with flexible transmissions (2011) 2011 IEEE International Conference on Robotics and Automation (ICRA), Pp. 5541–5547. IEEE; Julier, S., Uhlmann, J., Unscented filtering and nonlinear estimation (2004) Proc. IEEE, 92 (3), pp. 401-422; Wan, E.A., van Der Merwe, R., The unscented Kalman filter for nonlinear estimation (2000) Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. the IEEE2000, Pp. 153–158. IEEE; Wu, M., Smyth, A., Application of the unscented kalman filter for real-time nonlinear structural system identification (2007) Struct. Control Health Monit., 14, pp. 971-990; Schuy, J., Beckerle, P., Faber, J., Wojtusch, J., Rinderknecht, S., von Stryk, O., Dimensioning and evaluation of the elastic element in a variable torsion stiffness actuator (2013) IEEE/ASME International Conference on Advanced Intelligent Mechatronics; de Souza Neto, E.A., Perić, D., Owen, D.R.J., (2008) Computational Methods for Plasticity, , Wiley, Hoboken; Ott, C., (2008) English Cartesian Impedance Control of Redundant and Flexible-Joint Robots, , Springer, Heidelberg; Kaur, N., Kaur, A., A review on tuning of extended kalman filter using optimization techniques for state estimation (2016) Int. J. Comput. Appl., 145 (15), pp. 1-5","Velasco-Guillen, R.J.; Elastic Lightweight Robotics Group, Germany; email: rodrigo.velasco@tu-dortmund.de",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85107041920
"Markovich T., Honig S., Oron-Gilad T.","57224123976;57201589559;16042913400;","Closing the Feedback Loop: The Relationship Between Input and Output Modalities in Human-Robot Interactions",2020,"Springer Proceedings in Advanced Robotics","12",,,"29","42",,,"10.1007/978-3-030-42026-0_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107030799&doi=10.1007%2f978-3-030-42026-0_3&partnerID=40&md5=2caac7057f29c731bb883262d242dae1","Ben-Gurion University of the Negev, Beer-Sheva, Israel","Markovich, T., Ben-Gurion University of the Negev, Beer-Sheva, Israel; Honig, S., Ben-Gurion University of the Negev, Beer-Sheva, Israel; Oron-Gilad, T., Ben-Gurion University of the Negev, Beer-Sheva, Israel","Previous studies suggested that communication modalities used for human control and robot feedback influence human-robot interactions. However, they generally tended to focus on one part of the communication, ignoring the relationship between control and feedback modalities. We aim to understand whether the relationship between a user’s control modality and a robot’s feedback modality influences the quality of the interaction and if so, find the most compatible pairings. In a laboratory Wizard-of-Oz experiment, participants were asked to guide a robot through a maze by using either hand gestures or vocal commands. The robot provided vocal or motion feedback to the users across the experimental conditions forming different combinations of control-feedback modalities. We found that the combinations of control-feedback modalities affected the quality of human-robot interaction (subjective experience and efficiency) in different ways. Participants showed less worry and were slower when they communicated with the robot by voice and received vocal feedback, compared to gestural control and receiving vocal feedback. In addition, they felt more distress and were faster when they communicated with the robot by gestures and received motion feedback compared to vocal control and motion feedback. We also found that providing feedback improves the quality of human-robot interaction. In this paper we detail the procedure and results of this experiment. © 2020, Springer Nature Switzerland AG.","Feedback by motion cues; Feedback loop; Human-robot interaction; Navigation task; Stimulus-response compatibility",,,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","Acknowledgments. This research was supported by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Center at Ben-Gurion University of the Negev. The second author, SH is also supported by Ben-Gurion University of the Negev through the High-tech, Bio-tech and Chemo-tech Scholarship.",,,,,,,,,,"Dubberly, H., Pangaro, P., Haque, U., ON MODELING What is interaction?: Are there different types? (2009) Interactions, 16 (1), pp. 69-75; Mirnig, N., Riegler, S., Weiss, A., Tscheligi, M., A case study on the effect of feedback on itinerary requests in human-robot interaction (2011) 2011 IEEE RO-MAN, pp. 343-349. , , pp. , IEEE, July; Mohammad, Y., Nishida, T., Interaction between untrained users and a miniature robot in a collaborative navigation controlled experiment (2008) Int. J. Inf. Acquis., 5 (4), pp. 291-307; Redden, E.S., Carstens, C.B., Pettitt, R.A., (2010) Intuitive Speech-Based Robotic Control (No. ARL-TR-5175); Perrin, X., Chavarriaga, R., Ray, C., Siegwart, R., Millán, J.D.R., A comparative psychophysical and EEG study of different feedback modalities for HRI (2008) Proceedings of the 3Rd ACM/IEEE International Conference on Human Robot Interaction, pp. 41-48. , , pp. , ACM, March; Greenwald, A.G., A choice reaction time test of ideomotor theory (1970) J. Exp. Psychol., 86, pp. 20-25; Greenwald, A.G., Sensory feedback mechanisms in performance control: With special reference to the ideo-motor mechanism (1970) Psychol. Rev., 77 (2), p. 73; Proctor, R.W., Vu, K.P.L.: Stimulus-Response Compatibility Principles: Data, Theory, and Application. CRC Press, Boca Raton (2006); Wickens, C.D., Sandry, D.L., Vidulich, M., Compatibility and resource competition between modalities of input, central processing, and output (1983) Hum. 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"Huang J., Wu W., Zhang Z., Chen Y.","56326298100;57211553119;57201427541;57203833448;","A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems",2020,"IEEE Access","8",,,"197853","197862",,2,"10.1109/ACCESS.2020.3035348","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102885228&doi=10.1109%2fACCESS.2020.3035348&partnerID=40&md5=9d040a2176f35fa6240fbce49794d085","College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","Huang, J., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Wu, W., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Zhang, Z., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Chen, Y., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","In this paper, a novel human decision-making behavior model is proposed for human-robot interaction (HRI) for the control of multi-robot systems (MRS). The proposed human drift diffusion model (HDDM) combines the traditional drift diffusion model (DDM) and the null-space-based behavioral control (NSBC) method by introducing a data-processing station and a human cognitive system. In the HDDM, the evolution of human-decision information is computed. By using a threshold of such information to trigger human interaction, accurate human decision-making timing can be obtained. In addition, a cooperative controller is designed for robots to follow human instructions. Simulations under various scenarios showthat by using the proposedHDDMand the controller, robots can complete human instructions more accurately comparing to traditional methods. An experiment using a group of quadrotors subject to external wind disturbances also demonstrate the effectiveness of the proposedHDDMin real-world uncertain environments. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Human decisionmaking behavior model; Human drift diffusion model; Human intervention; Multi-robot system","Behavioral research; Cognitive systems; Controllers; Data handling; Decision making; Diffusion; Industrial robots; Man machine systems; Multipurpose robots; Robot learning; Drift-diffusion model; Human cognitive systems; Human decision making; Human robot Interaction (HRI); Multirobot systems; Null-space-based behavioral control; Processing stations; Uncertain environments; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61603094","This work was supported in part by the National Natural Science Foundation of China under Grant 61603094.",,,,,,,,,,"Huang, J., Zhou, N., Cao, M., Adaptive fuzzy behavioral control of second-order autonomous agents with prioritized missions: Theory and experiments (2019) Ieee Trans. Ind. Electron, 66 (12), pp. 9612-9622. , Dec; Ren, W., Beard, R.W., (2008) Distributed Consensus in Multi-Vehicle Cooper-Ative Control, , London, U.K.: Springer; Hu, W., Liu, L., Feng, G., Output consensus of heterogeneous linear multi-agent systems by distributed event-triggered/self-triggered strategy (2017) Ieee Trans. Cybern, 47 (8), pp. 1914-1924. , , Aug; Cruz, J., Leader-follower strategies for multilevel systems (1978) Ieee Trans. Autom. Control, 23 (2), pp. 244-255. , Apr; He, S., Wang, M., Dai, S.-L., Luo, F., Leader-follower formation control of USVs with prescribed performance and collision avoidance (2019) Ieee Trans. Ind. Informat, 15 (1), pp. 572-581. , Jan; Ren, W., Beard, R.W., Decentralized scheme for spacecraft formation-ying via the virtual structure approach (2004) J. Guid., Control, Dyn., 27 (1), pp. 73-82. , Jan; Balch, T., Arkin, R.C., Behavior-based formation control for multirobot teams (1998) Ieee Trans. Robot. 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Robot., 25 (5), pp. 985-994. , Oct","Chen, Y.; College of Electrical Engineering and Automation, China; email: ytchen_fzu@163.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102885228
"Awais M., Saeed M.Y., Malik M.S.A., Younas M., Asif S.R.I.","57217768600;57191104285;55916938800;57213343722;57222495268;","Intention based comparative analysis of human-robot interaction",2020,"IEEE Access","8",,,"205821","205835",,1,"10.1109/ACCESS.2020.3035201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102873314&doi=10.1109%2fACCESS.2020.3035201&partnerID=40&md5=aae036537a96823904a656ee74b8fd61","Department of Software Engineering, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Department of Information Technology, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan","Awais, M., Department of Software Engineering, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Saeed, M.Y., Department of Software Engineering, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Malik, M.S.A., Department of Information Technology, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Younas, M., Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Asif, S.R.I., Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan","Human-robot interaction is inevitable due to the increase of the autonomous intelligent machines in the human vicinity. The autonomous machines should synchronize with the interacting human. The key factor for synchronized Human-Robot Interaction (HRI) is a human intention. The interacting machine must have the clue about the intention of the interacting human for a useful interaction. In this review paper, recently proposed intention-based approaches for human-robot interaction are discussed. The approaches are categorized concerning different aspects, e.g., application area, specialized and generalized estimation techniques, etc. The review categorized the recently proposed approaches into five categories. The categorization is mainly based on the application areas of the intention recognition approaches. The type of approaches includes general and application-specific approaches. The application areas include synchronized physical human-assistance, human-synchronized vehicles, etc. The vehicles are synchronized with the driver and the pedestrian. The study highlighted the currently active as well as the dormant areas of the intention-based human-robot interaction and indicated new directions. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Intention based bionics; Intention based safe vehicles; Intention based teleoperation; Intention recognition","Man machine systems; Synchronization; Application specific; Autonomous machines; Comparative analysis; Estimation techniques; Human intentions; Human robot Interaction (HRI); Intelligent machine; Intention recognition; Human robot interaction",,,,,,"The authors would like to thank the Government College University Faisalabad (GCUF), Pakistan, to provide resources for this research.",,,,,,,,,,"Awais, M., (2013) Bayreuth University Library, , https://eref.uni-bayreuth.de/view/person/Awais=3AMuhammad=3A=3A.html; Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction (2018) Appl. Mech. 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Mag., 13 (1), pp. 76-78. , Mar; Forlizzi, J., DiSalvo, D., Service robots in the domestic environment: A study of the roomba vacuum in the home (2006) Proc. Hum. Robot Interact., pp. 258-265; Miura, J., Shirai, Y., Parallel scheduling of planning and action of a mobile robot based on planning-action consistency (1999) Proc. Workshop Scheduling Meet Real-Time Monit. Dyn. Uncertain World, , http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.7143, Stockholm, Sweden, Online; Krishna, K.M., Alami, R., Simeon, T., Safe proactive plans and their execution (2006) Robot. Auton. Syst., 54 (3), pp. 244-255. , Mar; Armano, G., Cherchi, G., Vargiu, E., An agent architecture for planning in a dynamic environment (2001) Proc. Congr. Italian Assoc. Artif. Intell., in Lecture Notes in Computer Science, 2175, pp. 388-394; Finzi, A., Orlandini, A., Human-robot interaction through mixed-initiative planning for rescue and search rovers (2005) Proc. Congr. Italian Assoc. Artif. Intell., in Lecture Notes in Computer Science, 3673, pp. 483-494; Awais, M., Henrich, D., Proactive premature intention estimation for intuitive human-robot collaboration (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4098-4103. , Vilamoura, Portugal, Oct; https://www.irobot.com/roomba, Accessed: 2020","Awais, M.; Department of Software Engineering, Pakistan; email: muhammadawais@gcuf.edu.pk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102873314
"Bhalaji R.K.A., Bathrinath S., Ponnambalam S.G., Saravanasankar S.","57206469714;54779403600;7005929538;23975020200;","Analyze the factors influencing human-robot interaction using MCDM method",2020,"Materials Today: Proceedings","39",,,"100","104",,,"10.1016/j.matpr.2020.06.316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102449574&doi=10.1016%2fj.matpr.2020.06.316&partnerID=40&md5=9534d30e8ef594bc5f850a5c6f535a91","Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India; Universiti Malaysia, Pekan, Pahang, 26000, Malaysia","Bhalaji, R.K.A., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India; Bathrinath, S., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India; Ponnambalam, S.G., Universiti Malaysia, Pekan, Pahang, 26000, Malaysia; Saravanasankar, S., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India","Robots play a key role in medical equipment manufacturing industry by safeguarding human workers from hazardous environment and risky jobs. Human robot interaction (HRI) is one of the robotic features that are enhanced in industrial robots. They mimic human behavior while arriving at a decision, contributing to the proficiency of the product. Tasks involving human cognitive skills and flexibility in the workers are combined with robots to obtain high-level accuracy, repeatability, and speed. Further, more challenges are to be met for achieving an effective human-robot interaction. In this paper, risk factors affecting the interaction between both robot and humans are discussed, and a contextual case is performed in a top south Indian medical equipment manufacturing industry. Industrial experts' inputs and relevant literature are considered to recognize the risk factors. Multi-Criteria decision-making method (MCDM) like DEMATEL (Decision Making Trial and Evaluation Laboratory) is used to analyze the risk factors influencing HRI in the assembly section. The paper's findings show that automation level and reliability of the robot are the most influential factor in the assembly section and need more attention to control and reduce the risk factor for the effective assembly. © 2020 The Authors. Published by Elsevier Ltd.","DEMATEL; HRI; India; Medical equipments; Multi-criteria decision making",,,,,,,,,,,,,,,,,"Erden, M.S., Jonkman, J.A., Physical human-robot interaction by observing actuator currents (2012) Int. J. Rob. Autom., 27 (3), p. 233; Zheng, M., Liu, P.X., Max, Q.H.M., Interpretation of human and robot emblematic gestures: How do they differ? (2019) Int. J. Rob. Autom., 34 (1), pp. 55-70; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on human–robot trust in time-critical situations (2017) IEEE Trans. Hum.-Mach. Syst., 47 (4), pp. 425-436; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Hoff, K.A., Bashir, M., Trust in automation: Integrating empirical evidence on factors that influence trust (2015) Hum. Factors, 57 (3), pp. 407-434; Johannsmeier, L., Haddadin, S., A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes (2016) IEEE Rob. Autom. Lett., 2 (1), pp. 41-48; Tsarouchi, P., Makris, S., Chryssolouris, G., Human–robot interaction review and challenges on task planning and programming (2016) Int. J. Comput. Integr. Manuf., 29 (8), pp. 916-931; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Bathrinath, S., Charan, V.S., Ponnambalam, S.G., Saravanasankar, S., Identification and evaluation of criteria of agile manufacturing using dematel: a case from an indian metal fabrication industry (2019) J. Modern Manuf. Syst. Technol., 2 (1), pp. 61-74; Bhalaji, R.K.A., Bathrinath, S., Ponnambalam, S.G., Saravanasankar, S., A fuzzy decision-making trial and evaluation laboratory approach to analyse risk factors related to environmental health and safety aspects in the healthcare industry (2019) Sādhanā, 44 (3), p. 55; Alkan, A., Canbay, K., Akman, G., Aladağ, Z., Researching usage of globe culture dimensions in organizational management by using dematel method (2019) Sakarya Univ. J. Sci., 23 (2), pp. 282-290; Xu, H., Deng, Y., Dependent evidence combination based on decision-making trial and evaluation laboratory method (2019) Int. J. Intell. Syst., 34 (7), pp. 1555-1571; Qarnain, S.S., Muthuvel, S., Bathrinath, S., Analyzing factors necessitating conservation of energy in residential buildings of Indian subcontinent: A DEMATEL approach (2020) Mater. Today: Proc.; Bhalaji, R.K.A., Bathrinath, S., Ponnambalam, S.G., Saravanasankar, S., A soft computing methodology to analyze sustainable risks in surgical cotton manufacturing companies (2020) Sādhanā, 45 (1), pp. 1-22; Karuppiah, K., Sankaranarayanan, B., Ali, S.M., Chowdhury, P., Paul, S.K., An integrated approach to modeling the barriers in implementing green manufacturing practices in SMEs (2020) J. Cleaner Prod., 121737","Bhalaji, R.K.A.; Department of Mechanical Engineering, India; email: bathri@gmail.com","Devaraju A.Palanisamy D.",,"Elsevier Ltd","3rd International Conference on Advanced Materials and Modern Manufacturing, ICAMMM 2020","3 April 2020 through 4 April 2020",,167571,22147853,,,,"English","Mater. Today Proc.",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85102449574
"Guerra-Zubiaga D.A., Nasajpour-Esfahani N., Siddiqui B., Kamperman K., II","23060428300;57222062476;57222071401;57222072710;","Implementing a novel framework to create tacit knowledge models to support human-robot interactions",2020,"ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)","2B-2020",,,"","",,,"10.1115/IMECE2020-23803","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101270241&doi=10.1115%2fIMECE2020-23803&partnerID=40&md5=003f9cc2858d067a6b58f91e7a6968da","Kennesaw State University, Kennesaw, Georgia, United States; Kennesaw State University, Marietta, GA, United States","Guerra-Zubiaga, D.A., Kennesaw State University, Kennesaw, Georgia, United States; Nasajpour-Esfahani, N., Kennesaw State University, Marietta, GA, United States; Siddiqui, B., Kennesaw State University, Marietta, GA, United States; Kamperman, K., II, Kennesaw State University, Marietta, GA, United States","While it is not only important to synthesize instruments, controls, and robotics, it is also essential to connect these elements to people to achieve the future of automation. Whether in an operating room with surgical robots or in an earthquake disaster zone where an operator is aided by search and rescue drones, interaction between machines and humans is becoming central to increasing productivity. Industry 4.0 trends such as Internet of Things (IoT) and digital manufacturing are the early adopters of human-machine interfaces that support manufacturing automation. Such models must consider various aspects of process implementation such as explicit, implicit, and tacit knowledge to properly mimic a human's performance. However, most inquiries in this field use expressed information instead of tacit knowledge due to an unfulfilled need for an industrial tacit knowledge framework. Tacit knowledge is difficult to learn and transfer if an operator's logic is never revealed. In response, this research provides a knowledge model to structure, categorize, and reuse tacit knowledge for advanced manufacturing operations. The model is implemented in a human-robot interaction by capturing valuable experiences using digital tools such as Tecnomatix for further reuse in a variety of industrial applications. © 2020 The Author(s). This is an Open Access article under the CC BY license.","Collaborative robots; Digital manufacturing; Next generation automation systems; Tacit knowledge framework","Digital devices; End effectors; Industrial research; Industrial robots; Internet of things; Man machine systems; Manufacture; User experience; Advanced manufacturing; Digital manufacturing; Earthquake disaster; Human Machine Interface; Internet of Things (IOT); Manufacturing Automation; Process implementation; Search and rescue; Human robot interaction",,,,,,,,,,,,,,,,"Guerra-Zubiaga, David A., Briceno, Simon, Product Lifecycle Management Tools to Support Next Generation of Mechatronic Systems at Aerospace Industry (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Djuric, A., Urbanic, R., Rickli, J., A Framework for Collaborative Robot (CoBot) Integration in Advanced Manufacturing Systems SAE Int. J. Mater. Manf, 9 (2), p. 2016; Guerra-Zubiaga, David A., Tacit Knowledge Elicitation Techniques Applied to Complex Manufacturing Processes (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Cai, Y., Starly, B., Cohen, P., Lee, Y., Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing (2017) Procedia Manufacturing, 10, pp. 1031-1042; Bassi, L., Industry 4.0: Hope, hype or revolution? (2017) 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI); Rosen, R., Wichert, G. V., Lo, G., Bettenhausen, K. D., About the Importance of Autonomy and Digital Twins for the Future of Manufacturing (2015) IFAC-PapersOnLine, 48 (3), pp. 567-572; Mourtzis, Dimitris, The Role of Simulation in Digital Manufacturing: Applications and Outlook (2015) International Journal of Computer Integrated Manufacturing, 28 (1), pp. 3-24. , Jan. EBSCOhost; Guerra-Zubiaga, D.A., Young, R.I.M., Design of a Manufacturing Knowledge Model (2008) International Journal of Computer Integrated Manufacturing, 12 (5), pp. 526-539; Guerra-Zubiaga, David A., Tacit Knowledge Elicitation Techniques Applied to Complex Manufacturing Processes (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Guerra-Zubiaga, David A., Newman, Stephen, Creating A New Energy Efficiency Tool for Manufacturing Automation to Support Next Generation Sustainable Eco-Factories (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Norton, A., Ober, W., Baraniecki, L., Shane, D., Skinner, A., Yanco, H., Perspectives on Human-Robot Team Performance from an Evaluation of the DARPA Robotics Challenge (2018) the DARPA Robotics Challenge Finals: Humanoid Robots to The Rescue, pp. 631-666. , Springer, Cham; Yamane, Katsu, Anderson, Stuart O., Hodgins, Jessica K., Controlling Humanoid Robots with Human Motion Data: Experimental Validation (2010) IEEE-RAS International Conference on Humanoid Robots, , Nashville, TN, USA, December 6-8, 2010; Lu, Y., Cecil, J., An Internet of Things (IoT)based collaborative framework for advanced manufacturing (2016) International Journal Of Advanced Manufacturing Technology, 84 (5-8), p. 1141; Calitz, A. P., Poisat, P., Cullen, M., The future African workplace: The use of collaborative robots in manufacturing (2017) South African Journal of Human Resource Management, 15 (1), pp. 1-11; Maurice, P., Padois, V., Measson, Y., Bidaud, P., Human-Oriented Design of Collaborative Robots (2017) International Journal of Industrial Ergonomics, 57, pp. 88-102; Ball, A., Patel, M., Mcmahon, C., Green, S., Clarkson, J., Culley, S., A Grand Challenge:Immortal Information and Through-Life Knowledge Management (KIM) (2008) International Journal of Digital Curation, 1, pp. 53-59; Mulder, U., Whiteley, A., Emerging and capturing tacit knowledge:a methodology for a bounded environment (2007) Journal of Knowledge Management; Augustsson, S., Olsson, J., Christiernin, L., Bolmsjö, G., (2014) How to Transfer Information between Collaborating Human Operators and Industrial Robots in an Assembly; Esmaeili, H., Thwaites, H., Woods, P., (2017) Immersive Virtual Environments for Tacit Knowledge Transfer Focusing on Gestures: A Workflow",,,"American Society of Mechanical Engineers (ASME)","American Society of Mechanical Engineers (ASME)","ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020","16 November 2020 through 19 November 2020",,167181,,9780791884492,,,"English","ASME Int Mech Eng Congress Expos Proc",Conference Paper,"Final","",Scopus,2-s2.0-85101270241
"Deuerlein C., Langer M., Seßner J., Heß P., Franke J.","57205293231;57221979822;57021762100;56400248900;57169710000;","Human-robot-interaction using cloud-based speech recognition systems",2020,"Procedia CIRP","97",,,"130","135",,1,"10.1016/j.procir.2020.05.214","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100856364&doi=10.1016%2fj.procir.2020.05.214&partnerID=40&md5=bab74d2476ec7da6c1785ca928320ec6","Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems, Egerlandstr. 7, Erlangen, 91058, Germany","Deuerlein, C., Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Langer, M., Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Seßner, J., Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems, Egerlandstr. 7, Erlangen, 91058, Germany; Heß, P., Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Franke, J., Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems, Egerlandstr. 7, Erlangen, 91058, Germany","Progress in natural speech processing has enabled significantly more powerful speech processing systems, primarily due to the use of machine learning technologies. In order to integrate cloud-based speech recognition systems for human-robot interaction, an interface for the voice control of a lightweight robot was developed. The main contribution of this work is the design and implementation of a software interface to recognize commands via cloud-based speech processing and the subsequently conversion into machine-readable code. Requirements for the evaluation of different cloud-services for the control of robots are determined. Furthermore, the control architecture for the robot is modeled and implemented. An example application, which enables users to control robot movements via speech, is realized as a proof of concept and for additional studies. This application includes the basic features of cloud-based speech processing: intent recognition from utterances, slot filling and dialogue-based interaction. Lastly, the influence of background noise on process safety was examined within an experiment. It turns out that a feasible process reliability can be achieved with the system despite the presence of background noises. © 2020 The Author(s).","Cloud-service; Human-robot-interaction; Industrial robot; Speech control","Man machine systems; Social robots; Speech processing; Control architecture; Design and implementations; Intent recognition; Machine learning technology; Process reliability; Processing systems; Software interfaces; Speech recognition systems; Speech recognition",,,,,"Bundesministerium für Bildung und Forschung, BMBF; Bundesministerium für Bildung und Forschung, BMBF","This work was conducted within the project MRK&MoCap4Robots (13FH008IX6), funded by the German Federal Ministry of Education and Research (BMBF). We are thankful for the support and funding from the BMBF.",,,,,,,,,,"Almansor, E.H., Hussain, F.K., Survey on intelligent chatbots: State-of-the-art and future research directions (2019) Proceedings of the 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS), 13-15, p. 534; (2019), https://developer.amazon.com/en-GB/docs/alexa/ask-overviews/build-skills-with-the-alexa-skills-kit.html, Amazon, Alexa Skill Kit:, accessed: 2019-11-24; Assefi, M., Wittie, M., Knight, A., Impact of network performance on cloud speech recognition (2015) Proceedings of the 24th International Conference, p. 1; Asseffi, M., Liu, G., Wittie, M.P., Izurieta, C., (2020) An Experimental Evaluation of Apple Siri and Google Speech Recognition, , Department of Computer Science, Montana State University; Braun, D., Hernandez, A., Matthes, F., Langen, M., Evaluating Natural Language Understanding Services for Conversational Question Answering Systems (2017) Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, p. 174. , Association for Computational Linguistics; Google, , https://dialogflow.com/docs/reference/agent, 2019. Dialogflow API reference:, accessed: 2019-11-24; Graves, A., Mohammed, A.-R., Hinton, G., Speech recognition with deep recurrent neural networks (2013) Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), p. 6645; Gustavsson, P., Syberfeldt, A., Brewster, R., Wang, L., Human-robot collaboration demonstrator combining speech recognition and haptic control (2017) Proceedings of the 50th CIRP Conference on Manufacturing Systems, p. 396; Jorge, C.A.F., Mol, A.C.A., Pereira, C.M.N.A., Aghina, M.A.C., Human-system interface based on speech recognition: Application to a virtual nuclear power plant control desk (2010) Prog. Nucl. Energy, 52, p. 379; Karpov, A., Ronzhin, A., Kipyatkova, I., An assistive bi-modal user interface integrating multi-channel speech recognition and computer vision (2011) Human-Computer Interaction, Part II, p. 454. , Springer-Verlag Berlin-Heidelberg; (2019) Language Understanding (LUIS) Documentation: Learn How Language Understanding Enables Your Applications to Understand What A Person Wants in Their Own Words, , https://docs.microsoft.com/en-gb/azure/cognitive-services/luis, Micosoft, accessed: 2019-11-24; Morbini, F., Audhkhasi, K., Sagae, K., Artstein, R., Which ASR should i choose for my dialogue system? (2013) Proceedings of the SIGDIAL Conference, pp. 394-403. , Metz, France; Park, S., Kim, Y., Matson, E.T., Jang, H., An intuitive interaction system for fire safety using a speech recognition technology (2015) Proceedings of the 6th International Conference, p. 388; Sharan, S., Nguyen, T.Q., Nauth, P., Araujo, R., Implementation and testing of voice control in a mobile robot for navigation (2019) Proceedings of the IEEE/ASME, p. 145; Skraba, A., Kolozvari, A., Kofjac, D., Stojanovic, R., Development of cyber-physical speech-controlled wheelchair for disabled persons (2019) Proceedings of the 22nd Euromicro Conference, p. 456; Wang, L., Tong, R., Leung, C.C., Ni, C., Cloud-based automatic speech recognition systems for southeast Asian (2017) Proceedings of the Languages Proceedings of the International Conference on Orange Technologies (ICOT), p. 147. , Singapore Kent Ridge Campus, National University of Singapore; Wölfel, K., Henrich, D., Grounding of uncertain force parameters in spoken robot commands (2020) Advances in Service and Industrial Robotics, p. 194. , Springer International Publishing; Zinchenko, K., Wu, C.Y., Song, K.T., A study on speech recognition control for a surgical robot (2017) IEEE Transactions on Industrial Informatics, 13 (2), p. 607","Deuerlein, C.; Technische Hochschule Nürnberg, Keßlerplatz 12, Germany; email: christian.deuerlein@th-nuernberg.de","Makris S.",,"Elsevier B.V.","8th CIRP Conference of Assembly Technology and Systems, CATS 2020","29 September 2020 through 1 October 2020",,167017,22128271,,,,"English",,Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85100856364
"Hashemi A., McPhee J.","57215457448;7005199260;","Trajectory planning for a human-robot interaction rehabilitation system using direct-collocation optimization",2020,"International Conference of Control, Dynamic Systems, and Robotics",,,"134","1","2",,,"10.11159/cdsr20.134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100746222&doi=10.11159%2fcdsr20.134&partnerID=40&md5=3d29120aeda11cedaa58289e6d8068b4","University of Waterloo, 200 University Ave W., Waterloo, Canada","Hashemi, A., University of Waterloo, 200 University Ave W., Waterloo, Canada; McPhee, J., University of Waterloo, 200 University Ave W., Waterloo, Canada","One of the essential considerations in Human-Robot Interaction (HRI) research is the trajectory planning phase [1]. This issue becomes vital in rehabilitation robotics due to the sensitive nature of the problem and patient involvement. An appropriate trajectory is conducive to improved clinical progress whereas an unsuitable practice can lead to the aggravation of the patient status. Due to the prevalence of stroke in recent years, this study aims to find the task-space manipulation trajectory for a 2 degrees of freedom (DOF) post-stroke end-effector-based rehabilitation robot, developed by Quanser, the Toronto Rehabilitation Institute (TRI), and the University of Waterloo. This planar robot provides upper-limb repetitive practices by moving the patient’s hand in the horizontal plane. The model of the robot is coupled with a 2D upper extremity musculoskeletal model, consisting of 6 lumped muscles, to form the HRI nonlinear dynamic system [2, 3]. This model includes four states, 2 robot angles and angular velocities, and 8 inputs, 2 robot motor torques and 6 arm muscle activations. An optimization-based trajectory planning scheme is presented and a multi-variable single-phase continuous-time optimization problem is set up to acquire the end-effector trajectory. Previous research on the trajectory planning of rehabilitation robots has considered a predefined path and found the corresponding parameters [4, 5]. In this study, however, the decision variables are free at the optimization points and splines are used only after optimization, in the interpolation phase. Other studies have considered kinematic and skeletal dynamic human models in the planar frameworks without considering muscles [6,7]. The integration of the human arm model with muscle dynamics in the optimization has the merit of producing a clinically plausible solution. A direct-collocation method is used to solve the problem, with the help of an open-source optimization library in MATLAB called “OptimTraj” [8]. The decision variables are the robot states, and the HRI nonlinear dynamics is considered as the equality constraint: © 2020, Avestia Publishing. All rights reserved.",,,,,,,,,,,,,,,,,,"Meng, Q., Xie, Q., Deng, Z., Yu, H., A general kinematics model for trajectory planning of upper limb exoskeleton robots (2019) International Conference on Intelligent Robotics and Applications, 11745, pp. 63-75. , LNAI; Ghannadi, B., Mehrabi, N., McPhee, J., Development of a human-robot dynamic model to support model-based control design of an upper limb rehabilitation robot (2015) Barcelona: ECCOMAS Thematic Conference on Multibody Dynamics; Mehrabi, N., Razavian, R. S., McPhee, J., A Physics-Based Musculoskeletal Driver Model to Study Steering Tasks (2015) J. Comput. Nonlinear Dyn, 10 (2), pp. 1-8; Rosati, G., Volpe, G., Biondi, A., Trajectory planning of a two-link rehabilitation robot arm (2007) IFToMM World Congress; Kalani, H., Akbarzadeh, A., Nabavi, S. N., Moghimi, S., Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot (2018) Intell. Serv. Robot, 11 (2), pp. 187-205; Tang, Sh., Chen, L., Barsotti, M., Hu, L., Li, Y., Wu, X., Bai, L., Hou, W., Kinematic synergy of multi-DOF movement in upper limb and its application for rehabilitation exoskeleton motionplanning (2019) Frontiers in Neurorobotics, 13. , (November); Nagengast, A. J., Braun, D. A., Wolpert, D. M., Optimal control predicts human performance on objects with internal degrees of freedom,"" (2009) PLoS Comput Biol, 5 (6), p. e1000419; Kelly, M., An introduction to trajectory optimization: How to do your own direct collocation (2017) SIAM Rev, 59 (4), pp. 849-904; Berret, B., Chiovetto, E., Nori, F., Pozzo, T., Evidence for composite cost functions in arm movement planning: an inverse optimal control approach (2011) PLoS Comput Biol, 7 (10), p. e1002183",,,,"Avestia Publishing","7th International Conference on Control, Dynamic Systems, and Robotics, CDSR 2020","9 November 2020 through 11 November 2020",,253669,23685433,,,,"English","Int. Conf. Control Dyn. Syst. Robot.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100746222
"Yang B., Sun B., Zhang Q., Wang Y., Li Y., Yang J., Gu L.","57221124306;55413100100;7407964126;54390454800;56414362100;56176391300;57221126694;","Emotion Detection During Human-Robots Interaction in Visual Reality Environment",2020,"Communications in Computer and Information Science","1336",,,"79","92",,,"10.1007/978-981-33-4932-2_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098291423&doi=10.1007%2f978-981-33-4932-2_6&partnerID=40&md5=8da95a63b8486347fb30c27469c90c12","Shenyang University of Technology, Shenyang, 110870, China","Yang, B., Shenyang University of Technology, Shenyang, 110870, China; Sun, B., Shenyang University of Technology, Shenyang, 110870, China; Zhang, Q.; Wang, Y.; Li, Y.; Yang, J.; Gu, L.","With the increasing of aging and disability population, care robots are going to be an assistant for elderly and disabled people. However, it is not satisfied any more with just ordering them to carry out some simple assignments mechanically. It is desirable that the robot is capable to avoid negative emotions of objects, and make them more secure and comfortable feelings psychologically during the work process. In this paper, in order to explore how objective environmental factors influence subjective feelings independently, an emotion detecting and recognition platform was developed including a virtual-reality-interact immersive environment for inducing object emotions and an implemented physiological signal detection device in which physiological signals including Electrocardiograph (ECG), Electromyogram(EMG), Electrodermal activity (EDA), functional near-infrared spectroscopy (fNIRS) were recorded synchronously. Verification experiments were executed and the results showed its availabilities and efficiencies of the proposed emotion detection system. © 2020, Springer Nature Singapore Pte Ltd.","Emotion detection; Physiological signal; Virtual environment","Behavioral research; Infrared devices; Near infrared spectroscopy; Physiology; Robotics; Signal detection; Virtual reality; Detection device; Electrodermal activity; Emotion detection; Environmental factors; Functional near-infrared spectroscopy (fnirs); Immersive environment; Physiological signals; Visual realities; Human robot interaction",,,,,,,,,,,,,,,,"Zhou, Q., Multi-layer affective computing model based on emotional psychology (2017) Electronic Commerce Res, 18 (1), pp. 109-124. , https://doi.org/10.1007/s10660-017-9265-8; Reeves, B., Nass, C., The media equation: How people treat computers, television, and new media like real people and places (1996) Philos. Soc. Sci., 30 (1), pp. 120-124; Negi, S., Arai, T., Psychological assessment of humanoid robot appearance and performance using virtual reality (2008) Robot and Human Interactive Communication, pp. 718-719. , pp; Morales, Y., Miyashita, T., Social robotic wheelchair centered on passenger and pedestrian comfort (2017) Robot. Autonomous Syst., 87 (1), pp. 355-362; Gruebler, A., Berenz, V., Coaching robot behavior using continuous physiological affective feedback (2011) International Conference on Humanoid Robots, pp. 26-28. , pp; Sidney, M., The affective computing approach to affect measurement (2017) Emotion Rev, 10 (2), pp. 174-183; Deaudelin, C., Dussault, M., Human-computer interaction: A review of the research on its affective and social aspects (2003) Can. J. Learn. Technol., 29 (1), pp. 89-110; Schacter, D., Wegner, D., Emotion and motivation (2016) Psychology; Hagemann, D., Waldstein, S., Central and autonomic nervous system integration in emotion (2003) Brain Cogn, 52 (1), pp. 79-87; Verma, G., Tiwary, U., Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals (2014) Neuroimage, 10 (2), pp. 162-172; Stone, R., Small, C., Virtual natural environments for restoration and rehabilitation in healthcare (2014) Intell. Syst. Ref. Libr., 24 (1), pp. 497-521; Chirico, A., Cipresso, P., Effectiveness of immersive videos in inducing awe: An experimental study (2017) Sci. Rep., 7 (1), pp. 12-18; Ulrich, R., Natural versus urban scenes: Some psychophysiological effects (1981) Environ. Behav., 13 (5), pp. 523-556; Felnhofer, A., Kothgassner, O., Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios (2015) Int. J. Hum.-Comput. Stud., 8 (2), pp. 48-56; Mohammadpour, M., Hashemi, S.M.R., Houshmand, N., (2017) Classification of Eeg-Based Emotion for BCI Applications; Michiko, O., Yoshiki, K., E6(S)-2 measurement of fNIRS to estimate emotion in a VR system (2017) Japanese J. Ergon.; Wexler, B.E., Warrenburg, S., Schwartz, G.E., EEG and EMG responses to emotion-evoking stimuli processed without conscious awareness (1992) Neuropsychologia, 30 (12), pp. 1065-1079; Gouizi, K., Maaoui, C., Reguig, F.B., Negative emotion detection using EMG signal (2014) International Conference on Control. IEEE; Canento, F., Fred, A., Silva, H., Multimodal biosignal sensor data handling for emotion recognition (2011) IEEE, pp. 647-650. , pp","Yang, B.; Shenyang University of TechnologyChina; email: 1274670125@qq.com","Qian J.Liu H.Zhou D.Cao J.",,"Springer Science and Business Media Deutschland GmbH","1st International Conference on Robotics and Rehabilitation Intelligence, ICRRI 2020","9 September 2020 through 11 September 2020",,253409,18650929,9789813349315,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85098291423
"Tausch A., Kluge A.","57219250178;23397227500;","The best task allocation process is to decide on one’s own: effects of the allocation agent in human–robot interaction on perceived work characteristics and satisfaction",2020,"Cognition, Technology and Work",,,,"","",,,"10.1007/s10111-020-00656-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097901764&doi=10.1007%2fs10111-020-00656-7&partnerID=40&md5=1e47c426d12b72c0e92c6240258a042e","Scientific Management Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany","Tausch, A., Scientific Management Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Kluge, A., Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany","New technologies are ever evolving and have the power to change human work for the better or the worse depending on the implementation. For human–robot interaction (HRI), it is decisive how humans and robots will share tasks and who will be in charge for decisions on task allocation. The aim of this online experiment was to examine the influence of different decision agents on the perception of a task allocation process in HRI. We assume that inclusion of the worker in the allocation will create more perceived work resources and will lead to more satisfaction with the allocation and the work results than a decision made by another agent. To test these hypotheses, we used a fictional production scenario where tasks were allocated to the participant and a robot. The allocation decision was either made by the robot, by an organizational unit, or by the participants themselves. We then looked for differences between those conditions. Our sample consisted of 151 people. In multiple ANOVAs, we could show that satisfaction with the allocation process, the solution, and with the result of the work process was higher in the condition where participants themselves were given agency in the allocation process compared to the other two. Those participants also experienced more task identity and autonomy. This has implications for the design of allocation processes: The inclusion of workers in task allocation can play a crucial role in leveraging the acceptance of HRI and in designing humane work systems in Industry 4.0. © 2020, The Author(s).","Autonomy; Decision-making; Human–robot interaction (HRI); Task allocation; Task identity; Trust","Computer applications; Human computer interaction; Allocation decision; Allocation process; Decision agent; On-line experiments; Organizational units; Robot interactions; Task allocation; Work process; Human robot interaction",,,,,,,,,,,,,,,,"Abdallah, S., Lesser, V., Modeling task allocation using a decision theoretic model (2005) Proceedings of 4Th International Joint Conference on Autonomous Agents and Multiagent Systems, , AAMAS; Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R., Task planning for human–robot interaction (2005) Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence Innovative Context-Aware Services Usages and Technologies, p. 81. , https://doi.org/10.1145/1107548.1107574, Bailly G, Crowley JL, ACM, New York, NY; Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R., Toward human-aware robot task planning (2006) AAAI Spring Symposium: To Boldly Go Where No human–robot Team has Gone Before, pp. 39-46; Baker, M., Yanco, H.A., Autonomy mode suggestions for improving human–robot interaction (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 2948-2953. , IEEE; Bakker, A.B., Demerouti, E., Job demands-resources theory: taking stock and looking forward (2017) J Occup Health Psychol, 22, pp. 273-285; Brockner, J., Wiesenfeld, B.M., An integrative framework for explaining reactions to decisions: interactive effects of outcomes and procedures (1996) Psychol Bull, 120, p. 189; Bucknall, T., Thomas, S., Critical care nurse satisfaction with levels of involvement in clinical decisions (1996) J Adv Nurs, 23, pp. 571-577; Cheung, J.H., Sinclair, R.R., Shi, J., Wang, M., Do job demands of Chinese manufacturing employees predict positive or negative outcomes? A test of competing hypotheses (2015) Stress Health, 31, pp. 432-442; Cosenzo, K., Chen, J., Reinerman-Jones, L., Barnes, M., Nicholson, D., Adaptive automation effects on operator performance during a reconnaissance mission with an unmanned ground vehicle (2010) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 25, pp. 2135-2139. , SAGE Publications, Los Angeles, CA; Dabholkar, P.A., Sheng, X., Consumer participation in using online recommendation agents: effects on satisfaction, trust, and purchase intentions (2012) Serv Ind J, 32, pp. 1433-1449; Debernard, S., Vanderhaegen, F., Millot, P., An experimental investigation of dynamic allocation of tasks between air traffic controller and AI systems (1993) Analysis, design and evaluation of man-machine systems 1992, pp. 95-100. , Strassen HG, (ed), Elsevier, The Hague; Driscoll, J.W., Trust and participation in organizational decision making as predictors of satisfaction (1978) Acad Manag J, 21, pp. 44-56; Fantini, P., Pinzone, M., Sella, F., Taisch, M., Collaborative robots and new product introduction: Capturing and transferring human expert knowledge to the operators (2017) International Conference on Applied Human Factors and Ergonomics, pp. 259-268. , Springer; Fitts, P.M., (1951) Human Engineering for an Effective Air-Navigation and Traffic-Control System, , National Research Council, Division of Anthropology and Psychology, Committee on Aviation Psychology; Fitzsimons, G.J., Greenleaf, E.A., Lehmann, D.R., Decision and consumption satisfaction: Implications for channel relations (1997) Marketing Studies Center Working Paper Series, p. 313; Flemisch, F., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., Beller, J., Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations (2012) Cognit Technol Work, 14, pp. 3-18; Flemisch, F., Abbink, D.A., Itoh, M., Pacaux-Lemoine, M.-P., Weßel, G., (2019) Cognit Technol Work, 21, pp. 555-568; Freedy, A., Visser, E., Weltman, G., Coeyman, N., Measurement of trust in human–robot collaboration (2007) Proceedings of the 2007 International Conference on Collaborative Technologies and Systems, pp. 106-114. , IEEE, Orlando, FL; Frey, D., Jonas, E., Die Theorie der kognizierten Kontrolle [theory of cognized control] (2002) Theorien der Sozialpsychologie, Band III, pp. 13-50. , Frey D, Martin I, (eds), 3, Verlag Hans Huber, Bern; Gaede, C., Ranz, F., Echelmeyer, W., Hummel, V., A study on challenges in the implementation of human–robot collaboration (2018) J Eng Manag Oper, 1, pp. 29-39; Gombolay, M.C., Gutierrez, R.A., Clarke, S.G., Sturla, G.F., Shah, J.A., Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams (2015) Auton Robot, 39, pp. 293-312; Goodrich, M.A., Olsen, D.R., Crandall, J.W., Palmer, T.J., Experiments in adjustable autonomy (2001) Proceedings of IJCAI Workshop on Autonomy, Delegation and Control: Interacting with Intelligent Agents, pp. 1624-1629. , Seattle, WA; Habib, L., Pacaux-Lemoine, M., Millot, P., A method for designing levels of automation based on a human–machine cooperation model (2017) IFAC Pap Online, 50, pp. 1372-1377; Hacker, W., Sachse, P., (2014) Allgemeine Arbeitspsychologie: Psychische Regulation von Tätigkeiten [work psychology: psychological regualtion of tasks], , Hogrefe, Göttingen; Hackman, J.R., Oldham, G.R., Development of the job diagnostic survey (1975) J Appl Psychol, 60, p. 159; Hackman, J.R., Oldham, G.R., (1980) Work redesign, , Addison-Wesley, Reading; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., de Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human–robot interaction (2011) Hum Factors, 53, pp. 517-527; Hervé, M., Pavie, X., O'Keeffe, M., (2000) Valuing people to create value: an innovative approach to leveraging motivation at work, , World Scientific, Singapore; Hoc, J.-M., Lemoine, M.-P., Cognitive evaluation of human-human and human–machine cooperation modes in air traffic control (1998) Int J Aviat Psychol, 8, pp. 1-32; Hughes, R., Considering the vignette technique and its application to a study of drug injecting and HIV risk and safer behaviour (1998) Sociol Health Illn, 20, pp. 381-400; Inagaki, T., Itoh, M., Human’s overtrust in and overreliance on advanced driver assistance systems: a theoretical framework (2013) Int J Veh Technol, 2013, pp. 1-8; Jian, J.Y., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) Int J Cogn Ergon, 4, pp. 53-71; Kaber, D.B., Riley, J., Adaptive automation of a dynamic control task based on secondary task workload measurement (1999) Int J Cogn Ergon, 3, pp. 169-187; Keen, P.G., Decision support systems: A research perspective (1980) Decision Support Systems: Issues and Challenges: Proceedings of an International Task Force Meeting, pp. 23-44; Lantz, P.M., Satisfaction with surgery outcomes and the decision process in a population-based sample of women with breast cancer (2005) Health Serv Res, 40, pp. 745-767; Locke, E.A., Schweiger, D.M., Latham, G.P., Participation in decision making: when should it be used? (1986) Organ Dyn, 14, pp. 65-79; Michalos, G., Spiliotopoulos, J., Makris, S., Chryssolouris, G., A method for planning human robot shared tasks (2018) CIRP J Manuf Sci Technol, 22, pp. 76-90; Moray, N., Inagaki, T., Itoh, M., Adaptive automation, trust, and self-confidence in fault management of time-critical tasks (2000) J Exp Psychol Appl, 6, p. 44; Morgeson, F.P., Humphrey, S.E., The Work Design Questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the nature of work (2006) J Appl Psychol, 91, pp. 1321-1339; Munzer, T., Mollard, Y., Lopes, M., Impact of robot initiative on human–robot collaboration (2017) Paper Presented at the HRI 2017 - ACM/IEEE International Conference on human–robot Interaction, , Vienna; Nikolakis, N., Kousi, N., Michalos, G., Makris, S., Dynamic scheduling of shared human–robot manufacturing operations (2018) Proc CIRP, 72, pp. 9-14; Older, M.T., Waterson, P.E., Clegg, C.W., A critical assessment of task allocation methods and their applicability (1997) Ergon, 40, pp. 151-171; Onnasch, L., Wickens, C.D., Li, H., Manzey, D., Human performance consequences of stages and levels of automation: an integrated meta-analysis (2014) Hum Factors, 56, pp. 476-488; Parasuraman, R., Sheridan, T.B., Wickens, C.D., A model for types and levels of human interaction with automation (2000) IEEE Trans Syst Man Cybern Part A Syst Hum, 30, pp. 286-297; Rajaonah, B., Tricot, N., Anceaux, F., Millot, P., The role of intervening variables in driver–ACC cooperation (2008) Int J Hum Comput Stud, 66, pp. 185-197; Romero, D., Noran, O., Stahre, J., Bernus, P., Fast-Berglund, Å., Towards a human-centred reference architecture for next generation balanced automation systems: Human-automation symbiosis (2015) IFIP International Conference on Advances in Production Management Systems, Springer, pp. 556-566; Rücker, D., Hornfeck, R., Paetzold, K., Investiagting ergonomics in the context of human–robot collaboration as a sociotechnical system (2018) Paper Presented at the Advances in Human Factors in Robots and Unmanned Systems, , AHFE; Ryan, J.C., Banerjee, A.G., Cummings, M.L., Roy, N., Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations (2014) IEEE Trans Cybern, 44, pp. 761-773; Rystedt, L.W., Devereux, J., Sverke, M., Comparing and combining the demand-control-support model and the effort reward imbalance model to predict long-term mental strain (2007) Eur J Work Organ Psychol, 16, pp. 261-278; Schmidt, K., Cooperative work and its articulation: requirements for computer support (1994) Trav Hum, 57, pp. 345-366; Schmidtler, J., Knott, V., Hölzel, C., Bengler, K., Human centered assistance applications for the working environment of the future (2015) Occup Ergon, 12, pp. 83-95; Shaw, J.C., Wild, E., Colquitt, J.A., To justify or excuse?: a meta-analytic review of the effects of explanations (2003) J Appl Psychol, 88, p. 444; Sheridan, T.B., Supervisory control of remote manipulators, vehicles and dynamic processes: experiments in command and display aiding (1984) Advances in man-machine systems research, pp. 49-137. , Rouse WB, (ed), JAI Press, New York; Sims, H.P., Szilagyi, A.D., Keller, R.T., The measurement of job characteristics (1976) Acad Manag J, 19, pp. 195-212; Slemp, G.R., Vella-Brodrick, D.A., Optimising employee mental health: the relationship between intrinsic need satisfaction, job crafting, and employee well-being (2013) J Happiness Stud, 15, pp. 957-977; Spector, P.E., Perceived control by employees: a meta-analysis of studies concerning autonomy and participation at work (1986) Hum Relat, 39, pp. 1005-1016; Stanton, N.A., Salmon, P.M., Walker, G.H., Baber, C., Jenkins, D.P., (2005) Human factors methods. A practical guide for engineering and design, , Ashgate, Farnham; Stegmann, S., van Dick, R., Ullrich, J., Charalambous, J., Menzel, B., Egold, N., Wu, T.T.-C., Der Work Design Questionnaire [the work design questionnaire] (2010) Zeitschrift für Arbeits- und Organisationspsychologie, 54, pp. 1-28; Thielsch, M.T., Meeßen, S.M., Hertel, G., Trust and distrust in information systems at the workplace (2018) PeerJ, 6; Trist, E., The evolution of socio-technical systems: A conceptual framework and an action research program (1981) Occas Pap, 2. , http://sistemas-humano-computacionais.wdfiles.com/local--files/capitulo%3Aredes-sociotecnicas/Evolution_of_socio_technical_systems.pdf; Tsarouchi, P., Matthaiakis, A.-S., Makris, S., Chryssolouris, G., On a human–robot collaboration in an assembly cell (2017) Int J Comput Integr Manuf, 30, pp. 580-589; van Den Steen, E., (2006) The limits of authority: Motivation versus coordination, , MIT Sloan Research Paper No 4626-06; Wieland, R., Hammes, M., Wuppertaler Screening Instrument Psychische Beanspruchung (WSIB) – Beanspruchungsbilanz und Kontrollerleben als Indikatoren für gesunde Arbeit (2014) J Psychol Alltagshandelns, 7, pp. 30-50; Williams, L.A., DeSteno, D., Pride and perseverance: the motivational role of pride (2008) J Personal Soc Psychol, 94, pp. 1007-1017; Zhang, S., Fitzsimons, G.J., Choice-process satisfaction: the influence of attribute alignability and option limitation (1999) Organ Behav Hum Decis Process, 77, pp. 192-214","Tausch, A.; Scientific Management Products and Work Systems, Germany; email: Tausch.alina@baua.bund.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,14355558,,,,"English","Cogn. Technol. Work",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85097901764
"Huang Z., Miyauchi G., Gomez A.S., Bird R., Kalsi A.S., Jansen C., Liu Z., Parsons S., Sklar E.","57195285915;57216351940;57220899465;57216345422;57216334497;54791016500;57216340745;57203087931;7003818615;","An Experiment on Human-Robot Interaction in a Simulated Agricultural Task",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12228 LNAI",,,"221","233",,1,"10.1007/978-3-030-63486-5_25","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097844590&doi=10.1007%2f978-3-030-63486-5_25&partnerID=40&md5=ebc12a30069251f09303c7e7084fa198","King’s College London, London, United Kingdom; University of Sheffield, Sheffield, United Kingdom; University of Lincoln, Lincoln, United Kingdom","Huang, Z., King’s College London, London, United Kingdom; Miyauchi, G., University of Sheffield, Sheffield, United Kingdom; Gomez, A.S., King’s College London, London, United Kingdom; Bird, R., King’s College London, London, United Kingdom; Kalsi, A.S., King’s College London, London, United Kingdom; Jansen, C., King’s College London, London, United Kingdom; Liu, Z., King’s College London, London, United Kingdom; Parsons, S., King’s College London, London, United Kingdom, University of Lincoln, Lincoln, United Kingdom; Sklar, E., King’s College London, London, United Kingdom, University of Lincoln, Lincoln, United Kingdom","On the farm of the future, a human agriculturist collaborates with both human and automated labourers in order to perform a wide range of tasks. Today, changes in traditional farming practices motivate robotics researchers to consider ways in which automated devices and intelligent systems can work with farmers to address diverse needs of farming. Because farming tasks can be highly specialised, though often repetitive, a human-robot approach is a natural choice. The work presented here investigates a collaborative task in which a human and robot share decision making about the readiness of strawberries for harvesting, based on visual inspection. Two different robot behaviours are compared: one in which the robot provides decisions with more false positives and one in which the robot provides decisions with more false negatives. Preliminary experimental results conducted with human subjects are presented and show that the robot behaviour with more false positives is preferred in completing this task. © 2020, Springer Nature Switzerland AG.","Agriculture; Computer vision; Human-robot collaboration; Machine learning","Agricultural robots; Agriculture; Decision making; Intelligent systems; Robotics; Automated device; Collaborative tasks; False negatives; False positive; Farming practices; Human robots; Human subjects; Visual inspection; Human robot interaction",,,,,"China Scholarship Council, CSC","Supported by China Scholarship Council.",,,,,,,,,,"The Common Agricultural Policy and Agriculture in Europe FAQ. European Commission (Jun 2013), , https://europa.eu/rapid/press-releaseMEMO-13-631en.htm; Bargoti, S., Underwood, J., Deep fruit detection in orchards (2017) IEEE International Conference on Robotics and Automation; Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., Huang, T., Revisiting RCNN: On awakening the classification power of faster RCNN (2018) ECCV 2018. LNCS, 11219, pp. 473-490. , Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.), pp., Springer, Cham, https://doi.org/10.1007/978-3-030-01267-0 28; Chernova, S., Veloso, M., Interactive policy learning through confidence-based autonomy (2009) J. Artif. Intell. Res., 34, pp. 1-25; Dong, J., Zheng, L., Quality classification of Enoki mushroom caps based on CNN. In: IEEE 4th International Conference on Image (2019) Vision and Computing; Girshick, R., Fast R-CNN (2015) IEEE International Conference on Computer Vision; Hughes, J., Scimeca, L., Ifrim, I., Maiolino, P., Iida, F., Achieving robotically peeled lettuce (2018) IEEE Robot. Autom. Lett., 3 (4), pp. 4337-4342; Kahneman, D., Tversky, A., Prospect theory: An analysis of decision under risk (2013) Handbook of the Fundamentals of Financial Decision Making: Part I, , World Scientific; Kang, H., Chen, C., Fast implementation of real-time fruit detection in apple orchards using deep learning (2020) Comput. Electron. Agric., 168; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization, , CoRR; Koenig, N., Matarić, M.J., Robot life-long task learning from human demonstrations: A Bayesian approach (2017) Auton. Robots, 41 (5), pp. 1173-1188; Lamb, N., Chuah, M.C., A strawberry detection system using convolutional neural networks (2018) IEEE International Conference on Big Data; Likert, R., A technique for the measurement of attitudes (1932) Arch. Psychol., 140, pp. 44-55; (2017) Contributions to UK Agriculture, , www.nfuonline.com/assets/93419, Feb; Pinto, C., Furukawa, J., Fukai, H., Tamura, S., (2017) Classification of Green Coffee Bean Images Basec on Defect Types Using Convolutional Neural Network, , IEEE International Conference on Advanced Informatics, Concepts, Theory, and Applications; Quigley, M., (2009) ROS: An Open-Source Robot Operating System. In: ICRA OSS Workshop; Redmon, J., Farhadi, A., YOLO9000: Better, faster, stronger (2017) IEEE Conference on Computer Vision and Pattern Recognition; Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement. Arxiv, 1804, p. 02767; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards real-time object detection with region proposal networks (2015) Advances in Neural Information Processing Systems; Rogatko, A., Litwin, S., Phase II studies: Which is worse, false positive or false negative? (1996) J. Natl Cancer Inst., 88 (7), p. 462; Rosenthal, S., Biswas, J., Veloso, M., An effective personal mobile robot agent through symbiotic human-robot interaction (2010) 9Th International Joint Conference on Autonomous Agents and Multi-Agent Systems; Santos, T.T., de Souza, L.L., Dos Santos, A.A., Avila, S., Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association (2020) Comput. Electron. Agric., 170; Schneider, E., Sklar, E.I., Parsons, S., Özgelen, A.T., Auction-based task allocation for multi-robot teams in dynamic environments (2015) TAROS 2015. LNCS (LNAI), 9287, pp. 246-257. , https://doi.org/10.1007/978-3-319-22416-929, Dixon, C., Tuyls, K. (eds.), pp., Springer, Cham; Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., Liang, Z., Apple detection during different growth stages in orchards using the improved YOLOv3 model (2019) Comput. Electron. Agric., 157, pp. 417-426; Yu, Y., Zhang, K., Yang, L., Zhang, D., Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN (2019) Comput. Electron. Agric., 163","Huang, Z.; King’s College LondonUnited Kingdom; email: zhuoling.huang@kcl.ac.uk","Mohammad A.Dong X.Russo M.",,"Springer Science and Business Media Deutschland GmbH","21th Annual Conference on Towards Autonomous Robotics, TAROS 20120","16 September 2020 through 16 September 2020",,252749,03029743,9783030634858,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097844590
"Hoffman G., Zhao X.","35726707500;57189046346;","A Primer for Conducting Experiments in Human-Robot Interaction",2020,"ACM Transactions on Human-Robot Interaction","10","1","3412374","","",,3,"10.1145/3412374","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097575448&doi=10.1145%2f3412374&partnerID=40&md5=e0bc881c561433f2f288a65c8545289f","Cornell University, Ithaca, NY, United States; University of Chicago, Chicago, IL, United States","Hoffman, G., Cornell University, Ithaca, NY, United States; Zhao, X., University of Chicago, Chicago, IL, United States","We provide guidelines for planning, executing, analyzing, and reporting hypothesis-driven experiments in Human-Robot Interaction (HRI). The intended audience are researchers in the field of HRI who are not trained in empirical research but who are interested in conducting rigorous human-participant studies to support their research. Following the chronological order of research activities and grounded in updated research practices in psychological and behavioral sciences, this primer covers recommended methods and common pitfalls for defining research questions, identifying constructs and hypotheses, choosing appropriate study designs, operationalizing constructs as variables, planning and executing studies, sampling, choosing statistical tools for data analysis, and reporting results. © 2020 Owner/Author.","Experimental studies; research methods; statistical analysis","Agricultural robots; Behavioral research; Man machine systems; Robot programming; Sampling; Statistical mechanics; Behavioral science; Chronological order; Empirical research; Human robot Interaction (HRI); Research activities; Research questions; Statistical tools; Study design; Human robot interaction",,,,,,,,,,,,,,,,"Acemoglu, D., Restrepo, P., Robots and jobs: Evidence from US labormarkets (2020) Journal of Political Economy, 128 (6), pp. 2188-2244; Admoni, H., Dragan, A., Srinivasa, S.S., Scassellati, B., Deliberate delays during robot-to-human handovers improve compliance with gaze communication (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction. 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Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85097575448
"Kopp T., Baumgartner M., Kinkel S.","57201388519;57218326101;6701428946;","Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework",2020,"International Journal of Advanced Manufacturing Technology",,,,"","",,7,"10.1007/s00170-020-06398-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097442312&doi=10.1007%2fs00170-020-06398-0&partnerID=40&md5=793ccebc27f39a00c7bb2f6f28b10792","Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Kopp, T., Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany; Baumgartner, M., Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany; Kinkel, S., Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies (n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. Finally, we argue for more application-oriented research that focuses on practically relevant factors to guide HRI research, inform cobot development, and support companies in overcoming apparent barriers. © 2020, The Author(s).","Collaborative robots; Empirical research; Framework; Human-robot collaboration; Industrial human-robot interaction; Success factors","Electronic assessment; Employment; Industrial robots; Man machine systems; Manufacture; Occupational risks; Personnel; Application scenario; Collaborative robots; Comprehensive two-dimensional; Human robot Interaction (HRI); Internal communications; Manufacturing companies; Practical importance; Technology-related factors; Social robots",,,,,,,,,,,,,,,,"(2019), World Robotics Report 2019 - Industrial Robots, Frankfurt; Steil, J.J., Maier, G.W., Kollaborative Roboter: universale Werkzeuge in der digitalisierten und vernetzten Arbeitswelt (2017) Handbuch Gestaltung digitaler und vernetzter Arbeitswelten, Living reference work, continuously updated edition, 57, pp. 1-24. , Maier GW, Engels G, Steffen E, (eds), Springer, Berlin; Schou, C., Madsen, O., A plug and produce framework for industrial collaborative robots (2017) Int J Adv Robot Syst, 14, pp. 1-10; Müller-Abdelrazeq, S.L., Schönefeld, K., Haberstroh, M., Hees, F., Interacting with collaborative robots—a study on attitudes and acceptance in industrial contexts (2019) SOCIAL ROBOTS: Technological, societal and ethical aspects of human-robot, 4, pp. 101-117. , Korn O, (ed), SPRINGER NATURE, Berlin; Giffi, C., Wellener, P., Dollar, B., (2018) 2018 Deloitte and The Manufacturing Institute skills gap and future of work study, , Deloitte Insights, London; Wang, Y., Ma, H.-S., Yang, J.-H., Wang, K.S., Industry 4.0: a way from mass customization to mass personalization production (2017) Adv Manuf, 5, pp. 311-320; Görke, M., Blankemeyer, S., Pischke, D., Oubari, A., Raatz, A., Nyhuis, P., Sichere und akzeptierte Kollaboration von Mensch und Maschine (2017) ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 112, pp. 41-45; Charalambous, G., Fletcher, S.R., Webb, P., The development of a Human Factors Readiness Level tool for implementing industrial human-robot collaboration (2017) Int J Adv Manuf Technol, 91, pp. 2465-2475; Meißner, A., Trübswetter, A., Mensch-Roboter-Kollaboration in der Produktion: Kritische Würdigung etablierter Technikakzeptanzmodelle und neue Erkenntnisse in der Akzeptanzforschung (2018) Technische Unterstützungssysteme, Die Die Menschen Wirklich Wollen: Dritte transdisziplinäre Konferenz, pp. 223-233. , Weidner R, Karafillidis A, Helmut-Schmidt-Universität, Hamburg; Vincent, J., Taipale, S., Sapio, B., (2015) Social robots from a human perspective, , Springer International Publishing, Cham; Savela, N., Turja, T., Oksanen, A., Social acceptance of robots in different occupational fields: a systematic literature review (2018) Int J Soc Robot, 10, pp. 493-502; Horstmann, A.C., Bock, N., Linhuber, E., Szczuka, J.M., Straßmann, C., Krämer, N.C., Do a robot's social skills and its objection discourage interactants from switching the robot off? 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In:, Singapore, pp","Kopp, T.; Institute for Learning and Innovation in Networks, Moltkestraße 30, Germany; email: tobias.kopp@hs-karlsruhe.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85097442312
"Permatasari D.A., Fakhrurroja H., Machbub C.","57209274119;55869180100;56619583100;","Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method",2020,"International Journal on Advanced Science, Engineering and Information Technology","10","5",,"1881","1888",,3,"10.18517/ijaseit.10.5.7606","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097429165&doi=10.18517%2fijaseit.10.5.7606&partnerID=40&md5=bbf3503012f556275c70f426da786790","School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia","Permatasari, D.A., School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia; Fakhrurroja, H., School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia; Machbub, C., School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia","Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management. © xx xx","cosine similarity; dialogue manager; finite state machine; Google cloud speech; human-robot interaction; TF-IDF",,,,,,,"This work was supported by the Post Graduate Team Research 2018 from the Ministry of Research, Technology and Higher Education, Republic of Indonesia.",,,,,,,,,,"Bui, T. H., (2006) Multimodal Dialogue Management - State of the art; Maharani, D. A., Fakhrurroja, H., Riyanto, Machbub, C., Hand gesture recognition using K-means clustering and Support Vector Machine (2018) ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics, pp. 1-6; Meng, L., Huang, M., Dialogue intent classification with long short-term memory networks (2018) Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 10619, pp. 42-50. , LNAI; Hsieh, M. C., Hung, W. S., Lin, S. W., Luo, C. H., Designing an assistive dialog agent for a case of spinal cord injury (2009) Proceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009; Sadohara, K., Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities (2013) Proc. Fourth Work. Speech Lang. Process. Assist. Technol, pp. 93-98; Schwarzler, S., Schenk, J., Ruske, G., Wallhoff, F., A multi-agent framework for a hybrid dialog management system (2009) 2009 IEEE International Conference on Multimedia and Expo, pp. 958-961; Holzapfel, H., A dialogue manager for multimodal human-robot interaction and learning of a humanoid robot (2008) Ind. Rob, 35 (6), pp. 528-535; Lee, C., Cha, Y. S., Kuc, T. Y., Implementation of dialogue system for intelligent service robots (2008) 2008 International Conference on Control, Automation and Systems, ICCAS 2008; Raux, A., Eskenazi, M., A Finite-State Turn-Taking Model for Spoken Dialog Systems (2009) Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL; Yi, S., Jung, K., A Chatbot by Combining Finite State Machine, Information Retrieval, and Bot-Initiative Strategy (2017) Alexa Price Proc, pp. 1-10; Su, B., Kuan, T., Tseng, S., Wang, J., Su, P., Improved TF-IDF weight method based on sentence similarity for spoken dialogue system (2016) 2016 International Conference on Orange Technologies (ICOT), pp. 36-39; Lee, C., Jung, S., Kim, S., Lee, G. G., Example-based dialog modeling for practical multi-domain dialog system (2009) Speech Commun; Fakhrurroja, H., Permatasari, D. A., Purwarianti, A., Machbub, C., Dialogue Management for Human Robot Interaction Using Artificial Intelligence Markup Language (2018) ICEECS 2018 International Conference on Electrical Engineering and Computer Science; Zhang, X., LeCun, Y., Character-level Convolutional Networks for Text Classificatio (2015) Advances in Neural Information Processing Systems, 28; Petcu, D., Craciun, C., Rak, M., Towards a Cross Platform Cloud API - Components for Cloud Federation (2011) CLOSER; Google Speech API (2017) Google Cloud Platform, , Google; Assefi, M., Liu, G., Wittie, M. P., Izurieta, C., An Experimental Evaluation of Apple Siri and Google Speech Recognition (2015) Proccedings 2015 ISCA SEDE; Chiu, C. C., State-of-the-Art Speech Recognition with Sequence-to-Sequence Models (2018) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Manning, C. D., Raghavan, P., Schutze, H., (2009) An Introduction to Information Retrieval, , Cambridge University Press; Agarwal, N., Rawat, M., Vijay, M., Comparative Analysis Of Jaccard Coefficient and Cosine Similarity for Web Document Similarity Measure (2014) Int. J. Adv. Res. Eng. Technol; Gomaa, H., (2016) Real-Time Software Design For Embedded Systems, , New York: Cambridge University Press",,,,"Insight Society",,,,,20885334,,,,"English","Int. J. Adv. Sci. Eng. Inf. Technol.",Article,"Final","",Scopus,2-s2.0-85097429165
"Nguyen Van T., Yi S.-Y., Bui Khoi P.","56811211200;7201404755;57201260426;","Hedge algebras-based admittance controller for safe natural human–robot interaction",2020,"Advanced Robotics","34","24",,"1546","1558",,1,"10.1080/01691864.2020.1852958","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097415163&doi=10.1080%2f01691864.2020.1852958&partnerID=40&md5=69f4cfc9e35967836cf0a3d59bd7c37f","Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea; School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam","Nguyen Van, T., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea; Yi, S.-Y., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea; Bui Khoi, P., School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam","In physical human–robot interaction (pHRI), the identification of inertia and damping matrices in the dynamic admittance model is still an open problem. Besides, the natural interaction is rarely considered in previous studies while it is crucial to obtain the effective cooperation. To this end, a fuzzy-based admittance controller is presented, in which the end-effector's velocity is adaptively adjusted via the external wrench and transmitted power without the identification of inertia and damping matrices. Besides, this fuzzy-based admittance controller also guarantees the natural cooperation between human and robot. Unfortunately, there is no formulated linkage of the fuzzy sets with the natural linguistic term semantics. As a consequence, human experts must utilize the order relationships between the terms of interest when formulating the fuzzy rule-based knowledge of the fuzzy controller. This paper presents an alternative admittance controller for pHRI based on an algebraic approach to linguistic hedges in fuzzy logic to overcome the existing shortcomings of previous admittance controllers. In addition, this paper also considers end-effector's full degree of freedom to guarantee the natural human–robot interaction. The proposed admittance controller is experimentally evaluated on a teaching task set-up using 6-DOF manipulator. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","admittance control; fuzzy control; Hedge algebras; natural linguistic semantics; physical human–robot interaction","Agricultural robots; Controllers; Damping; Degrees of freedom (mechanics); End effectors; Fuzzy inference; Fuzzy logic; Matrix algebra; Semantics; Wooden fences; Algebraic approaches; Degree of freedom; Fuzzy controllers; Fuzzy rule based; Linguistic hedges; Natural interactions; Robot interactions; Transmitted power; Social robots",,,,,"National Research Foundation of Korea, NRF: NRF-2018R1D1A1B07044841","This research was partly supported by the research program funded by the National Research Foundation of Korea [grant number NRF-2018R1D1A1B07044841].",,,,,,,,,,"(2011) Robot for industrial environments–safety requirements–part 1: robot, technical report, , Geneva, Switzerland: International Organization for Standardization; Navarro, B., Cherubini, A., Fonte, A., An ISO10218-compliant adaptive damping controller for safe physical human-robot interaction 2016 IEEE International Conference on Robotics and Automation (ICRA), , Stockholm, Sweden; 2016. p. 3043–3048; Jindai, M., Watanabe, T., A handshake robot system based on a shake-motion leading model 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Nice, France; 2008. p. 3330–3335; Kovács, S., Vincze, D., Gacsi, M., Fuzzy automaton based human-robot interaction SAMI 2010 8th IEE International Symposium on Applied Machine Intelligence and Informatics, , Herlany, Slovakia; 2010. p. 165–169; Kovács, S., Vincze, D., Gacsi, M., Interpolation based fuzzy automaton for human-robot interaction (2009) 9th IFAC Symposium on Robot Control, 42 (16), pp. 317-322; Papageorgiou, D., Doulgeri, Z., A Kinematic controller for human-robot handshaking using internal motion adaption 2015 IEEE International Conference on Robotics and Automation, , WA, USA; 2015. p. 5622–5627; Dimeas, F., Mouolianitis, V., Papakonstantinou, C., Manipulator performance constraints in Cartesian admittance control for human-robot cooperation 2016 IEEE International Conference on Robotics and Automation (ICRA), , Stockholm, Sweden; 2016. p. 3049–3054; Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans Syst Man Cybern, SMC-15 (1), pp. 116-132; Mamdani, E., Twenty years of fuzzy control: experiences gained and lessons learnt IEEE International Conference on Fuzzy Systems, , San Francisco, CA, USA; 1993. p. 339–344; Wang, L., Stable adaptive fuzzy control of nonlinear systems (1993) IEEE Trans Fuzzy Syst, 1 (2), pp. 146-155; Wang, L., (1997) A course in fuzzy systems and control, , Upper Saddle River (NJ: Prentice-Hall; Su, C., Stepanenko, Y., Adaptive control of a class of nonlinear systems with fuzzy logic (1994) IEEE Trans Fuzzy Syst, 2 (4), pp. 285-294; Yang, Y.S., Jia, X.L., Zhou, C.J., Robust adaptive fuzzy control for a class of uncertain nonlinear systems 4th International Conference of Intelligent Techniques Soft Computing Nuclear Science Engineering, , Bruges, Belgium; 2000. p. 303–311; Dimeas, F., Aspragathos, N., Fuzzy learning variable admittance control for human-robot cooperation 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, , Chicago (IL), USA; 2014. p. 4770–4775; Jamil, M., Jalani, J., Ahmad, A., A new approach of active compliance control via fuzzy logic control for multifingered robot hand First International Conference on Intelligent Robots and Systems, , 2014. p. 4770–4775; Khoi, P., Toan, N., Application of fuzzy logic for controlling mechanism of relative manipulation robot (MRM robot) (2016) J Sci Technol, 54 (3), pp. 385-401; Toan, N., Khoi, P., A control solution for closed-form mechanisms of relative manipulation based on fuzzy approach (2019) Int J Adv Rob Syst, 16 (2), pp. 1-11; Toan, N., Kim, J., Kim, K., Application of fuzzy logic to damping controller for safe human-robot interaction 14th International Conference on Ubiquitous Robots and Ambient intelligence, , Jeju, South Korea; 2017. p. 109–113; Toan, N., Khoi, P., Fuzzy-based admittance controller for safe natural human-robot interaction (2019) Adv Robot, 33 (15-16), pp. 815-823; Ho, N., Wechler, W., Hedge algebras: An algebraic approach to structure of sets of linguistic truth values (1990) Fuzzy Sets Syst, 35 (3), pp. 281-293; Ho, N., Wechler, W., Extended hedge algebras and their application to fuzzy logic (1992) Fuzzy Sets Syst, 52 (3), pp. 259-281; Ho, N., Nam, H., An algebraic approach to linguistic hedges in Zadeh’s fuzzy logic (2002) Fuzzy Sets Syst, 129 (2), pp. 229-254; Ho, N., Tran, T., Pham, D., Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application (2014) Knowl Based Syst, 67, pp. 244-262; Khoi, P., Toan, N., Hedge-algebras-based controller for mechanisms of relative manipulation (2018) Int J Prec Eng Manuf, 19 (3), pp. 377-385; Phong, P., Dong, D., Khang, T., Hedge algebra based type-2 fuzzy logic system and its application to predict survival time of myeloma patients International Conference on Knowledge and Systems Engineering, , 2009. p. 13–18; Bui, H., Tran, D., Vu, N., Optimal fuzzy control of an inverted pendulum (2012) J Vib Control, 18 (14), pp. 2097-2110; Bui, H., Nguyen, C., Bui, V., Vibration control of uncertain structures with actuator saturation using hedge-algebras-based fuzzy controller (2015) J Vib Control, 23 (12), pp. 1-19; Duc, N., Vu, N., Tran, D., A study on the application of hedge algebras to active fuzzy control of a seism-excited structure (2012) J Vib Control, 18 (14), pp. 2186-2200; Nguyen, C., Pedrycz, W., Duong, T., A genetic design of linguistic terms for fuzzy rule based classifiers (2013) Int J Approx Reason, 54 (1), pp. 1-21; Vukadinović, D., Bašić, M., Nguyen, C., Hedge-algebra-based voltage controller for a self-excited induction generator (2014) Control Eng Pract, 30, pp. 78-90; Ali, O., Ali, A., Sumait, B., Comparison between the effects of different types of membership functions on fuzzy logic controller performance (2015) Int J Emerging Eng Res Technol, 3 (3), pp. 76-83; Naaz, S., Alam, A., Biswas, R., Effect of different defuzzification methods in a fuzzy based load balancing application (2011) Int J Comput Sci Issues, 8 (1), pp. 261-267; Buss, S., (2003) 3D computer graphics: A mathematical introduction with OpenGL, , New York (NY: Cambridge University Press; Tsumugiwa, T., Yokogawa, R., Yoshida, K., Stability analysis for impedance control of robot for human-robot cooperative task system 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, , Sendai, Japan; 2004. p. 3883–3888; Mussa-Ivaldi, F., Hogan, N., Bizzi, E., Neural, mechanical, and geometric factors subserving arm posture in humans (1985) J Neurosci, 5 (10), pp. 2732-2743; Phan, K.B., Ha, H.T., Hoang, S.V., Eliminating the effect of uncertainties of cutting forces by fuzzy controller for robots in millings process (2020) Appl Sci, 10 (5), p. 1685; Cat, H.N., Nhu, L.V., Le Xuan, V., An Interpolative reasoning method based on hedge algebras and its application to a problem of fuzzy control Proceedings of the 10th WSEAS International Conference on Computers, Vouliagmeni, , Athens, Greece, July 13–15, 2006, 526–534; Ho, N.C., Lan, V.N., Viet, L.X., Optimal hedge-algebras-based controller: Design and application (2008) Fuzzy Sets Syst, 159, pp. 968-989","Bui Khoi, P.; School of Mechanical Engineering, Viet Nam; email: khoi.phanbui@hust.edu.vn",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85097415163
"Feil-Seifer D., Haring K.S., Rossi S., Wagner A.R., Williams T.","14033850500;55639550500;55619320887;7401456914;7404172627;","Where to Next the Impact of COVID-19 on Human-Robot Interaction Research",2020,"ACM Transactions on Human-Robot Interaction","10","1","3405450","","",,26,"10.1145/3405450","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097195243&doi=10.1145%2f3405450&partnerID=40&md5=4ab65f200571ff4bd776e18d0a79ddd6","University of Nevada, Reno, Italy","Feil-Seifer, D., University of Nevada, Reno, Italy; Haring, K.S., University of Nevada, Reno, Italy; Rossi, S., University of Nevada, Reno, Italy; Wagner, A.R., University of Nevada, Reno, Italy; Williams, T., University of Nevada, Reno, Italy","The COVID-19 pandemic will have a profound and long-lasting impact on the entire scientific endeavor. Scientists already are adapting research programs to adapt to changes in what is prioritized - and what is possible; educators are changing the way that the next generation of researchers are trained, and flagship conferences in many fields are being cancelled, postponed, and fundamentally transformed. These broad-reaching changes are particularly impactful to human-oriented domains such as human-robot interaction (HRI). Because in-person human-subject experiments can take a year or more to conduct, the research we will see published in the field in the immediate future may appear to be ""business as usual,""with accounts of laboratory studies with large numbers of in-person participants. The research currently being performed, however, is of course a different story entirely. Studies that were under way when the current crisis began will be truncated, resulting either in work that cannot be published or in work whose true impact is difficult to accurately assess. Yet HRI research performed in the coming years will be changed in fundamentally different ways; the inability to perform - or expect future performance of - in-person human subjects research, especially research involving tactile or multiparty interaction, will change both the dominant methodological techniques employed by HRI researchers and the very research questions that the field chooses to - and is able to - address. These challenges demand that HRI researchers identify precisely how the field can maintain research quality and impact while the ability to conduct human-subject studies is severely impaired for an undetermined amount of time. A natural inclination may be simply to wait the crisis out in the hope of a speedy return to normalcy; however, in this article, we argue that the community can also take this opportunity to reevaluate and refocus how research in this field is conducted and how students are mentored in ways that will yield benefits for years to come after the current crisis has ended. © 2020 Owner/Author.","COVID-19 impact; Human-robot interaction; research","Agricultural robots; Man machine systems; Business-as-usual; Future performance; Human robot Interaction (HRI); Human subject experiments; Laboratory studies; Multi-party interactions; Research programs; Research questions; Human robot interaction",,,,,,,,,,,,,,,,"Aust, F., Diedenhofen, B., Ullrich, S., Musch, J., Seriousness checks are useful to improve data validity in online research (2013) Behavior Research Methods, 45 (2), pp. 527-535; Carlson, Z., Lemmon, L., Higgins, M., Frank, D., Salek Shahrezaie, R., Feil-Seifer, D., Perceived mistreatment and emotional capability following aggressive treatment of robots and computers (2019) International Journal of Social Robotics, 11 (5), pp. 727-739. , https://doi.org/10.1007/s12369-019-00599-8, (Oct. 2019); Cesta, A., Cortellessa, G., Orlandini, A., Tiberio, L., Long-term evaluation of a telepresence robot for the elderly: Methodology and ecological case study (2016) International Journal of Social Robotics, 8, pp. 421-441. , https://doi.org/10.1007/s12369-016-0337-z; Visser De, E.J., Peeters, M.M.M., Jung, M.F., Kohn, S., Shaw, T.H., Pak, R., Neerincx, M.A., Towards a theory of longitudinal trust calibration in human-robot teams (2020) International Journal of Social Robotics, 12, pp. 459-478; Edwards, C., Edwards, A., Spence, P.R., Westerman, D., Initial interaction expectations with robots: Testing the human-to-human interaction script (2016) Communication Studies, 67 (2), pp. 227-238; Fitter, N.T., Raghunath, N., Cha, E., Sanchez, C.A., Takayama, L., Mataric, M.J., Are we there yet Comparing remote learning technologies in the university classroom (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 2706-2713; Sophie Haring, K., Watanabe, K., Silvera-Tawil, D., Velonaki, M., Matsumoto, Y., Touching an Android robot: Would you do it and how (2015) Proceedings of the 2015 International Conference on Control, Automation, and Robotics, pp. 8-13. , IEEE, Los Alamitos, CA; Blake Jackson, R., Williams, T., Smith, N., Exploring the role of gender in perceptions of robotic noncompliance (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction., pp. 559-567; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., An affective guide robot in a shopping mall (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction., pp. 173-180; Kwon, M., Biyik, E., Talati, A., Bhasin, K., Losey, D.P., Sadigh, D., When humans aren't optimal: Robots that collaborate with risk-aware humans (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI'20). ACM, pp. 43-52. , https://doi.org/10.1145/3319502.3374832, New York, NY; Picard, R.W., (2000) Affective Computing, , MIT Press, Cambridge, MA; Pu, L., Moyle, W., Jones, C., Todorovic, M., The effectiveness of social robots for older adults: A systematic review and meta-analysis of randomized controlled studies (2018) Gerontologist, 59 (1), pp. e37-e51. , https://doi.org/10.1093/geront/gny046; Riether, N., Hegel, F., Wrede, B., Horstmann, G., Social facilitation with social robots (2012) Proceedings of the 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI'12). IEEE, pp. 41-47. , Los Alamitos, CA; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on human-robot trust in time-critical situations (2017) IEEE Transactions on Human-Machine Systems, 47 (4), pp. 425-436; Rossi, S., Rossi, A., Dautenhahn, K., The secret life of robots: Perspectives and challenges for robot's behaviours during non-interactive tasks (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-020-00650-z, Published online. April 23 2020; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human-robot social interaction outside the lab (2006) Proceedings of the 9th IEEE International Workshop on Advanced Motion Control. IEEE, pp. 596-601. , Los Alamitos, CA; Zhi Tan, X., Vazquez, M., Carter, E.J., Morales, C.G., Steinfeld, A., Inducing bystander interventions during robot abuse with social mechanisms (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 169-177; Vollmer, A.-L., Read, R., Trippas, D., Belpaeme, T., Children conform, adults resist: A robot group induced peer pressure on normative social conformity (2018) Science Robotics, 3 (21), p. eaat7111; Wagner, A.R., Robinette, P., Howard, A., Modeling the human-robot trust phenomenon: A conceptual framework based on risk (2018) ACM Transactions on Interactive Intelligent Systems, 8 (4), pp. 1-24; Wladis, C., Samuels, J., Do online readiness surveys do what they claim Validity, reliability, and subsequent student enrollment decisions (2016) Computers & Education, 98, pp. 39-56; Yang, G.-Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Dario, P., Combating COVID-19-The role of robotics in managing public health and infectious diseases (2020) Science Robotics, 5 (40), p. eabb5589. , https://robotics.sciencemag.org/content/5/40/eabb5589.full.pdf",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85097195243
"Stock-Homburg R., Hannig M., Lilienthal L.","56364282100;57225793828;57220182073;","Conversational Flow in Human-Robot Interactions at the Workplace: Comparing Humanoid and Android Robots",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"578","589",,,"10.1007/978-3-030-62056-1_48","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097184142&doi=10.1007%2f978-3-030-62056-1_48&partnerID=40&md5=0310700bcc9ae565a6c8efe9e9832a33","Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany","Stock-Homburg, R., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany; Hannig, M., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany; Lilienthal, L., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany","This article deals with the conversational flow during human-robot interactions at the workplace. We conducted an experimental field study in a company setting. Drawing on computer-as-social-actor (CASA) paradigm, we argue that employees might mindlessly apply social rules in conversations with service robots which is reflected in the degree of conversational flow. We examined to which extent the structure of the Conversational Skills Rating Scale (CSRS) can be replicated with real-life human-robot interactions and whether there are differences in conversational flow between humanoid robots, android robots and humans. We could show that the conversational flow in such service settings at the workplace is based on the factors expressiveness, coordination and composure. The results show that android robots can evoke a higher level of expressiveness in employees than humanoid robots. To our knowledge, our study is the first to compare different aspects of conversational flow towards service robots in a real workplace setting. Since conversational flow can create social bonds and satisfy social needs, companies should use this knowledge when considering using robots in customer or employee contact. © 2020, Springer Nature Switzerland AG.","Android robots; CASA paradigm; Conversational flow; Human likeness; Human-robot interaction; Humanoid robots; Robots at workplace; Service robots","Anthropomorphic robots; Customer satisfaction; Man machine systems; Mobile robots; Personnel; Robotics; Field studies; Humanoid robot; Rating scale; Service robots; Social actors; Social needs; Social rules; Human robot interaction",,,,,,,,,,,,,,,,"Koudenburg, N., Postmes, T., Gordijn, E.H., Conversational flow and entitativity: The role of status (2014) Br. J. Soc. 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Res., 16, pp. 64-73","Hannig, M.; Technical University of Darmstadt, Hochschulstraße 1, Germany; email: martin.hannig@bwl.tu-darmstadt.de","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097184142
"Ayub A., Wagner A.R.","57205025514;7401456914;","Teach Me What You Want to Play: Learning Variants of Connect Four Through Human-Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"502","515",,,"10.1007/978-3-030-62056-1_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097178555&doi=10.1007%2f978-3-030-62056-1_42&partnerID=40&md5=63c895f6d952ea4a73151bd53064d31a","The Pennsylvania State University, State College, PA  16802, United States","Ayub, A., The Pennsylvania State University, State College, PA  16802, United States; Wagner, A.R., The Pennsylvania State University, State College, PA  16802, United States","This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demonstration, active learning, and game theory allowing a robot to leverage its developing representation of the game to conduct question/answer sessions with a person, thus filling in gaps in its knowledge. The paper demonstrates a method for teaching a robot the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results show that the robot can learn arbitrary win conditions for the game with little prior knowledge of the win conditions and then play the game with a human utilizing the learned win conditions. Our experiments also show that some questions are more important for learning the game’s win conditions. We believe that this method could be broadly applied to a variety of interactive learning scenarios. (A preliminary version of this paper was accepted at[5]). © 2020, Springer Nature Switzerland AG.","Active learning; Game theory; Human-robot interaction; Interactive games; Social learning","Game theory; Knowledge management; Learning systems; Robotics; Active Learning; Filling in; Game-theoretic; Interactive games; Interactive learning; Learning by demonstration; Prior knowledge; Human robot interaction",,,,,,"Acknowledgements. This work was funded in part by Penn State’s Teaching and Learning with Technology (TLT) Fellowship, and an award from Penn State’s Institute for CyberScience.","This work was funded in part by Penn State?s Teaching and Learning with Technology (TLT) Fellowship, and an award from Penn State?s Institute for CyberScience.",,,,,,,,,"Angluin, D., (2001) Queries Revisited, , https://doi.org/10.1007/3-540-45583-33, Abe, N., Khardon, R., Zeugmann, T. (eds.) ALT 2001. LNCS, vol. 2225, pp. 12–31. Springer, Heidelberg; Ayub, A., Wagner, A.R., (2018) Learning to Win Games in a Few Examples: Using Game-Theory and Demonstrations to Learn the Win Conditions of a Connect Four Game, , https://doi.org/10.1007/978-3-030-05204-134, Ge, S.S., et al. (eds.) ICSR 2018. LNCS (LNAI), vol. 11357, pp. 349–358. Springer, Cham; Ayub, A., Wagner, A.R., Cognitively-inspired model for incremental learning using a few examples (2020) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 222-223; Ayub, A., Wagner, A.R., Online learning of objects through curiosity-driven active learning (2020) IEEE Roman (Workshop on Lifelong Learning for Long-Term Human-Robot Interaction; Ayub, A., Wagner, A.R., (2020) A Robot that Learns Connect Four Using Game Theory and Demonstrations; Ayub, A., Wagner, A.R., (2020) Tell Me What This Is: Few-Shot Incremental Object Learning by a Robot; Berlekamp, E., Conway, J.H., Guy, R., (1982) Winning Ways for Your Mathematical Plays: Games in General, , Academic Press; Buchsbaum, D., Bridgers, S., Weisberg, D.S., Gopnik, A., The power of possibility: Causal learning, counterfactual reasoning, and pretend play (2012) Philos. Trans. Royal Soc. B Biol. 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(ed.), Springer, Cham, https://doi.org/10.1007/978-3-030-14174-5 16; Yu, T., Finn, C., Xie, A., Dasari, S., Zhang, T., Abbeel, P., Levine, S., (2018) One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning; Zare, M., Ayub, A., Liu, A., Sudhakara, S., Wagner, A., Passonneau, R., Dialogue policies for learning board games through multimodal communication (2020) Proceedings of the 21Th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Association for Computational Linguistics, pp. 339-351; Zare, M., Ayub, A., Wagner, A.R., Passonneau, R.J., Show me how to win: A robot that uses dialog management to learn from demonstrations (2019) Proceedings of the 14Th International Conference on the Foundations of Digital Games, pp. 1-7","Ayub, A.; The Pennsylvania State UniversityUnited States; email: aja5755@psu.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097178555
"Khavas Z.R., Ahmadzadeh S.R., Robinette P.","57202646373;13807679300;25823528900;","Modeling Trust in Human-Robot Interaction: A Survey",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"529","541",,6,"10.1007/978-3-030-62056-1_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097174866&doi=10.1007%2f978-3-030-62056-1_44&partnerID=40&md5=573b34f532218d613a041a1b7d4d956d","Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA  01854, United States; Computer Science, University of Massachusetts Lowell, Lowell, MA  01854, United States","Khavas, Z.R., Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA  01854, United States; Ahmadzadeh, S.R., Computer Science, University of Massachusetts Lowell, Lowell, MA  01854, United States; Robinette, P., Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA  01854, United States","As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI[22], however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling. © 2020, Springer Nature Switzerland AG.","Human-robot interaction; Human-robot trust; Modeling trust in HRI; Trust calibration in HRI; Trust measurement","End effectors; Man machine systems; Robotics; Human robot Interaction (HRI); Human robots; Model trusts; Robotic collaborators; Robotic systems; Team performance; Trust modeling; Trust models; Social robots",,,,,,,,,,,,,,,,"Ajenaghughrure, I.B., Sousa, S.C., Kosunen, I.J., Lamas, D., Predictive model to assess user trust: A psycho-physiological approach (2019) Proceedings of the 10Th Indian Conference on Human-Computer Interaction, pp. 1-10; Atoyan, H., Duquet, J.R., Robert, J.M., Trust in new decision aid systems (2006) Proceedings of the 18Th Conference on l’Interaction Homme-Machine, pp. 115-122; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefits of interactions with physically present robots over video-displayed agents (2011) Int. 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IEEE; Wang, N., Pynadath, D.V., Hill, S.G., Trust calibration within a human-robot team: Comparing automatically generated explanations (2016) 2016 11Th ACM/IEEE International Conference on HRI, pp. 109-116. , IEEE; Xu, A., Dudek, G., Trust-driven interactive visual navigation for autonomous robots (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3922-3929; Xu, A., Dudek, G., Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations (2015) 2015 10Th ACM/IEEE International Conference on HRI, pp. 221-228; Xu, A., Dudek, G., Maintaining efficient collaboration with trust-seeking robots (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3312-3319. , IEEE; Xu, A., Dudek, G., Towards modeling real-time trust in asymmetric human–robot collaborations (2016) Robotics Research. STAR, 114, pp. 113-129. , https://doi.org/10.1007/978-3-319-28872-77, Inaba, M., Corke, P. (eds.) , Springer, Cham; Yagoda, R.E., Gillan, D.J., You want me to trust a robot? The development of a human-robot interaction trust scale (2012) Int. J. Soc. Robot., 4 (3), pp. 235-248. , https://doi.org/10.1007/s12369-012-0144-0; Yang, X.J., Unhelkar, V.V., Li, K., Shah, J.A., Evaluating effects of user experience and system transparency on trust in automation (2017) 2017 12Th ACM/IEEE International Conference on HRI, pp. 408-416. , IEEE; You, S., Robert, L.P., Jr., Human-robot similarity and willingness to work with a robotic co-worker (2018) Proceedings of the 2018 ACM/IEEE International Conference on HRI, pp. 251-260","Khavas, Z.R.; Electrical and Computer Engineering, United States; email: Zahra_RezaeiKhavas@student.uml.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097174866
"Paradeda R.B., Ferreira M.J., Martinho C., Paiva A.","14028916600;56159560400;6506952254;7102683774;","The Importance of the Person’s Assertiveness in Persuasive Human-Robot Interactions",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"516","528",,,"10.1007/978-3-030-62056-1_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097169740&doi=10.1007%2f978-3-030-62056-1_43&partnerID=40&md5=45b38eeb39a96a6f6609118df34d3080","Instituto Superior Técnico, Lisbon, Portugal; INESC -ID, University of Lisbon, Lisbon, Portugal; State University of Rio Grande do Norte, Natal, Brazil; Interactive Technologies Institute, Funchal, Portugal","Paradeda, R.B., Instituto Superior Técnico, Lisbon, Portugal, INESC -ID, University of Lisbon, Lisbon, Portugal, State University of Rio Grande do Norte, Natal, Brazil; Ferreira, M.J., Instituto Superior Técnico, Lisbon, Portugal, INESC -ID, University of Lisbon, Lisbon, Portugal, Interactive Technologies Institute, Funchal, Portugal; Martinho, C., Instituto Superior Técnico, Lisbon, Portugal, INESC -ID, University of Lisbon, Lisbon, Portugal; Paiva, A., Instituto Superior Técnico, Lisbon, Portugal, INESC -ID, University of Lisbon, Lisbon, Portugal","In social robotics, user-personalised messages is a technique that can be used to persuade a person to do something. Those messages can be personalised according to the personality of whom will receive this message. So, it is essential to know the personality traits of the target. However, we have many traits, e.g., a person can be extroverted, assertive, logical, among others. Because of that, it is challenging to create a strategy that can reach all the traits. In this sense, we chose to focus our persuasion strategies to approach the assertive trait of a person. The strategies were applied in a storytelling scenario with an autonomous social robot behaving assertively using strategies to suggesting the person change the decision with assertive messages and nonverbal persuasive techniques. Besides, we take into account the assertiveness level of the participant to measure if this level influences the acceptance of robot suggestion or not. We observed from the results that a person’s assertiveness level might influence the perception regarding the persuasive agent and the decisions-made in the task. © 2020, Springer Nature Switzerland AG.","Assertiveness; Human-robot interaction; Personality traits; Persuasion","Robotics; Personality traits; Persuasion strategy; Social robotics; Human robot interaction",,,,,"Fundo Regional para a Ciência e Tecnologia, FRCT; Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação, ARDITI: M1420-09-5369-000001; Fundação para a Ciência e a Tecnologia, FCT; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 201833/2014-0; Universidade Federal do Rio Grande do Norte, UFRN","We would like to thank the State University of Rio Grande do Norte (UERN), National Council for Scientific and Technological Development (CNPq) program Science without Border: 201833/2014-0 - Brazil and Agência Regional para o Desenvolvimento e Tecnologia (ARDITI) - M1420-09-5369-000001, for PhD grants to the first and second author, respectively. This work was also supported by Funda¸cão para a Ciência e a Tecnologia: (FCT) - UIDB/50021/2020.","We would like to thank the State University of Rio Grande do Norte (UERN), National Council for Scientific and Technological Development (CNPq) program Science without Border: 201833/2014-0-Brazil and Ag?ncia Regional para o Desenvolvimento e Tecnologia (ARDITI)-M1420-09-5369-000001, for PhD grants to the first and second author, respectively. This work was also supported by Funda??o para a Ci?ncia e a Tecnologia: (FCT)-UIDB/50021/2020.",,,,,,,,,"Adnan, M., Mukhtar, H., Naveed, M., Persuading students for behavior change by determining their personality type (2012) 2012 15Th International Multitopic Conference (INMIC), pp. 439-449. , IEEE; Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) Proceedings of the 8Th ACM/IEEE International Conference on Human-Robot Interaction, pp. 325-332. , IEEE; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Chidambaram, V., Chiang, Y., Mutlu, B., Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 293-300. , ACM; Costa, P.T., McCrae, R.R., Revised NEO Personality Inventory (NEO PI-R) and NEP Five-Factor Inventory (NEO-FFI). professional manual (1992) Psychological Assessment Resources Lutz, FL; Elliott, T.R., Gramling, S.E., Personal assertiveness and the effects of social support among college students (1990) J. Couns. 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(eds.), Springer, Cham; Paradeda, R., Ferreira, M.J., Martinho, C., Paiva, A., Communicating assertiveness in robotic storytellers (2018) ICIDS 2018. LNCS, 11318, pp. 442-452. , https://doi.org/10.1007/978-3-030-04028-451, Rouse, R., Koenitz, H., Haahr, M. (eds.) , Springer, Cham; Paradeda, R., Ferreira, M.J., Martinho, C., Paiva, A., Would you follow the suggestions of a storyteller robot? (2018) ICIDS 2018. LNCS, 11318, pp. 489-493. , https://doi.org/10.1007/978-3-030-04028-457, Rouse, R., Koenitz, H., Haahr, M. (eds.), Springer, Cham; Paradeda, R.B., Hashemian, M., Rodrigues, R.A., Paiva, A., How facial expressions and small talk may influence trust in a robot (2016) ICSR 2016. LNCS (LNAI), 9979, pp. 169-178. , https://doi.org/10.1007/978-3-319-47437-317, Agah, A., Cabibihan, J.-J., Howard, A.M., Salichs, M.A., He, H. (eds.) , Springer, Cham; Paradeda, R.B., Ferreira, M.J., Oliveira, R., Martinho, C., Paiva, A., The role of assertiveness in a storytelling game with persuasive robotic non-player characters (2019) Proceedings of the The Annual Symposium on Computer-Human Interaction in Play. October 22–25, 2019. Barcelona, Spain, pp. 453-465; Paradeda, R., Ferreira, M.J., Oliveira, R., Martinho, C., Paiva, A., What makes a good robotic advisor? The role of assertiveness in human-robot interaction (2019) ICSR 2019. LNCS (LNAI, 11876, pp. 144-154. , https://doi.org/10.1007/978-3-030-35888-414, Salichs, M.A., et al. (eds.), Springer, Cham; Paradeda, R.B., Martinho, C., Paiva, A., Persuasion based on personality traits: Using a social robot as storyteller (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’17, ACM, New York, NY, USA, pp. 367-368; Rolls, E.T., (2013) Emotion and Decision Making Explained, , Oxford University Press","Paradeda, R.B.; Instituto Superior TécnicoPortugal; email: raul.paradeda@tecnico.ulisboa.pt","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097169740
"Chen M., Hsu D., Lee W.S.","57190131729;34569904000;55663394400;","Guided Exploration of Human Intentions for Human-Robot Interaction",2020,"Springer Proceedings in Advanced Robotics","14",,,"921","938",,1,"10.1007/978-3-030-44051-0_53","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097163270&doi=10.1007%2f978-3-030-44051-0_53&partnerID=40&md5=84dfea668395dd88d22ba1b71f8c4e8c","National University of Singapore, Singapore, 117417, Singapore","Chen, M., National University of Singapore, Singapore, 117417, Singapore; Hsu, D., National University of Singapore, Singapore, 117417, Singapore; Lee, W.S., National University of Singapore, Singapore, 117417, Singapore","Robot understanding of human intentions is essential for fluid human-robot interaction. Intentions, however, cannot be directly observed and must be inferred from behaviors. We learn a model of adaptive human behavior conditioned on the intention as a latent variable. We then embed the human behavior model into a principled probabilistic decision model, which enables the robot to (i) explore actively in order to infer human intentions and (ii) choose actions that maximize its performance. Furthermore, the robot learns from the demonstrated actions of human experts to further improve exploration. Preliminary experiments in simulation indicate that our approach, when applied to autonomous driving, improves the efficiency and safety of driving in common interactive driving scenarios. © 2020, Springer Nature Switzerland AG.",,,,,,,"National Research Foundation Singapore, NRF; Singapore-MIT Alliance for Research and Technology Centre, SMART: MOE2016-T2-2-068","Acknowledgments. This work is supported in part by the Singapore NRF through the SMART Future Urban Mobility IRG and MOE AcRF grant MOE2016-T2-2-068.",,,,,,,,,,"Abbeel, P., Ng, A.Y., Apprenticeship learning via inverse reinforcement learning (2004) Proceedings of the International Conference on Machine Learning, p. 1. , , p. , ACM; Astington, J.W., The Child’s Discovery of the Mind (1993) Vol, 31. , Harvard University Press, Cambridge; Bai, H., Cai, S., Ye, N., Hsu, D., Lee, W.S., Intention-aware online POMDP planning for autonomous driving in a crowd (2015) Proceedings of the IEEE International Conference on Robotics & Automation, Pp. 454–460. IEEE; Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D., Intention-aware motion planning (2013) Proceedings International Workshop on the Algorithmic Foundations of Robotics, pp. 475-491. , Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds.), pp. , Springer, Heidelberg; Bratman, M.: Intention, Plans and Practical Reason. 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(eds.), pp. , Springer, Heidelberg; Rubinstein, A., (1998) Modeling Bounded Rationality, , MIT Press, Cambridge; Sadigh, D., Sastry, S.S., Seshia, S.A., Dragan, A., Information gathering actions over human internal state (2016) Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 66-73. , , pp. , IEEE; Sadigh, D., Sastry, S., Seshia, S.A., Dragan, A.D., Planning for autonomous cars that leverage effects on human actions (2016) Proceedings of the Robotics: Science & Systems; Sunberg, Z.N., Ho, C.J., Kochenderfer, M.J., The value of inferring the internal state of traffic participants for autonomous freeway driving (2017) American Control Conference (ACC), pp. 3004-3010. , , pp. , IEEE; Vert, J.P., Tsuda, K., Schölkopf, B., A primer on kernel methods (2004) Kernel Methods Comput. Biol., 47, pp. 35-70","Chen, M.; National University of SingaporeSingapore; email: chenmin@comp.nus.edu.sg",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85097163270
"Pedersen J.E., Christensen K.W., Herath D., Jochum E.","57220186362;57220181144;8366358100;57074708900;","I Like the Way You Move: A Mixed-Methods Approach for Studying the Effects of Robot Motion on Collaborative Human Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"73","84",,1,"10.1007/978-3-030-62056-1_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097147271&doi=10.1007%2f978-3-030-62056-1_7&partnerID=40&md5=06ab32f13c72ba7b5fdef3264fd5d136","Aalborg University, Aalborg, Denmark; University of Canberra, Canberra, Australia","Pedersen, J.E., Aalborg University, Aalborg, Denmark; Christensen, K.W., Aalborg University, Aalborg, Denmark; Herath, D., University of Canberra, Canberra, Australia; Jochum, E., Aalborg University, Aalborg, Denmark","Human robot collaboration is an increasingly relevant area within Human Robot Interaction. As robots move into dynamic environments and engage in collaborative tasks with people, there is a need to further understand how perceptual and communication cues facilitate and support Human Robot Collaboration. Building on prior Human Robot Interaction studies, we developed a mixed-methods approach for studying the effects of expressive movements of an industrial robot arm engaged in a collaborative drawing task. The purpose was to evaluate the effects of different movement qualities on participant experience while collaborating with a robot. We present our approach and the results of the experiments. Although we did not identify any significant difference in interactions where the robot moved expressively, our study highlights the importance of in-the-wild experiments and strategies for combining qualitative and quantitative methodologies. © 2020, Springer Nature Switzerland AG.","Collaborative robots; Human-factors; Human-robot interaction; Mixed-methods","Industrial robots; Man machine systems; Robotics; Collaborative drawing; Collaborative tasks; Dynamic environments; Human-robot collaboration; Mixed method; Quantitative methodology; Robot motion; Social robots",,,,,,,,,,,,,,,,"Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) Int. J. 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Hum.-Robot Interact, 7 (1), p. 3. , https://doi.org/10.1145/3209540; Tresset, P., Deussen, O., Artistically skilled embodied agents Proceedings of AISB2014; (2018) Issues in Science and Technology, Vol., 35 (1). , https://issues.org/sougwen-chung/; Hoffman, G., Wendy, J., Designing robots with movement in mind (2014) J. Hum.-Robot Interact., 3 (1), pp. 91-122. , https://doi.org/10.5898/JHRI.3.1.Hoffman; Riek, L.D., Wizard of oz studies in HRI: A systematic review and new reporting guide-lines (2012) J. Hum.-Robot Interact., 1 (1), pp. 119-136. , https://doi.org/10.5898/JHRI.1.1.Riek; Hinwood, D., Ireland, J., Jochum, E.A., Herath, D., A proposed wizard of OZ architecture for a human-robot collaborative drawing task (2018) ICSR 2018. 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Usability Stud., 4 (3), pp. 114-123; Pei, Y.C., Chen, J.L., Wong, A.M., Tseng, K.C., An evaluation of the design and usability of a novel robotic bilateral arm rehabilitation device for patients with stroke (2017) Front. Neurorobot., 11. , https://doi.org/10.3389/fnbot.2017.00036; Mattos, L.S., Deshpande, N., Barresi, G., Guastini, L., Peret-Ti, G., A novel computerized surgeon-machine interface for robot-assisted laser phonomi-crosurgery (2014) Laryngoscope, 124 (8), pp. 1887-1894; Herath, D.C., Jochum, E., Vlachos, E., An experimental study of embodied interaction and human perception of social presence for interactive robots in public settings (2018) IEEE Trans. Cogn. Dev. Syst., 10 (4); Jordan, B., Henderson, A., Interaction analysis: Foundations and practice (1995) J. Learn. Sci., 4, pp. 39-103. , https://doi.org/10.1207/s15327809jls0401","Herath, D.; University of CanberraAustralia; email: damith.herath@canberra.edu.au","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097147271
"Trainer T., Taylor J.R., Stanton C.J.","57220176815;57195346873;23092202700;","Choosing the Best Robot for the Job: Affinity Bias in Human-Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"490","501",,,"10.1007/978-3-030-62056-1_41","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097133781&doi=10.1007%2f978-3-030-62056-1_41&partnerID=40&md5=ac0f44637a7a8408b48c16042f72fdc7","MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia","Trainer, T., MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia; Taylor, J.R., MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia; Stanton, C.J., MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia","Humans subconsciously judge others as being either similar or dissimilar to themselves, manifesting as an unconscious preference, or affinity bias, for those who are perceived to be similar. In human-to-human interaction, affinity bias can significantly influence trust formation and lead to discrimination, for example, in decisions related to recruitment and team selection. We investigate whether affinity bias is observed in human-robot interaction during team formation with social agents that differ in gender and skin tone. In this study, we asked 61 participants to order the resumés of 24 different avatars that varied in gender, skin tone, and competency under the pretext of choosing the “best” avatars to be the participant’s teammate. Then, using a wizard-of-oz style experiment, participants performed a task with two avatar teammates (one most preferred and one least preferred) to measure trust. Results showed that while avatars were predominantly chosen based upon competency, avatar appearance generated an affinity bias in resumé sorting, and participants were more likely to trust their preferred teammate. © 2020, Springer Nature Switzerland AG.","Affinity bias; Human-robot interaction; Trust","Man machine systems; Robotics; Human-to-human interactions; Skin tone; Social agents; Team formation; Wizard of Oz; Social robots",,,,,,,,,,,,,,,,"Louis, C., (2019) Unconscious Bias: The Affinity Bias, , https://podojo.com/unconscious-bias-the-affinity-bias/, Retrieved from, Accessed 20 May 2020; Turnbull, H., (2014) The Affinity Bias Conundrum: The Illusion of Inclusion Part III, , https://diversityjournal.com/13763-affinity-bias-conundrum-illusion-inclusion-part-iii/.Accessed20May2020, Retrieved from; Turnbull, H., The Illusion of Inclusion: Global Inclusion, Unconscious Bias, and the Bottom Line, , Business Expert Press, NY, USA (2016); Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) Br. J. Soc. Psychol., 51 (4), pp. 724-731; D’Souza, G., Colarelli, S., Team member selection decisions for virtual versus face-to-face teams (2010) Comput. Hum. Behav., 26 (4), pp. 630-635; Staats, C., Understanding implicit bias: What educators should know (2016) Educ. Digest, 82 (1), pp. 29-38; Derous, E., Ryan, A., Nguyen, H., Multiple categorization in resume screening: Examining effects on hiring discrimination against Arab applicants in field and lab settings (2012) J. Org. Behav., 3 (4), pp. 544-570; Cheng, Y., Sun, P., Chen, N., The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers and instructors (2018) Comput. Educ., 126, pp. 399-416; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics, pp. 1935-1972. , https://doi.org/10.1007/978-3-319-32552-1_72, Siciliano, B., Khatib, O. (eds.), Springer, Cham; Murphy, J., Gretzel, U., Pesonen, J., Marketing robot services in hospitality and tourism: The role of anthropomorphism (2019) J. Travel Tourism Mark., 36 (7), pp. 784-795; Carlucci, F., Nardi, L., Iocchi, L., Nardi, D., Explicit representation of social norms for social robots (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4191-4196. , IEEE; Crisp, R.J., Hewstone, M., (2007) Multiple Social Categorization, , Elsevier Academic Press, San Diego, CA; Linville, P., Salovey, P., Fischer, G., Stereotyping and perceived distributions of social characteristics: An application to ingroup-outgroup perception (1986) Prejudice, Discrimination and Racism, pp. 165-208. , Dovidio, J., Gaertner, L. (eds.) , Academic Press, Orlando, FL; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) Br. J. Soc. Psychol., 51 (4), pp. 724-731; Gong, L., The boundary of racial prejudice: Comparing preferences for computer-synthesized white, black, and robot characters (2007) Comput. Hum. Behav., 24, pp. 2074-2093; Kim, M.-S., Kim, E.-J., Humanoid robots as “The Cultural Other”: Are we able to love our creations? (2013) AI & Soc, 28, pp. 309-318; Sheridan, T., Human-Robot interaction: Status & challenges (2016) Hum. Factors: J. Hum. Factors Ergon Soc., 58 (4), pp. 525-532; Kuchenbrandt, D., Eyssel, F., Bobinger, S., Neufeld, M., When a robot’s group membership matters (2013) Int. J. Soc. Robot., 5, pp. 409-417; Bartneck, C., Yogeeswaran, K., Min Ser, Q., Wang, S., Sparrow, R., Eyssel, F., Robots and racism (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 196-204. , ACM. New York, NY, USA; Addison, K., Yogeeswaran, K., Bartneck, C., Robots can be more than black and white: Examining racial bias towards robots (2019) Proceedings of AAAI/ACM Conference on Artificial Intelligence, Ethics & Society., pp. 493-498. , Honolulu; Ventre-Dominey, J., Embodiment into a robot increases its acceptability (2019) Sci. Rep., 9 (1), pp. 1-10; Peck, T., Seinfeld, S., Aglioti, S., Slater, M., Putting yourself in the skin of a black avatar reduces implicit racial bias (2013) Conscious. Cogn., 22, pp. 779-787; Singh, J., Rukta, N., Attitude of in and out-group employees and leader member exchange (2018) Int. J. Eng. Technol. Sci. Res., 5 (3), pp. 441-445; Stroessner, S., Benitez, J., The social perception of humanoid and non-humanoid robots: Effects of gendered and machine-like features (2019) Int. J. Soc. Robot., 11 (2), pp. 305-315; Trovato, G., Lucho, C., Paredes, R., She’s electric-the influence of body proportions on perceived gender of robots across cultures (2018) Robot, 7 (3), p. 50; Carpenter, J., Davis, J., Erwin-Stewart, N., Lee, T., Bransford, J., Vye, N., Gender representation and humanoid robots designed for domestic use (2009) Int. J. Soc. Robot., 1 (26), pp. 261-265; Siegel, M., Breazeal, C., Norton, M., Persuasive robotics: The influence of robot gender on human behaviour (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Pp. 2563–2568. IEEE; Skvoretz, J., Bailey, J., “Red, white, yellow, blue, all out but you”: Status effects on team formation, and expectation states theory (2016) Soc. Psychol. Q., 79 (2), pp. 136-155; Riek, L., Howard, D., A code of ethics for the human-robot interaction profession (2014) Proc. We Robot; Simpson, J., Psychological foundations of trust (2007) Curr. Dir. Psychol. 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Psychol., 66, pp. 519-545; Ghazali, A., Ham, J., Barakova, E., Markopoulos, P., Effects of robot facial characteristics and gender in persuasive human-robot interaction (2018) Front. Robot. AI, 5 (73), pp. 1-15; Cameron, D., Framing factors: The importance of context and the individual in understanding trust in human-robot interaction (2015) IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), , Hamburg, Germany; Bendick, M., Nunes, A., Developing the research basis for controlling bias in hiring (2012) J. Soc. Issues, 68 (2), pp. 238-262; Wirts, A., Discriminatory intent and implicit bias: Title VII liability and unwitting discrimination (2017) Boston Coll. Law Rev., 58 (2), pp. 809-856; Broadbent, E., Interactions with robots: The truths we reveal about ourselves (2017) Ann. Rev. Psychol., 68 (1), pp. 627-652; Avatarmaker Homepage. https://avatarmaker.com/. Accessed 20 May 2020; TTSMP3 Homepage, , https://ttsmp3.com, Accessed 20 May 2020","Stanton, C.J.; MARCS Institute, Australia; email: c.stanton@westernsydney.edu.au","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097133781
"Kont M., Alimardani M.","57220182371;55836794100;","Engagement and Mind Perception Within Human-Robot Interaction: A Comparison Between Elderly and Young Adults",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"344","356",,3,"10.1007/978-3-030-62056-1_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097131336&doi=10.1007%2f978-3-030-62056-1_29&partnerID=40&md5=96dce9bb75007811b4b182932dd88c5a","Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands","Kont, M., Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands; Alimardani, M., Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands","People can feel engaged and attribute human-like traits when interacting with a social robot and reveal this unconsciously to observers. Studies have suggested that behavioral signals such as facial expressions, posture, speech and laughter play an important role in identifying engagement in Human-Robot Interaction (HRI), however the effect of these factors in different age groups, as well as their relationship with mind attribution towards robots remains unclear. This study examined 24 elderly people and 24 university students on facial expressions, laughter and speech during an interaction with a NAO-robot. In addition, self-reported engagement level and mind perception scores were collected after the interaction and analyzed. Results showed that elderly had a significantly lower report of engagement with the robot, which was positively correlated with their perception of mind capacity in the robot. Furthermore, for both elderly and students, there was a negative trend between self-reported mind perception and observed behavioral engagement with the robot. Findings of this study could be employed in the design and evaluation of future HRI scenarios. © 2020, Springer Nature Switzerland AG.","Behavior analysis; Elderly; Engagement; Human-Robot Interaction (HRI); Mind perception; Social robots; Young adults","Man machine systems; Robotics; Design and evaluations; Elderly people; Engagement levels; Facial Expressions; Human like; Human robot Interaction (HRI); University students; Young adults; Social robots",,,,,,,,,,,,,,,,"Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2); Fasola, J., Mataric, M.J., Using socially assistive human–robot interaction to motivate physical exercise for older adults (2012) Proc. IEEE, 100 (8), pp. 2512-2526; Nomura, T., Takeuchi, S., The elderly and robots: From experiments based on comparison with younger people (2011) Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence; Alimardani, M., Qurashi, S., Mind perception of a sociable humanoid robot: A comparison between elderly and young adults (2020) ROBOT 2019. AISC, 1093, pp. 96-108. , https://doi.org/10.1007/978-3-030-36150-1_9, Silva, M.F., Luís Lima, J., Reis, L.P., Sanfeliu, A., Tardioli, D. (eds.) , Springer, Cham; Stafford, R.Q., Macdonald, B.A., Jayawardena, C., Wegner, D.M., Broadbent, E., Does the robot have a mind? Mind perception and attitudes towards robots predict use of an eldercare robot (2014) Int. J. Social Robot., 6 (1), pp. 17-32. , https://doi.org/10.1007/s12369-013-0186-y; Piasek, J., Wieczorowska-Tobis, K., Acceptance and long-term use of a social robot by elderly users in a domestic environment (2018) 2018 11Th International Conference on Human System Interaction (HSI), pp. 478-482. , IEEE; Sinnema, L., Alimardani, M., The attitude of elderly and young adults towards a humanoid robot as a facilitator for social interaction (2019) ICSR 2019. LNCS (LNAI), Vol. 11876, pp. 24-33. , https://doi.org/10.1007/978-3-030-35888-4_3, Salichs, M.A., et al. (eds.), Springer, Cham; Flandorfer, P., Population ageing and socially assistive robots for elderly persons: The importance of sociodemographic factors for user acceptance (2012) Int. J. Popul. Res. 2012; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) Int. J. Social Robot., 7 (4), pp. 465-478. , https://doi.org/10.1007/s12369-015-0298-7; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) 2010 5Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375-382. , IEEE; Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P.W., Paiva, A., Automatic analysis of affective postures and body motion to detect engagement with a game companion (2011) Proceedings of the 6Th International Conference on Human-Robot, pp. 305-312; Leite, I., Autonomous disengagement classification and repair in multiparty child-robot interaction (2016) 2016 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 525-532; Rudovic, O., Lee, J., Mascarell-Maricic, L., Schuller, B.W., Picard, R.W., Measuring engagement in robot-assisted autism therapy: A cross-cultural study (2017) Front. Robot. AI, 4, p. 36; Salam, H., Celiktutan, O., Hupont, I., Gunes, H., Chetouani, M., Fully automatic analysis of engagement and its relationship to personality in human-robot interactions (2016) IEEE Access, 5, pp. 705-721; Baxter, P., de Jong, C., Aarts, R., de Haas, M., Vogt, P., The effect of age on engagement in preschoolers’ child-robot interactions (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 81-82; Oh, S., Oh, Y.H., Ju, D.Y., Understanding the preference of the elderly for companion robot design (2020) AHFE 2019. AISC, 962, pp. 92-103. , https://doi.org/10.1007/978-3-030-20467-9_9, Chen, J. (ed.), Springer, Cham; Nomura, T., Sasa, M., Investigation of differences on impressions of and behaviors toward real and virtual robots between elder people and university students (2009) 2009 IEEE International Conference on Rehabilitation Robotics, pp. 934-939. , IEEE; Oh, Y.H., Ju, D.Y., Age-related differences in fixation pattern on a companion robot (2020) Sensors, 20 (13), p. 3807; Riek, L.D., Wizard of OZ studies in HRI: A systematic review and new reporting guidelines (2012) J. Hum.-Robot Interact., 1 (1), pp. 119-136; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3; Lee, K.M., Peng, W., Jin, S.A., Yan, C., Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human–robot interaction (2006) J. Commun., 56 (4), pp. 754-772; Drejing, K., Thill, S., Hemeren, P., Engagement: A traceable motivational concept in human-robot interaction (2015) 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 956-961. , IEEE; Hall, J., Tritton, T., Rowe, A., Pipe, A., Melhuish, C., Leonards, U., Perception of own and robot engagement in human–robot interactions and their dependence on robotics knowledge (2014) Robot. Auton. Syst., 62 (3), pp. 392-399; Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E., Towards engagement models that consider individual factors in HRI: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human–robot assembly task (2017) Int. J. Social Robot., 9 (1), pp. 63-86. , https://doi.org/10.1007/s12369-016-0357-8; Biswas, M., Romeo, M., Cangelosi, A., Jones, R.B., Are older people any different from younger people in the way they want to interact with robots? Scenario based survey (2019) J. Multimod. User Interfaces, 14 (1), pp. 61-72. , https://doi.org/10.1007/s12193-019-00306-x; Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M.T., Socially assistive robots in elderly care: A mixed-method systematic literature review (2014) Int. J. Hum.-Comput. Interact., 30 (5), pp. 369-393; Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kato, K., Cultural differences in attitudes towards robots (2005) Proceedings of the AISB Symposium on Robot Companions: Hard Problems and Open Challenges in Human-Robot Interaction, pp. 1-4; Evers, V., Maldonado, H., Brodecki, T., Hinds, P., Relational vs. Group self-construal: Untangling the role of national culture in HRI (2008) 2008 3Rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 255-262; Joosse, M., Lohse, M., Evers, V., Lost in proxemics: Spatial behavior for cross-cultural HRI (2014) HRI 2014 Workshop on Culture-Aware Robotics, pp. 1-6","Alimardani, M.; Department of Cognitive Science and AI, Netherlands; email: m.alimardani@uvt.nl","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097131336
"Strazdas D., Hintz J., Felbberg A.-M., Al-Hamadi A.","57220084480;57220088201;57220091452;57199426558;","Robots and Wizards: An Investigation into Natural Human-Robot Interaction",2020,"IEEE Access","8",,"9264629","207635","207642",,5,"10.1109/ACCESS.2020.3037724","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096826835&doi=10.1109%2fACCESS.2020.3037724&partnerID=40&md5=7df6eab5757d7cf9a6c826974236f960","Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany; Department of Experimental Psychology, Otto-von-Guericke Universität Magdeburg, Magdeburg, 39106, Germany","Strazdas, D., Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany; Hintz, J., Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany; Felbberg, A.-M., Department of Experimental Psychology, Otto-von-Guericke Universität Magdeburg, Magdeburg, 39106, Germany; Al-Hamadi, A., Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany","The goal of the study was to research different communication modalities needed for intuitive Human-Robot Interaction. This study utilizes a Wizard of Oz prototyping method to enable a restriction-free, intuitive interaction with an industrial robot. The data from 36 test subjects suggests a high preference for speech input, automatic path planning and pointing gestures. The catalogue developed during this experiment contains intrinsic gestures suggesting that the two most popular gestures per action can be sufficient to cover the majority of users. The system scored an average of 74% in different user interface experience questionnaires, while containing forced flaws. These findings allow a future development of an intuitive Human-Robot interaction system with high user acceptance. © 2013 IEEE.","Activity recognition; cooperative systems; gesture recognition; human-robot interaction; intelligent robots; interactive systems; robot control; robot learning; telerobotics","Industrial robots; Man machine systems; Surveys; User experience; User interfaces; Communication modalities; Intuitive interaction; Pointing gestures; Speech input; User acceptance; Wizard of Oz; Social robots",,,,,"03ZZ04X02B; Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448L, 03ZZ0470; Bundesministerium für Gesundheit, BMG","This work was supported in part by the Federal Ministry of Education and Research of Germany (BMBF) RoboAssist under Grant 03ZZ0448L, in part by the HuBa under Grant 03ZZ0470, and in part by the Robo-Lab within the Zwanzig20 Alliance 3Dsensation, under Grant 03ZZ04X02B.",,,,,,,,,,"Buhr, D., Soziale Innovationspolitik F-ur Die Industrie 4.0, , http://library.fes.de/pdf-les/wiso/11494.pdf, Accessed: Sep. 1, 2020; Behrens, R., Elkmann, N., Workshop: Sicherheit in der menschroboterkollaboration (2016) Proc. 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User Interfaces (IUI), pp. 193-200; Hudson, S.E., Fogarty, J., Atkeson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J., Predicting human interruptibility with sensors: A wizard of Oz feasibility study (2003) Proc. New Horizons CHI, pp. 257-264; Serrano, M., Nigay, L., A wizard of oz component-based approach for rapidly prototyping and testing input multimodal interfaces (2010) J. Multi-modal User Interface, 3 (3), pp. 215-225. , Apr; Dow, S., Macintyre, B., Lee, J., Oezbek, C., Bolter, J.D., Gandy, M., Wizard of oz support throughout an iterative design process (2005) IEEE Pervas. Comput., 4 (4), pp. 18-26. , Oct; Zaeh, M., Vogl, W., Interactive laser-projection for programming industrial robots (2006) Proc. IEEE/ACM Int. Symp. Mixed Augmented Real-ity, pp. 125-128. , Oct; Norberto Pires, J., Robot by voice: Experiments on commanding an industrial robot using the human voice (2005) Ind. Robot Int., J., 32 (6), pp. 505-511. , Dec; Reinhart, G., Vogl, W., Kresse, I., A projection-based user interface for industrial robots (2007) Proc. IEEE Symp. Virtual Environ. Hum.-Comput. Interface Meas. Syst., pp. 67-71. , Jun; Brogardh, T., Present and future robot control development-An industrial perspective (2007) Annu. Rev. Control, 31 (1), pp. 69-79. , http://www.sciencedirect.com/science/article/pii/S1367578807000077; Bischoff, R., Kazi, A., Perspectives on augmented reality based humanrobot interaction with industrial robots (2004) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 3226-3231. , Dec; Speicher, M., Nebeling, M., GestureWiz: A human-powered gesture design environment for user interface prototypes (2018) Proc. CHI Conf. Hum. Factors Comput. Syst., pp. 1-11; Hoffman, G., Openwoz: A runtime-con-gurable wizard-of-oz framework for human-robot interaction (2016) Proc. AAAI Spring Symp. Ser., pp. 1-5; Brooke, J., (2020) SUS-A Quick and Dirty Usability Scale, , www.TBIStaffTraining.info, Accessed: Sep. 1; Finstad, K., (2010) The Usability Metric for User Experience, , https://academic.oup.com/iwc/article-abstract/22/5/323/682940; Lewis, J.R., Psychometric evaluation of the PSSUQ using data from-ve years of usability studies (2002) Int. J. Hum.-Comput. Interact., 14 (3-4), pp. 463-488. , https://www.tandfonline.com/action/journalInformationjournalCode=hihc20, Sep; Lewis, J., Psychometric evaluation of an after-scenario questionnaire for computer usability studies: The ASQ (1991) SIGCHI Bull., 23, pp. 78-81. , Jan; Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data (1977) Proc. Biometrics, pp. 159-174. , Dec; Schober, P., Boer, C., Schwarte, L.A., Correlation coef-cients: Appropriate use and interpretation (2018) Anesthesia Analgesia, 126 (5), pp. 1763-1768. , May; Guilford, J.P., (1936) Psychometric Methods, , New York, NY, USA: McGrawHill; Wu, P.C., Wang, R., Kin, K., Twigg, C., Han, S., Yang, M.H., Chien, S.Y., DodecaPen: Accurate 6DoF tracking of a passive stylus (2017) Proc. 30th Annu. ACM Symp. User Interface Softw. Technol., pp. 365-374. , Oct","Strazdas, D.; Neuro-Information Technology, Germany; email: dominykas.strazdas@ovgu.de
Al-Hamadi, A.; Neuro-Information Technology, Germany; email: ayoub.al-hamadi@ovgu.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85096826835
"Roy R.N., Drougard N., Gateau T., Dehais F., Chanel C.P.C.","55904560000;55936524900;56032393200;13005898500;36681830500;","How can physiological computing benefit human-robot interaction?",2020,"Robotics","9","4","100","1","24",,6,"10.3390/robotics9040099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096654806&doi=10.3390%2frobotics9040099&partnerID=40&md5=ac1b319bd6cb0a71aef56a70373377c8","ISAE-SUPAERO, Université de Toulouse, France; ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France","Roy, R.N., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France; Drougard, N., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France; Gateau, T., ISAE-SUPAERO, Université de Toulouse, France; Dehais, F., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France; Chanel, C.P.C., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France","As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators’ MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator’s MS to improve human-robot operations’ safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Automated planning; Human-robot interaction; Mental state monitoring; Mixed-initiative; Passive BCI; Physiological computing; Teleoperation; Telerobotics",,,,,,,,,,,,,,,,,"Van Erp, J., Lotte, F., Tangermann, M., Brain-computer interfaces: Beyond medical applications (2012) Computer, 45, pp. 26-34. , [CrossRef]; Cinel, C., Valeriani, D., Poli, R., Neurotechnologies for human cognitive augmentation: Current state of the art and future prospects (2019) Front. Hum. Neurosci, 13, p. 13. , [CrossRef] [PubMed]; Vasic, M., Billard, A., Safety issues in human-robot interactions Proceedings of the 2013 IEEE International Conference on Robotics and Automation, pp. 197-204. , Karlsruhe, Germany, 6–10 May 2013; Zacharaki, A., Kostavelis, I., Gasteratos, A., Dokas, I., Safety bounds in human robot interaction: A survey (2020) Saf. Sci, 127, p. 104667. , [CrossRef]; Goodrich, M.A., Schultz, A.C., Human-robot Interaction: A Survey (2007) Found. Trends Hum.-Comput. 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IEEE, 94, pp. 1425-1444. , [CrossRef]; Atrash, A., Pineau, J., A Bayesian method for learning POMDP observation parameters for robot interaction management systems (2010) Proceedings of the POMDP Practitioners Workshop, , Toronto, BC, Canada, 12 May; Hoey, J., Poupart, P., von Bertoldi, A., Craig, T., Boutilier, C., Mihailidis, A., Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process (2010) Comput. Vis. Image Underst, 114, pp. 503-519. , [CrossRef]; Dehais, F., Goudou, A., Lesire, C., Tessier, C., Towards an anticipatory agent to help pilots (2005) Proceedings of the AAAI 2005 Fall Symposium “From Reactive to Anticipatory Cognitive Embodied Systems, , Arlington, VA, USA, 4–6 November; Cummings, M.L., Mitchell, P.J., Predicting controller capacity in supervisory control of multiple UAVs (2008) IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum, 38, pp. 451-460. , [CrossRef]; Dehais, F., Peysakhovich, V., Scannella, S., Fongue, J., Gateau, T., Automation surprise in aviation: Real-time solutions Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2525-2534. , Seoul, Korea, 18–23 April 2015; Nikolaidis, S., Ramakrishnan, R., Gu, K., Shah, J., Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks (2015) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 189-196. , ACM: New York, NY, USA, [CrossRef]; Atrash, A., Pineau, J., A bayesian reinforcement learning approach for customizing human-robot interfaces Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 355-360. , Sanibel Island, FL, USA, 8–11 February 2009; Regis, N., Dehais, F., Rachelson, E., Thooris, C., Pizziol, S., Mickaël Causse, C.T., Formal Detection of Attentional Tunneling in Human Operator-Automation Interactions (2014) IEEE Trans. Hum.-Mach. Syst, 44, pp. 326-336; Nicolas-Alonso, L.F., Gomez-Gil, J., Brain Computer Interfaces, a Review (2012) Sensors, 12, pp. 1211-1279. , [CrossRef]; Ghosh, D., (2012) An Adaptive Human Brain to ComputerInterface System for Robotic or Wheel-Chair based Navigational Tasks, , Master’s Thesis, TUM Technische Iniversität München, KTH Industrial Engineering and Management Master of Science, Stockholm, Sweden; Aricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Bonelli, S., Golfetti, A., Pozzi, S., Benhacene, R., Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment (2016) Front. Hum. Neurosci, 10, p. 539; Chanel, C.P., Roy, R.N., Dehais, F., Drougard, N., Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction (2020) Sensors, 20, p. 296. , [CrossRef]; Appriou, A., Cichocki, A., Lotte, F., Modern Machine-Learning Algorithms: For Classifying Cognitive and Affective States From Electroencephalography Signals (2020) IEEE Syst. Man Cybern. Mag, 6, pp. 29-38. , [CrossRef]; Pongsakornsathien, N., Lim, Y., Gardi, A., Hilton, S., Planke, L., Sabatini, R., Kistan, T., Ezer, N., Sensor networks for aerospace human-machine systems (2019) Sensors, 19, p. 3465. , [CrossRef]; Cavallo, F., Limosani, R., Fiorini, L., Esposito, R., Furferi, R., Governi, L., Carfagni, M., Design impact of acceptability and dependability in assisted living robotic applications (2018) Int. J. Interact. Des. Manuf, 12, pp. 1167-1178. , [CrossRef]; Gimhae, G.N., Six human factors to acceptability of wearable computers (2013) Int. J. Multimed. Ubiquitous Eng, 8, pp. 103-114; Dias, R.D., Ngo-Howard, M.C., Boskovski, M.T., Zenati, M.A., Yule, S.J., Systematic review of measurement tools to assess surgeons’ intraoperative cognitive workload (2018) Br. J. Surg, 105, pp. 491-501. , [CrossRef] [PubMed]; Omurtag, A., Roy, R.N., Dehais, F., Chatty, L., Garbey, M., Tracking mental workload by multimodal measurements in the operating room (2019) Neuroergonomics, pp. 99-103. , Elsevier: Amsterdam, The Netherlands; Memar, A.H., Esfahani, E.T., Objective Assessment of Human Workload in Physical Human-robot Cooperation Using Brain Monitoring (2019) ACM Trans. Hum. Robot Interact, 9, pp. 1-21. , [CrossRef]","Roy, R.N.; ISAE-SUPAERO, France; email: raphaelle.roy@isae-supaero.fr
Roy, R.N.; ANITI—Artificial and Natural Intelligence Toulouse Institute, France; email: raphaelle.roy@isae-supaero.fr",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85096654806
"Rauchbauer B., Hmamouche Y., Bigi B., Prévot L., Ochs M., Chaminade T.","56607418800;57170656400;6506614361;57202367740;13608908400;8339688200;","Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fMRI scanning",2020,"LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings",,,,"668","675",,3,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096592111&partnerID=40&md5=dc7843cd98622660dc46999edb3d2e02","Aix Marseille Université, CNRS, INT, Marseille, France; Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France; Aix Marseille Université, CNRS, LNC, Marseille, France; Aix Marseille Université, CNRS, LIS, Marseille, France; Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France","Rauchbauer, B., Aix Marseille Université, CNRS, INT, Marseille, France, Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France, Aix Marseille Université, CNRS, LNC, Marseille, France; Hmamouche, Y., Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France, Aix Marseille Université, CNRS, LIS, Marseille, France; Bigi, B., Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France; Prévot, L., Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France; Ochs, M., Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France; Chaminade, T., Aix Marseille Université, CNRS, INT, Marseille, France","In this paper we present investigation of real-life, bi-directional conversations. We introduce the multimodal corpus derived from these natural conversations alternating between human-human and human-robot interactions. The human-robot interactions were used as a control condition for the social nature of the human-human conversations. The experimental set up consisted of conversations between the participant in a functional magnetic resonance imaging (fMRI) scanner and a human confederate or conversational robot outside the scanner room, connected via bidirectional audio and unidirectional videoconferencing (from the outside to inside the scanner). A cover story provided a framework for natural, real-life conversations about images of an advertisement campaign. During the conversations we collected a multimodal corpus for a comprehensive characterization of bi-directional conversations. In this paper we introduce this multimodal corpus which includes neural data from functional magnetic resonance imaging (fMRI), physiological data (blood flow pulse and respiration), transcribed conversational data, as well as face and eye-tracking recordings. Thus, we present a unique corpus to study human conversations including neural, physiological and behavioral data. © European Language Resources Association (ELRA), licensed under CC-BY-NC","Artificial agents; Conversation; Physiology","Eye tracking; Functional neuroimaging; Magnetic resonance imaging; Man machine systems; Physiology; Scanning; Video conferencing; Behavioral data; Bi-directional; Blood flow; Experimental set up; Functional magnetic resonance imaging; Multi-modal; Neural data; Physiological data; Social robots",,,,,"ANR-16-CONV-0002; Aix-Marseille Université, AMU; Fondation pour la Recherche Médicale, FRM: SPF20171039127","Research supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and AAP-ID-17-46-170301-11.1 by the Excellence Initiative of Aix-Marseille University (A*MIDEX), a French ""Investissement d'Avenir"" programme and the Institute for Language, Communication and the brain (ILCB ANR-16CONV-0002). BR is supported by the Fondation pour la Recherche M?dicale (FRM, SPF20171039127).",,,,,,,,,,"Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: a back-projected human-like robot head for multiparty human-machine interaction (2012) Cognitive behavioural systems, pp. 114-130. , Springer; Amos, B., Ludwiczuk, B., Satyanarayanan, M., Openface: A general-purpose face recognition library with mobile applications (2016) CMU School of Computer Science, 6; Ashburner, J., A fast diffeomorphic image registration algorithm (2007) Neuroimage, 38 (1), pp. 95-113; Barnes, B. K., Discourse particles in French conversation:(eh) ben, bon, and enfin (1995) French Review, pp. 813-821; Bigi, B., A multilingual text normalization approach (2011) Language and Technology Conference, pp. 515-526. , Springer; Bigi, B., SPPAS-multi-lingual approaches to the automatic annotation of speech (2015) The Phonetician-International Society of Phonetic Sciences, (111-112), pp. 54-69; Blache, P., Bertrand, R., Ferré, G., Creating and exploiting multimodal annotated corpora: the ToMA project (2009) Multimodal corpora, pp. 38-53. , Springer; Boersma, P., Praat, a system for doing phonetics by computer (2002) Glot International, 5; Britton, J. C., Taylor, S. F., Berridge, K. C., Mikels, J. A., Liberzon, I., Differential subjective and psychophysiological responses to socially and nonsocially generated emotional stimuli (2006) Emotion, 6 (1), p. 150; Chaminade, T., An experimental approach to study the physiology of natural social interactions (2017) Interaction Studies, 18 (2); Dennett, D. C., (1989) The intentional stance, , Cambridge, MA, USA: MIT press; Ellis, Y., Laughing together: Laughter as a feature of affiliation in French conversation (1997) Journal of French Language Studies, 7 (2), pp. 147-161; Frith, C. D., Frith, U., Interacting minds-a biological basis (1999) Science, 286 (5445), pp. 1692-1695; Gorgolewski, K., Esteban, O., Schaefer, G., Wandell, B., Poldrack, R., OpenNeuro-a free online platform for sharing and analysis of neuroimaging data (2017) Organization for Human Brain Mapping, 1677 (2). , Vancouver, Canada; Gravano, A., Hirschberg, J., Benuš, Š., Affirmative cue words in task-oriented dialogue (2011) Computational Linguistics, 38 (1), pp. 1-39; Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J., Iglesias, S., Stephan, K. E., The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data (2017) Journal of Neuroscience Methods, 276, pp. 56-72. , https://doi.org/https://doi.org/10.1016/j.jneumeth.2016.10.019; Ochs, M., Jain, S., Blache, P., Toward an automatic prediction of the sense of presence in virtual reality environment (2018) Proceedings of the 6th International Conference on Human-Agent Interaction, pp. 161-166. , ACM; Prévot, L., Bigi, B., Bertrand, R., A quantitative view of feedback lexical markers in conversational French (2013) Proceedings of the SIGDIAL 2013 Conference, pp. 87-91; Prévot, L., Gorish, J., Mukherjee, S., Annotation and classification of french feedback communicative functions (2015) The 29th Pacific Asia Conference on Language, Information and Computation; Rauchbauer, B., Nazarian, B., Bourhis, M., Ochs, M., Prévot, L., Chaminade, T., Brain activity during reciprocal social interaction investigated using conversational robots as control condition (2019) Philosophical Transactions of the Royal Society B, 374 (1771), p. 20180033; Schiffrin, D., (1987) Discourse markers, , Cambridge University Press; Smedt, T. De, Daelemans, W., Pattern for python (2012) Journal of Machine Learning Research, 13 (Jun), pp. 2063-2067; Swerts, M., Filled pauses as markers of discourse structure (1998) Journal of Pragmatics, 30 (4), pp. 485-496; Whitfield-Gabrieli, S., Nieto-Castanon, A., Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks (2012) Brain Connectivity, 2 (3), pp. 125-141; Wood, E., Baltrusaitis, T., Zhang, X., Sugano, Y., Robinson, P., Bulling, A., Rendering of eyes for eye-shape registration and gaze estimation (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 3756-3764; Bigi, B., SPPAS - Multi-lingual Approaches to the Automatic Annotation of Speech (2015) In ""the Phonetician"" - International Society of Phonetic Sciences, pp. 54-69. , http://www.sppas.org/, ISSN 0741-6164 111-112 2015-I-II, pages retrieved from; Boersma, P., Weenink, D., (2018) Praat: doing phonetics by computer [Computer program], , http://www.praat.org/, Version 6.0.37, retrieved 14 March 2018 from; (2015) Équipe ORTOLANG, , https://www.ortolang.fr, Open Resources and Tools for LANGuage (Ortolang) 7.1.1, ORTOLANG ISSN 2417-7482; Gorgolewski, K., Esteban, O., Schaefer, G., Wandell, B., Poldrack, R., OpenNeuro-a free online platform for sharing and analysis of neuroimaging data (2017) Organization for Human Brain Mapping, 1677 (2). , https://openneuro.org/, Vancouver, Canada",,"Calzolari N.Bechet F.Blache P.Choukri K.Cieri C.Declerck T.Goggi S.Isahara H.Maegaard B.Mariani J.Mazo H.Moreno A.Odijk J.Piperidis S.","Amazon AWS;Bertin;Lenovo;Ontotex;Vecsys;Vocapia","European Language Resources Association (ELRA)","12th International Conference on Language Resources and Evaluation, LREC 2020","11 May 2020 through 16 May 2020",,164155,,9791095546344,,,"English","LREC - Int. Conf. Lang. Resour. Eval., Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85096592111
"Madhumitha G., Nandhini M., Senthilnathan R.","57194039543;57197795245;57135495100;","Development of pass-through augmented reality interface for human robot interaction",2020,"International Journal of Advanced Mechatronic Systems","8","2-3",,"75","85",,,"10.1504/IJAMECHS.2020.111307","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096532208&doi=10.1504%2fIJAMECHS.2020.111307&partnerID=40&md5=8f0a9bce7b17279b78d103ffaa54cc33","Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, India","Madhumitha, G., Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, India; Nandhini, M., Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, India; Senthilnathan, R., Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, India","In this technology driven world, human machine interaction is indispensable. Conventional human machine interface techniques pose the challenges of extensive training and increased cognitive load. Usage of hand gestures, face and speech recognition, eye ball tracking, etc. are in the upward trend to overcome these limitations and prove to be more intuitive and user friendly. In this paper, a vision-based pass-through augmented reality system is developed that enables the user to command the mobile robot for various mobility and navigation tasks. A pass-through augmented reality setup is achieved by a combination of a typical virtual reality headset and a stereo camera with depth perception. The stereo camera and a Leap Motion sensor mounted on a wearable VR headset acts as the vision system for the user and the gesture recognition system respectively. Necessary graphical user interface with the list of functions and information will appear on the VR headset display from which the user can select the option/command to be given to the mobile robot. The gesture made by the user in the GUI is detected and communicated to the robot through wireless means to perform the corresponding task. Copyright © 2020 Inderscience Enterprises Ltd.","Augmented reality; Human-machine interaction; Mixed reality",,,,,,,"This project is funded by the Selective Academic Excellence Initiative of SRM Institute of Science and Technology, Kattankulathur. The authors also extend their acknowledgements to Mr. Sandeep Soleti, Mr. Amitansh V. Singh and Mr. Mohammad Jasim for their valuable contribution to the project.",,,,,,,,,,"Arifin, Y., Sastria, T.G., Barlian, E., User experience metric for augmented reality application: a review (2018) Procedia Computer Science, 135 (1), pp. 648-656; Bhamare, M., Mahajan, K.J., Virtual automation using mixed reality and Leap Motion control (2017) International Research Journal of Engineering and Technology (IRJET), 4 (12), pp. 916-918; Cannan, J., Hu, H., (2013) Human-machine Interaction (HMI): A Survey, , School of Computer Science and Electronic Engineering, University of Essex; Flavián, C., Ibáñez-Sánchez, S., Orús, C., The impact of virtual, augmented and mixed reality technologies on the customer experience (2019) Journal of Business Research, 100 (1), pp. 547-560; Igorevic, R.R., Guo, J., (2011) Hand Gesture Recognition and Interaction with 3D Stereo Camera, , Department of Computer Science, Australian National University; Kerefeyn, S., Maleshkov, S., Manipulation of virtual objects through a Leap Motion optical sensor (2015) International Journal of Computer Sciences Issues (IJCSI), 12 (5), pp. 52-57; Leap Motion Sensor, , https://www.leapmotion.com/, [online] (accessed 3 July 2019); Liu, H., Wang, L., Gesture recognition for human robot collaboration: a review (2017) International Journal of Industrial Ergonomics, 68 (1), pp. 355-367; Oculus Rift VR Headset, , https://www.oculus.com/rift/, [online] (accessed 3 July 2019); Pourazar, B., Meruvia-Pastor, O., A comprehensive framework for evaluation of stereo correspondence solutions in immersive augmented and virtual realities (2016) Journal of Virtual Reality and Broadcasting, 13 (1), p. 2. , Articl; Sauer, M., Driewer, F., Gollnitz, M., Schilling, K., Potential and challenges of stereo augmented reality for mobile robot teleoperation (2007) Computer Science VII: Robotics and Telematics, , IFAC, University of Wurzburg, Germany; Sauer, M., Zeiger, F., Schilling, K., Mixed-reality user interface for mobile robot teleoperation in ad-hoc networks (2010) 2nd IFAC Symposium on Telematics Applications, , Politehnica University, Timisoara, Romania; (2016) The Future of Augmented Reality: Hololens, , http://spectrum.ieee.org/NorthAmerican, June [online] (accessed 3 July 2019); Zed Mini Stereo Camera, , https://www.stereolabs.com/zedmini/, [online] (accessed 3 July 2019); Zhu, J., Wang, X., Rosenman, M., Fusing multiple sensors information into mixed reality-based user interface for robot teleoperation (2009) Proceedings of IEEE International Conference on Systems, Man and Cybernetics, , San Antonio, TX, USA","Madhumitha, G.; Department of Mechatronics Engineering, India; email: madhumig@srmist.edu.in",,,"Inderscience Publishers",,,,,17568412,,,,"English","Int. J. Adv. Mechatronic Syst.",Article,"Final","",Scopus,2-s2.0-85096532208
"Wei D., Zhang R., Saadatzi M.N., Olowo O.O., Popa D.O.","57195065540;57195069295;36642836100;57193411592;7102861346;","Organic Piezoresistive Pressure Sensitive Robotic Skin for Physical Human-Robot Interaction",2020,"Proceedings of the ASME Design Engineering Technical Conference","1",,"V001T01A013","","",,,"10.1115/DETC2020-22604","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096345026&doi=10.1115%2fDETC2020-22604&partnerID=40&md5=d682008b15fa4273ab114b09f3c8ac4f","Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States","Wei, D., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Zhang, R., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Saadatzi, M.N., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Olowo, O.O., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Popa, D.O., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States","Pressure sensitive robotic skins have long been investigated for applications to physical human-robot interaction (pHRI). Numerous challenges related to fabrication, sensitivity, density, and reliability remain to be addressed under various environmental and use conditions. In our previous studies, we designed novel strain gauge sensor structures for robotic skin arrays. We coated these star-shaped designs with an organic polymer piezoresistive material, Poly (3, 4-ethylenedioxythiophene)-ploy(styrenesulfonate) or PEDOT: PSS and integrated sensor arrays into elastomer robotic skins. In this paper, we describe a dry etching photolithographic method to create a stable uniform sensor layer of PEDOT:PSS onto starshaped sensors and a lamination process for creating doublesided robotic skins that can be used with temperature compensation. An integrated circuit and load testing apparatus was designed for testing the resulting robotic skin pressure performance. Experiments were conducted to measure the loading performance of the resulting sensor prototypes and results indicate that over 80% sensor yields are possible with this fabrication process. Copyright © 2020 ASME.","Fabrication; Lamination; Pedot:pss; Phri; Robotic skin sensor","Conducting polymers; Design; Dry etching; Integrated circuit testing; Load testing; Man machine systems; Robotics; Strain gages; Fabrication process; Integrated sensor arrays; Loading performance; Physical human-robot interactions; Physical humanrobot interaction (phri); Piezoresistive materials; Poly-3 ,4-ethylenedioxythiophene; Temperature compensation; Human robot interaction",,,,,"National Science Foundation, NSF: 1828355; Office of Experimental Program to Stimulate Competitive Research, EPSCoR: 1849213","This work was supported by National Science Foundation awards MRI #1828355 and EPSCOR #1849213. We would like to thank Joshua Baptist and the staffs of University of Louisville’s Micro Nano Technology Center (MNTC) for their great suggestions and help in fabricating sensor samples.",,,,,,,,,,"Bartolozzi, C., Natale, L., Nori, F., Metta, G., Robots with a sense of touch (2016) Nature materials, 15 (9), p. 921; Johnsson, M., Balkenius, C., Sense of touch in robots with self-organizing maps (2011) IEEE Transactions on Robotics, 27 (3), pp. 498-507; Ward-Cherrier, B., Cramphorn, L., Lepora, N. F., Tactile manipulation with a TacThumb integrated on the open-hand M2 gripper (2016) IEEE Robotics and Automation Letters, 1 (1), pp. 169-175; Mukai, T., Onishi, M., Odashima, T., Hirano, S., Luo, Z., Development of the tactile sensor system of a human-interactive robot ""RI-MAN"" (2008) IEEE Transactions on robotics, 24 (2), pp. 505-512; Hoshi, T., Shinoda, H., Robot skin based on toucharea-sensitive tactile element Proc. Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 3463-3468. , IEEE; Inaba, M., Hoshino, Y., Nagasaka, K., Ninomiya, T., Kagami, S., Inoue, H., A full-body tactile sensor suit using electrically conductive fabric and strings Proc. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS'96, pp. 450-457. , IEEE; Lumelsky, V. J., Shur, M. S., Wagner, S., Sensitive skin (2001) IEEE Sensors Journal, 1 (1), pp. 41-51; Wang, C., Hwang, D., Yu, Z., Takei, K., Park, J., Chen, T., Ma, B., Javey, A., User-interactive electronic skin for instantaneous pressure visualization (2013) Nature materials, 12 (10), p. 899; Argall, B. D., Billard, A. G., A survey of tactile human-robot interactions (2010) Robotics and autonomous systems, 58 (10), pp. 1159-1176; Dahiya, R. S., Metta, G., Valle, M., Sandini, G., Tactile sensing-from humans to humanoids (2009) IEEE transactions on robotics, 26 (1), pp. 1-20; Saadatzi, M. N., Baptist, J. R., Yang, Z., Popa, D. O., Modeling and Fabrication of Scalable Tactile Sensor Arrays for Flexible Robot Skins (2019) IEEE Sensors Journal; Baptist, J., Zhang, R., Wei, D., Saadatzi, M., Popa, D., Fabrication of strain gauge sensor arrays for tactile skins Proc. Proc. SPIE.; Das, S. K., Baptist, J. R., Sahasrabuddhe, R., Lee, W. H., Popa, D. O., Package analysis of 3D-printed piezoresistive strain gauge sensors Proc. Sensors for Next-Generation Robotics III, International Society for Optics and Photonics, p. 985905; Saadatzi, M. N., Baptist, J. R., Wijayasinghe, I. B., Popa, D. O., Characterization of large-area pressure sensitive robot skin Proc. Smart Biomedical and Physiological Sensor Technology XIV, International Society for Optics and Photonics, p. 102160G; Nothnagle, C., Baptist, J. R., Sanford, J., Lee, W. H., Popa, D. O., Wijesundara, M. B., EHD printing of PEDOT: PSS inks for fabricating pressure and strain sensor arrays on flexible substrates Proc. Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, International Society for Optics and Photonics, p. 949403; Saadatzi, M. N., Yang, Z., Baptist, J. R., Sahasrabuddhe, R. R., Wijayasinghe, I. B., Popa, D. O., Parametric investigation of scalable tactile sensors Proc. Smart Biomedical and Physiological Sensor Technology XIV, International Society for Optics and Photonics, p. 102160A","Wei, D.; Department of Electrical and Computer Engineering, United States; email: danming.wei@louisville.edu",,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020","17 August 2020 through 19 August 2020",,164730,,9780791883907,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85096345026
"She Y., Gu Z., Song S., Su H.-J., Wang J.","56545216500;57219491844;57200117296;8379272100;7701333092;","A continuously tunable stiffness arm with cable-driven mechanisms for safe physical human-robot interaction",2020,"Proceedings of the ASME Design Engineering Technical Conference","10",,"V010T10A060","","",,,"10.1115/DETC2020-22035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096113665&doi=10.1115%2fDETC2020-22035&partnerID=40&md5=e4af94f67774880489f390140324a689","Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA  15213, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States","She, Y., Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Gu, Z., Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA  15213, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","In this paper, we present a continuously tunable stiffness arm for safe physical human-robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human-robot interaction via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/actuators, variable stiffness links for safe physical human-robot interactions are much less studied. This paper details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26 % given a morphing angle of 90◦. The study demonstrates that the compliant link design could be a promising approach to address safety concerns for safe physical human-robot interactions. Copyright © 2020 ASME",,"3D printers; Cables; Machine design; Man machine systems; Physical addresses; Robotic arms; Robotics; Safety engineering; Stiffness; Universal joints; Cable-driven mechanisms; Design and modeling; Lateral stiffness; Mechanical compliance; Physical human-robot interactions; Static experiments; Tunable stiffness; Variable stiffness; Social robots",,,,,"National Science Foundation, NSF: CMMI-1637656","This material is based upon work supported by the National Science Foundation under Grant No: CMMI-1637656. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"Mutlu, B., Forlizzi, J., Robots in organizations: the role of workflow, social, and environmental factors in human-robot interaction (2008) Human-Robot Interaction (HRI), 2008 3rd ACM/IEEE International Conference on, pp. 287-294. , IEEE; Wolf, S., Hirzinger, G., A new variable stiffness design: Matching requirements of the next robot generation (2008) IEEE International Conference on Robotics and Automation, pp. 1741-1746. , 2008. ICRA; Wolf, S., Eiberger, O., Hirzinger, G., The DLR FSJ: Energy based design of a variable stiffness joint 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 5082-5089; Friedl, W., Höppner, H., Petit, F., Hirzinger, G., Wrist and forearm rotation of the DLR hand arm system: Mechanical design, shape analysis and experimental validation 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1836-1842; Ménard, T., Grioli, G., Bicchi, A., A stiffness estimator for agonistic #x2013;antagonistic variable-stiffness-actuator devices IEEE Transactions on Robotics, 30 (5), pp. 1269-1278; Garabini, M., Passaglia, A., Belo, F., Salaris, P., Bicchi, A., Optimality principles in variable stiffness control: The VSA hammer 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3770-3775; Schiavi, R., Grioli, G., Sen, S., Bicchi, A., VSA-II: a novel prototype of variable stiffness actuator for safe and performing robots interacting with humans (2008) IEEE International Conference on Robotics and Automation, pp. 2171-2176. , 2008. ICRA; Tonietti, G., Schiavi, R., Bicchi, A., Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 526-531; Zinn, M., Khatib, O., Roth, B., Salisbury, J. K., Playing it safe [human-friendly robots IEEE Robotics Automation Magazine, 11 (2), pp. 12-21; Migliore, S. A., Brown, E. A., DeWeerth, S. P., Biologically inspired joint stiffness control Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 4508-4513; Koganezawa, K., Mechanical stiffness control for antagonistically driven joints 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1544-1551; Pratt, G. A., Williamson, M. M., Series elastic actuators 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems 95.’Human Robot Interaction and Cooperative Robots’, Proceedings, 1, pp. 399-406. , vol.1; She, Y., Meng, D., Cui, J., Su, H.-J., On the impact force of human-robot interaction: Joint compliance vs. link compliance (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6718-6723. , IEEE; She, Y., Song, S., Su, H.-J., Wang, J., A comparative study on the effect of mechanical compliance for a safe physical human–robot interaction (2020) Journal of Mechanical Design, 142 (6); Park, J.-J., Kim, B.-S., Song, J.-B., Kim, H.-S., Safe link mechanism based on nonlinear stiffness for collision safety Mechanism and Machine Theory, 43 (10), pp. 1332-1348; Zhang, M., Laliberté, T., Gosselin, C., Force capabilities of two-degree-of-freedom serial robots equipped with passive isotropic force limiters Journal of Mechanisms and Robotics, 8 (5), p. 051002; López-Martínez, J., Blanco-Claraco, J. L., García-Vallejo, D., Giménez-Fernández, A., Design and analysis of a flexible linkage for robot safe operation in collaborative scenarios Mechanism and Machine Theory, 92, pp. 1-16; She, Y., Su, H.-J., Hurd, C. J., Shape optimization of 2d compliant links for design of inherently safe robots (2015) ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V05BT08A004-V05BT08A004. , American Society of Mechanical Engineers; She, Y., Su, H.-J., Meng, D., Song, S., Wang, J., Design and modeling of a compliant link for inherently safe corobots (2018) Journal of Mechanisms and Robotics, 10 (1), p. 011001; Hines, L., Arabagi, V., Sitti, M., Shape memory polymer-based flexure stiffness control in a miniature flapping-wing robot (2012) IEEE Transactions on Robotics, 28 (4), pp. 987-990; Galloway, K. C., Clark, J. E., Koditschek, D. E., Variable stiffness legs for robust, efficient, and stable dynamic running (2013) Journal of Mechanisms and Robotics, 5 (1), p. 011009; She, Y., Su, H.-J., Lai, C., Meng, D., Design and prototype of a tunable stiffness arm for safe human-robot interaction (2016) ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, , American Society of Mechanical Engineers Digital Collection; She, Y., Su, H.-J., Meng, D., Lai, C., Design and modeling of a continuously tunable stiffness arm for safe physical human–robot interaction (2020) Journal of Mechanisms and Robotics, 12 (1); Howell, L. L., Midha, A., A method for the design of compliant mechanisms with small-length flexural pivots (1994) Journal of mechanical design, 116 (1), pp. 280-290; Howell, L. L., Midha, A., Parametric deflection approximations for end-loaded, large-deflection beams in compliant mechanisms (1995) Journal of Mechanical Design, 117 (1), pp. 156-165; Howell, L. L., (2001) Compliant mechanisms, , John Wiley & Sons; Park, J.-J., Kim, B.-S., Song, J.-B., Kim, H.-S., Safe link mechanism based on nonlinear stiffness for collision safety (2008) Mechanism and Machine Theory, 43 (10), pp. 1332-1348; López-Martínez, J., Blanco-Claraco, J. L., García-Vallejo, D., Giménez-Fernández, A., Design and analysis of a flexible linkage for robot safe operation in collaborative scenarios (2015) Mechanism and Machine Theory, 92, pp. 1-16; Zhang, M., Laliberté, T., Gosselin, C., Force capabilities of two-degree-of-freedom serial robots equipped with passive isotropic force limiters (2016) Journal of Mechanisms and Robotics, 8 (5), p. 051002",,,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020","17 August 2020 through 19 August 2020",,164730,,9780791883990,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85096113665
"Ötting S.K., Masjutin L., Steil J.J., Maier G.W.","57192939244;57219892967;6701553064;7201409088;","Let’s Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human–Robot Interaction at Work",2020,"Human Factors",,,,"","",,3,"10.1177/0018720820966433","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095954429&doi=10.1177%2f0018720820966433&partnerID=40&md5=d1c68a9c36a9caf4da93e8c26ec90738","Bielefeld University, Germany; Technische Universität Braunschweig, Germany","Ötting, S.K., Bielefeld University, Germany; Masjutin, L., Bielefeld University, Germany; Steil, J.J., Technische Universität Braunschweig, Germany; Maier, G.W., Bielefeld University, Germany","Objective: This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human–robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Background: Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing. Method: Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study. Results: Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown. Conclusion: Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features’ effects. Application: Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context. © Copyright 2020, Human Factors and Ergonomics Society.","adaptability; autonomy; design features; human likeness; interface","Controllers; Job analysis; Machine design; Personnel; Empirical research; Implementation cost; Multiple features; Robot interactions; Situation awareness; Specific effects; Structured coding; Task performance; Social robots; article; awareness; effect size; empirical research; employee; experimental study; human; human experiment; meta analysis; robotics; satisfaction; task performance; trust; visibility; workload; workplace",,,,,,,,,,,,,,,,"Adalgeirsson, S.O., Breazeal, C., MeBot: A robotic platform for socially embodied presence (2010) HRI ’10: Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction, pp. 15-22. , http://dl.acm.org/citation.cfm?id=1734454.1734467, In,., IEEE Press, (, –; 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"Porzel R., Cangalovic V.","21741148500;57212534241;","What say you: An ontological representation of imperative meaning for human-robot interaction",2020,"CEUR Workshop Proceedings","2708",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095605279&partnerID=40&md5=20963ea340ddf494fd8a751122c9a0ca","Digital Media Lab., Bremen University, Bremen, Germany; Applied Linguistics, Bremen University, Bremen, Germany","Porzel, R., Digital Media Lab., Bremen University, Bremen, Germany; Cangalovic, V., Applied Linguistics, Bremen University, Bremen, Germany","There is a distinct difference between modeling linguistic knowledge and knowledge that is expressed linguistically. The research effort described herein concerns the latter, i.e. how to model the output of a natural language understanding system in a formal ontology in such a way that robotic agents can carry out the tasks given to them via natural language. For this, we build on a given foundational ontology that is suitable for our requirements and introduce the basic modeling principles and design patterns to model and represent the meaning of instructions. Linguistically, our model is informed by constructional approaches to language understanding, i.e. embodied- and fluid construction grammar. Ontologically, we base our model on the foundational framework provided by the Dolce Ultra Light + Descriptions and Situations ontology. The proposed model, called SOMA-SAY, is part of a larger system of ontologies that are employed in the Everyday Activity Science and Engineering collaborative research center. Copyright © 2020 for this paper by its authors.","Everyday activities; Natural language understanding; Robotics","Linguistics; Ontology; Collaborative research; Fluid construction grammars; Foundational ontologies; Language understanding; Linguistic knowledge; Natural language understanding; Ontological representation; Science and engineering; Human robot interaction",,,,,,,,,,,,,,,,"Beetz, M, Bessler, D, Haidu, A, Pomarlan, M, Bozcuoglu, AK, Bartels, G., Know Rob 2.0 - A 2nd Generation Knowledge Processing Framework for Cognition-Enabled Robotic Agents (2018) Proceedings - IEEE International Conference on Robotics and Automation, pp. 512-519; Steels, L., Basics of Fluid Construction Grammar [Journal Article] (2017) Constructions and Frames, 9 (2), pp. 178-225; Lakoff, G., (1987) Women, Fire, and Dangerous Things: What Categories Reveal about the Mind, , University of Chicago Press; Feldman, J, Lakoff, G, Bailey, D, Narayanan, S, Regier, T, Stolcke, A., L0—The First Five Years of an Automated Language Acquisition Project (1996) AI Review, 10, pp. 103-129; Farrar, S, Langendoen, T., A Linguistic Ontology for the Semantic Web (2004) GLOT, 7, pp. 97-100. , 06; Niles, I, Pease, A., Towards a Standard Upper Ontology (2001) Proceedings of the International Conference on Formal Ontology in Information Systems - Volume 2001. 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Berlin, Heidelberg: Springer Berlin Heidelberg; Masolo, C, Borgo, S, Gangemi, A, Guarino, N, Oltramari, A., Wonderweb deliverable d18, ontology library (final) (2003) ICT project, p. 31. , 33052; Cimiano, P, Buitelaar, P, Frank, A, Racioppa, S, Sintek, M, Kiesel, M, LingInfo: Design and Applications of a Model for the Integration of Linguistic Information in Ontologies (2006) Proceedings of the OntoLex Workshop at LREC, pp. 28-32. , ELRA; Bateman, JA, Hois, J, Ross, R, Tenbrink, T., A Linguistic Ontology of Space for Natural Language Processing (2010) Artif Intell, 174 (14), pp. 1027-1071. , Sep; Mascardi, V, Cordì, V, Rosso, P., A comparison of upper ontologies (disi-tr-06-21) (2008) Dipartimento di Informatica e Scienze dell’Informazione, Universit´r degli Studi di Genova, 35, p. 16146; Mossakowski, T., The Distributed Ontology, Model and Specification Language–DOL (2016) International Workshop on Algebraic Development Techniques, pp. 5-10. , Springer; Fillmore, C., The mechanisms of construction grammar (1988) Berkeley Linguistics Society, 14, pp. 35-55; Goldberg, AE., (1995) Constructions: A Construction Grammar Approach to Argument Structure, , University of Chicago Press; Fillmore, C, Kay, P., (1999) Construction grammar, , Stanford, CA: CSLI; Chang, N, Feldman, J, Porzel, R, Sanders, K., Scaling Cognitive Linguistics: Formalisms for Language Understanding (2002) Proceedings of the 1st Workshop On Scalable Natural Language Understanding; Katz, J., (1980) Propositional Structure and Illocutionary Force, , Mass.: Harvard University Press; Gangemi, A, Mika, P., Understanding the Semantic Web through Descriptions and Situations (2003) Proceedings of the ODBASE Conference, , Springer; Johnson, M., (1987) The Body in the Mind Metaphors, , University of Chicago Press; Langacker, RW., (1987) Foundations of Cognitive Grammar, 1. , Stanford University Press; Baker, CF, Fillmore, CJ, Lowe, JB., The Berkeley FrameNet Project (1998) Proceedings of the 17th International Conference on Computational Linguistics, 1, pp. 86-90. , ACL’98/COLING’98. USA: Association for Computational Linguistics; Narayanan, S., Moving Right Along: A Computational Model of Metaphoric Reasoning about Events (1999) Proceedings of the Sixteenth National Conference of Artificial Intelligence, , Menlo Park; Pfau, J, Porzel, R, Pomarlan, M, Cangalovic, VS, Grudpan, S, Höffner, S, Give MEANinGS to Robots with Kitchen Clash: A VR Human Computation Serious Game for World Knowledge Accumulation (2019) Entertainment Computing and Serious Games. vol. 11863 of Lecture Notes in Computer Science, pp. 85-96. , Springer; Hedblom, MM, Kutz, O, Mossakowski, T, Neuhaus, F., Between Contact and Support: Introducing a logic for image schemas and directed movement (2017) 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), pp. 256-268",,"Hammar K.Kutz O.Dimou A.Hahmann T.Hoehndorf R.Masolo C.Vita R.",,"CEUR-WS","2020 Joint Ontology Workshops, JOWO 2020","31 August 2020 through 7 October 2020",,164284,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85095605279
"Taschdjian Z.","57219593253;","Why Did the Robot Cross the Road?: A User Study of Explanation in Human-Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12424 LNCS",,,"527","537",,,"10.1007/978-3-030-60117-1_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094173650&doi=10.1007%2f978-3-030-60117-1_39&partnerID=40&md5=80eccc65e3802fdaf48b36d7a4588a84","Georgia Institute of Technology, Atlanta, GA  30332, United States; H2O.ai, Mountain View, CA  94043, United States","Taschdjian, Z., Georgia Institute of Technology, Atlanta, GA  30332, United States, H2O.ai, Mountain View, CA  94043, United States","This work documents a pilot user study evaluating the effectiveness of contrastive, causal and example explanations in supporting human understanding of AI in a hypothetical commonplace human-robot interaction (HRI) scenario. In doing so, this work situates “explainable AI” (XAI) in the context of the social sciences and suggests that HRI explanations are improved when informed by the social sciences. © 2020, Springer Nature Switzerland AG.","Explainable AI; HCI; Human-robot interaction","Behavioral research; Human computer interaction; Human robot Interaction (HRI); Human understanding; User study; Human robot interaction",,,,,,,,,,,,,,,,"Ackermann, E., Piaget’s Constructivism (2001) Papert’s Constructionism: What’s the Difference?, Vol, p. 5; Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N., Intelligible models for healthcare (2015) Predicting Pneumonia Risk and Hospital; Proceedings of the 21St ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD 2015, pp. 1721-1730. , https://doi.org/10.1145/2783258.2788613; Chapman, A., Hadfield, M., Chapman, C., Qualitative research in healthcare: An introduction to grounded theory using thematic analysis (2015) J. R. Coll. Physicians Edinb., 45 (3), pp. 201-205. , https://doi.org/10.4997/JRCPE.2015.305; Gopnik, A., The post-piaget era (1996) Psychol. Sci., 7 (4), pp. 221-225. , https://doi.org/10.1111/j.1467-9280.1996.tb00363.x; Grudin, J., AI and HCI: Two fields divided by a common focus (2009) AI Mag, 30 (4), p. 48. , https://doi.org/10.1609/aimag.v30i4.2271; Gunning, D., DARPA’s explainable artificial intelligence (XAI) program (2019) Proceedings of the 24Th International Conference on Intelligent User Interfaces (IUI 2019), , https://doi.org/10.1145/3301275.3308446, Association for Computing Machinery, New York; Halpern, J.Y., Causes and explanations: A structural-model approach. Part I: Causes (2005) Br. J. Philos. Sci., 56 (4), pp. 843-887. , https://doi.org/10.1093/bjps/axi147; Johnson-Laird, P.N., Mental models and human reasoning (2010) Proc. Natl. Acad. Sci., 107 (43). , https://doi.org/10.1073/pnas.1012933107; Jonassen, D., Operationalizing mental models: Strategies for assessing mental models to support meaningful learning and design supportive learning environments (1995) The First International Conference on Computer Support for Collaborative Learning (CSCL 1995), pp. 182-186. , https://doi.org/10.3115/222020.222166, L. Erlbaum Associates Inc., Mahwah; Kim, B., (2015) Interactive and Interpretable Machine Learning Models for Human Machine Collaboration, , MIT, Cambridge; Kozyrkov, C., (2018) Explainable AI won’t Deliver. Here’s Why, , https://hackernoon.com/explainable-ai-wont-deliver-here-swhy-6738f54216be, Accessed 11 Oct 2019; Joyner, D.A., (2018) Intelligent Evaluation and Feedback in Support of a Credit-Bearing; Penstein Rosé, C., AIED 2018 (2018) LNCS (LNAI), 10948. , https://doi.org/10.1007/978-3-319-93846-2.MOOC, Springer, Cham; Kuang, C., Can AI Be Taught to Explain Itself? The New York Times, , https://www.nytimes.com/2017/11/21/magazine/can-aibe-taught-to-explain-itself.html, 21 November 2017; Li, X., Huan, J., Constructivism learning: A learning paradigm for transparent predictive analytics (2017) Proceedings of the 23Rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD 2017, pp. 285-294. , https://doi.org/10.1145/3097983.3097994; Malinsky, D., Danks, D., Causal discovery algorithms: A practical guide (2018) Philos. Compass, 13 (1). , https://doi.org/10.1111/phc3.12470; Miller, T., (2018) Explanation in Artificial Intelligence: Insights from the Social Sciences, , http://arxiv.org/abs/1706.07269, [Cs]; Moreno, R., Mayer, R., Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends (2007) Educ. Psychol. Rev., 19 (3), pp. 309-326. , https://doi.org/10.1007/s10648-007-9047-2; Páez, A., The pragmatic turn in explainable artificial intelligence (XAI) (2019) Minds Mach, 29 (3), pp. 441-459. , https://doi.org/10.1007/s11023-019-09502-w; Papert, S., One AI or many? (1988) Daedalus, 117 (1), pp. 1-14. , www.jstor.org/stable/20025136, Accessed 19 Feb 2020; Parusniková, Z., Popper and hume: Two great skeptics (2019) The Impact of Critical Rationalism, , Sassower, R., Laor, N. (eds.), Palgrave Macmillan, Cham; https://design.google/library/ai/; Reece, B.C., Aristotle’s four causes of action (2019) Australas. J. Philos., 97 (2), pp. 213-227. , https://doi.org/10.1080/00048402.2018.1482932; Rudin, C., (2019) Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead., , http://arxiv.org/abs/1811.10154, September; Springer, A., Hollis, V., Whittaker, S., Dice in the black box: User experiences with an inscrutable algorithm (2017) AAAI Spring Symposium-Technical Report, SS-17-01-, pp. 427-430; Vasconcelos, M., Cardonha, C., Gonçalves, B., Modeling epistemological principles for bias mitigation in ai systems: An illustration in hiring decisions (2018) Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society – AIES 2018, pp. 323-329. , https://doi.org/10.1145/3278721.3278751; Winikoff, M., Debugging agent programs with why? Questions (2017) Proceedings of the 16Th Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2017), pp. 251-259. , International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC","Taschdjian, Z.; Georgia Institute of TechnologyUnited States; email: ztaschdjian@gatech.edu","Stephanidis C.Kurosu M.Degen H.Reinerman-Jones L.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human Computer Interaction,HCII 2020","19 July 2020 through 24 July 2020",,250389,03029743,9783030601164,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85094173650
"Setchi R., Dehkordi M.B., Khan J.S.","6603499046;55510123800;57216273029;","Explainable robotics in human-robot interactions",2020,"Procedia Computer Science","176",,,"3057","3066",,4,"10.1016/j.procs.2020.09.198","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093362632&doi=10.1016%2fj.procs.2020.09.198&partnerID=40&md5=0250699a8285a172630b17414cca0093","Research Centre in AI, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff, CF24 3AA, United Kingdom; Centre for Advanced Studies, Dr. A.P.J.Abdul Kalam Technical University, Lucknow, India","Setchi, R., Research Centre in AI, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff, CF24 3AA, United Kingdom; Dehkordi, M.B., Research Centre in AI, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff, CF24 3AA, United Kingdom; Khan, J.S., Centre for Advanced Studies, Dr. A.P.J.Abdul Kalam Technical University, Lucknow, India","This paper introduces a new research area called Explainable Robotics, which studies explainability in the context of human-robot interactions. The focus is on developing novel computational models, methods and algorithms for generating explanations that allow robots to operate at different levels of autonomy and communicate with humans in a trustworthy and human-friendly way. Individuals may need explanations during human-robot interactions for different reasons, which depend heavily on the context and human users involved. Therefore, the research challenge is identifying what needs to be explained at each level of autonomy and how these issues should be explained to different individuals. The paper presents the case for Explainable Robotics using a scenario involving the provision of medical health care to elderly patients with dementia with the help of technology. The paper highlights the main research challenges of Explainable Robotics. The first challenge is the need for new algorithms for generating explanations that use past experiences, analogies and real-time data to adapt to particular audiences and purposes. The second research challenge is developing novel computational models of situational and learned trust and new algorithms for the real-time sensing of trust. Finally, more research is needed to understand whether trust can be used as a control variable in Explainable Robotics. © 2020 The Authors. Published by Elsevier B.V.","AI; Explainable AI; Explainable Robotics; Explanation; Reasoning; Robotics","Computation theory; Computational methods; Knowledge based systems; Man machine systems; Medical robotics; Robotics; Computational model; Control variable; Level of autonomies; Medical health; Patients with dementia; Real time sensing; Real-time data; Research challenges; Social robots",,,,,"C82092; Llywodraeth Cymru; European Regional Development Fund, ERDF","The authors would like to acknowledge the support of the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS) operation C82092, partially funded by the European Regional Development Fund (ERDF) through the Welsh Government.",,,,,,,,,,"(2019) Explainable AI: The Basics, , https://royalsociety.org/-/media/policy/projects/explainable-ai/AIand-interpretability-policy-briefing.pdf; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 101-108. , Christchurch, New Zealand, 7-10 March 2016; Edmonds, M., Gao, F., Liu, H., Xie, X., Qi, S., Rothrock, B., Zhu, Y., A tale of two explanations: Enhancing human trust by explaining robot behavior (2019) Science Robotics, 4 (37); Core, M.G., Lane, H.C., van Lent, M., Gamboc, D., (2006) Building Explainable Artificial Intelligence Systems, pp. 1766-1773. , MIT Press, AAAI; Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Müller, H., Causability and explainability of artificial intelligence in medicine (2019) Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9 (4); Kiesenberg, P., Weippl, E.W., Holzinger, A., Trust for the doctor-in-the-loop (2016) European Research Consortium for Informatics and Mathematics (ERCIM) News: Tackling Big Data in the Life Sciences, 104 (1), pp. 32-33; Hamburg, M.A., Collins, F., The path to personalized medicine (2010) New England Journal of Medicine, 363 (4), pp. 301-304; Hudec, M., Bednarova, E., Holzinger, A., Augmenting statistical data dissemination by short quantified sentences of natural language (2018) Journal of Official Statistics (JOS), 34, pp. 981-1010; Skirpan, M., Yeh, T., Designing a moral compass for the future of computer vision using speculative analysis (2017) Proc. Of IEEE Computer Vision and Pattern Recognition, pp. 64-73; Montavon, G., Samek, W., Muller, K.-R., Methods for interpreting and understanding deep neural networks (2018) Digital Signal Processing, 73, pp. 1-15; Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Lee, S.-I., From local explanations to global understanding with explainable AI for trees (2020) Nature Machine Intelligence, 2 (1), pp. 2522-5839; Schoenborn, J.M., Althoff, K.-D., Recent trends in XAI: A broad overview on current approaches, methodologies and interactions (2019) Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR) Workshop, pp. 1-10; Poulin, B., Eisner, R., Szafron, D., Lu, P., Greiner, R., Wishart, D.S., Fyshe, A., Anvik, J., Visual explanation of evidence with additive classifiers (2006) National Conference on Artificial Intelligence, AAAI, pp. 1822-1829; Szafron, D., Lu, P., Greiner, R., Wishart, D.S., Poulin, B., Eisner, R., Lu, Z., Meeuwis, D., Proteome analyst: Custom predictions with explanations in a web-based tool for high-throughput proteome annotations (2004) Nucleic Acids Research, 32 (S2), pp. W365-W371; Veruggio, G., Operto, F., Roboethics: A bottom-up interdisciplinary discourse in the field of ap-plied ethics in robotics (2006) International Review of Information Ethics, 6; Deng, B., The robot's dilemma (2015) Nature, 523. , 2 July 2015; Körtner, T., Ethical challenges in the use of social service robots for elderly people (2016) Zeitschrift Für Gerontologie und Geriatrie, 49, pp. 303-307; Sharkey, N., Sharkey, A., Robotic surgery and ethical challenges (2012) Medical Robotics- Minimally Invasive Surgery, pp. 276-291; Hameed, I.A., Tan, Z.-H., Thomsen, N., Duan, X., User acceptance of social robots (2016) ACHI 2016: The Ninth International Conference on Advances in Computer-Human Interactions, pp. 274-279; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Foundations and Trends in Human-Computer Interaction, 1 (3), pp. 203-275; Sheridan, T.B., (2002) Humans and Automation: System Design and Research Issues, , John Wiley and Sons; Schilling, M., Kopp, S., Wachsmuth, S., Wrede, B., Ritter, H., Brox, T., Nebel, B., Burgard, W., Towards a multidimensional perspective on shared autonomy (2016) 2016 AAAI Fall Symposium Series; Nikolaidis, S., Zhu, Y.X., Hsu, D., Srinivasa, S., Human-robot mutual adaptation in shared autonomy (2017) Proc ACM SIGCHI, pp. 294-302. , Mar; 2017; Beer, J.A., Toward a framework for levels of robot autonomy in human-robot interaction (2014) J Hum Robot Interact, 3 (2), pp. 74-99; Scholtz, J., Theory and evaluation of human robot interactions (2003) Proc. Hawaii International Conference on System Science, 36; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artif. Intell., 247, pp. 45-69; Lombrozo, T., The structure and function of explanations (2006) TRENDS in Cognitive Sciences, 10 (10), pp. 464-470; Pennington, N., Hastie, R., Reasoning in explanation-based decision making (1993) Cognition, 49, pp. 123-163; Falcon, A., Aristotle on Causality (2019) The Stanford Encyclopedia of Philosophy, , Edward N. Zalta (ed.), Stanford University, USA; Wilkinson, S., Levels and kinds of explanation: Lessons from neuropsychiatry (2014) Frontiers of Philosophy, 5. , Article","Setchi, R.; Research Centre in AI, United Kingdom; email: setchi@cf.ac.uk","Cristani M.Toro C.Zanni-Merk C.Howlett R.J.Jain L.C.Jain L.C.",,"Elsevier B.V.","24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020","16 September 2020 through 18 September 2020",,163593,18770509,,,,"English",,Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85093362632
"Vo T., Lyons J.B.","57208575959;15623605700;","Producing an Immersive Experience Using Human-Robot Interaction Stimuli",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12425 LNCS",,,"266","276",,,"10.1007/978-3-030-60128-7_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092934520&doi=10.1007%2f978-3-030-60128-7_21&partnerID=40&md5=639b69f99896d18f9184989ad7b73a9d","Ball Aerospace Corporation, Broomfield, United States; Air Force Research Laboratory, WPAFB, Dayton, United States","Vo, T., Ball Aerospace Corporation, Broomfield, United States; Lyons, J.B., Air Force Research Laboratory, WPAFB, Dayton, United States","Human-Robot Interaction (HRI) is an emerging topic within contemporary science and this topic spans the literatures between engineering, psychology, computer science, artificial intelligence, machine learning and robotics. The study of HRI requires scenarios and other affordances from which to contextualize the HRI to study the factors that shape human attitudes, behaviors, and biases as they relate to robots. The current paper details the rationale, software/hardware, and contextual considerations associated with the creation of HRI stimuli used in experiments, specifically the creation of a set of video stimuli. These stimuli centered on the concept of an autonomous security robot (ASR) and provided a scenario wherein humans (research confederates) would interact with the ASR in a realistic scenario. A video was created to foster a realistic visual and auditory representation of the HRI encounter from a dynamic perspective – meaning shifting perspectives between a first-person experiential vantage point to a birds-eye-view of the broader situation. The scenario used involved a security context where the confederates sought access to a secure facility. The ASR’s role was to examine visitor access credentials and determine if the visitor was authorized or not. The robot’s behaviors, while scripted, were depicted as autonomous in the video and included verbal interactions/instructions as well as physical limb motions, gestures, and instructions. Several important features of the stimuli were considered in its creation, namely: realism, immersive experience, and simulated vulnerability of the human to the robot. The current paper walks through each of these considerations with details provided in our approach. To date, the HRI stimuli have been used in multiple experiments and have demonstrated flexibility in addressing multiple research questions in the domain of HRI. © 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.","Artificial intelligence; Autonomous robots; Human-Robot Interaction; Immersive experiences; Security robots","Artificial intelligence; Behavioral research; Engineering education; Human computer interaction; User experience; Auditory representation; Autonomous security; Human robot Interaction (HRI); Important features; Multiple research; Realistic scenario; Software/hardware; Verbal interaction; Social robots",,,,,,,,,,,,,,,,"(2012) Office of the under Secretary of Defense for Acquisition, , Technology and Logistics, Washington, DC; (2016) Office of the under Secretary of Defense for Acquisition, Technology, and Logistics, , Washington, DC; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., de Visser, E.J., Parasuraman, R., A metaanalysis of factors affecting trust in humanrobot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Lyons, J.B., Vo, T., Wynne, K.T., Mahoney, S., Nam, C.S., Gallimore, D., Trusting autonomous robots: The role of reliability and stated social intent Human Factors, , in press; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrated model of organizational trust (1995) Acad. Manag. Rev., 20, pp. 709-734; Robinette, P., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) HRI 2016, 11Th ACM/IEEE International Conference on Human-Robot Interaction, Pp. 101–108; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (Faulty) robot? Effects of error, task type, personality on humanrobot cooperation and trust (2015) HRI 2015, 10Th ACM/IEEE International Conference on Humanrobot Interaction, pp. 1-8; Booth, S., Tomkin, J., Pfister, H., Waldo, J., Gajos, K., Nagpal, R., Piggybacking robots: Humanrobot overtrust in University dormitory security (2017) HRI 2017, 11Th ACM/IEEE International Conference on Humanrobot Interaction, Pp. 426–434; Wiggers, K., (2017) Meet the 400Pound Robots that Will Soon Patrol Parking Lots, Offices, and Malls, , https://www.digitaltrends.com/cooltech/knightscoperobotsinterview/; Inbar, O., Meyer, J., Manners matter: Trust in robotic peacekeepers (2015) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), pp. 185-189; Long, S.K., Karpinskymosely, N.D., Bliss, J., Trust of simulated robotic peacekeepers among resident and expatriate Americans (2017) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61, pp. 2091-2095; Lyons, J.B., Being transparent about transparency: A model for humanrobot interaction (2013) Trust and Autonomous Systems: Papers from the AAAI Spring Symposium (Technical Report SS1307), , Sofge, D., Kruijff, G.J., Lawless, W.F. (eds.) , AAAI Press, Menlo Park; Gallimore, D., Lyons, J.B., Vo, T., Mahoney, S., Wynne, K.T., Trusting robocop: Genderbased effects on trust of an autonomous robot (2019) Front. Psychol.: Humanmedia Interact., 10, p. 482; Lyons, J.B., Nam, C.S., Jessup, S.A., Vo, T.Q., Wynne, K.T., The role of individual differences as predictors of trust in autonomous security robots (2020) Proceedings of IEEE Humanmachine Systems Conference, Rome, IT","Lyons, J.B.; Air Force Research Laboratory, United States; email: joseph.lyons.6@us.af.mil","Stephanidis C.Harris D.Li W.-C.Schmorrow D.D.Fidopiastis C.M.Zaphiris P.Ioannou A.Ioannou A.Fang X.Sottilare R.A.Schwarz J.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human-Computer Interaction,HCII 2020","19 July 2020 through 24 July 2020",,249799,03029743,9783030601270,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85092934520
"Galin R., Meshcheryakov R., Kamesheva S.","57209776926;23035794100;57217104550;","Distributing Tasks in Multi-agent Robotic System for Human-Robot Interaction Applications",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12336 LNAI",,,"99","106",,1,"10.1007/978-3-030-60337-3_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092934301&doi=10.1007%2f978-3-030-60337-3_10&partnerID=40&md5=9cb1cda3fc009f5ae70060e835c1bcc6","V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Galin, R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Meshcheryakov, R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Kamesheva, S., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Human-robot interaction become a trend in robotics and happen in a wide range of situations. This research paper describes human and collaborative robots interaction behind traditional paradigms for robots in a shared workspace. This research shows that humans and robots distributing task in collaborative interaction of multi-agent robotic system. Different methods for distributing tasks on global and local levels in multi-agent robotic system analyzed as part of research work. Characteristics of tasks distribution algorithms are considered. The particle swarm algorithm for task distribution presented as an example. © 2020, Springer Nature Switzerland AG.","Collaboration; Human-robot interaction; Model; Multi-agent system; Safety-oriented behavior","Man machine systems; Multi agent systems; Robotics; Collaborative interaction; Collaborative robots; Distribution algorithms; Multi-agent robotic systems; Particle swarm algorithm; Research papers; Shared-workspace; Task distribution; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331","Acknowledgements. The reported study was partially funded by RFBR according to the research project № 19-08-00331.",,,,,,,,,,"Ermishin, K., Yuschenko (2016) A: Collaborative Mobile Robots-A New Stage of Development of Service Robotics. J. Robot. Tech. Cybern., 3 (12), pp. 3-9; Matheson, E., Human-robot collaboration in manufacturing applications: A review (2019) Robotics, 8 (4), p. 100. , https://doi.org/10.3390/robotics8040100; Galin, R., Meshcheryakov, R., Review on human–robot interaction during collaboration in a shared workspace (2019) ICR 2019. LNCS (LNAI), 11659, pp. 63-74. , https://doi.org/10.1007/978-3-030-26118-4_7, Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) , Springer, Cham; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , https://doi.org/10.1016/j.mechatronics.2018.02.009; Robots and robotic devices – Safety requirements for industrial robots – Part 1, 2: Robot systems and integration (2011) Geneva; (2013) ISO: 12100:2010–11 Safety of Machinery-General Principles for Design – Risk Assessment and Risk Reduction, , Standard, International Organization for Standardization; ISO/TS 15066:2016 Robots and Robotic Devices – Collaborative Robots, , https://www.iso.org/standard/62996.html, Accessed 11 May 2020; Lazarte, M., Robots and Humans Can Work Together with New ISO Guidance, , https://www.iso.org/news/2016/03/Ref2057.html, Accessed 13 May 2020; Galin, R.R., Meshcheryakov, R.V., Human-robot interaction efficiency and human-robot collaboration (2020) Robotics: Industry 4.0 Issues & New Intelligent Control Paradigms. SSDC, 272, pp. 55-63. , https://doi.org/10.1007/978-3-030-37841-7_5, Kravets, A. (ed.) , Springer, Cham; Kaiser, L., Schlotzhauer, A., Brandstötter, M., Safety-related risks and opportunities of key design-aspects for industrial human-robot collaboration (2018) ICR 2018. LNCS (LNAI), 11097, pp. 95-104. , https://doi.org/10.1007/978-3-319-99582-3_11, Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.), Springer, Cham; Vorotnikov, S., Ermishin, K., Nazarova, A., Yuschenko, A., Multi-agent robotic systems in collaborative robotics (2018) ICR 2018. LNCS (LNAI), 11097, pp. 270-279. , https://doi.org/10.1007/978-3-319-99582-3_28, Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.), Springer, Cham; Magrini, E., Human-robot coexistence and interaction in open industrial cells (2020) Robot. Comput.-Integr. Manuf., 61. , https://doi.org/10.1016/j.rcim.2019.101846; Schmidtler, J., Knott, V., Hölzel, C., Bengler, K., Human centered assistance applications for the working environment of the future (2015) Occup. Ergon., 12 (3), pp. 83-95; Hoffman, G., Evaluating fluency in human–robot collaboration (2019) IEEE Trans. Hum.-Mach. Syst., pp. 1-10. , https://doi.org/10.1109/thms.2019.2904558; Lin, F., Hsu, J.Y., Cooperation protocols in multi-agent robotic systems (1997) Auton. Robots, 4, pp. 175-198. , https://doi.org/10.1023/a:1008813631823; Charalambous, G., Human-automation collaboration in manufacturing: Identifying key implementation factors (2013) ICMR 2013, pp. 301-306. , Cranfield University, UK; Galin, R., Cobots and the benefits of their implementation in intelligent manufacturing (2020) IOP Conf. Ser.: Mater. Sci. Eng., 862. , https://doi.org/10.1088/1757-899x/862/3/032075; Nazarova, A.V., Zhai, M., Distributed solution of problems in multi agent robotic systems (2019) Smart Electromechanical Systems. SSDC, 174, pp. 107-124. , https://doi.org/10.1007/978-3-319-99759-9_9, Gorodetskiy, A.E., Tarasova, I.L. (eds.) , Springer, Cham; Liu, C., Tomizuka, M., Designing the robot behavior for safe human–robot interactions (2017) Trends in Control and Decision-Making for Human–Robot Collaboration Systems. SSDC, pp. 241-270. , https://doi.org/10.1007/978-3-319-40533-9_11, Wang, Y., Zhang, F. (eds.) , Springer, Cham; Kennedy, J., Eberhart, R., Particle swarm optimization (1995) IEEE International Conference on Neural Networks, pp. 1942-1948; Wang, Y., Zeng, J., A survey of a multi-objective particle swarm optimization algorithm (2010) CAAI Trans. Intell. Syst., 5 (5), pp. 377-384; Azzouz, R., Bechikh, S., Ben Said, L., Dynamic multi-objective optimization using evolutionary algorithms: A survey (2017) Recent Advances in Evolutionary Multi-Objective Optimization. ALO, 20, pp. 31-70. , https://doi.org/10.1007/978-3-319-42978-6_2, Bechikh, S., Datta, R., Gupta, A. (eds.) , Springer, Cham","Galin, R.; V.A. Trapeznikov Institute of Control Sciences of Russian Academy of SciencesRussian Federation; email: rinat.r.galin@yandex.ru","Ronzhin A.Rigoll G.Meshcheryakov R.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Interactive Collaborative Robotics, ICR 2020","7 October 2020 through 9 October 2020",,249569,03029743,9783030603366,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85092934301
"Karatas N., Sekino H., Tanaka T.","55221289500;57219306141;56434786400;","Utilization of human-robot interaction for the enhancement of performer and audience engagement in performing art",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12427 LNCS",,,"348","358",,,"10.1007/978-3-030-60152-2_26","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092181330&doi=10.1007%2f978-3-030-60152-2_26&partnerID=40&md5=4944a7f85848b45b0054849fa6a20dc3","Nagoya University, Nagoya, 464-8601, Japan; Stony Brook University, Stony Brook, NY  11794, United States","Karatas, N., Nagoya University, Nagoya, 464-8601, Japan; Sekino, H., Stony Brook University, Stony Brook, NY  11794, United States; Tanaka, T., Nagoya University, Nagoya, 464-8601, Japan","Recently, computer-supported interactive technologies have played a significant role as complementary tools in creation of extraordinary artworks. These technologies have been used in art to explore their utility to compose new creative concepts, and to enrich the dimensions of artistic performances to strengthen the engagement between the performer and the audience. Since integrating the audience into the artistic performance has a significant role in enhancing an individual pleasure, a robotic medium holds great potential in bringing about new opportunities for artistic performances. In this preliminary study, we observed the effects of eye gazing behaviours of a minimal robot on audience engagement and connectedness in regard to an artistic performance. With this paper, the results from the data of a limited number of participants show that the audience tended to be distracted by the robot’s existence, however, the gazing behavior of the robot maintain a feeling of connectivity between the robot and the audience. © Springer Nature Switzerland AG 2020.","Engagement; Human robot interaction; Performing arts","Human computer interaction; Complementary tools; Eye-gazing; Interactive technology; Performing arts; Human robot interaction",,,,,,,,,,,,,,,,"Striner, A., McNally, B., (2017) Transitioning between audience and performer: co-designing interactive music performances with children, , https://doi.org/10.1145/3027063.3053171, arXiv preprint arXiv:1702.06236; Jessop, E.N., Torpey, P.A., Bloomberg, B., Music and technology in death and the powers (2011) NIME, pp. 349-354. , May; Freeman, J., (2005) Large Audience Participation, Technology, and Orchestral Performance, , ICMC; Hassib, M., Schneegass, S., Eiglsperger, P., Henze, N., Schmidt, A., Alt, F., EngageMeter: a system for implicit audience engagement sensing using electroen-cephalography (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 5114-5119. , May; Yan, S., Ding, G., Li, H., Sun, N., Wu, Y., Guan, Z., Huang, T., Enhancing audience engagement in performing arts through an adaptive virtual environment with a brain-computer interface (2016) Proceedings of the 21st International Conference on Intelligent User Interfaces, pp. 306-316. , https://doi.org/10.1145/2856767.2856768, March; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5495-5502. , https://doi.org/10.1109/IROS.2009.5354145, IEEE, October; Mutlu, B., Kanda, T., Forlizzi, J., Hodgins, J., Ishiguro, H., Conversational gaze mechanisms for humanlike robots (2012) ACM Trans. Interact. Intell. Syst. (TiiS), 1 (2), pp. 1-33. , https://doi.org/10.1145/2070719.2070725; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708-713. , https://doi.org/10.1109/IROS.2005.1545011, IEEE, August; Kidd, C.D., Breazeal, C., Effect of a robot on user perceptions (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), 4, pp. 3559-3564. , https://doi.org/10.1109/IROS.2004.1389967, IEEE, September; Griffin, R., Baron-Cohen, S., The intentional stance: Developmental and neurocog-nitive perspectives (2002) Daniel Dennett, pp. 83-116; Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H., Hagita, N., Footing in human-robot conversations: how robots might shape participant roles using gaze cues (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 61-68. , https://doi.org/10.1145/1514095.1514109, March; Karatas, N., Tamura, S., Fushiki, M., Okada, M., The effects of driving agent gaze following behaviors on human-autonomous car interaction (2018) ICSR 2018. LNCS (LNAI), 11357, pp. 541-550. , https://doi.org/10.1007/978-3-030-05204-153, Ge, S.S., et al. (eds) Springer, Cham; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: a review (2017) J. Human-Robot Interact, 6 (1), pp. 25-63. , https://doi.org/10.5898/JHRI.6.1.Admoni; Komatsu, T., Kurosawa, R., Yamada, S., How does the difference between users’ expectations and perceptions about a robotic agent affect their behavior? (2012) Int. J. Soc. Robot, 4 (2), pp. 109-116; Have We Lost Our Anthropological Imagination?, , https://www.epicpeople.org/anthropological-imagination/, Accessed 02 October 2017; https://www.therobotreport.com/swiss-startup-makes-good-on-broadway/, Swiss startup makes good on Broadway. Accessed 29 July 2016","Karatas, N.; Nagoya UniversityJapan; email: karatas@mirai.nagoya-u.ac.jp","Stephanidis C.Salvendy G.Wei J.Yamamoto S.Mori H.Meiselwitz G.Nah F.F.-H.Siau K.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,249359,03029743,9783030601515,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85092181330
"Zhong Q., Zheng C., Zhang H.","25653248900;57219265400;57007699100;","Research on discriminative skeleton-based action recognition in spatiotemporal fusion and human-robot interaction",2020,"Complexity","2020",,"8717942","","",,1,"10.1155/2020/8717942","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091978497&doi=10.1155%2f2020%2f8717942&partnerID=40&md5=07ee2f80441ff4104e5f7245f30639bf","Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710054, China","Zhong, Q., Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710054, China; Zheng, C., Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China; Zhang, H., Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China","A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system. © 2020 Qiubo Zhong et al.",,"Classification (of information); Convolutional neural networks; Information filtering; Man machine systems; Musculoskeletal system; Speech recognition; Adaptive feature extractions; Classification results; Competitive performance; Convolutional networks; Discrimination informations; Information sequences; Spatio-temporal fusions; Temporal and spatial; Social robots",,,,,"jg20190487; 2020SCG090; State Key Laboratory for Manufacturing Systems Engineering: sklms2019011","This material is based upon work funded by the State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University Foundation of China, under grant no. sklms2019011, ""13th Five-Year Plan"" Talent Training Project of Higher Education in Zhejiang Province under grant no. jg20190487, and Research Project of Educational Science Planning in Zhejiang Province under grant no. 2020SCG090.",,,,,,,,,,"Simonyan, K., Zisserman, A., (2014) Two-stream Convolutional Networks for Action Recognition in Videos; Bagautdinov, T., Alahi, A., Fleuret, F., Fua, P., Savarese, S., Social Scene Understanding: End-to-end Multi-person Action Localization and Collective Activity Recognition, , Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) July 2017 Honolulu, HI, USA 2-s2.0-85041892279; Wang, H., Schmid, C., Action Recognition with Improved Trajectories, pp. 3551-3558. , Proceedings of the IEEE International Conference on Computer Vision (ICCV) December 2013 Sydney, Australia 2-s2.0-84898805910; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime Multi-person 2d Pose Estimation Using Part Affinity Fields, , Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) July 2017 Honolulu, HI, USA 2-s2.0-85042428134; Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J., Cascaded Pyramid Network for Multi-person Pose Estimation, , Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition June 2018 Salt Lake City, UT, USA 2-s2.0-85061772116; Liu, J., Shahroudy, A., Xu, D., Wang, G., Spatio-temporal LSTM with trust gates for 3d human action recognition (2016) Computer Vision-ECCV 2016, 9907, pp. 816-833. , 2-s2.0-84990059379; Li, C., Zhong, Q., Xie, D., Pu, S., Skeleton-based Action Recognition with Convolutional Neural Networks, , Proceedings of the IEEE International Conference on Multimedia & Expo Workshops ICMEW July 2017 Hong Kong, China 2-s2.0-85031695454; Yan, S., Xiong, Y., Lin, D., (2018) Spatial Temporal Graph Convolutional Networks for Skeleton-based Action Recognition, , http://arxiv.org/abs/1801.07455v2; Yang, C., Jiang, Y., Na, J., Li, Z., Cheng, L., Su, C.-Y., Finite-time convergence adaptive fuzzy control for dual-arm robot with unknown kinematics and dynamics (2019) Ieee Transactions on Fuzzy Systems, 27 (3), pp. 574-588. , 2-s2.0-85052604492; Shi, L., Zhang, Y., Cheng, J., Lu, H., (2019) Skeleton-based Action Recognition with Multi-stream Adaptive Graph Convolutional Networks, , https://arxiv.org/abs/1912.06971; Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W., (2020) Disentangling and Unifying Graph Convolutions Fo Skeleton-based Action Recognition, , https://arxiv.org/abs/2003.14111; Yang, C., Jiang, Y., He, W., Na, J., Li, Z., Xu, B., Adaptive parameter estimation and control design for robot manipulators with finite-time convergence (2018) Ieee Transactions on Industrial Electronics, 65 (10), pp. 8112-8123. , 2-s2.0-85041508667; Yang, C., Luo, J., Pan, Y., Liu, Z., Su, C.-Y., Personalized variable gain control with tremor attenuation for robot teleoperation (2018) Ieee Transactions on Systems, Man, and Cybernetics: Systems, 48 (10), pp. 1759-1770. , 2-s2.0-85018863536; Peng, W., Hong, X., Chen, H., Zhao, G., Learning graph convolutional network for skeleton-based human action recognition by neural searching (2020) Proceedings of the Aaai Conference on Artificial Intelligence, 34 (3), pp. 2669-2676; Obinata, Y., Yamamto, T., (2020) Temporal Extension Module for Skeleton-based Action Recognition, , https://arxiv.org/abs/2003.08951; Shahroudy, A., Liu, J., Ng, T.-T., Wang, G., Ntu Rgb + D: A Large Scale Dataset for 3d Human Activity Analysis, pp. 1010-1019. , Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas, NV, USA 2-s2.0-84986252370; Kay, W., Carreira, J., Simonyan, K., (2017) The Kinetics Human Action Video Dataset, , https://arxiv.org/abs/1705.06950; Shi, L., Zhang, Y., Cheng, J., Lu, H., Two-stream Adaptive Graph Convolutional Networks for Skeleton-based Action, , Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) June 2019 Long Beach, CA, USA","Zhong, Q.; Robotics Institute, China; email: zhongqiubo@nbut.edu.cn",,,"Hindawi Limited",,,,,10762787,,,,"English","Complexity",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85091978497
"Megalingam R.K., Manaswini M., Yannam J., Naick V.S., Chowdary G.N.","35119069500;57216287217;57219228453;57210336095;57210337979;","Human Robot Interaction on Navigation platform using Robot Operating System",2020,"Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020",,,"9171065","898","905",,2,"10.1109/ICISC47916.2020.9171065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091772063&doi=10.1109%2fICISC47916.2020.9171065&partnerID=40&md5=484da35d93bbe6c2c97579396175d8f2","Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India","Megalingam, R.K., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Manaswini, M., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Yannam, J., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Naick, V.S., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Chowdary, G.N., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India","In the last few decades, automation has been one of the leading and emerging technologies to develop sophisticated and robust robotic solutions for various applications like military, agriculture, transport, shopping, house, hospitals, etc. One of these emerging fields is service robotics which is implemented in the shop, home, and hospital environments. As these domains deal with the interaction between Humans and Robots, the concept called Human-Robot Interaction (HRI) has come into existence. In service robotics, voice is the most important mode of communication for HRI. This research proposes the implementation and integration of such HRI on an autonomous navigation system using the Robot Operating System (ROS). This paper also discusses the system architecture that is developed for this research. © 2020 IEEE.","autonomous; integration; mapping; navigation; speech","Agricultural robots; Hospitals; Man machine systems; Military applications; Navigation systems; Robot learning; Robotics; Autonomous navigation systems; Emerging technologies; Hospital environment; Human robot Interaction (HRI); Robot operating system; Robot operating systems (ROS); Robotic solutions; System architectures; Social robots",,,,,,"It is gladdening to thank Humanitarian Technology Labs and the Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Kollam who provided us with a highly encouraging work environment and lab facilities, which was the backbone for completion of this project.",,,,,,,,,,"Voisan, E., Paulis, B., Precup, R., Dragan, F., Ros-based robot navigation and human interaction in indoor environment (2015) 2015 Ieee 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics, Timisoara, pp. 31-36; https://www.google.com; https://www.who.int/news-room/fact-sheets/detail/ageing-and-health; Tharaniya Soundhari, M., Brilly Sangeetha, S., Intelligent interface based speech recognition for home automation using android application (2015) 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1-11. , Coimbatore; Li, R., Liu, J., Zhang, L., Hang, Y., Lidar/mems imu integrated navigation (slam) method for a small uav in indoor environments (2014) 2014 Dgon Inertial Sensors and Systems (ISS), pp. 1-15. , Karlsruhe; Opromolla, R., Fasano, G., Rufino, G., Grassi, M., Savvaris, A., Lidar-inertial integration for uav localization and mapping in complex environments (2016) 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 649-656. , Arlington, va; Megalingam, R.K., Vishnu, S., Sekhar, S., Sasikumar, V., Sreekumar, S., Nair, T.R., Design and implementation of an android application for smart shopping (2019) 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0470-0474. , Chennai, India; Kannan Megalingam, R., Ravi Teja, C., Sreekanth, S., Raj, A., Ros based autonomous indoor navigation simulation using slam algorithm (2018) International Journal of Pure and Applied Mathematics, 118 (7), pp. 199-205; Megalingam, R.K., Nair, R.N., Prakhya, S.M., Automated voice based home navigation system for the elderly and the physically challenged (2011) 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), pp. 1-5. , Chennai; Mishra, S., Kankal, R., Lunawat, S., Ram, P., Gupta, V., Interactive automation interface using android app and speech recognition over wireless lan and internet (2016) International Conference & Workshop on Electronics & Telecommunication Engineering (ICWET 2016), pp. 93-99. , Mumbai; Gatesichapakorn, S., Takamatsu, J., Ruchanurucks, M., Ros based autonomous mobile robot navigation using 2d lidar and rgb-d camera (2019) 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), pp. 151-154. , Bangkok, Thailand; Cheng, Y., Wang, G.Y., Mobile robot navigation based on lidar (2018) 2018 Chinese Control and Decision Conference (CCDC), pp. 1243-1246. , Shenyang; Zhang, Y., Lu, Z., Wang, C., Liu, C., Wang, Y., Voice control dual arm robot based on ros system (2018) 2018 Ieee International Conference on Intelligence and Safety for Robotics (ISR), pp. 232-237. , Shenyang; Li, Z., Xiong, Y., Zhou, L., Ros-based indoor autonomous exploration and navigation wheelchair (2017) 2017 10th International Symposium on Computational Intelligence and Design (ISCID), pp. 132-135. , Hangzhou",,,,"Institute of Electrical and Electronics Engineers Inc.","4th International Conference on Inventive Systems and Control, ICISC 2020","8 January 2020 through 10 January 2020",,162544,,9781728128139,,,"English","Proc. Int. Conf. Inven. Syst. Control, ICISC",Conference Paper,"Final","",Scopus,2-s2.0-85091772063
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","Formal Verification of Human-Robot Interaction in Healthcare Scenarios",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12310 LNCS",,,"303","324",,4,"10.1007/978-3-030-58768-0_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091577651&doi=10.1007%2f978-3-030-58768-0_17&partnerID=40&md5=7d38e35ffa4c0ccb5b814b58b096023b","Politecnico di Milano, Milan, Italy; McMaster University, Hamilton, Canada","Lestingi, L., Politecnico di Milano, Milan, Italy; Askarpour, M., McMaster University, Hamilton, Canada; Bersani, M.M., Politecnico di Milano, Milan, Italy; Rossi, M., Politecnico di Milano, Milan, Italy","We present a model-driven approach for the creation of formally verified scenarios involving human-robot interaction in healthcare settings. The work offers an innovative take on the application of formal methods to human modeling, as it incorporates physiology-related aspects. The model, based on the formalism of Hybrid Automata, includes a stochastic component to capture the variability of human behavior, which makes it suitable for Statistical Model Checking. The toolchain is meant to be accessible to a wide range of professional figures. Therefore, we have laid out a user-friendly representation format for the scenario, from which the full formal model is automatically generated and verified through the Uppaal tool. The outcome is an estimation of the probability of success of the mission, based on which the user can refine the model if the result is not satisfactory. © 2020, Springer Nature Switzerland AG.","Healthcare robotics; Human-robot interaction; Model-driven approach; Service robots; Statistical Model Checking","Behavioral research; Formal verification; Health care; Man machine systems; Model checking; Stochastic models; Stochastic systems; Automatically generated; Human behaviors; Hybrid automatons; Model driven approach; Probability of success; Statistical model checking; Stochastic component; User friendly; Human robot interaction",,,,,,"Aknowledgements. The Italian Ministry of Education, University and Research is acknowledged for the support provided through the Project “Department of Excellence LIS4.0 - Lightweight and Smart Structures for Industry 4.0”.",,,,,,,,,,"(2020), https://github.com/LesLivia/hritoolchain, HRI Toolchain; Adam, C., Johal, W., Pellier, D., Fiorino, H., Pesty, S., Social human-robot interaction: a new cognitive and affective interaction-oriented architecture (2016) ICSR 2016. LNCS (LNAI), 9979, pp. 253-263. , https://doi.org/10.1007/978-3-319-47437-325, Agah, A., Cabibihan, J.-J., Howard, A.M., Salichs, M.A., He, H. (eds) Springer, Cham; Agha, G., Palmskog, K., A survey of statistical model checking (2018) ACM Trans. Model. Comput. Simul. (TOMACS), 28 (1), pp. 1-39; Alur, R., The algorithmic analysis of hybrid systems (1995) Theoret. Comput. Sci, 138 (1), pp. 3-34; Alur, R., Dill, D.L., A theory of timed automata (1994) Theoret. Comput. Sci, 126 (2), pp. 183-235; Arai, R., Schlingloff, H., Model-based performance prediction by statistical model checking an industrial case study of autonomous transport robots (2017) Concurrency, Specification and Programming; Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F., Formal model of human erroneous behavior for safety analysis in collaborative robotics (2019) Robot. Comput.-Integr. Manuf, 57, pp. 465-476; Askarpour, M., Menghi, C., Belli, G., Bersani, M., Pelliccione, P., Mind the gap: robotic mission planning meets software engineering (2020) Proceedings of the 8th International Conference on Formal Methods in Software Engineering; Bersani, M.M., Soldo, M., Menghi, C., Pelliccione, P., Rossi, M., PuRSUE-from specification of robotic environments to synthesis of controllers (2020) Formal Aspects Comput, pp. 1-41. , https://doi.org/10.1007/s00165-020-00509-0; Chettibi, T., Haddad, M., Lehtihet, H., Khalil, W., Suboptimal trajectory generation for industrial robots using trapezoidal velocity profiles (2006) IROS, pp. 729-735. , IEEE; David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B., Uppaal SMC tutorial (2015) Int. J. Softw. Tools Technol. Transfer, 17 (4), pp. 397-415. , https://doi.org/10.1007/s10009-014-0361-y; Zhao, X., Towards integrating formal verification of autonomous robots with battery prognostics and health management (2019) SEFM 2019. LNCS, 11724, pp. 105-124. , https://doi.org/10.1007/978-3-030-30446-16, Ölveczky, P.C., Salaün, G. (eds) Springer, Cham; Foughali, M., Ingrand, F., Seceleanu, C., Statistical model checking of complex robotic systems (2019) SPIN 2019. LNCS, 11636, pp. 114-134. , https://doi.org/10.1007/978-3-030-30923-77, Biondi, F., Given-Wilson, T., Legay, A. (eds) Springer, Cham; de Graaf, M.M., Ben Allouch, S., van Dijk, J.A., Why would I use this in my home? A model of domestic social robot acceptance (2019) Hum.-Comput. Interact, 34 (2), pp. 115-173; Hadley, M., A deterministic model of the free will phenomenon (2018) J. Conscious. Explor. Res, 9 (1), pp. 01-19; Halder, R., Proença, J., Macedo, N., Santos, A., Formal verification of ROS-based robotic applications using timed-automata (2017) International FME Workshop on Formal Methods in Software Engineering (FormaliSE), pp. 44-50. , IEEE; Herd, B., Miles, S., McBurney, P., Luck, M., Quantitative analysis of multiagent systems through statistical model checking (2015) EMAS 2015. LNCS (LNAI), 9318, pp. 109-130. , https://doi.org/10.1007/978-3-319-26184-37, Baldoni, M., Baresi, L., Dastani, M. (eds) Springer, Cham; (2010) Safety of machinery-General principles for design-Risk assessment and risk reduction, , ISO 12100: ISO; (2014) Robots and robotic devices-Safety requirements for personal care robots, , ISO 13482: ISO; (2006) Safety of machinery-Safety-related parts of control systems-Part 1: General principles for design, , ISO 13849–1: ISO; Jaber, M.Y., Givi, Z., Neumann, W.P., Incorporating human fatigue and recovery into the learning-forgetting process (2013) Appl. Math. Model, 37 (12–13), pp. 7287-7299; Jacobs, T., Virk, G.S., ISO 13482-the new safety standard for personal care robots (2014) International Symposium on Robotics, pp. 1-6. , VDE; Konz, S., Work/rest: Part ii-the scientific basis (knowledge base) for the guide 1 (2000) Ergonomics Guidelines and Problem Solving, 1 (401), p. 38; Larsen, K.G., Pettersson, P., Yi, W., Uppaal in a nutshell (1997) Int. J. Softw. Tools Technol. Transfer, 1 (1), pp. 134-152. , https://doi.org/10.1007/s100090050010; Luckcuck, M., Farrell, M., Dennis, L.A., Dixon, C., Fisher, M., Formal specification and verification of autonomous robotic systems: a survey (2019) ACM Comput. Surv. (CSUR), 52 (5), pp. 1-41; Payne, P., Lopetegui, M., Yu, S., A review of clinical workflow studies and methods (2019) Cognitive Informatics. HI, pp. 47-61. , https://doi.org/10.1007/978-3-030-16916-94, Zheng, K., Westbrook, J., Kannampallil, T.G., Patel, V.L. (eds) Springer, Cham; Porfirio, D., Sauppé, A., Albarghouthi, A., Mutlu, B., Authoring and verifying human-robot interactions (2018) Proceedings of ACM Symposium on User Interface Software and Technology, pp. 75-86; Rasouli, A., Tsotsos, J.K., Autonomous vehicles that interact with pedestrians: a survey of theory and practice (2019) IEEE Trans. Intell. Transp. Syst, 21, pp. 900-918; Sinkaram, C., Rajakumar, K., Asirvadam, V., Modeling battery management system using the lithium-ion battery (2012) International Conference on Control System, Computing and Engineering, pp. 50-55. , IEEE; Vicentini, F., Askarpour, M., Rossi, M.G., Mandrioli, D., Safety assessment of collaborative robotics through automated formal verification (2019) IEEE Trans. Rob, , https://doi.org/10.1109/TRO.2019.2937471; Webster, M., Formal verification of an autonomous personal robotic assistant (2014) AAAI Spring Symposium Series","Lestingi, L.; Politecnico di MilanoItaly; email: livia.lestingi@polimi.it","de Boer F.Cerone A.",,"Springer Science and Business Media Deutschland GmbH","18th International Conference on Software Engineering and Formal Methods, SEFM 2020","14 September 2020 through 18 September 2020",,245269,03029743,9783030587673,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85091577651
"Seiler J., Schäfer N., Latsch B., Chadda R., Hessinger M., Beckerle P., Kupnik M.","57219113421;57201306601;57219113155;57210174545;56031511500;36964076200;14060455400;","Wearable vibrotactile interface using phantom tactile sensation for human-robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12272 LNCS",,,"380","388",,,"10.1007/978-3-030-58147-3_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091290104&doi=10.1007%2f978-3-030-58147-3_42&partnerID=40&md5=61469517fb70fbaf900a6b353bf00eab","Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Technische Universität Dortmund, Robotics Research Institute, Otto-Hahn-Straße 8, Dortmund, 44227, Germany; Technische Universität Darmstadt, Institute for Mechatronic Systems, Otto-Berndt-Straße 2, Darmstadt, 64287, Germany","Seiler, J., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Schäfer, N., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Latsch, B., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Chadda, R., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Hessinger, M., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Beckerle, P., Technische Universität Dortmund, Robotics Research Institute, Otto-Hahn-Straße 8, Dortmund, 44227, Germany, Technische Universität Darmstadt, Institute for Mechatronic Systems, Otto-Berndt-Straße 2, Darmstadt, 64287, Germany; Kupnik, M., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany","We present a wearable vibrotactile feedback device consisting of four linear resonant actuators (LRAs) that are able to generate virtual stimuli, known as phantom tactile sensation, for human-robot interaction. Using an energy model, we can control the location and intensity of the virtual stimuli independently. The device consists of mostly 3D-printed rigid and flexible components and uses commercially available haptic drivers for actuation. The actuators have a rated frequency of 175 Hz which is close to the highest skin sensitivity regarding vibrations (150 to 300 Hz). Our experiment was conducted with a prototype consisting of two bracelets applied to the forearm and upper arm of six participants. Eight possible circumferential angles were stimulated, of which four originated from real actuators and four were generated by virtual stimuli. The responses given by the participants showed a nearly linear relationship within ± 10 ∘ for the responded angle against the presented stimulus angle. These results show that phantom tactile sensation allows for an increase of spatial resolution to design vibrotactile interfaces for human-robot interaction with fewer actuators. © The Author(s) 2020.","Human-robot interface; Phantom tactile sensation; Vibrotactile feedback","3D printers; Actuators; Biofeedback; Machine design; Man machine systems; Phantoms; Wearable technology; Circumferential angles; Flexible components; Linear relationships; Linear resonant actuators; Spatial resolution; Tactile sensation; Vibro-tactile feedbacks; Vibro-tactile interfaces; Human robot interaction",,,,,"Deutsche Forschungsgemeinschaft, DFG: KA 417/32-1, KU 3498/3-1, SPP 2134","Acknowledgements. This research received support from the Deutsche For-schungsgemeinschaft (DFG) under grants KU 3498/3-1 and KA 417/32-1 within the priority program The Active Self (SPP 2134).",,,,,,,,,,"Beckerle, P., A human-robot interaction perspective on assistive and rehabilitation robotics (2017) Front. Neurorobot, 11, p. 24; Ajoudani, A., Progress and prospects of the human-robot collaboration (2018) Auton. Robot, 42, pp. 957-975; Elliott, L.R., Coovert, M.D., Redden, E.S., Overview of meta-analyses investigating vibrotactile versus visual display options (2009) HCI 2009. LNCS, 5611, pp. 435-443. , Jacko, J.A. (ed) Springer, Heidelberg; Burke, J.L., Comparing the effects of visual-auditory and visual-tactile feedback on user performance: a meta-analysis (2006) Proceedings of the 8th International Conference on Multimodal Interfaces-ICMI 2006, Banff, Alberta, Canada, p. 108. , ACM Press; Bimbo, J., Teleoperation in cluttered environments using wearable haptic feedback (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, pp. 3401-3408. , IEEE, September; Louison, C., Ferlay, F., Mestre, D.R., Spatialized vibrotactile feedback improves goal-directed movements in cluttered virtual environments (2018) Int. J. Hum.-Comput. Interact, 34, pp. 1015-1031; Erp, J.B.F.V., Waypoint navigation with a vibrotactile waist belt (2005) ACM Trans. Appl. Percept, 2, pp. 106-117; Bark, K., Effects of vibrotactile feedback on human learning of arm motions (2015) IEEE Trans. Neural Syst. Rehabil. Eng, 23, pp. 51-63; Pacchierotti, C., Sinclair, S., Solazzi, M., Frisoli, A., Hayward, V., Prattichizzo, D., Wearable Haptic Systems for the Fingertip and the Hand: taxonomy, review, and perspectives (2017) IEEE Trans. Haptics, 10, pp. 580-600; Pezent, E., Tasbi: multisensory squeeze and vibrotactile wrist haptics for augmented and virtual reality (2019) 2019 IEEE World Haptics Conference (WHC), Tokyo, Japan, pp. 1-6. , IEEE, July; Tsetserukou, D., Tachi, S., Efficient object exploration and object presentation in TeleTA, Teleoperation system with Tactile feedback (2009) World Haptics 2009-Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Salt Lake City, UT, USA, pp. 97-102. , IEEE; Schaetzle, S., VibroTac: an ergonomic and versatile usable vibrotactile feedback device (2010) 19th International Symposium in Robot and Human Interactive Communication, Viareggio, Italy, pp. 670-675. , IEEE, September; Verrillo, R.T., Fraioli, A.J., Smith, R.L., Sensation magnitude of vibrotactile stimuli (1969) Percept. Psychophysics, 6, pp. 366-372; Barghout, A., Cha, J., El Saddik, A., Kammerl, J., Steinbach, E., Spatial resolution of vibrotactile perception on the human forearm when exploiting funneling illusion (2009) 2009 IEEE International Workshop on Haptic Audio visual Environments and Games, Lecco, Italy, pp. 19-23. , IEEE, November; Salazar Luces, J.V., Okabe, K., Murao, Y., Hirata, Y., A phantom-sensation based paradigm for continuous vibrotactile wrist guidance in two-dimensional space (2018) IEEE Robot. Autom. Lett, 3, pp. 163-170; Park, G., Choi, S., Tactile information transmission by 2D stationary phantom sensations (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems-CHI 2018, Montreal QC, Canada, pp. 1-12. , ACM Press; Békésy, G.V., Sensations on the skin similar to directional hearing, beats, and harmonics of the ear (1957) J. Acoust. Soc. Am, 29, pp. 489-501; Békésy, G.V., Funneling in the nervous system and its role in loudness and sensation intensity on the skin (1958) J. Acoust. Soc. Am, 30, pp. 399-412; Israr, A., Poupyrev, I., Tactile brush: drawing on skin with a tactile grid display (2011) Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems-CHI 2011, Vancouver, BC, Canada, p. 2019, , ACM Press; Choi, S., Kuchenbecker, K.J., Vibrotactile display: Perception, technology, and applications (2013) Proc. IEEE, 101, pp. 2093-2104; Bolanowski, S.J., Gescheider, G.A., Verrillo, R.T., Hairy skin: psychophysical channels and their physiological substrates (1994) Somatosens. Mot. Res, 11, pp. 279-290; Békésy, G.V., Wever, E.G., (1960) Experiments in Hearing, , McGraw-Hill, New York City","Schäfer, N.; Technische Universität Darmstadt, Merckstraße 25, Germany; email: schaefer@must.tu-darmstadt.de","Nisky I.Hartcher-O'Brien J.Wiertlewski M.Smeets J.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020","6 September 2020 through 9 September 2020",,244859,03029743,9783030581466,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85091290104
"Ikeuchi K., Matsushita Y., Sagawa R., Kawasaki H., Mukaigawa Y., Furukawa R., Miyazaki D.","57192153568;35956654700;55822540700;55335068200;15521483500;7005535093;57210669338;","Robot Vision, Autonomous Vehicles, and Human Robot Interaction",2020,"Advances in Computer Vision and Pattern Recognition",,,,"289","303",,,"10.1007/978-3-030-56577-0_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091134605&doi=10.1007%2f978-3-030-56577-0_12&partnerID=40&md5=a0b777f1a1386aeef675c2e2df8819c1","Applied Robotics Research, Microsoft Corporation, Redmond, WA, United States; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Ibaraki, Tsukuba, Japan; Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan; Division of Information Science, Nara Institute of Science and Technology, Nara, Japan; Faculty of Information Sciences, Hiroshima City University, Hiroshima, Japan; Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan","Ikeuchi, K., Applied Robotics Research, Microsoft Corporation, Redmond, WA, United States; Matsushita, Y., Graduate School of Information Science and Technology, Osaka University, Osaka, Japan; Sagawa, R., Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Ibaraki, Tsukuba, Japan; Kawasaki, H., Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan; Mukaigawa, Y., Division of Information Science, Nara Institute of Science and Technology, Nara, Japan; Furukawa, R., Faculty of Information Sciences, Hiroshima City University, Hiroshima, Japan; Miyazaki, D., Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan","Sensors are indispensable to robots. Active-lighting sensors, for example, work in a robust manner, even under severe conditions. They are utilized heavily for various robot tasks, such as object localization/recognition, navigation, and manipulation. In this chapter, we review two robotics applications: simultaneous localization and mapping (SLAM) for navigation tasks and learning-from-observation (LfO) for manipulation tasks. © 2020, Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Dissanayake, MG, Newman, P, Clark, S, Durrant-Whyte, HF, Csorba, M, A solution to the simultaneous localization and map building (slam) problem (2001) IEEE Trans Robot Autom, 17 (3), pp. 229-241; Xue, L, Ono, S, Banno, A, Oishi, T, Sato, Y, Ikeuchi, K, Global 3d modeling and its evaluation for large-scale highway tunnel using laser range sensor (2012) SEISAN KENKYU, 64 (2), pp. 155-160; Besl, PJ, McKay, ND, Method for registration of 3-d shapes (1992) Sensor fusion IV: control paradigms and data structures, 1611, pp. 586-606. , International Society for Optics and Photonics; Oishi, T, Kurazume, R, Nakazawa, A, Ikeuchi, K, Fast simultaneous alignment of multiple range images using index images (2005) Fifth international conference on 3-d digital imaging and modeling (3DIM’05). IEEE, pp. 476-483; Ono, S, Ikeuchi, K, Self-position estimation for virtual 3d city model construction with the use of horizontal line laser scanning (2004) Int J ITS Res, 2, pp. 67-75; Bolles, RC, Baker, HH, Marimont, DH, Epipolar-plane image analysis: an approach to determining structure from motion (1987) Int J Comput Vis (IJCV), 1 (1), pp. 7-55; Ikeuchi, K, Suehiro, T, Tanguy, P, Wheeler, M, Assembly plan from observation (1991) Annual research review. The Robotics Institute, Carnegie Mellon University, pp. 37-53; Ikeuchi, K, Suehiro, T, Toward an assembly plan from observation. I. Task recognition with polyhedral objects (1994) IEEE Trans Robot Autom, 10 (3), pp. 368-385; Suehiro, T, Ikeuchi, K, Towards an assembly plan from observation: part II: correction of motion parameters based on fact contact constraints (1992) Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), 3, pp. 2095-2102. , IEEE; Ikeuchi, K, Kawade, M, Suehiro, T, Assembly task recognition with planar, curved and mechanical contacts (1993) Proceedings of the IEEE international conference on robotics and automation (ICRA). IEEE, pp. 688-694; Takamatsu, J, Morita, T, Ogawara, K, Kimura, H, Ikeuchi, K, Representation for knot-tying tasks (2006) IEEE Trans Robot, 22 (1), pp. 65-78; Kuhn, HW, Tucker, AW, Linear inequalities and related systems (1956) Annals of mathematics studies, , Princeton University Press; Nakaoka, S, Nakazawa, A, Kanehiro, F, Kaneko, K, Morisawa, M, Hirukawa, H, Ikeuchi, K, Learning from observation paradigm: leg task models for enabling a biped humanoid robot to imitate human dances (2007) Int J Robot Res, 26 (8), pp. 829-844; Ikeuchi, K, Ma, Z, Yan, Z, Kudoh, S, Nakamura, M, Describing upper-body motions based on labanotation for learning-from-observation robots (2018) Int J Comput Vis (IJCV), 126 (12), pp. 1415-1429; Guest, AH, (2013) Labanotation: the system of analyzing and recording movement, , Routledge","Ikeuchi, K.; Applied Robotics Research, United States; email: katsuike@microsoft.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,21916586,,,,"English","Adv. Comput. Vis. Pattern Recognit.",Book Chapter,"Final","",Scopus,2-s2.0-85091134605
"Morillo-Méndez L., Mozos O.M.","57211823245;55663022800;","Towards human-based models of behaviour in social robots: Exploring age-related differences in the processing of gaze cues in human-robot interaction",2020,"CEUR Workshop Proceedings","2655",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090886466&partnerID=40&md5=288666d88157648881076366384b9226","AASS, Örebro University, Örebro, SE-701 82, Sweden","Morillo-Méndez, L., AASS, Örebro University, Örebro, SE-701 82, Sweden; Mozos, O.M., AASS, Örebro University, Örebro, SE-701 82, Sweden","The emergence of robotic systems offers many opportunities for older adults (OA) to support their daily life activities. Therefore, there is a need to study social interactions between OA and robots better. One important aspect of social communication is the use of non-verbal cues, of which eye gaze has proven to be of special interest both in the fields of social cognition and HRI. In this paper, we review previous work on HRI with OA and propose an experiment to compare the influence of gaze behaviour of robots on older and younger users. These findings will allow a better design and adaptation of social robots to age-related changes in aspects of social cognition. © 2020 CEUR-WS. All rights reserved.",,"Artificial intelligence; Machine design; Stairs; Age-related changes; Age-related differences; Daily life activities; Gaze behaviours; Robotic systems; Social cognition; Social communications; Social interactions; Social robots",,,,,"754285","This work was funded by EU H2020 Marie Sk lodowska-Curie grant No 754285, and by the Knut and Alice Wallenberg foundation WASP-AI programme.","This work was funded by EU H2020 Marie Sklodowska-Curie grant No 754285, and by the Knut and Alice Wallenberg foundation WASP-AI programme.",,,,,,,,,"Andrist, Sean, Mutlu, Bilge, Tapus, Adriana, Look like me: Matching robot personality via gaze to increase motivation (2015) Conference on Human Factors in Computing Systems - Proceedings, pp. 3603-3612. , 2015-April, pages Association for Computing Machinery, apr; Aly, Amir, Tapus, Adriana, Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human-robot interaction (2016) Autonomous Robots, 40 (2), pp. 193-209. , feb; Blankenship, Virginia, Hnat, Steven M., Hess, Thomas G., Brown, Donald R., Reciprocal in¬ teraction and similarity of personality attributes (1984) Journal of Social and Personal Relationships, 1 (4), pp. 415-432; Cañigueral, Roser, Hamilton, Antonia F.de C., The role of eye gaze during natural social interac¬ tions in typical and autistic people (2019) Frontiers in Psychology, 10 (MAR), p. 560; Costa, Paul T, Herbst, Jeffrey H, McCrae, Robert R, Siegler, llene C, Personality at midlife: Stability, intrinsic maturation, and response to life events (2000) Assessment, 7 (4), pp. 365-378; Crompton, Catherine J., MacPherson, Sarah E., Human agency beliefs affect older adults' inter¬ action behaviours and task performance when learning with computerised partners (2019) Computers in Human Behavior, 101, pp. 60-67; Corriero, Elena F., Counterbalancing (2017) The SAGE Encyclopedia of Communication Research Methods, pp. 277-281. , Mike Allen, editor, pages SAGE Publications, Inc; Deutsch, Inbal, Erel, Hadas, Paz, Michal, Hoffman, Guy, Zuckerman, Oren, Home robotic devices for older adults: Opportunities and concerns (2019) Computers in Human Behavior, 98, pp. 122-133. , (April); Field, Andy, (2018) Discovering Statistics Using IBM SPSS Statistics, , SAGE, London, 5th edition; Fasola, Juan, Mataric, Maja J., Evaluation of a spatial language interpretation framework for natural human-robot interaction with older adults (2015) Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 301-308. , Institute of Electrical and Electronics Engineers Inc., nov; Federico, Francesca, Marotta, Andrea, Orsolini, Margherita, Casagrande, Maria, Aging in cognitive control of social processing: evidence from the attention network test (2020) Aging, Neuropsychology, and Cognition; Feingold-Polak, Ronit, Elishay, Avital, Shahar, Yonat, Stein, Maayan, Edan, Yael, Levy-Tzedek, Shelly, Differences between young and old users when interacting with a humanoid robot: A qualitative usability study (2018) Paladyn, 9 (1), pp. 183-192. , feb; Goldberg, Lewis R., A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models. 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Dunham, editors, pages Psychology Press; (2019) World Population Prospects 2019: Highlights, , United Nations. Technical Report 141; Yildirim, Elif, Buyiikiscan, Ezgi Soncu, Giirvit, Hakan, Affective theory of mind in human aging: is there any relation with executive functioning? (2020) Aging, Neuropsychology, and Cognition, 27 (2), pp. 207-219. , mar; Zarkowski, Mateusz, Multi-party Turn-Taking in Repeated Human-Robot Interactions: An Interdisciplinary Evaluation (2019) International Journal of Social Robotics, 11 (5), pp. 693-707. , dec; Zhang, Yanxia, Beskow, Jonas, Kjellström, Hedvig, Look but Don't Stare: Mutual Gaze Interac¬ tion in Social Robots (2017) International Conference on Social Robotics, LNAI, 10652, pp. 556-566. , pages Springer Verlag; Zafrani, Oded, Nimrod, Galit, Towards a Holistic Approach to Studying Human-Robot Interaction in Later Life (2019) Gerontologist, 59 (1), pp. E26-E36",,"Rudolph S.Marreiros G.",,"CEUR-WS","9th European Starting AI Researchers' Symposium, STAIRS 2020","29 August 2020 through 30 August 2020",,162587,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85090886466
"MajidiRad A.H., Maniar P., Yihun Y., He H.","57197794404;57218686201;51061874300;56231174200;","Human Factors to Develop a Safety Guard Model in Human-Robot Interaction",2020,"Mechanisms and Machine Science","83",,,"271","286",,,"10.1007/978-3-030-43929-3_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090030512&doi=10.1007%2f978-3-030-43929-3_24&partnerID=40&md5=5dcd163be7570c04b06b6bc242a6bd29","Department of Mechanical Engineering, Wichita State University, Wichita, United States; Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, United States","MajidiRad, A.H., Department of Mechanical Engineering, Wichita State University, Wichita, United States; Maniar, P., Department of Mechanical Engineering, Wichita State University, Wichita, United States; Yihun, Y., Department of Mechanical Engineering, Wichita State University, Wichita, United States; He, H., Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, United States","In this research, a strategy to incorporate an assist-as-needed scheme into human-robot collaboration during an object co-lifting and manipulation task is developed. Surface Electromyography (sEMG) signals from the upper-arm muscles are collected and analyzed to quantify fatigue while the subjects are collaborating with a Universal Robot (UR5). Rated perceived fatigue (RPF) is then utilized to quantify fatigue levels based on personal perception of the subject using the intervals of 25%, 50%, 75% and 100%. This perceived information is correlated with the change in interaction load at the object-robot interface to quantify the modifying factor needed to stay in the optimal human-robot collaboration (HRC) condition. © 2020, Springer Nature Switzerland AG.",,"Fatigue level; Human-robot collaboration; Manipulation task; Modifying factors; Personal perception; Robot interface; Safety guards; Surface electromyography; Social robots",,,,,,,,,,,,,,,,"International Federation of Robotics (IFR): Executive Summary World Robotics 2016 Industrial Robots, , Technical report; Pellegrinelli, S., Orlandini, A., Pedrocchi, N., Umbrico, A., Tolio, T., Motion planning and scheduling for human and industrial-robot collaboration (2017) CIRP Ann, 66 (1), pp. 1-4; Sadeghniiat-Haghighi, K., Yazdi, Z., Fatigue management in the workplace (2015) Ind Psychiatry J, 24 (1), p. 12; Craig, A., Tran, Y., Wijesuriya, N., Nguyen, H., Regional brain wave activity changes associated with fatigue (2012) Psychophysiology, 49 (4), pp. 574-582; MajidiRad A, Yihun YS (2019) Upper limb rehabilitation and mobility assistance using robotic devices: a review. In: Proceedings of the ASME 2019 international design engineering technical conferences and computers and information in engineering conference, 43rd mechanisms and robotics conference, Anaheim, California, USA, 18–21 August 2019, vol 5A, V05AT07A039. ASME. https://doi.org/10.1115/DETC2019-98063; Luttmann, A., Jāger, M., Sökeland, J., Laurig, W., Electromyographical study on surgeons in urology. II. Determination of muscular fatigue (1996) Ergonomics, 39 (2), pp. 298-313; Basmajian, J.V., de Luca, C.J., (1985) Muscles Alive, , Williams & Wilkins, New York; Adhikari, V., Majidrad, A., Yihun, Y., Desai, J., Assist-as-needed controller to a task-based knee rehabilitation exoskeleton (2018) 2018 40Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, pp. 3212-3215. , , pp; Mielke, E.A., Townsend, E.C., Killpack, M.D., (2017) Analysis of Rigid Extended Object Co-Manipulation by Human Dyads: Lateral Movement Characterization, , http://arxiv.org/abs/1702.00733, arXiv preprint; Feth, D., Groten, R., Peer, A., Hirche, S., Buss, M., Performance related energy exchange in haptic human-human interaction in a shared virtual object manipulation task (2009) World Haptics 2009-Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. IEEE, pp. 338-343. , pp; Bian, F., Ren, D., Li, R., Liang, P., Improving stability in physical human-robot interaction by estimating human hand stiffness and a vibration index (2018) Ind Robot Int J, 46, pp. 529-540; Li, H., Zhang, L., Kawashima, K., Operator dynamics for stability condition in haptic and teleoperation system: A survey (2018) Int J Med Robot Comput Assist Surg, 14 (2); Švarnỳ, P., Hoffmann, M., (2018) Safety of Human-Robot Interaction through Tactile Sensors and Peripersonal Space Representations, , http://arxiv.org/abs/1805.07256, arXiv preprint; Phinyomark, A., Thongpanja, S., Hu, H., Phukpattaranont, P., Limsakul, C., The usefulness of mean and median frequencies in electromyography analysis (2012) Computational Intelligence in Electromyography Analysis-A Perspective on Current Applications and Future Challenges, pp. 195-220. , , pp; Petrofsky, J.S., Glaser, R.M., Phillips, C.A., Lind, A.R., Williams, C., Evaluation of the amplitude and frequency components of the surface EMG as an index of muscle fatigue (1982) Ergonomics, 25 (3), pp. 213-223; Chopp, J.N., Fischer, S.L., Dickerson, C.R., On the feasibility of obtaining multiple muscular maximal voluntary excitation levels from test exertions: A shoulder example (2010) J Electromyogr Kinesiol, 20 (5), pp. 896-902; McDonald, A.C., Mulla, D.M., Keir, P.J., Using EMG amplitude and frequency to calculate a multimuscle fatigue score and evaluate global shoulder fatigue (2018) Hum Factors, 61, pp. 526-536. , https://doi.org/10.1177/0018720818794604; Kouzaki, M., Shinohara, M., Masani, K., Fukunaga, T., Force fluctuations are modulated by alternate muscle activity of knee extensor synergists during low-level sustained contraction (2004) J Appl Physiol, 97 (6), pp. 2121-2131; Basmajian, J.V., Muscle fatigue and time-dependent parameters of the surface EMG signal (1985) Muscles Alive Their Functions Revealed by Electromyography, pp. 201-222. , , pp; Williams, P.L., Warwick, R., Dyson, M., Bannister, L.H., (1980) Gray’s Anatomy, 378. , , vol , Churchill Livingstone, Edinburgh; Merletti, R., Parker, P.A., Parker, P.J., (2004) Electromyography: Physiology, Engineering, and Non-Invasive Applications, Vol, p. 11. , Wiley, London; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for EMG signal classification (2012) Expert Syst Appl, 39 (8), pp. 7420-7431; Boostani, R., Moradi, M.H., Evaluation of the forearm EMG signal features for the control of a prosthetic hand (2003) Physiol Meas, 24 (2), p. 309; Al-Mulla, M.R., Sepulveda, F., Colley, M., 2012) sEMG techniques to detect and predict localised muscle fatigue EMG Methods for Evaluating Muscle and Nerve Function, pp. 157-186. , , pp; Cifrek, M., Medved, V., Tonković, S., Ostojić, S., Surface EMG based muscle fatigue evaluation in biomechanics (2009) Clin Biomech, 24 (4), pp. 327-340; Oskoei, M.A., Hu, H., Gan, J.Q., Manifestation of fatigue in myoelectric signals of dynamic contractions produced during playing PC games. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society. IEEE (2008) Pp 315–318; Thongpanja, S., Phinyomark, A., Phukpattaranont, P., Limsakul, C., Time-dependent EMG power spectrum features of Biceps Brachii during isotonic exercise (2010) J Sports Sci Technol, 10 (2S), pp. 314-318; Kebria, P.M., Al-Wais, S., Abdi, H., Nahavandi, S., Kinematic and dynamic modelling of UR5 manipulator. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC) (2016) IEEE, pp. 004229-004234. , , pp","Yihun, Y.; Department of Mechanical Engineering, United States; email: yimesker.yihun@wichita.edu",,,"Springer",,,,,22110984,,,,"English","Mech. Mach. Sci.",Book Chapter,"Final","",Scopus,2-s2.0-85090030512
"Val-Calvo M., Alvarez-Sanchez J.R., Ferrandez-Vicente J.M., Fernandez E.","57195318553;56786654900;56268451400;57203722548;","Affective Robot Story-Telling Human-Robot Interaction: Exploratory Real-Time Emotion Estimation Analysis Using Facial Expressions and Physiological Signals",2020,"IEEE Access","8",,"9133100","134051","134066",,4,"10.1109/ACCESS.2020.3007109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089306806&doi=10.1109%2fACCESS.2020.3007109&partnerID=40&md5=224d52b404287276f8c4946b8f9a3f11","Departamento de Inteligencia Artificial, Universidad Nacional de Educación A Distancia (UNED), Madrid, 28040, Spain; Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Polit∅cnica de Cartagena, Cartagena, 30202, Spain; Instituto de Bioingeniería, Universidad Miguel HernÆndez, Elche, 03202, Spain; CIBER-BBN, Madrid, 28029, Spain","Val-Calvo, M., Departamento de Inteligencia Artificial, Universidad Nacional de Educación A Distancia (UNED), Madrid, 28040, Spain, Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Polit∅cnica de Cartagena, Cartagena, 30202, Spain; Alvarez-Sanchez, J.R., Departamento de Inteligencia Artificial, Universidad Nacional de Educación A Distancia (UNED), Madrid, 28040, Spain; Ferrandez-Vicente, J.M., Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Polit∅cnica de Cartagena, Cartagena, 30202, Spain; Fernandez, E., Instituto de Bioingeniería, Universidad Miguel HernÆndez, Elche, 03202, Spain, CIBER-BBN, Madrid, 28029, Spain","Affective human-robot interaction is still an active area of research in part due to the great advances in artificial intelligence. Now, the design of autonomous devices that work in real therapeutic environments has become a plausible reality. Affective human-robot interaction requires a robot to analyze the emotional state of the human interlocutor and interpret emotional responses that can be used, not merely in the interaction but, for example, to provoke desired therapeutic responses. It is, therefore, necessary to broaden experimental techniques into more realistic paradigms, where the capacity of emotion estimation can be completely explored. This exploratory paper proposes a realistic experimental paradigm in which the robot employs a dramatic story to evoke emotions in the users, and tests previously self-designed methodologies to be able to make estimates of the users' emotional state in real-time. Regardless of the multiple impediments and restrictions, and all the aspects that could still be improved, this paper can outline the feasibility of the proposed methodology in realistic scenarios. © 2013 IEEE.","Affective state; blood volume pressure; EEG; emotion estimation; face emotion recognition; galvanic skin response; human-robot interaction; real-time","Artificial intelligence; Intelligent robots; Man machine systems; Autonomous devices; Emotion estimation; Emotional response; Emotional state; Experimental techniques; Facial Expressions; Physiological signals; Realistic scenario; Social robots",,,,,"Fundación Séneca; Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018-098969-B-100; Generalitat Valenciana, GVA: PROMETEO/2019/119","This work was supported in part by the Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia under Project Programa de Ayudas a Grupos de Excelencia de la Región de Murcia, in part by the Ministerio de Ciencia, Innovación y Universidades under Project RTI2018-098969-B-100, and in part by the Generalitat Valenciana under Project PROMETEO/2019/119.",,,,,,,,,,"Picard, R.W., (2000) Affective Computing, , Cambridge MA USA: MIT Press; Vidler, V., Use puppets to reach the emotionally disturbed (1972) Instructor, 81, pp. 9-68. , May; Currant, N., The expansive educational value of puppets (1985) Acad. 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Alvarez-Sanchez, J.R.; Departamento de Inteligencia Artificial, Spain; email: jras@dia.uned.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85089306806
"Kremer L., Sen S., Eigenstetter M.","57206721323;57212636810;56515392700;","Human-Robot Interaction in Health Care: Focus on Human Factors",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12182 LNCS",,,"654","667",,,"10.1007/978-3-030-49062-1_45","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088753832&doi=10.1007%2f978-3-030-49062-1_45&partnerID=40&md5=cd9391052471e5f143719c12f5c44faf","Niederrhein University of Applied Sciences, Krefeld, Germany","Kremer, L., Niederrhein University of Applied Sciences, Krefeld, Germany; Sen, S., Niederrhein University of Applied Sciences, Krefeld, Germany; Eigenstetter, M., Niederrhein University of Applied Sciences, Krefeld, Germany","In-patient and out-patient care processes in the health care system are strongly affected by the effects of demographic change and a tense skill shortage. Digitization - both as a digitizing transformation process and as a process of implementing technical innovations such as robots - is intended to counteract these processes. Not only for safe interaction with a care robot, but also for its acceptance, humans must be able to understand and predict the robot’s movements. The ability to make predictions about the movements of a robot/technology and to be willing to interact with it is linked to the construct of situation awareness. In an experimental setting, a care situation was simulated in the full-scope simulator and the effects of the interaction on the situation awareness and the acceptance of the robot were investigated. The experiment was realized with 33 participants. The methods used were the SAGAT technique and an adapted UTAUT questionnaire. Significant correlations between UTAUT predictors and added measures were found. A stepwise hierarchical regression model found Performance Expectancy and Attitude as significant predictors of acceptance, but none of the other UTAUT and added factors were identified to be significant predictors. There was no significant effect between situation awareness and acceptance. A descriptive analysis of the measures of situation awareness showed an average to good situation awareness across all participants. © 2020, Springer Nature Switzerland AG.","Health care; Human-robot interaction; Situation awareness; Technology acceptance; UTAUT","Health care; Regression analysis; Social robots; Demographic changes; Descriptive analysis; Full scope simulators; Health-care system; Hierarchical regression models; Situation awareness; Technical innovation; Transformation process; Human computer interaction",,,,,,,,,,,,,,,,"Lux, T., Breil, B., Dörries, M., Digitalisierung im Gesundheitswesen — zwischen Datenschutz und moderner Medizinversorgung (2017) Wirtschaftsdienst, 97 (10), pp. 687-703. , https://doi.org/10.1007/s10273-017-2200-8; Bernnat, R., Bauer, M., Schmidt, H., Bieber, N., Heusser, N., Schönfeld, R., (2017) Effizienzpoten-ziale durch eHealth, , https://www.strategyand.pwc.com/de/de/studie/effizienzpotentiale-durch-ehealth.pdf, Accessed 30 Jan 2020; Thiel, R., (2018) #SmartHealthSystems: Digitalisierungsstrategien im internationalen Vergle-ich; Bendel, O., Surgical, therapeutic, nursing and sex robots in machine and information ethics (2015) Machine Medical Ethics. ISCASE, 74, pp. 17-32. , https://doi.org/10.1007/978-3-319-08108-3_2, van Rysewyk, S.P., Pontier, M. (eds) Springer, Cham; Klein, B., Graf, B., Schlömer, I.F., Rossberg, H., Röhricht, K., Baumgarten, S., (2018) Robotik in der Gesundheitswirtschaft: einsatzfelder und potenziale, , Medhochzwei, Heidelberg; Endsley, M.R., Toward a theory of situation awareness in dynamic systems (1995) Hum. Factors, 37 (1), pp. 32-64. , https://doi.org/10.1518/001872095779049543; Xu, J., Anders, S., Pruttianan, A., Human performance measures for the evaluation of process control human-system interfaces in high-fidelity simulations (2018) Appl. Ergon, 73, pp. 151-165. , https://doi.org/10.1016/j.apergo.2018.06.008, (PMID: 30098630); Nitsch, V., Situation awareness in autonomous service robots (2013) 10. Berliner Werkstatt Mensch-Maschine-Systeme; Schuster, D., Jentsch, F., Measurement of situation awareness in human-robot teams (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 1496-1500. , https://doi.org/10.1177/1071181311551311; Bolstad, C.A., Cuevas, H.M., Integrating situation awareness assessment into test and evaluation (2010) ITEA J, 31, p. 240; Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology (1989) MIS Q, 13 (3), pp. 319-340. , https://doi.org/10.2307/249008; Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: toward a unified view (2003) MIS Q, 27 (3), pp. 425-478; Heerink, M., Krose, B., Evers, V., Wielinga, B., Measuring acceptance of an assistive social robot: a suggested toolkit (2009) The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 528-533. , IEEE; Benmessaoud, C., Kharrazi, H., MacDorman, K.F., Facilitators and barriers to adopting robotic-assisted surgery: contextualizing the unified theory of acceptance and use of technology (2011) PLoS One, 6 (1), p. e16395. , https://doi.org/10.1371/journal.pone.0016395, (PMID: 21283719); Williams, M.D., Rana, N.P., Dwivedi, Y.K., The unified theory of acceptance and use of technology (UTAUT): a literature review (2015) J. 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Internet Res, 18 (12), p. e337. , https://doi.org/10.2196/jmir.6003, PMID: 28011445","Kremer, L.; Niederrhein University of Applied SciencesGermany; email: lisanne.kremer@hs-niederrhein.de","Kurosu M.",,"Springer","Thematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242229,03029743,9783030490614,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088753832
"Fischer N., Sträter O.","57218312040;6602760426;","Design Methods for Human-Robot-Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12198 LNCS",,,"106","118",,,"10.1007/978-3-030-49904-4_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088753494&doi=10.1007%2f978-3-030-49904-4_8&partnerID=40&md5=c800b16244606fb6bf3dc9205f3ffc12","Volkswagen Group Components, Kassel, Germany; University of Kassel, Kassel, Germany","Fischer, N., Volkswagen Group Components, Kassel, Germany; Sträter, O., University of Kassel, Kassel, Germany","An experimental set-up was made to measure a human’s reaction to a cooperating robot. The conclusion is a proposal of construction methods regarding layout, production planning and path planning of the robot. © 2020, Springer Nature Switzerland AG.",,"Ergonomics; Health; Health risks; Human computer interaction; Machine design; Production control; Risk management; Robot programming; Safety engineering; Co-operating robots; Construction method; Design method; Experimental set up; Made-to-measure; Production Planning; Human robot interaction",,,,,,,,,,,,,,,,"Arai, T., Kato, R., Fujita, M., Assessment of operator stress induced by robot collaboration in assembly (2010) CIRP Ann. – Manuf. 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A study of human-robot interaction in a collaborative task (2004) Human-Computer Interaction, pp. 151-181. , Lawrence Erlbaum Associates Inc., United States; Holzträger, D., (2012) Gesundheitsförderliche Mitarbeiterführung-Gestaltung von Maßnahmen der betrieblichen Gesundheitsförderung für Führungskräfte, , Rainer Hampp Verlag, Mering; Koppenborg, M., Lungfiel, A., Naber, B., Nickel, P., Auswirkung von Autonomie und Geschwindigkeit in der virtuellen Mensch-Roboter-Kollaboration (2013) Chancen durch Arbeits-, Produkt-und Systemgestaltung, pp. 417-420. , Gesellschaft für Arbeitswissenschaft (ed) GfA-Press, Krefeld; Lysaght, R., Hill, S.G., Dick, A.O., (1989) Technical Report 851-Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies, , U.S. Army Research Institute, Fort Bliss, Texas; Mori, M., The Uncanny Valley: The Original Essey by Masahiro Mori (translated by MacDorman & Minato) (2012), https://spectrum.ieee.org/automaton/robotics/humanoids/the-uncanny-valley, Robotics & Automation Magazine, June; Müller, H.J., Krummennacher, J., Schubert, T., (2015) Aufmerksamkeit und Handlungssteuerung-Grundlagen für die Anwendung, , https://doi.org/10.1007/978-3-642-41825-9, Springer, Heidelberg; Nomura, T., Kanda, T., Suzuki, T., (2005) Experimental investigation into influence of negative attitudes toward robots on human-robot interaction, , http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.59.6397&rep=rep1&type=pdf, Springer, London Accessed 03 Jan 2019; Ortmann, U., Guhlke, B., (2016) Konzepte zur sozial-und humanverträglichen Gestaltung von Industrie 4.0, , http://www.unibielefeld.de/soz/las/TA/itsowl/, (2014). Accessed 04 Nov; Ostermann, B., (2014) Entwicklung eines Konzepts zur sicheren Personenerfassung als Schutzeinrich-tung an kollaborierenden Robotern, , http://nbn-resolving.de/urn/resolver.pl?urn=urn%3Anbn%3Ade%3Ahbz%3A468-20140702-113540-2, Accessed 11 Sep 2015; Pfendler, C., Zur Messung der mentalen Beanspruchung mit dem NASA-Task Load Index (1990) Zeitschrift für Arbeitswissenschaft, pp. 158-163; Pfendler, C., Vergleich der Zwei-Ebenen-Intensitäts-Skala und des NASA Task Load Index bei der Beanspruchungsbewertung während Lernvorgängen (1993) Z. Arbeitswissenschaft, 47, pp. 26-33; Posner, M., Orienting of attention (1980) Q. J. Exp. Psychol, 32, pp. 3-25; Reich, N., Eyssel, F., Attitudes towards service robots in domestic environments: the role of personality characteristics, individual interests, and demographic variables (2013) Paladyn – J. Behav. Robot, 4 (2), pp. 123-130; Rüdiger, A., Was tun, wenn die Roboter kommen? (2017) CIOBRIEFING-Technology-Update für IT-Manager, 8, pp. 18-21; Rumsch, W.-C., Kruse, D.J., Köhler, F., (2014) Technology Watch Studie: Roboter in Kooperation: Mensch-Roboter-Produkt, , Innovationsgesellschaft für fortgeschrittene Produktionssysteme in der Fahrzeugindutsrie mgH, Berlin; Schmitz, W., Reckter, B., Riskante neue Arbeitswelt (2019) VDI Nachrichten, 9, p. 1; Siegert, H.-J., Bocionek, S., (1996) Robotik: Programmierung Intelligenter Roboter, , https://doi.org/10.1007/978-3-642-80067-2, Springer, Heidelberg; Sträter, O., (2005) Cognition and Safety-An Integrated Approach to Systems Design and Assessment, , Ashgate, Hampshire; Thiemermann, S., (2005) Direkte Mensch-Roboter-Kooperation in der Kleinteilemontage mit einem SCARA-Roboter, , Jost-Jetter Verlag, Heimsheim; Zühlke, D., (2012) Nutzergerechte Entwicklung von Mensch-Maschine-Systemen, , https://doi.org/10.1007/978-3-642-22074-6, Springer, Heidelberg","Fischer, N.; Volkswagen Group ComponentsGermany; email: nadja.fischer@volkswagen.de","Duffy V.G.",,"Springer","11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242209,03029743,9783030499037,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088753494
"Yılmaz E., Fal M., Acartürk C.","57218312261;57190405035;25652940800;","The role of gaze as a deictic cue in human robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12197 LNAI",,,"466","478",,,"10.1007/978-3-030-50439-7_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088744941&doi=10.1007%2f978-3-030-50439-7_32&partnerID=40&md5=ab36db527830cc5c9093e3f5990e382d","Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey; TAI, Turkish Aerospace Industries, Ankara, Turkey","Yılmaz, E., Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey; Fal, M., Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey, TAI, Turkish Aerospace Industries, Ankara, Turkey; Acartürk, C., Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey","Gaze has a major role in social interaction. As a deictic reference, gaze aims at attracting visual attention of a communication partner to a referred entity in the environment. Gaze direction in natural faces is a well-investigated domain of research at behavioral and neurophysiological levels. However, our knowledge about deictic role of gaze in Human Robot Interaction is limited. The present study focuses on a comparative analysis of the deictic role of gaze direction in alternative face morphologies. We report an experimental study that investigated deictic gaze in a virtual reality environment. Human participants identified object locations by utilizing deictic gaze cues provided by avatar faces, as well as natural human faces. Our findings reveal a facilitating role in the accuracy of objection detection in favor of gaze embedded in natural faces compared to gaze embedded in synthetic avatar face morphologies. © Springer Nature Switzerland AG 2020.","Deictic gaze; Human-avatar interaction; Human-robot interaction; Joint attention; Synthetic faces","Behavioral research; Human computer interaction; Visual communication; Communication partners; Comparative analysis; Gaze direction; Human faces; Object location; Social interactions; Virtual-reality environment; Visual Attention; Human robot interaction",,,,,,,,,,,,,,,,"Perrett, D., Smith, P., Potter, D., Visual cells in the temporal cortex sensitive to face view and gaze direction (1985) Proceedings of the Royal Society of London, Series B, Biological Sciences, 223 (1232), pp. 293-317. , Royal Society; Stiefelhagen, R., Zhu, J., Head orientation and gaze direction in meetings (2002) CHI 2002 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2002), pp. 858-859. , Association for Computing Machinery, New York City; Katzenmaier, M., Stiefelhagen, B., Schultz, T., Identifying the addressee in human-human-robot interactions based on head pose and speech (2004) ICMI 2004-Sixth International Conference on Multimodal Interfaces, pp. 144-151. , Association for Computing Machinery, New York City; Tan, Z., Thomsen, N.B., Duan, X., iSocioBot: a multimodal interactive social robot (2018) Int. J. Soc. Robot, 10 (1), pp. 5-19; Galantucci, B., An experimental study of the emergence of human communication systems (2005) Cogn. Sci, 29 (5), pp. 737-767; Sebanz, N., Bekkering, H., Knoblich, G., Joint action: bodies and minds moving together (2006) Trends Cogn. Sci, 10 (2), pp. 70-76; Haddadin, S., Croft, E., Erratum to: Physical human–robot interaction (2016) Springer Handbook of Robotics, p. E1. , https://doi.org/10.1007/978-3-319-32552-1_81, Siciliano, B., Khatib, O. (eds) Springer, Cham; Fang, R., Doering, M., Chai, J.Y., Embodied collaborative referring expression generation in situated human-robot interaction (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 271-278. , Association for Computing Machinery, New York City; Lemaignan, S., Warnier, M., Sisbot, E.A., Artificial cognition for social human–robot interaction: an implementation (2017) Artif. Intell, 247 (1), pp. 45-69; Piwek, P., Salience in the generation of multimodal referring acts (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 207-210; Admoni, H., Scassellati, B., Social eye gaze in human-robot Interaction: a review (2017) J. Hum.-Robot Interact, 6 (1), pp. 25-63; Ruhland, K., Peters, C.E., Andrist, S., A review of eye gaze in virtual agents, social robotics and HCI: behaviour generation, user interaction and perception (2015) Comput. Graph. Forum, 34 (6), pp. 299-326; Yücel, Z., Salah, A.A., Meriçli, Ç., Joint attention by gaze interpolation and saliency (2013) IEEE Trans. Cybern, 43 (3), pp. 829-842; Breazeal, C., Dautenhahn, K., Kanda, T., Social robots (2016) Springer Handbook of Robotics, pp. 1935-1961. , https://doi.org/10.1007/978-3-319-32552-1, Siciliano, B., Khatib, O. (eds), 2nd edn. Springer Handbooks, Heidelberg; Devault, D., Kariaeva, N., Kothari, A., An information-state approach to collaborative reference (2005) Proceedings of the ACL Interactive Poster and Demonstration Sessions, pp. 1-4; Mutlu, B., Shiwa, T., Kanda, T., Footing in human-robot conversations: how robots might shape participant roles using gaze cues (2009) Hum. Factors, 2 (1), pp. 61-68; Shannon, C.E., A mathematical theory of communication (1948) Bell Syst. Tech. J, 1 (1), pp. 379-423; Holmqvist, K., Andersson, R., Paradigms-visual search (2011) Eye Tracking: A Comprehensive Guide to Methods and Measures, pp. 409-413. , Oxford University Press, Oxford","Acartürk, C.; Institute of Informatics, Turkey; email: acarturk@metu.edu.tr","Schmorrow D.D.Fidopiastis C.M.",,"Springer","14th International Conference on Augmented Cognition, AC 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242429,03029743,9783030504380,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088744941
"Williams T., Hirshfield L., Tran N., Grant T., Woodward N.","7404172627;16303680200;57209796697;57205024437;57218272953;","Using augmented reality to better study human-robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12190 LNCS",,,"643","654",,5,"10.1007/978-3-030-49695-1_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088527885&doi=10.1007%2f978-3-030-49695-1_43&partnerID=40&md5=8a3e9e28500a7c355e65f499253110ff","MIRRORLab, Colorado School of Mines, Golden, CO, United States; SHINE Lab, University of Colorado, Boulder, CO, United States","Williams, T., MIRRORLab, Colorado School of Mines, Golden, CO, United States; Hirshfield, L., SHINE Lab, University of Colorado, Boulder, CO, United States; Tran, N., MIRRORLab, Colorado School of Mines, Golden, CO, United States; Grant, T., SHINE Lab, University of Colorado, Boulder, CO, United States; Woodward, N., MIRRORLab, Colorado School of Mines, Golden, CO, United States","In the field of Human-Robot Interaction, researchers often techniques such as the Wizard-of-Oz paradigms in order to better study narrow scientific questions while carefully controlling robots’ capabilities unrelated to those questions, especially when those other capabilities are not yet easy to automate. However, those techniques often impose limitations on the type of collaborative tasks that can be used, and the perceived realism of those tasks and the task context. In this paper, we discuss how Augmented Reality can be used to address these concerns while increasing researchers’ level of experimental control, and discuss both advantages and disadvantages of this approach. © Springer Nature Switzerland AG 2020.","Augmented Reality; Human-Robot Interaction; Research Methods","Augmented reality; Human computer interaction; Mixed reality; Collaborative tasks; Experimental control; Perceived realisms; Wizard of Oz; Human robot interaction",,,,,"National Science Foundation, NSF","This work was funded in part by grant IIS-1909864 from the National Science Foundation.",,,,,,,,,,"Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robot. Auton. 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Augmented reality to enable human-robot cooperative search (2018) International Symposium on Safety, Security, and Rescue Robotics; Amor, H.B., Ganesan, R.K., Rathore, Y., Ross, H., Intention projection for human-robot collaboration with mixed reality cues (2018) VAM-HRI","Williams, T.; MIRRORLab, United States; email: twilliams@mines.edu","Chen J.Y.C.Fragomeni G.",,"Springer","12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242159,03029743,9783030496944,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088527885
"Hempel T., Al-Hamadi A.","57217701662;57199426558;","SLAM-based multistate tracking system for mobile human-robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12131 LNCS",,,"368","376",,2,"10.1007/978-3-030-50347-5_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087533586&doi=10.1007%2f978-3-030-50347-5_32&partnerID=40&md5=0467294eb815418bf2281a74b756d3b9","Neuro-Information Technology (NIT), Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, 39106, Germany","Hempel, T., Neuro-Information Technology (NIT), Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, 39106, Germany; Al-Hamadi, A., Neuro-Information Technology (NIT), Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, 39106, Germany","The transfer from the utilization of simple robots for specifically predefined tasks to the integration of generalized autonomous systems poses a number of challenges for the collaboration between humans and robots. These include the independent orientation of robots in unknown environments and the intuitive interaction with human cooperation partners. We present a robust human-robot interaction (HRI) system that proactively searches for interaction partners and follows them in unknown real environments. For this purpose, an algorithm for simultaneous localization and mapping of the environment is integrated along with a dynamic system for determination of the partner’s willingness and the tracking of the partner’s localization. Interruptions of interactions are recovered by a separate recovery mode that is able to identify prior collaboration partners. © Springer Nature Switzerland AG 2020.","Human-robot interaction; Interaction willingness detection; Slam","Image analysis; Man machine systems; SLAM robotics; Tracking (position); Autonomous systems; Collaboration partner; Human robot Interaction (HRI); Intuitive interaction; Real environments; Recovery modes; Simultaneous localization and mapping; Tracking system; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448G-L","This work is funded by the Federal Ministry of Education and Research (BMBF) (RoboAssist no. 03ZZ0448G-L) within 3Dsensation alliance.",,,,,,,,,,"Awais, M., Henrich, D., Human-robot collaboration by intention recognition using probabilistic state machines (2010) 19th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2010), pp. 75-80. , https://doi.org/10.1109/RAAD.2010.5524605, June; Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M., Visual object tracking using adaptive correlation filters (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544-2550; Bradski, G., The OpenCV library (2000) Dr. Dobb’s J. Softw. Tools; Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O., Monoslam: real-time single camera slam (2007) IEEE Trans Pattern Anal. Mach. Intell, 29, pp. 1052-1067; Engel, J., Koltun, V., Cremers, D., (2016) Direct sparse odometry, , http://arxiv.org/abs/1607.02565, CoRR abs/1607.02565; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2015) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874; Kerl, C., Sturm, J., Cremers, D., Dense visual slam for RGB-D cameras (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2100-2106. , https://doi.org/10.1109/IROS.2013.6696650, November; Li, S., Zhang, L., Diao, X., Deep-learning-based human intention prediction using RGB images and optical flow (2019) J. Intell. Robot. Syst, 97 (1), pp. 95-107. , https://doi.org/10.1007/s10846-019-01049-3; Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges (2003) Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), IJCAI, Acapulco, Mexico; Mur-Artal, R., Montiel, J.M.M., Tardós, J.D., (2015) ORB-SLAM: a versatile and accurate monocular slam system, , CoRR abs/1502.00956; Mur-Artal, R., Tardos, J.D., ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras (2017) IEEE Trans. Rob, 33 (5), pp. 1255-1262. , https://doi.org/10.1109/tro.2017.2705103; Newcombe, R.A., Lovegrove, S., Davison, A.J., DTAM: dense tracking and mapping in real-time (2011) ICCV, pp. 2320-2327. , Metaxas, D.N., Quan, L., Sanfeliu, A., Gool, L.J.V. (eds) IEEE; Pire, T., Fischer, T., Castro, G., De Cristóforis, P., Civera, J., Berlles, J., S-PTAM: stereo parallel tracking and mapping (2017) Robot. Auton. Syst, 93, pp. 27-42. , https://doi.org/10.1016/j.robot.2017.03.019; Redmon, J., Farhadi, A., (2018) Yolov3: an incremental improvement, , http://arxiv.org/abs/1804.02767.cite, arxiv:1804.02767Comment. Technical report; Svenstrup, M., Tranberg, S., Andersen, H.J., Bak, T., Pose estimation and adaptive robot behaviour for human-robot interaction (2009) 2009 IEEE International Conference on Robotics and Automation, pp. 3571-3576. , https://doi.org/10.1109/ROBOT.2009.5152690, May; Tistarelli, M., Grosso, E., Human face analysis: from identity to emotion and intention recognition (2010) ICEB 2010. LNCS, 6005, pp. 76-88. , https://doi.org/10.1007/978-3-642-12595-911, Kumar, A., Zhang, D. (eds) Springer, Heidelberg; Wolf, L., Hassner, T., Taigman, Y., Effective unconstrained face recognition by combining multiple descriptors and learned background statistics (2011) IEEE Trans. Pattern Anal. Mach. Intell, 33 (10), pp. 1978-1990. , https://doi.org/10.1109/TPAMI.2010.230","Hempel, T.; Neuro-Information Technology (NIT), Universitaetsplatz 2, Germany; email: thorsten.hempel@ovgu.de","Campilho A.Karray F.Wang Z.",,"Springer","17th International Conference on Image Analysis and Recognition, ICIAR 2020","24 June 2020 through 26 June 2020",,241149,03029743,9783030503468,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85087533586
"Huang P., Wang H., Wang Y., Liu Z., Samuel O.W., Yu M., Li X., Chen S., Li G.","57201918519;57037447900;57216644565;56344609500;57188828713;56591026900;56647927000;35236039500;7407054228;","Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction",2020,"Computational and Mathematical Methods in Medicine","2020",,"5694265","","",,3,"10.1155/2020/5694265","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084169941&doi=10.1155%2f2020%2f5694265&partnerID=40&md5=89cd24f858621156de0fc29247a3b29f","CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 100049, China","Huang, P., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Wang, H., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Wang, Y., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Liu, Z., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Samuel, O.W., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Yu, M., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China; Li, X., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Chen, S., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Li, G., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China","Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis-(LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems. © 2020 Pingao Huang et al.",,"Discriminant analysis; Man machine systems; Motion estimation; Muscle; Quality control; Social robots; Analysis windows; Classification tasks; EMG-based control; External interference; Linear discriminant analysis; Movement intention recognition; Movement recognition; Surface electromyogram; Biomedical signal processing; adult; Article; classifier; discriminant analysis; electromyogram; feature extraction; feature selection; female; forearm; human; human experiment; limb movement; male; multiclass classification; normal human; robotics; signal noise ratio; signal processing; upper limb; automated pattern recognition; computer interface; devices; electric conductivity; electromyography; electronic device; equipment design; movement (physiology); physiology; robotics; signal processing; skeletal muscle; upper limb; gold; Electric Conductivity; Electromyography; Equipment Design; Gold; Humans; Movement; Muscle, Skeletal; Pattern Recognition, Automated; Robotics; Signal Processing, Computer-Assisted; Upper Extremity; User-Computer Interface; Wearable Electronic Devices",,"gold, 7440-57-5; Gold",,,"National Natural Science Foundation of China, NSFC: U1613222 81927804 61803361; Dream Project of Ministry of Science and Technology of the People's Republic of China","Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems. National Key R&D Program of China 2018YFA0701400 National Natural Science Foundation of China U1613222 81927804 61803361 Ministry of Science and Technology of the People's Republic of China 2016YFE0124100 Shenzhen Science and Technology Plan Project JCYJ20160331174854880 Guangdong Science and Technology Project 2019B090915002 Shenzhen Institute of Artificial Intelligence and Robotics for Society",,,,,,,,,,"Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M., A review of wearable sensors and systems with application in rehabilitation (2012) Journal of NeuroEngineering and Rehabilitation, 9 (1), p. 21. , 2-s2.0-84859905662; Rodgers, P., Kirstein, T., Tröster, G., Wearable systems for health care applications (2004) Methods of Information in Medicine, 43 (43), pp. 232-238; Long, Y., Du Z.-J., Wang W.-D., Dong, W., Human motion intent learning based motion assistance control for a wearable exoskeleton (2018) Robotics and Computer-Integrated Manufacturing, 49, pp. 317-327. , 2-s2.0-85027548318; 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Teramae, T., Kushida, D., Takemori, F., Kitamura, A., Construction of an intelligent massage system based on human skin-muscle elasticity (2011) Electronics and Communications in Japan, 94 (10), pp. 26-33. , 2-s2.0-80053334159; Kitamura, X.Z., Sun, H.L., Yue, X.Y., A highly stretchable carbon nanotubes/thermoplastic polyurethane fiber-shaped strain sensor with porous structure for human motion monitoring (2018) Composites Science and Technology, 168, pp. 126-132. , 2-s2.0-85053494354; Yang, P.K., Lin, L., Yi, F., A flexible, stretchable and shape-adaptive approach for versatile energy conversion and self-powered biomedical monitoring (2015) Advanced Materials, 27 (25), pp. 3817-3824. , 2-s2.0-84934282756; Wang, Y., Wang, L., Yang, T.T., Wearable and highly sensitive graphene strain sensors for human motion monitoring (2014) Advanced Functional Materials, 24 (29), pp. 4666-4670. , 2-s2.0-84905506878; Boland, C.S., Khan, U., Backes, C., Sensitive, high-strain, high-rate bodily motion sensors based on graphene-rubber composites (2014) Acs Nano, 8 (9), pp. 8819-8830. , 2-s2.0-84919937169; 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Lim, S., Son, D., Kim, J., Transparent and stretchable interactive human machine interface based on patterned graphene heterostructures (2015) Advanced Functional Materials, 25 (3), pp. 375-383. , 2-s2.0-84920983061; Song, W., Gan, B., Jiang, T., Nanopillar arrayed triboelectric nanogenerator as a self-powered sensitive sensor for a sleep monitoring system (2016) Acs Nano, 10 (8), pp. 8097-8103. , 2-s2.0-84983420692; Cai, G.F., Wang, J.X., Qian, K., Extremely stretchable strain sensors based on conductive self-healing dynamic cross-links hydrogels for human-motion detection (2017) Advanced Science, 4; Trung, T.Q., Ramasundaram, S., Hwang, B.U., An all-elastomeric transparent and stretchable temperature sensor for body-attachable wearable electronics (2016) Advanced Materials, 28 (3), p. 502. , 2-s2.0-84955175542; Muth, J.T., Vogt, D.M., Truby, R.L., Embedded 3D printing of strain sensors within highly stretchable elastomers (2014) Advanced Materials, 26 (36), pp. 6307-6312. , 2-s2.0-84907947600; 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Math. Methods Med.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084169941
"Liu J., Wang M., Wang P., Hou F., Meng C., Hashimoto K., Guo S.","37034409100;57215321364;57213430555;57216506514;25723467600;57195605981;7403650479;","Cost-Efficient Flexible Supercapacitive Tactile Sensor with Superior Sensitivity and High Spatial Resolution for Human-Robot Interaction",2020,"IEEE Access","8",,"9051698","64836","64845",,,"10.1109/ACCESS.2020.2984511","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083706673&doi=10.1109%2fACCESS.2020.2984511&partnerID=40&md5=aa4905b2e9f43ea8abe210d1808f180f","State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China; Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China; School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China","Liu, J., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Wang, M., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Wang, P., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Hou, F., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Meng, C., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Hashimoto, K., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Guo, S., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China, Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China","Tactile sensing is crucial for the safety, accuracy and robustness of the human-robot interactions in the fields of wearable equipment, service robots and healthcare robots. Although many efforts have been made, it still requires much work to develop functional and reliable flexible tactile sensors with superior sensitivity, wide measurement range, high spatial resolution and low cost based on simple structures and easy fabrication. Here, this paper introduces a flexible supercapacitive tactile sensor with outstanding and balanced performance. The tactile sensor contains two layers of flexible electrodes and a layer of ionic-gel coated microfiber matrix to form a supercapacitive sensing structure. The flexible electrodes and the ionic microfiber matrix are processed with scalable techniques such as screen printing and gel-coating, which guarantee the ultra-flexibility and low fabrication cost. The experimental data suggests strong linearity in the pressure-capacitance relationship and high sensitivity (135.9 nF $\cdot $ kPa $^{-1}\cdot $ cm2 or 27.11 kPa-1). Wide pressure measurement range (from 0 kPa to 1200 kPa) is also achieved by balancing structure parameters. Dynamic responses of the tactile sensors could accurately reflect the applied pressure cycles from human-finger tapping and machine pressing. The tactile sensor can map the pressure distribution with a high spatial resolution (&gt;2 points $\cdot $ mm-2) when connected with the specially designed electric circuitry. The spatial resolution from sub-mm to large area makes it promising for various sensing applications in human-robot interactions, from finger touch to body contact. The developed tactile sensor in this study owns superior applicability and universality which makes it a trustworthy candidate to benefit various applications in robotics, flexible electronics and bioengineered equipment. © 2013 IEEE.","Human-robot interaction; supercapacitive; tactile sensor","Balancing; Electrodes; Flexible electronics; Image resolution; Man machine systems; Microfibers; Screen printing; Easy fabrication; Flexible electrodes; Flexible tactile sensors; High spatial resolution; Sensing applications; Simple structures; Spatial resolution; Structure parameter; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51605136; Natural Science Foundation of Hebei Province: E2017202248, E2017202270; National Key Research and Development Program of China, NKRDPC: 2016YFE0128700; Hebei Provincial Key Research Projects: 19211817D","This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 51605136, in part by the National Key Research and Development Plan under Grant 2016YFE0128700, in part by the Natural Science Foundation of Hebei Province under Grant E2017202248 and Grant E2017202270, and in part by the Key Research and Development Plan of Hebei Province under Grant 19211817D.",,,,,,,,,,"Dahiya, R.S., Metta, G., Valle, M., Sandini, G., Tactile sensing_ from humans to humanoids (2010) Ieee Trans. Robot., 26 (1), pp. 1-20. , Feb; Sekitani, T., Nakajima, H., Maeda, H., Fukushima, T., Aida, T., Hata, K., Someya, T., Stretchable active-matrix organic light-emitting diode display using printable elastic conductors (2009) Nature Mater., 8 (6), pp. 494-499. , Jun; Fan, Z., Ho, J.C., Takahashi, T., Yerushalmi, R., Takei, K., Ford, A.C., Chueh, Y.-L., Javey, A., Toward the development of printable nanowire electronics and sensors (2009) Adv. Mater., 21 (37), pp. 3730-3743. , Oct; Wu, W., Wen, X., Wang, Z.L., Taxel-addressable matrix of vertical-nanowire piezotronic transistors for active and adaptive tactile imaging (2013) Science, 340 (6135), pp. 952-957. , May; Mukai, T., Hirano, S., Nakashima, H., Kato, Y., Sakaida, Y., Guo, S., Hosoe, S., Development of a nursing-care assistant robot RIBA that can lift a human in its arms (2010) Proc. IEEE/RSJ Int. Conf. Intell. 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Mater., 29 (12). , Mar; Mannsfeld, S.C.B., Tee, B.C.-K., Stoltenberg, R.M., Chen, C.V.H.-H., Barman, S., Muir, B.V.O., Sokolov, A.N., Bao, Z., Highly sensitive fiexible pressure sensors with microstructured rubber dielectric layers (2010) Nature Mater., 9 (10), pp. 859-864. , Oct; Li, R., Si, Y., Zhu, Z., Guo, Y., Zhang, Y., Pan, N., Sun, G., Pan, T., Super-capacitive iontronic nanofabric sensing (2017) Adv. Mater., 29 (36). , Sep; Li, S., Pan, N., Zhu, Z., Li, R., Li, B., Chu, J., Li, G., Pan, T., All-in-One iontronic sensing paper (2019) Adv. Funct. Mater., 29 (11). , Mar; Wang, S., Xu, J., Wang, W., Wang, G.-J.-N., Rastak, R., Molina-Lopez, F., Chung, J.W., Bao, Z., Skin electronics from scalable fabrication of an intrinsically stretchable transistor array (2018) Nature, 555 (7694), pp. 83-88. , Mar; Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., Matusik, W., Learning the signatures of the human grasp using a scalable tactile glove (2019) Nature, 569 (7758), pp. 698-702. , May; Lee, W., Kim, D., Rivnay, J., Matsuhisa, N., Lonjaret, T., Yokota, T., Yawo, H., Someya, T., Integration of organic electrochemical and field-effect transistors for ultrafiexible, high tem-poral resolution electrophysiology arrays (2016) Adv. Mater., 28 (44), pp. 9722-9728. , Nov; Lee, S., Reuveny, A., Reeder, J., Lee, S., Jin, H., Liu, Q., Yokota, T., Someya, T., A transparent bending-insensitive pressure sensor (2016) Nature Nanotechnol., 11 (5), pp. 472-478. , May; Tee, B.C.-K., Chortos, A., Dunn, R.R., Schwartz, G., Eason, E., Bao, Z., Tunable fiexible pressure sensors using microstructured elas-tomer geometries for intuitive electronics (2014) Adv. Funct. Mater., 24 (34), pp. 5427-5434. , Sep; Liu, J., Liu, N., Hashimoto, K., Meng, C., Guo, S., Touch position identification based on a fiexible array-less supercapacitive tactile sensor (2019) Aip Adv., 9 (1). , Jan; Nie, B., Xing, S., Brandt, J.D., Pan, T., Droplet-based interfacial capacitive sensing (2012) Lab Chip, 12 (6), pp. 1110-1118; Nie, B., Li, R., Cao, J., Brandt, J.D., Pan, T., Flexible transparent iontronic _lm for interfacial capacitive pressure sensing (2015) Adv. Mater., 27 (39), pp. 6055-6062. , Oct; Yang, X., Wang, Y., Sun, H., Qing, X., A fiexible ionic liquid-polyurethane sponge capacitive pressure sensor (2019) Sens. Actuators A, Phys., 285, pp. 67-72. , Jan; Yang, X., Wang, Y., Qing, X., A fiexible capacitive pressure sensor based on ionic liquid (2018) Sensors, 18 (7), p. 2395; Matsuhisa, N., Inoue, D., Zalar, P., Jin, H., Matsuba, Y., Itoh, A., Yokota, T., Someya, T., Printable elastic conductors by in situ for-mation of silver nanoparticles from silver _akes (2017) Nature Mater., 16 (8), pp. 834-840. , Aug; Trung, T.Q., Lee, N.-E., Flexible and stretchable physical sensor integrated platforms for wearable human-activity monitoringand personal healthcare (2016) Adv. Mater., 28 (22), pp. 4338-4372. , Jun; Hyun, W.J., Secor, E.B., Hersam, M.C., Frisbie, C.D., Francis, L.F., High-resolution patterning of graphene by screen printing with a silicon stencil for highly fiexible printed electronics (2015) Adv. Mater., 27 (1), pp. 109-115. , Jan; Khan, S., Lorenzelli, L., Dahiya, R.S., Technologies for printing sensors and electronics over large fiexible substrates: A review (2015) Ieee Sensors J., 15 (6), pp. 3164-3185. , Jun; Kumar, S., Liu, G., Schloerb, D.W., Srinivasan, M.A., Viscoelastic characterization of the primate finger pad in vivo by microstep indentation and three-dimensional finite element models for tactile sensation studies (2015) J. Biomech. Eng., 137 (6). , Jun; Maheshwari, V., Saraf, R., Tactile devices to sense touch on a par with a human finger (2008) Angew. Chem. Int. Ed., 47 (41), pp. 7808-7826. , Sep; Guo, S., Shiraoka, T., Inada, S., Mukai, T., A two-ply polymer-based fiexible tactile sensor sheet using electric capacitance (2014) Sensors, 14 (2), pp. 2225-2238","Guo, S.; State Key Laboratory of Reliability and Intelligence of Electrical Equipment, China; email: guoshijie@fudan.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85083706673
"Jeong J., Yang J., Baltes J.","57216362871;57216364098;6603911725;","Robot magic show: Human-robot interaction",2020,"Knowledge Engineering Review",,,"e15","","",,2,"10.1017/S0269888920000211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083346589&doi=10.1017%2fS0269888920000211&partnerID=40&md5=060d0d64cf18664471ed9d882a2a5565","National Taiwan Normal University, 106, Heping E. Road, Sec. 1, Taipei City, 10610, Taiwan","Jeong, J., National Taiwan Normal University, 106, Heping E. Road, Sec. 1, Taipei City, 10610, Taiwan; Yang, J., National Taiwan Normal University, 106, Heping E. Road, Sec. 1, Taipei City, 10610, Taiwan; Baltes, J., National Taiwan Normal University, 106, Heping E. Road, Sec. 1, Taipei City, 10610, Taiwan","The use of robots in performance arts is increasing. But, it is hard for robots to cope with unexpected circumstances during a performance, and it is almost impossible for robots to act fully autonomously in such situations. IROS-HAC is a new challenge in robotics research and a new opportunity for cross-disciplinary collaborative research. In this paper, we describe a practical method for generating different personalities of a robot entertainer. The personalities are created by selecting speech or gestures from a set of options. The selection uses roulette wheel selection to select answers that are more closely aligned with the desired personality. In particular, we focus on a robot magician, as a good magic show includes good interaction with the audience and it may also include other robots and performers. The magician with a variety of personalities increased the audience immersion and appreciation and maintained the audience's interest. The magic show was awarded first prize in the competition for a comprehensive evaluation of technology, story, and performance. This paper contains both the research methodology and a critical evaluation of our research. © Cambridge University Press, 2020.",,"Artificial intelligence; Software engineering; Collaborative research; Comprehensive evaluation; Critical evaluation; Cross-disciplinary; Performance arts; Research methodologies; Robotics research; Roulette wheel selection; Human robot interaction",,,,,,,,,,,,,,,,"Baltes, J., Tu, K.Y., Sadeghnejad, S., Anderson, J., (2017), 32, pp. 1-14. , HuroCup: Competition for multi-event humanoid robot athletes. The Knowledge Engineering Review; Baltes, J., (2018), https://bit.ly/2JjeMlz, Call for Participation IEEE IROS 2018 Humanoid Application Challenge-Robot Magic Show. (Accessed 17 March 2019); Baltes, J., Sun, Y., Moon, H., 2017 competitions: Magical, manipulation, mercurial robots [competitions] (2018) Ieee Robotics & Automation Magazine, 25, pp. 8-15; Costa, P., McCrae, R., A five-factor theory of personality (1999) The Five-Factor Model of Personality: Theoretical Perspectives, 2, pp. 51-87; Lapeyre, M., Rouanet, P., Grizou, J., Nguyen, S., Depraetre, F., Le Falher, A., Oudeyer, P.Y., (2014), Poppy project: Open-source fabrication of 3D printed humanoid robot for science, education and art. In Digital Intelligence 2014, 6; Moon, H., Sun, Y., Baltes, J., Kim, S., The IROS 2016 competitions [competitions] (2017) Ieee Robotics & Automation Magazine, 24, pp. 20-29; Morris, K.J., Samonin, V., Anderson, J., Lau, M.C., Baltes, J., (2018), pp. 245-256. , a. Robot magic: A robust interactive humanoid entertainment robot. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems; Morris, K.J., Anderson, J., Lau, M.C., Baltes, J., (2018), b. Interaction and learning in a humanoid robot magic performance. In 2018 AAAI Spring Symposium Series; Robotics, S., (2019), http://www.seedrobotics.com/rh2d-manipulator.html, RH2D MANIPULATOR. (Accessed 17 March 2019); Simon, T., Joo, H., Matthews, I., Sheikh, Y., (2017), Hand keypoint detection in single images using multiview bootstrapping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1145-1153. References 'Simon et al. (2017)' were identical in the reference list, so the latter has been deleted to avoid duplication and subsequent references and the citations have been renumbered. Please check and confirm this was done correctly; Soegaard, M., Dam, R.F., (2013) The Encyclopedia of Human-Computer Interaction, , 2nd edition. The Interaction Design Foundation; Yang, J., Jeong, J., Baltes, J., (2019), pp. 73-78. , Humanoid robot magic: Various responses and communication. In Proceedings of the 25th International Symposium on Electronic Art-ISEA 2019 'Lux Aeterna'","Jeong, J.; National Taiwan Normal University, 106, Heping E. Road, Sec. 1, Taiwan; email: jslvjh@gmail.com",,,"Cambridge University Press",,,,,02698889,,KEREE,,"English","Knowl Eng Rev",Article,"Article in Press","",Scopus,2-s2.0-85083346589
"Rivera L.J.F., Chandrasekaran B.","57216126752;56740438200;","ROS-MATLAB Interface and Setup for a Fault Tolerant Robotic System Using Human Robot Interaction",2020,"2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",,,"9031178","568","575",,1,"10.1109/CCWC47524.2020.9031178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083086195&doi=10.1109%2fCCWC47524.2020.9031178&partnerID=40&md5=8fd987213b2d60de27ab82d67755a523","Florida Polytechnic University, Department of Electrical & Computer Engineering, Lakeland, FL, United States","Rivera, L.J.F., Florida Polytechnic University, Department of Electrical & Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical & Computer Engineering, Lakeland, FL, United States","A Fault Tolerant Control system is a crucial aspect to any type of application mainly in robotics. In this paper, the focus is on the setup and interfacing between MATLAB and the Robotic Operating System (ROS) towards the implementation of a fault tolerant robotic system architecture previously proposed. This paper is a work in progress and presents the preliminary efforts towards achieving the communication between the different components used in the robotic system. Also, some of the possible test cases to verify how to system reacts to certain faults is also presented. This aspect of communication between different modules in the robotic system is significant to make the system more efficient by combining the powerful MATLAB tools in conjunction with the Robot Operating System(ROS). © 2020 IEEE.","Fault Tolerance; Human Robot Interaction; Interfacing; MATLAB; ROS; Self-Healing; Self-Recovery","Fault tolerance; Fault tolerant computer systems; Man machine systems; MATLAB; Robotics; Fault tolerant control systems; Fault-tolerant; MATLAB tools; Robot operating systems (ROS); Robotic system architectures; Robotic systems; Test case; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Rivera, L.J.F., Ruiz, A.Y.R., Chandrasekaran, B., A robotic control system for fault tolerance and safety using human robot interaction (2019) 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0435-439. , January IEEE; Rivera, L.J.F., Chandrasekaran, B., Implementation of A Robotic Control System for Fault Tolerance and Safety Using Human Robot Interaction, , unpublished, accepted at 2020 IEEE/SICE International Symposium on System Integration (SII 2020; Eason, G., Noble, B., Sneddon, I.N., On certain integrals of Lipschit z-Hankel type involving products of Bessel functions (1955) Phil. Trans. Roy. Soc. London, A247, pp. 529-551. , April (references; Kirchner, D., Niemczyk, S., Geihs, K., (2014) RoSHA: A Multi-robot Self-healing Architecture, , In: Behnke S., Veloso M., Visser A., Xiong R. (eds) RoboCup 2013: Robot World Cup XVII. RoboCup 2013. Lecture Notes in Computer Science, vol 8371. Springer, Berlin, Heidelberg; Brooks, R., A robust layered control system for a mobile robot (1986) IEEE Journal on Robotics and Automation, 2 (1), pp. 14-23. , March; https://robohub.org/ros-lOl-intro-to-therobot-operating-systern/, Accessed on 16-09-2018; Zaman, S., Slany, W., Steinbauer, G., ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues (2011) 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC), pp. 1-5. , Riyadh; MATLAB & Simulink, , https://www.mathworks.com/discovery/what-is-matlab.htrn, What is MATLAB; Young, I.M., (1989) The Technical Writer 'S Handbook, , Mill Valley, CA: University Science; Corke, P., MATLAB toolboxes: Robotics and vision for students and teacher s (2007) IEEE Robotics & Automation Magazine, 14 (4), pp. 16-17. , Dec; MATLAB & Simulink, , https://www.mathworks.com/products/robotics.htmI, Robotics System Toolbox Accessed on16-04-2018; Enrique, F., Crespo, L.S., Mahtani, A., Martinez, A., (2015) Learning ROS for Robotics Programm Ing Your One-stop Guide to the Robot Operating System, , Birmingham: Packt Publishing; Repositories/Ubuntu-Community Help Wiki, , https://help.ubuntu.com/community/Repositories/Ubuntu, Ubuntu Documentation; http://wiki.ros.org/melodic/Installation/Ubuntu, Accessed on 18-01-2018; Getting Started with Robotics System Toolbox, , https://www.mathworks.com/help/robotics/getting-started-with-roboticssystem-toolbox.html, Accessed on 01-01-2019; Connect to A ROS Network-MATLAB & Simulink, , https://www.mathworks.com/help/robotics/examples/connect-to-a-rosnetwork.html; http://wiki.ros.org/diagnostics/TutorialslUsingtheRobotMonitor, Accessed on 19-06-2018; Lyu, M.R., (1995) Software Fault Tolerance, , Chichester: Wiley; Simulink-simulation and model-based design Simulation and Model-Based Design MATLAB &, , https://www.mathworks.com/products/simulinkhtrnl, Accessed on 21-05-2019",,"Chakrabarti S.Paul R.","IEEE Region 1;IEEE Region 6;IEEE USA;Institute of Engineering and Management (IEM);University of Engineering and Management (UEM);UNLV","Institute of Electrical and Electronics Engineers Inc.","10th Annual Computing and Communication Workshop and Conference, CCWC 2020","6 January 2020 through 8 January 2020",,158422,,9781728137834,,,"English","Annu. Comput. Commun. Workshop Conf., CCWC",Conference Paper,"Final","",Scopus,2-s2.0-85083086195
"Ruiz A.Y.R., Chandrasekaran B.","57208120529;56740438200;","A Robotic Control System Using Robot Operating System and MATLAB for Sensor Fusion and Human-Robot Interaction",2020,"2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",,,"9031184","550","555",,,"10.1109/CCWC47524.2020.9031184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083085926&doi=10.1109%2fCCWC47524.2020.9031184&partnerID=40&md5=201c1f8892394dbb0731d92c2f602c13","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Ruiz, A.Y.R., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Human-robot interaction is a key aspect in the area of robotics. In this paper, the implementation of a sensor fusion based robotic control system architecture using Robot Operating System (ROS) and MATLAB is presented. Also, a few test cases under different scenarios is also highlighted for a robot navigation task and using the proposed architecture. The robotic system architecture was previously proposed and the work in this paper focuses on the implementation aspect of this architecture. This paper is a work in progress and more sensors and modules will continue to be interfaced and implemented as the research progresses. © 2020 IEEE.","Control System Architecture; Human-Robot Interaction; Motion Control; ROS/MATLAB Interface; Sensor Fusion; Sensors; Simulink","Computer architecture; Control systems; Man machine systems; MATLAB; Robotics; Implementation aspects; Proposed architectures; Robot navigation; Robot operating system; Robot operating systems (ROS); Robotic control system; Robotic system architectures; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Ruiz, A.Y.R., Rivera, L.J.F., Chandrasekaran, B., A sensor fusion based robotic system architecture using human interaction for motion contro! (2019) 20 19 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), p. 00950100. , January IEEE; Ruiz, A.Y.R., Chandrasekaran, B., Implementation of a sensor fusion based robotic system architecture for motion control using human-robot interaction 2020 IEEE/SICE International Symposium on System Integration (SII 2020; Powering the World's Robots, , http://www.ros.org/, [Accessed: II-Sept-2019]; Simulink-simulation and model-based design Simulation and Model-Based Design MATLAB, , www.mathworks.comlproducts/simulink.htm, [Accessed 30-0ct-2019]; Mahdi, L., Reza Kangavari, M., Comprehensive architecture for intelligent adaptive interface in the field of singlehuman multiple-robot interaction (2018) ETRI Journal, 50 (4), pp. 483-498; Path Following with Obstacle Avoidance in Simulink-MATLAB & Simulink, , www.mathworks.comlhelp/robotics/examples/path-following-with100bstacle-avoidance-in-simulink.htm, Path Following with Obstacle Avoidance in Simulink®[Accessed: 1O-Oct-2019]; Sensor fusion using synthetic radar and vision data in simulink matlab & simulink Driving Scenario Designer, , www.mathworks.comlhelp/driving/examples/sensor-fusion-usingsynthetic-radar-and-vision-data-in-simulink.htm, Accessed 10-Oct2019; Ros.org,wiki.ros.orgIROS/Tutorials, [Accessed 22-0ct-2019]; Welcome to VirtualBox.org!, , www.virtualbox.org/, Oracle VM VirtualBox, [Accessed: I0-Nov-20 19]; Ros.org,wiki.ros.org/stdrsimulator/Tutorials/Teleopwithteleoptwistkeyboard, [Accessed 10-Nov-20 19]; MathWorks, , www.mathworks.comlproductslmatlab.htm, [Accessed: 17-0ct-2019]; Robot Operating System (ROS) MATLAB & Simulink, MathWorks, , www.mathworks.comlhelp/robotics/gs/robot-operating-system-ros.htm, Robot Operating System (ROS)[Accessed: 10-Sept-2019]; ROS Network Setup MATLAB & Simulink, , www.mathworks.comlhelp/robotics/ug/ros-network-setup.html, [Accessed: 2-Nov-2019]; Get Started with ROS-MATLAB & Simulink, , www.mathworks.comlhelp/robotics/examples/get-started-with-ros.htm, Get Started with ROS[Accessed: 30-0ct-2019]; Creating and Executing Test Cases MATLAB & Simulink Example, , www.mathworks.comlhelp/sldv/examples/creating-and-executing-testcases.htm, Creating and Executing Test Cases[Accessed: 2-Nov-2019]",,"Chakrabarti S.Paul R.","IEEE Region 1;IEEE Region 6;IEEE USA;Institute of Engineering and Management (IEM);University of Engineering and Management (UEM);UNLV","Institute of Electrical and Electronics Engineers Inc.","10th Annual Computing and Communication Workshop and Conference, CCWC 2020","6 January 2020 through 8 January 2020",,158422,,9781728137834,,,"English","Annu. Comput. Commun. Workshop Conf., CCWC",Conference Paper,"Final","",Scopus,2-s2.0-85083085926
"Andrews J., Lad M., Chandrasekaran B.","57216313842;57216313136;56740438200;","A Robotic System for Elderly Care and Monitoring using Human Robot Interaction",2020,"2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",,,"9031215","576","579",,,"10.1109/CCWC47524.2020.9031215","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083084843&doi=10.1109%2fCCWC47524.2020.9031215&partnerID=40&md5=2b69755562735a8906df9f7b4b30824c","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Andrews, J., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Lad, M., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Human-Robot Interaction is an emerging field in the area of robotics and growth in this field finds application in several areas of the society. In this paper an initial progress of the design of a robotic system for elderly care is proposed. The paper describes the basic architecture of this proposed system and presents an initial implementation of this robot. This is a work in progress, and the robotic system will be updated and developed as the research progresses when more sensors and other components are interfaced providing more functionalities to the robot. Also, another goal of the project is contribution in undergraduate electrical and computer engineering education where students understand and learn to work on both hardware and software aspects in the design of an assistive robotic system. © 2020 IEEE.","Human-Robot Interaction; Machine Learning; Motion Control; Robotic System Architecture; Sensors; Social Robotics","Computer hardware; Engineering research; Man machine systems; Robotics; Students; Assistive robotics; Elderly care; Electrical and computer engineering; Hardware and software; Robotic systems; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Sharkey, A., Sharkey, N., Children, the elderly, and interacti ve robots (2011) IEEE Robotics & Automation Magazine, 18 (1), pp. 32-38. , Apr; (2008), http://www.impactlab.net/2008/10/27lrosie-the-robot-maid-made-bytoyota, Say Goodbye To Housekeep ing, New Robot Maid. Impact Lab[last accessed: 4 November, 2019]; Hegel, F., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., (2009) Understanding Social Robots. in 2009 Second International Conferences on Advances in Computer-Human Interactions, pp. 169-174. , February. IEEE; Fong, T., Nourbakhsh, I., Dautenhahn, K., (2002) A Survey of Socially Interactive Robots : Concepts, Design and Applications: Technical Report CMU-RI-TR-02-29; Duffy, B.R., Rooney, C., O'Hare, G.M., O'Donoghue, R., What is a social robot (1999) 10th Irish Conference on Artificial Intelligence & Cognitive Science, , September University College Cork, Ireland, 1-3 September 1999; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care : A review (2009) Gerontechnology, 8 (2), pp. 94-103; Graf, B., Hans, M., Schraft, R.D., Care-o-bot II-development of a next generation robotic home assistant (2004) Autonomous Robots, 16 (2), pp. 193-205; Bahadori, S., Cesta, A., Grisetti, G., Iocchi, L., Leone, R., Nardi, D., Oddi, A., Rasconi, R., Robocare: An integrated robotic system for the domestic care of the elderly (2003) Proceedings of Workshop on Ambient Intelligence AI 1A-03, Pisa, , Italy; https:llwww.xilinx.comlproductslboards-and-kitslzcul04.html, [Accessed: 4-November-2019]; http://www.tul.com.twlProductsPYNQ-Z2.html, November-2019; http://www.pynq.io, [Accessed: 4-November-2019]; https:llwww.tensorflow.org/, [Accessed: 4-November-2019]; Gael, L., https:llwww.thingiverse.comlthing:90033, [Accessed: 4-November-2019]; http://pinktentacle.coml2008/11/, Motoman SDAIO [Accessed: 4November-2019]; https:llwccftech.comlsony-emotional-robots/, SONY, Emotional Robot [Accessed: 4-November-2019]; Westlund, J.K., Lee, J.J., Plummer, L., Faridi, F., Gray, J., Berlin, M., Quintus-Bo Sz, H., Breazeal, C., Tega: A social robot (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), p. 561; Sabelli, A.M., Kanda, T., Hagita, N., A conversational robot in an elderly care center : An ethnographic study (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 37-44. , March IEEE",,"Chakrabarti S.Paul R.","IEEE Region 1;IEEE Region 6;IEEE USA;Institute of Engineering and Management (IEM);University of Engineering and Management (UEM);UNLV","Institute of Electrical and Electronics Engineers Inc.","10th Annual Computing and Communication Workshop and Conference, CCWC 2020","6 January 2020 through 8 January 2020",,158422,,9781728137834,,,"English","Annu. Comput. Commun. Workshop Conf., CCWC",Conference Paper,"Final","",Scopus,2-s2.0-85083084843
"Tanausavaphol T., Neranon P., Vessakosol P., Phukpattaranont P.","57216256375;55842967500;35491966100;6602493030;","EMG – Based force estimation for dynamic muscle contractions in physical human-robot interaction",2020,"Journal of Mechanical Engineering Research and Developments","43","2",,"165","177",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082962631&partnerID=40&md5=ab715fb0b9d864bb531ca20504edf082","Department of Mechanical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand; Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand","Tanausavaphol, T., Department of Mechanical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand; Neranon, P., Department of Mechanical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand; Vessakosol, P., Department of Mechanical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand; Phukpattaranont, P., Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand","Electromyography is a method in the field of electrodiagnostic medicine for monitoring and assessing the electrical activity generated by skeletal muscles themselves. As reviewed, the study of human force estimation based on Electromyographical signals is such a crucial challenge, since it is further complicated by the dynamic nature of the human subject. Consequently, this paper aims to develop an effective algorithm to roughly approximate the human hand applied force during executing rectilinear-motion-machine interaction, in which a test influence variable, namely friction force against the object movement, was additionally exerted. This mathematical algorithm will be further implemented in a newly designed rehabilitation robot using EMG muscle force estimation instead of applying a costly multi-axis force/torque sensor. Artificial neural networks and Support vector machine approaches were successfully applied to distinctly classify the electromyography signals of human’s hand muscles detected by eight-channel EMG electrodes. After the set of tests was carried out, root mean square error was individually utilized to evaluate the quantitative performance of each technique. The experimental results illustrated that both approaches were considered acceptable for EMG–based force estimation for dynamic muscle contractions by indicating that the human applied force was validly estimated based on the EMG signals. Additionally, it can be implied that the more the resistant force applied against the object movement, the lower the force model estimated performance. © 2020 Zibeline International Publishing Sdn. Bhd.. All rights reserved.","Artificial neural networks; Electromyography; Human-robot interaction; Muscle force estimation; Support vector machine",,,,,,"Prince of Songkla University, PSU","We acknowledge financial support from research grants, research fund from Research and Development Office (RDO) and a 5-year bachelor's-master scholarship program for postgraduate students, Prince of Songkla University. The author would like to thank all the participants in the experiments conducted.",,,,,,,,,,"Artemiadis, P.K., Kyriakopoulos, K.J., EMG-based control of a robot arm using low-dimensional embeddings (2010) IEEE Trans. Robot., 26 (2), pp. 393-398. , Apr; Xie, P.J., Haleegoah, J., Research on Vision-Based Underwater Robot Positioning and Map Building Technology, p. 6; Oh, Y.T., Study of Driving Torque through Analysis of Dynamic Characteristics on Industrial Robot, p. 8; Bai, F., Chew, C.-M., Muscle force estimation with surface EMG during dynamic muscle contractions: A wavelet and ANN based approach (2013) 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4589-4592. , Osaka; Konrad, P., (2006) The ABC of EMG: A Practical Introduction to Kinesiological Electromyography, Version 1.4, , Noraxon USA, Inc; Kaewboon, W., Phukpattaranont, P., Limsakul, C., Upper limbs rehabilitation system for stroke patient with biofeedback and force (2013) The 6th 2013 Biomedical Engineering International Conference, pp. 1-5. , Amphur Muang, Krabi, Thailand; Lubecki, T.M., Bai, F., Chew, C.-M., Teo, C.L., Development of intuitive human-machine interface based on Electromyography for assistive robot (KAAD) (2011) 2011 IEEE/SICE International Symposium on System Integration (SII), pp. 908-913. , Kyoto, Japan; Hoozemans, M.J.M., Van Dieën, J.H., Prediction of handgrip forces using surface EMG of forearm muscles (2005) J. Electromyogr. Kinesiol., 15 (4), pp. 358-366. , Aug; Disselhorst-Klug, C., Schmitz-Rode, T., Rau, G., Surface electromyography and muscle force: Limits in sEMG–force relationship and new approaches for applications (2009) Clin. Biomech., 24 (3), pp. 225-235. , Mar; Ali, N., Ahmad, S., Aziz, S., Zaman, G., The Adomian decomposition method for solving HIV infection model of latently infected cells Matrix Science Mathematic, 3 (1), pp. 05-08; Jekwu, E.C., Precious, A.G., Evans, A.B., Evaluating the impact of unsteady viscous flow and presence of solid particles on pipeline surfaces during crude oil transport (2019) Acta Mechanica Malaysia, 2 (2), pp. 20-27; Gian, S.H., Kasim, S., Hassan, R., Zakaria, Z., Mahdin, H., Ramli, A.A., Md Fudzee, M.F., Salamat, M.A., Online activity duration management system for manufacturing company (2019) Acta Electronica Malaysia, 3 (2), pp. 01-08; Shazwan, K.S., Shahari, R., Che Amri, C.N.A., Mohd Tajuddin, N.S., Figs (Ficus carica L.): Cultivation method and production based in Malaysia (2019) Engineering Heritage Journal, 3 (2), pp. 6-8; Karlsson, S., Yu, J., Akay, M., Time-frequency analysis of myoelectric signals during dynamic contractions: A comparative study (2000) IEEE Trans. Biomed. Eng., 47 (2), pp. 228-238. , Feb; Staudenmann, D., Kingma, I., Stegeman, D.F., Van Dieën, J.H., Towards optimal multi-channel EMG electrode configurations in muscle force estimation: A high density EMG study (2005) J. Electromyogr. Kinesiol., 15 (1), pp. 1-11. , Feb; Staudenmann, D., Kingma, I., Daffertshofer, A., Stegeman, D.F., VanDieen, J.H., Improving EMG-based muscle force estimation by using a high-density EMG grid and principal component analysis (2006) IEEE Trans. Biomed. Eng., 53 (4), pp. 712-719. , Apr; Naeem, U.J., Xiong, C., Abdullah, A.A., EMG-muscle force estimation model based on back-propagation neural network (2012) 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings, pp. 222-227. , Tianjin, China; Castellini, C., Koiva, R., Using surface electromyography to predict single finger forces (2012) 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1266-1272. , Rome, Italy; Xiao, F., Wang, Y., Gao, Y., Zhu, Y., Zhao, J., Continuous estimation of elbow joint angle by multiple features of surface electromyographic using grey features weighted support vector machine (2017) J. Med. Imaging Health Inform., 7 (3), pp. 574-583. , Jun; Gao, Y., Luo, Y., Li, Q., Zhao, J., Continuous joint angle estimation by least support vector machine from time-delayed sEMG features (2018) Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology - ICBBT’18, pp. 13-17. , Amsterdam, Netherlands; Zarzycka, N., Załuska, S., Measurements of the forearm i inhabitants of the Lublin region (1989) Ann. Univ. Mariae Curie Sklodowska [Med.], 44, pp. 85-92; Mishra, M., Srivastava, M., A view of Artificial Neural Network (2014) 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), pp. 1-3. , Unnao, India; Keattisak, C., Supot, N., A comparison of prediction palm oil time series using polynomial artificial neural network and artificial neural network The 12th National Graduate Research Conference (GRC-2011); Jensen, C., Vasseljen, O., Westgaard, R.H., Estimating maximal EMG amplitude for the trapezius muscle: On the optimization of experimental procedure and electrode placement for improved reliability and increased signal amplitude (1996) J. Electromyogr. Kinesiol., 6 (1), pp. 51-58. , Mar; Kim, H.J., Lee, Y.S., Kim, D., Arm motion estimation algorithm using myo armband (2017) 2017 First IEEE International Conference on Robotic Computing (IRC), pp. 376-381. , Taichung, Taiwan; Jiang, N., Muceli, S., Graimann, B., Farina, D., Effect of arm position on the prediction of kinematics from EMG in amputees (2013) Med. Biol. Eng. Comput., 51 (1-2), pp. 143-151. , Feb; Bafitlhile, T.M., Li, Z., Applicability of ε-support vector machine and artificial neural network for flood forecasting in humid, semi-humid and semi-arid basins in China (2019) Water, 11 (1), p. 85. , Jan; Arabia, S., Akande, K.O., Owolabi, T.O., Twaha, S., Olatunji, S.O., Performance comparison of SVM and ANN in predicting compressive strength of concrete (2014) IOSR J. Comput. Eng., 16 (5), pp. 88-94; Yu, B., Wang, Y.T., Yao, J.B., Wang, J.Y., A comparison of the performance of ANN and SVM for the prediction of traffic accident duration (2016) Neural Netw. World, 26 (3), pp. 271-287","Neranon, P.; Department of Mechanical Engineering, Thailand; email: paramin.n@psu.ac.th",,,"Bangladesh University of Engineering and Technology",,,,,10241752,,JERDF,,"English","J Mech Eng Res Dev",Article,"Final","",Scopus,2-s2.0-85082962631
"Rivera L.J.F., Chandrasekaran B.","57216126752;56740438200;","Implementation of a Robotic Control System for Fault Tolerance and Safety using Human Robot Interaction",2020,"Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020",,,"9026237","1028","1032",,2,"10.1109/SII46433.2020.9026237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082612829&doi=10.1109%2fSII46433.2020.9026237&partnerID=40&md5=ed406b7c0e715c7db14736a2e8a5eb02","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Rivera, L.J.F., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Fault tolerance is important in a robotic system architecture because of the advancements in robotics which makes the robots much smarter and more capable on executing tasks. Thus fault tolerant and self-healing robotic control systems help prevent any cascade errors that can lead to the robot becoming inoperable or causing a safety hazard. The current paper is a work in progress describing the initial efforts of implementing a fault tolerant robotic system architecture and presents initial progress of this implementation where the robotic control system demonstrates a fault tolerance and a self-healing algorithm using human robot interaction in the process. © 2020 IEEE.","Fault Detection; Fault Tolerance; Human Robot Interaction; Motion Control; Robot Control; Self-Healing","Cascade control systems; Computer architecture; Fault detection; Fault tolerance; Man machine systems; Motion control; Robotics; Self-healing materials; Fault-tolerant; Robot controls; Robotic control system; Robotic system architectures; Safety hazards; Self-healing; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Edwards, G., Garcia, J., Tajalli, H., Popescu, D., Medvidovic, N., Sukhatme, G., Petrus, B., Architecture-driven selfadaptation and self-management in robotics systems (2009) 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, pp. 142-151. , May. IEEE; Kirchner, D., Niemczyk, S., Geihs, K., RoSHA: A multi-robot self-healing architecture (2013) Robot Soccer World Cup, pp. 304-315. , June. Springer, Berlin, Heidelberg; Powering the World's Robots, , http://www.ros.org/, ROS. org. [Accessed: 8-Nov-2019]; Robotics system toolbox Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , https://www.mathworks.com/products/robotics.html, [Accessed: 8-Nov-2019]; Rivera, L.J.F., Ruiz, A.Y.R., Chandrasekaran, B., A robotic control system for fault tolerance and safety using human robot interaction (2019) 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0435-0439. , January. IEEE",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/SICE International Symposium on System Integration, SII 2020","12 January 2020 through 15 January 2020",,158395,,9781728166674,,,"English","Proc. IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85082612829
"Ruiz A.Y.R., Chandrasekaranxs B.","57208120529;56740438200;","Implementation of a Sensor Fusion Based Robotic System Architecture for Motion Control using Human-Robot Interaction",2020,"Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020",,,"9026276","405","409",,1,"10.1109/SII46433.2020.9026276","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082608849&doi=10.1109%2fSII46433.2020.9026276&partnerID=40&md5=326ea919aba81cdfa77a67b3cf3db297","Florida Polytechnic University, Department of Electrical Computer Engineering, Lakeland, FL, United States","Ruiz, A.Y.R., Florida Polytechnic University, Department of Electrical Computer Engineering, Lakeland, FL, United States; Chandrasekaranxs, B., Florida Polytechnic University, Department of Electrical Computer Engineering, Lakeland, FL, United States","In this paper an initial implementation of a sensor fusion based robotic system architecture for motion control is presented. The architecture also consists of a human robot interaction interface to help the robotic system during the application. This is a work in progress and the paper presents the initial efforts covering the architectural implementation of the system that includes the experimental setup and the block diagram of the architecture. © 2020 IEEE.","Control System Architecture; Human-Robot Interaction; Motion Control; Robotics System Toolbox; ROS; Sensor Fusion; Simulink","Computer architecture; Man machine systems; Motion control; Robotics; Block diagrams; Control system architecture; Robotic system architectures; Robotic systems; Robotics systems; Sensor fusion; Simulink; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Ilbeygi, M., Reza Kangavari, M., Comprehensive architecture for intelligent adaptive interface in the field of single-human multiple-robot interaction (2018) ETRI Journal, 40 (4), pp. 483-498; Doroodgar, B., Ficocelli, M., Mobedi, B., Nejat, G., The search for survivors: Cooperative human-robot interaction in search and rescue environments using semi-autonomous robots (2010) 2010 IEEE International Conference on Robotics and Automation, , Anchorage, Alaska, USA, May; Kruijff, G.J.M., Experience in system design for human-robot teaming in urban search and rescue (2014) Field and Service Robotics, 92. , Yoshida K., Tadokoro S. (eds) . Springer Tracts in Advanced Robotics. Springer, Berlin, Heidelberg; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Transactions on Robotics, 33 (1). , February; Ragaglia, M., Maria Zanchettin, A., Rocco, P., Safety-aware trajectory scaling for Human-Robot Collaboration with Prediction of Human Occupancy (2015) 2015 International Conference on Advanced Robotics (ICAR), , Istanbul, Turkey, July; Sierra, S.D., Development of an interface for human-robot interaction on a robotic platform for gait assistance: Agora smart walker (2018) 2018 Ieee Andescon; Sierra, S.D., Development of an interface for human-robot interaction on a robotic platform for gait assistance: Agora smart walker (2018) 2018 Ieee Andescon; Ikemoto, S., Ben Amor, H., Minato, T., Jung, B., Ishiguro, H., Physical human-robot interaction mutual learning and adaptation (2012) IEEE Robotics & Automation Magazine, 19 (4). , December; Dieterle, T., Particke, F., Patino-Studencki, L., Thielecke, J., Sensor data fusion of LIDAR with stereo RGB-D camera for object tracking (2017) 2017 Ieee Sensors; Jian, S., Lou, Y., Application of motion control system for delta parallel robot (2017) 2017 IEEE International Conference on Information and Automation (ICIA); Rodriguez, I., Nottensteiner, K., Leidner, D., Kabecker, M., Stulp, F., Albu-Schaffer, A., Iteratively refined feasibility checks in robotic assembly sequence planning (2019) IEEE Robotics and Automation Letters, p. 1; Ruiz, R., Ariel, Y., Rivera Figueroa, L.J., Chandrasekaran, B., A sensor fusion based robotic system architecture using human interaction for motion control (2019) 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC); Powering the World's Robots, , http://www.ros.org/, ROS. org. [Accessed: 8-Nov-2019]; Robotics system toolbox Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , https://www.mathworks.com/products/robotics.html, [Accessed: 8-Nov-2019]; Automated driving system toolbox Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , www.mathworks.com/products/automated-driving.html, [Accessed: 8-Nov-2019]; Path following with obstacle avoidance in simulink Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , www.mathworks.com/help/robotics/examples/path-following-withobstacle-avoidance-in-simulink.html, [Accessed: 8-Nov-2019]",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/SICE International Symposium on System Integration, SII 2020","12 January 2020 through 15 January 2020",,158395,,9781728166674,,,"English","Proc. IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85082608849
"Koay K.L., Syrdal D.S., Dautenhahn K., Walters M.L.","9732723100;22986616600;7003305185;56214015200;","A narrative approach to human-robot interaction prototyping for companion robots",2020,"Paladyn","11","1",,"66","85",,4,"10.1515/pjbr-2020-0003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082311835&doi=10.1515%2fpjbr-2020-0003&partnerID=40&md5=89816d5ddaca0d23b85b7aa7c54fa8fb","Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; University of Waterloo, Waterloo, Canada","Koay, K.L., Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Syrdal, D.S., Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Dautenhahn, K., University of Waterloo, Waterloo, Canada; Walters, M.L., Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom","This paper presents a proof of concept prototype study for domestic home robot companions, using a narrative-based methodology based on the principles of immersive engagement and fictional enquiry, creating scenarios which are inter-connected through a coherent narrative arc, to encourage participant immersion within a realistic setting. The aim was to ground human interactions with this technology in a coherent, meaningful experience. Nine participants interacted with a robotic agent in a smart home environment twice a week over a month, with each interaction framed within a greater narrative arc. Participant responses, both to the scenarios and the robotic agents used within them are discussed, suggesting that the prototyping methodology was successful in conveying a meaningful interaction experience. © 2020 Kheng Lee Koay et al., published by De Gruyter 2020.","human-robot interactions; prototyping; robot companions; social robotics; user studies",,,,,,"Seventh Framework Programme, FP7: 287624; European Commission, EC: FP7 215554","We would like to thank our colleague Wan Ching (Steve) Ho, for his efforts in implementing and running the robots in the scenarios. We would also like to thank our colleague Joe Saunders for his proof-reading and sense-checking of the paper. The work described in this paper was conducted within the EU Integrated Projects LIREC (LIving with Robots and int Eractive Companions), funded by the European Commission under contract numbers FP7 215554 and partly funded by the ACCOMPANY project, a part of the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 287624.",,,,,,,,,,"Bedaf, S., Gelderblom, G.J., Syrdal, D.S., Lehmann, H., Michel, H., Hewson, D., Amirabdollahian, F., De Witte, L., ""which activities threaten independent living of elderly when becoming problematic: Inspiration for meaningful service robot functionality,"" (2014) Disability and Rehabilitation: Assistive Technology, 9 (6), pp. 445-452; Scopelliti, M., Giuliani, M., D'Amico, A., Fornara, F., Keates, S., Clarkson, P.J., Langdon, P., Robinson, P., (2004), pp. 257-266. , eds., London Springer; Frennert, S., Östlund, B., Eftring, H., ""would granny let an assistive robot into her home?,"" (2012) Social Robotics, pp. 128-137; Bethel, C.L., Murphy, R.R., ""review of human studies methods in HRI and recommendations,"" (2010) International Journal of Social Robotics, 2 (4), pp. 347-359. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000208893600002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3; Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K., (1998) Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256-265. , The lumiere project: Bayesian user modeling for inferring the goals and needs of software users,"" Morgan Kaufmann Publishers Inc; Whitworth, B., ""polite computing,"" (2005) Behaviour & Information Technology, 24 (5), pp. 353-363; Saez-Pons, J., Syrdal, D.S., Dautenhahn, K., ""what has happened today? memory visualisation of a robot companion to assist user's memory,"" (2015) Journal of Assistive Technologies, 9 (4), pp. 207-218. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000422588800004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3; Saunders, J., Syrdal, D.S., Koay, K.L., Burke, N., Dautenhahn, K., """"Teach me-show me"" - End-user personalization of a smart home and companion robot,"" (2016) IEEE Transactions on Human-Machine Systems, 46 (1), pp. 27-40. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000368829400003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3; Dautenhahn, K., ""robots we like to live with?-a developmental perspective on a personalized, life-long robot companion,"" (2004) 2004 ROMAN 13th IEEE International Workshop on Robot and Human Interactive Communication, pp. 17-22; Kidd, C.D., Breazeal, C., ""robots at home: Understanding long-term human-robot interaction,"" (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3230-3235; Fernaeus, Y., Håkansson, M., Jacobsson, M., Ljungblad, S., ""how do you play with a robotic toy animal?: A long-term study of pleo,"" (2010) Proceedings of the 9th International Conference on Interaction Design and Children, pp. 39-48; De Graaf, M.M., Allouch, S.B., Klamer, T., ""sharing a life with harvey: Exploring the acceptance of and relationship-building with a social robot,"" (2015) Computers in Human Behavior, 43, pp. 1-14. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000348889700002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3; Payr, S., (2013) Your Virtual Butler, pp. 134-178. , Virtual butlers and real people: Styles and practices in long-term use of a companion,"" Springer; Kleanthous, S., Christophorou, C., Tsiourti, C., Dantas, C., Wintjens, R., Samaras, G., Christodoulou, E., Salvendy, G., (2016), pp. 35-44. , eds., (Cham), Springer International Publishing; Martins, G.S., Santos, L., Dias, J., ""user-adaptive interaction in social robots: A survey focusing on non-physical interaction,"" (2019) International Journal of Social Robotics, 11, pp. 185-205. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000460006800014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3, Jan; Kompatsiari, K., Ciardo, F., Tikhanoff, V., Metta, G., Wykowska, A., ""on the role of eye contact in gaze cueing,"" (2018) Scientific Reports, 8, p. 17842. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000453224400003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3; Ciardo, F., De Tommaso, D., Beyer, F., Wykowska, A., Ge, S.S., Cabibihan, J.-J., Salichs, M.A., Wagner, A.R., (2018), pp. 441-450. , Á. Castro-González, eds.), (Cham), pp Springer International Publishing; Bedaf, S., Draper, H., Gelderblom, G.-J., Sorell, T., De Witte, L., ""can a service robot which supports independent living of older people disobey a command? 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The Book for Knudepunkt; Fernaeus, Y., Jacobsson, M., Ljungblad, S., Holmqvist, L.E., ""are we living in a robot cargo cult?,"" (2009) 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 279-280; Duque, I., Dautenhahn, K., Koay, K.L., Willcock, L., Christianson, B., ""knowledge-driven user activity recognition for a smart house - Development and validation of a generic and low-cost, resource-efficient system,"" (2013) Proceedings of the 6th International Conference on Advances in Computer-Human Interactions; Saunders, J., Burke, N., Koay, K.L., Dautenhahn, K., ""a user friendly robot architecture for re-ablement and co-learning in a sensorised home,"" (2013) Assistive Technology: From Research to Practice (Proc: From AAATE), 33, pp. 49-58; Koay, K.L., Syrdal, D., Dautenhahn, K., Arent, K., Kreczmer, B., (2011) Mixed Reality and Human-Robot Interaction, pp. 133-151. , et al ""Companion migration-initial participants' feedback from a video-based prototyping study,"" Springer; Segura, E.M., Cramer, H., Gomes, P.F., Nylander, S., Paiva, A., ""revive!: Reactions to migration between different embodiments when playing with robotic pets,"" (2012) Proceedings of the 11th International Conference on Interaction Design and Children, pp. 88-97; Kriegel, M., Aylett, R., Cuba, P., Vala, M., Paiva, A., (2011) International Workshop on Intelligent Virtual Agents, pp. 282-295. , Robots meet ivas: a mind-body interface for migrating artificial intelligent agents,"" Springer; Kriegel, M., Aylett, R., Koay, K.L., Casse, K., Dautenhahn, K., Cuba, P., Arent, K., ""digital body hopping-migrating artificial companions,"" (2010) Proceedings of Digital Futures' 10; Syrdal, D.S., Koay, K.L., Walters, M.L., Dautenhahn, K., ""the boy-robot should bark!-children's impressions of agent migration into diverse embodiments,"" (2009) Proceedings: New Frontiers of Human-Robot Interaction, A Symposium at AISB; Koay, K.L., Syrdal, D., Ho, W., Dautenhahn, K., ""prototyping realistic long-term human-robot interaction for the study of agent migration,"" (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 809-816; Koay, K.L., Lakatos, G., Syrdal, D.S., Gácsi, M., Bereczky, B., Dautenhahn, K., Miklósi, A., Walters, M.L., ""hey! 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"Yang L., Li Z., Lei Q., Xu J., Deng Y., Zhong Y.","57208427789;57200183907;56423257200;57218110539;57214761534;57215563808;","Teaching a robot to draw: Hand gesture demonstration based on human-robot interaction",2020,"Proceedings of SPIE - The International Society for Optical Engineering","11433",,"114330M","","",,1,"10.1117/12.2559707","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081179526&doi=10.1117%2f12.2559707&partnerID=40&md5=925f860b902ee2da680ab4164ad31d89","College of Mechanical Engineering, Changchun University of Technology, Changchun, China; Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China","Yang, L., College of Mechanical Engineering, Changchun University of Technology, Changchun, China; Li, Z., College of Mechanical Engineering, Changchun University of Technology, Changchun, China; Lei, Q., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China; Xu, J., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China; Deng, Y., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China; Zhong, Y., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China","With the development of collaborative robots, robot programming by demonstration (PBD) plays an important role in human-robot interaction, it aims to transfer new skills from observations of tasks demonstrated by humans to robots. In this paper, we proposed a new approach to teach a robot to draw pictures based on human fingertip recognition and hand motion tracking. Combining the robot operating system (ROS), OpenCV and Moveit (motion planning libraries), we capture the finger movement trajectory by using Kinect2 depth camera. Then the trajectory waypoint is sent to the ur5 robotic arm through topic communication to complete the trajectory tracking task. The experiment indicates that the proposed approach allows inexperienced users to efficiently teach a robot to track the demonstrated trajectory. © 2020 SPIE.","Fingertip recognition; Human-robot interaction; Programming by demonstration; Robot operating system","Computer vision; Demonstrations; Man machine systems; Motion capture; Motion planning; Motion tracking; Robot programming; Trajectories; Collaborative robots; Fingertip recognition; Hand motion tracking; Programming by demon-stration; Robot operating system; Robot operating systems (ROS); Robot programming by demonstration; Trajectory tracking; Human robot interaction",,,,,"2017215102008; 2015B010919002; 201907010012; 2016GJ004; Guangdong Innovative and Entrepreneurial Research Team Program: 2014ZT05G132; Shenzhen Peacock Plan: 201607010041, 201704030091","This research is supported by the Major Projects of Guangzhou City of China (Grant No. 201907010012)，the Guangdong Innovative and Entrepreneurial Research Team Program (Grant No.2014ZT05G132), Shenzhen Peacock Plan (Grant No.KQTD2015033117354154), the Major Projects of Guangzhou City of China (Grant No.201704030091, 201607010041), the Major Projects of Guangdong Province of China (Grant No.2015B010919002), the Major Projects of Dongguan City of China (Grant No.2017215102008), the Nansha District International Science and Technology Cooperation Project of Guangzhou City of China (Grant No.2016GJ004).",,,,,,,,,,"Han, J., Shao, L., Xu, D., Shotton, J., Enhanced computer vision with microsoft kinect sensor: A review (2013) IEEE TRANSACTIONS on CYBERNETICS, 43 (5). , Oct; Billard, A., Calinon, S., Dillmann, R., Schaal, S., Robot programming by demonstration (2008) Springer Handbook of Robotics, pp. 1371-1394. , Springer; Haage, M., Teaching assembly by demonstration using advanced human robot interaction and a knowledge integration framework (2017) Procedia Manufacturing, 11, pp. 164-173; Moe, S., Schjølberg, I., Real-time hand guiding of industrial manipulator in 5 dof using microsoft kinect and accelerometer (2013) IEEE RO-MAN.; Hyun Lim, G., Pedrosa, E., Amaral, F., Lau, N., Rich and robust human-robot interaction on gesture recognition for assembly tasks (2017) IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), , April 26-28, Coimbra, Portugal; Kurien, M., Kim, M., Kopsida, M., Brilakis, I., Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system (2018) Automation in Construction, 86, pp. 125-137. , February; Orlando Russo, L., Airò Farulla, G., Pianu, D., PARLOMA-A novel human-robot interaction system for deaf-blind remote communication (2015) International Journal of Advanced Robotic Systems, , January 1; Landa-Hurtado, Mamani-Macaya, Valenzuela-Coloma, Kinect-based trajectory teaching for industrial robots (2014) 14th Pan-American Congress of Applied Mechanics-PACAM XIV, , March 24-28; Ekvall, S., Kragic, D., Robot learning from demonstration: A task-level planning approach (2008) International Journal of Advanced Robotic Systems, 5 (3), pp. 223-234; Profanter, S., Perzylo, A., Somani, N., Rickert, M., Knoll, A., Analysis and semantic modeling of modality preferences in industrial human-robot interaction (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Sept 28-Oct 2, Hamburg, Germany; Marino, D.L., Manic, M., Fast trajectory simplification algorithm for natural user interfaces in robot programming by demonstration (2016) IEEE 25th International Symposium on Industrial Electronics (ISIE).; Lambrecht, J., Walzel, H., Kruger, J., Robust finger gesture recognition on handheld devices for spatial programming of industrial robots (2013) The 22nd IEEE International Symposium on Robot and Human Interactive Communication.; Heimann, O., Hügle, J., Krüger, J., (2017) Gesture Based Robot Programming Using Process Knowledge-An Example for Welding Applications, , 12-15 Sept; Zhang, X., Zhou, H., Cheng, H., Huang, Y., Teaching-playback of robot manipulator based on human gesture recognition and motion tracking (2015) Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics.; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2017) International Journal of Industrial Ergonomics.; Ganapathyraju, S., Hand gesture recognition using convexity hull defects to control an industrial robot (2013) International Conference on Instrumentation Control and Automation.; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing.; Feng, Z., Xu, S., Zhang, X., Real-time fingertip tracking and detection using kinect depth sensor for a new writing-in-the air system (2012) Proceedings of the 4th International Conference on Internet Multimedia Computing and Service.; Kumar Singh, A., Chakraborty, P., Nandi, G.C., Sketch drawing by NAO humanoid robot (2016) TENCON 2015-2015 IEEE Region 10 Conference.; Ju, Z., Ji, X., Li, J., Liu, H., An integrative framework of human hand gesture segmentation for human-robot interaction (2017) IEEE Systems Journal, 11 (3). , Sept; Morshidi, M., Tjahjadi, T., Gravity optimised particle filter for hand tracking (2014) Pattern Recognition.","Yang, L.; Changchun Univ. of Technology (China)China","Osten W.Nikolaev D.Zhou J.","American Science and Engineering Institute","SPIE","12th International Conference on Machine Vision, ICMV 2019","16 November 2019 through 18 November 2019",,157925,0277786X,9781510636439,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85081179526
"Shiomi M., Sumioka H., Ishiguro H.","8397042200;6505862269;26643316500;","Special issue on human-robot interaction in close distance",2020,"Journal of Robotics and Mechatronics","32","1",,"7","",,,"10.20965/jrm.2020.p0007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079879484&doi=10.20965%2fjrm.2020.p0007&partnerID=40&md5=b51f574ca8542941eaa033e0e44fed10","Department of Agent Interaction Design Lab, Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan; Presence Media Research Group, Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan; Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Osaka, Toyonaka, 560-8531, Japan","Shiomi, M., Department of Agent Interaction Design Lab, Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan; Sumioka, H., Presence Media Research Group, Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan; Ishiguro, H., Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Osaka, Toyonaka, 560-8531, Japan",[No abstract available],,,,,,,,,,,,,,,,,,,,,,"Fuji Technology Press",,,,,09153942,,,,"English","J. Rob. Mechatronics",Editorial,"Final","",Scopus,2-s2.0-85079879484
"Hsieh W.-F., Sato-Shimokawara E., Yamaguchi T.","57202001489;25031564100;56501037800;","Investigation of robot expression style in human-robot interaction",2020,"Journal of Robotics and Mechatronics","32","1",,"224","235",,,"10.20965/jrm.2020.p0224","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079877371&doi=10.20965%2fjrm.2020.p0224&partnerID=40&md5=77e567b592bd9823ccd363dac2fb5269","Department of Information and Communications Systems Engineering, Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Faculty of System Design, Tokyo Metropolitan Technology, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Faculty of System design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan","Hsieh, W.-F., Department of Information and Communications Systems Engineering, Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Sato-Shimokawara, E., Faculty of System Design, Tokyo Metropolitan Technology, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Yamaguchi, T., Faculty of System design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan","In our daily conversation, we obtain considerable information from our interlocutor’s non-verbal behaviors, such as gaze and gestures. Several studies have shown that nonverbal messages are prominent factors in smoothing the process of human-robot interaction. Our previous studies have shown that not only a robot’s appearance but also its gestures, tone, and other nonverbal factors influence a person’s impression of it. The paper presented an analysis of the impressions made when human motions are implemented on a humanoid robot, and experiments were conducted to evaluate impressions made by robot expressions to analyze the sensations. The results showed the relation between robot expression patterns and human preferences. To further investigate biofeedback elicited by different robot styles of expression, a scenario-based experiment was done. The results revealed that people’s emotions can definitely be affected by robot behavior, and the robot’s way of expressing itself is what most influences whether or not it is perceived as friendly. The results show that it is potentially useful to combine our concept into a robot system to meet individual needs. © 2020, Fuji Technology Press. All rights reserved.","Human-robot interaction; Nonverbal communication; Robot expression style; Robot impression analysis","Anthropomorphic robots; Behavioral research; Biofeedback; Man machine systems; Expression patterns; Expression styles; Humanoid robot; Impression analysis; Non-verbal communications; Nonverbal behavior; Robot behavior; Scenario-based; Human robot interaction",,,,,,,,,,,,,,,,"Borja, R., de La Pinta, J.R., Lvarez, A., Maestre, J.M., Inte-gration of service robots in the smart home by means of UPnP: A surveillance robot case study (2013) Robotics and Autonomous Systems, 61 (2), pp. 153-160; Huijnen, C., Badii, A., van den Heuvel, H., Caleb-Solly, P., Thiemert, D., Maybe it becomes a buddy, but do not call it a robot” – seamless cooperation between companion robotics and smart homes (2011) Ambient Intelligence,” Pp. 324-329, , D. V. Keyson et al. 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"Arora A.S., Arora A.","35736838900;55437932800;","The race between cognitive and artificial intelligence: Examining socio-ethical collaborative robots through anthropomorphism and xenocentrism in human-robot interaction",2020,"International Journal of Intelligent Information Technologies","16","1",,"1","16",,4,"10.4018/IJIIT.2020010101","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079781264&doi=10.4018%2fIJIIT.2020010101&partnerID=40&md5=7d2f2d66f1de1376be56694ffc1aa8ae","University of the District of Columbia, Washington, DC, United States","Arora, A.S., University of the District of Columbia, Washington, DC, United States; Arora, A., University of the District of Columbia, Washington, DC, United States","Research on human-robot interaction (HRI) is growing; however, focus on the congruent socio-behavioral HRI research fields of social cognition, socio-behavioral intentions, and code of ethics is lacking. Humans possess an inherent ability of integrating perception, cognition, and action; while robots may have limitations as they may not recognize an object or a being, navigate a terrain, and/ or comprehend written or verbal language and instructions. This HRI research focuses on issues and challenges for both humans and robots from social, behavioral, technical, and ethical perspectives. The human ability to anthropomorphize robots and adoption of 'intentional mindset' toward robots through xenocentrism have added new dimensions to HRI. Robotic anthropomorphism plays a significant role in how humans can be successful companions of robots. This research explores social cognitive intelligence versus artificial intelligence with a focus on privacy protections and ethical implications of HRI while designing robots that are ethical, cognitively and artificially intelligent, and social human-like agents. Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.","Anthropomorphism; Artificial Intelligence; Ethical Designs; Human-Robot Interaction (HRI); Intentional Mindset; Roboethics; Social Cognitive Intelligence; Xenocentrism","Artificial intelligence; Behavioral research; Machine design; Man machine systems; Philosophical aspects; Social robots; Anthropomorphism; Cognitive intelligence; Ethical designs; Human robot Interaction (HRI); Intentional Mindset; Roboethics; Xenocentrism; Intelligent robots",,,,,"National Science Foundation, NSF","Anshu Saxena Arora, Ph.D., PMP is Associate Professor of Marketing in the School of Business and Public Administration at the University of the District of Columbia. Previously, she worked as the Associate Professor of Marketing at the Jay S. Sidhu School of Business and Leadership, Wilkes University, Wilkes-Barre, PA. Before joining Wilkes University, Dr. Arora was Associate Professor of Marketing and Chairperson of Department of Business (Management, Marketing and Supporting Areas) in the College of Business Administration, Savannah State University, Savannah, Georgia, USA. She is a Certified Project Management Professional (PMP) from Project Management Institute (PMI), USA and holds a Foundation Certificate in IT Service Management from Information Systems Examination Board (ISEB), UK. Dr. Arora has been a Visiting Professor at ISC Paris Business School in France, University of California, Davis, and Thunderbird School of Global Management at Arizona State University. She has more than a decade of industrial and academic experience. She has worked in companies like Hyundai Motors India Limited, Lufthansa German Airlines, and Siemens India. In July 2019, Dr. Arora received the prestigious National Science Foundation (NSF) grant for establishing the Logistics & International Trade (LIT) Analytics Center of Excellence and developing STEM-Business focused LIT concentration program at the University of the District of Columbia. She is the Senior Editor of Marketing for the International Journal of Emerging Markets (IJoEM) published by Emerald Publications. She has published more than 40 research papers in national and international journals of repute, and has presented about 50 papers in national and international conferences. Her research interests and expertise include digital / DSMM marketing, marketing analytics, social media strategy mix and social media measurement, stereotypical advertising polysemy, consumer behavior, ambient advertising, innovative experiential learning models in marketing and logistics / supply chain management, relational supply chain strategy relationships, and project management.",,,,,,,,,,"Aggarwal, P., McGill, A.L., Is that car smiling at me? 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"Zekhnine C., Berrached N.E.","57201944895;56636254200;","Human-robots interaction by facial expression recognition",2020,"International Journal of Engineering Research in Africa","46",,,"76","87",,,"10.4028/www.scientific.net/JERA.46.76","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079193951&doi=10.4028%2fwww.scientific.net%2fJERA.46.76&partnerID=40&md5=fd3508db76d08ffb610eac4653e7d10d","University of Science and Technology of Oran Mohamed Boudiaf, PO Box 1505 El M’naouer, Oran, 31 000, Algeria","Zekhnine, C., University of Science and Technology of Oran Mohamed Boudiaf, PO Box 1505 El M’naouer, Oran, 31 000, Algeria; Berrached, N.E., University of Science and Technology of Oran Mohamed Boudiaf, PO Box 1505 El M’naouer, Oran, 31 000, Algeria","This paper presents a facial expressions recognition system to command both mobile and arm robot. The proposed system mainly consists of two modules: facial expressions recognition and robots command. The first module aims to extract the ROI (Region Of Interest like: mouth, eyes, eyebrow) using Gradient Vector Flow (GVF) snake segmentation and the Euclidian distance calculation (compatible with the MPEG-4 description of the six universal emotions). To preserve the temporal aspect of the processing from FEEDTUM database (video file), Time Delay Neural Network (TDNN) is used as classifier of the universal facial expressions such as happiness, sadness, surprise, anger, fear, disgust and neutral. While the second module, analyzes recognized facial expressions and translates them into a language to communicate with robots by establishing command law. © 2020 Trans Tech Publications Ltd, Switzerland.","Facial expression recognition (FER); GVF snake; Mobile and arm robot; MPEG-4 description; Time delay neural network (TDNN)","Classification (of information); Human robot interaction; Image segmentation; Motion Picture Experts Group standards; Neural networks; Time delay; Facial expression recognition; Facial expressions recognition; Gradient vector flow; GVF snake; MPEG-4 description; Snake segmentation; Time delay neural networks; Universal facial expressions; Face recognition",,,,,,,,,,,,,,,,"Mittal, R., Srivastava, P., George, A., Mukherjee, A., (2012) Autonomous Robot Control Using Facial Expressions, , IJCTE. 4(4; Ju, D.J.S., Kim, E.Y., Intelligent wheelchair (IW) interface using face and mouth recognition (1999) IEEE Trans on CSVT, 9 (4), pp. 551-564; Faria, P.M., Braga, R.A., Valgode, M.E., Reis, L.P., Interface framework to drive an intelligent wheelchair using facial expressions (1998) Third IEEE International Conference on Automatic Face and Gesture Recognition (FG ’98, pp. 124-129. , Nara, Japan; Shim, B., Kang, K.K., Lee, W.W., Won, S.J.B., Han, S.H., (2010) An Intelligent Control of Mobile Robot Based on Voice Command, International Conference on Control, , Automation and Systems, 2107, South Korea; Zhentao, L., Min, W., Weihua, C., Luefeng, C., Jianping, X., Ri, Z., Mengtian, Z., Junwei, M., A Facial Expression Emotion Recognition Based Human-robot Interaction System (2017) IEEE/CAA JAS, 4 (4), pp. 668-676; Irfan, E., Pentland, A., Coding, Analysis Interpretation and Recognition of Facial Expressions (1997) IEEE Trans, 19, pp. 757-763; Black, M.J., Yacoob, Y., Recognizing facial expressions in image sequences using local parametrized models of image motion (1997) IJCV, pp. 23-48; Tian, Y., Kanade, T., Cohn, J., Recognition Actions Units for Facial Expression Analysis (2001) IEEE Trans, 23 (2), pp. 97-115; Lanitis, A., Taylor, C.J., Coots, T.F., Automatic interpretation and coding of faces images using flexible models (1997) IEEE Trans, pp. 743-756; Zekhnine, C., Berrached, N.E., Realization of facial expressions recognition system by MLP neural network (2005) Fifth Conference on Electric Engineering, Polytechnic Military School, Algiers, Algeria; mlyons@atr.jp, www.jaffedatabase.com; Shinza, Y., Saito, Y., Kenmochi, Y., Kotani, K., Facial expression analysis by integrating information of feature-point positions and grey levels of facial images (2000) IEEE ICIP, Vancouver, Canada; Zekhnine, C., Berrached, N.E., Command of an arm manipulator robot by facial expression (2010) Second International Conference on Image and Signal Processing and Their Applications, , Biskra, Algeria; www.jade-hs.de/fileadmin/team/frank-wallhoff/feedtum.pdf; Kass, M., Witkin, A., Terzopoulos, D., Snakes: Active contour models (1988) IJCV, 1 (4), pp. 321-331; Cohen, I., (1992) Modèles Déformables 2-D Et 3-D: Application à La Segmentation d’Images Médicales; Zhou, Z., Wu, J., Wu, D., Zhu, Z., Sheng, Y., Human body contour tracking based on GVF-snake and DESO (2013) Journal of Computational Information Systems, 9, pp. 7525-7532; Qusay, O.M., Nasrudin, M.F., Broken character restoration using gradient vector flow and balloon force algorithm (2015) Journal of Theoretical and Applied Information Technology, 3 (73); Vylder, J.D., Ochoa, D., Wilfried, P., Chaerle, L., van Der Straeten, D., (2009) Tracking Multiple Objects Using Intensity-Gvf Snakes, , EUSIPCO; Hammal, Z., Caplier, A., Rombaut, M., (2004) Classification d’expressions Faciales Par La théorie De l'évidence, , LIS INPG, Grenoble; Denavit, J., Hartemberg, R.S., A kinematic notation for lower pair mechanism based on matrices (1955) JAM Trans ASME, 22, pp. 215-221; Zekhnine, C., Berrached, N.E., Facial expressions recognition: Development and application to HMI (2018) Sixth IFIP ICCIA'18, , Oran, Algeria","Zekhnine, C.; University of Science and Technology of Oran Mohamed Boudiaf, PO Box 1505 El M’naouer, Algeria; email: elnfi@yahoo.fr",,,"Trans Tech Publications Ltd",,,,,16633571,,,,"English","Int. J. Eng. Res. Afr.",Article,"Final","",Scopus,2-s2.0-85079193951
"Roesler O.","57208167935;","A Cross-Situational Learning Based Framework for Grounding of Synonyms in Human-Robot Interactions",2020,"Advances in Intelligent Systems and Computing","1093 AISC",,,"225","236",,,"10.1007/978-3-030-36150-1_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079100022&doi=10.1007%2f978-3-030-36150-1_19&partnerID=40&md5=d533e6fe2f9dfb1cc5115ded730007c0","Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium","Roesler, O., Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium","Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, not many considered grounding of synonyms and the majority of employed models only worked offline. In this paper, we try to fill this gap by introducing an online learning framework for grounding synonymous object and action names using cross-situational learning. Words are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. An interaction experiment between a human tutor and HSR robot is used to evaluate the proposed framework. The results show that the employed framework is able to successfully ground all used words. © 2020, Springer Nature Switzerland AG.","Cross-situational learning; Human-robot interaction; Language grounding","Man machine systems; Robotics; Action execution; Cross-situational learning; Geometric characteristics; Language grounding; Offline; Online learning; Robot joint; Human robot interaction",,,,,,,,,,,,,,,,"Aly, A., Taniguchi, A., Taniguchi, T., A generative framework for multimodal learning of spatial concepts and object categories: An unsupervised part-of-speech tagging and 3D visual perception based approach (2017) IEEE International Conference on Development and Learning and the International Conference on Epigenetic Robotics (Icdl-Epirob), , Lisbon, Portugal, September; Blythe, R.A., Smith, K., Smith, A.D.M., Learning times for large lexicons through cross-situational learning (2010) Cogn. Sci., 34, pp. 620-642; Clark, E.V., The principle of contrast: A constraint on language acquisition (1987) Mechanisms of Language Acquisition, pp. 1-33. , Lawrence Erlbaum Associates; Craye, C., Filliat, D., Goudou, J.F., Environment exploration for object-based visual saliency learning (2016) IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May; Dawson, C.R., Wright, J., Rebguns, A., Escárcega, M.V., Fried, D., Cohen, P.R., A generative probabilistic framework for learning spatial language IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), , Osaka, Japan, August 2013; Ester, M., Kriegel, H.P., Sander, J., Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise (1996) Proceedings of the 2Nd International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226-231. , Portland, Oregon, USA, August; Filin, S., Pfeifer, N., Segmentation of airborne laser scanning data using a slope adaptive neighborhood (2006) ISPRS J. Photogram. Remote Sens. (P&RS), 60, pp. 71-80; Fischler, M.A., Bolles, R.C., Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography (1981) Commun. ACM (CACM), 24 (6), pp. 381-395; Fisher, C., Hall, D.G., Rakowitz, S., Gleitman, L., When it is better to receive than to give: Syntactic and conceptual constraints on vocabulary growth (1994) Lingua, 92, pp. 333-375; Fontanari, J.F., Tikhanoff, V., Cangelosi, A., Ilin, R., Perlovsky, L.I., Cross-situational learning of object-word mapping using neural modeling fields (2009) Neural Netw, 22 (5-6), pp. 579-585; Fontanari, J.F., Tikhanoff, V., Cangelosi, A., Perlovsky, L.I., A cross-situational algorithm for learning a lexicon using neural modeling fields (2009) International Joint Conference on Neural Networks (IJCNN), Atlanta, GA, USA, June; Harnad, S., The symbol grounding problem (1990) Physica D, 42, pp. 335-346; Hubert, L., Arabie, P., Comparing partitions (1985) J. Classif., 2 (1), pp. 193-218; (2017) International Federation of Robotics: World Robotics 2017-Service Robots; Kemp, C.C., Edsinger, A., Torres-Jara, E., Challenges for robot manipulation in human environments (2007) IEEE Robot. Autom. Mag., 14 (1), pp. 20-29; Koster, K., Spann, M., MIR: An approach to robust clustering-application to range image segmentation (2000) IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 22 (5), pp. 430-444; Nguyen, A., Le, B., 3D point cloud segmentation: A survey (2013) 6Th IEEE Conference on Robotics, Automation and Mechatronics (RAM), , IEEE, Manila, November; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in python (2011) J. Mach. Learn. Res., 12, pp. 2825-2830; Pinker, S., (1989) Learnability and Cognition, , MIT Press, Cambridge; Roesler, O., Aly, A., Taniguchi, T., Hayashi, Y., A probabilistic framework for comparing syntactic and semantic grounding of synonyms through cross-situational learning ICRA-18 Workshop on Representing a Complex World: Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding, , Brisbane, Australia, May 2018; Roesler, O., Aly, A., Taniguchi, T., Hayashi, Y., Evaluation of word representations in grounding natural language instructions through computational human-robot interaction (2019) Proceedings of the 14Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , Daegu, South Korea, March; Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J., Fast 3D recognition and pose using the viewpoint feature histogram (2010) Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, pp. 2155-2162. , October; Sappa, A.D., Devy, M., Fast range image segmentation by an edge detection strategy (2002) Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling (3DIM), , Quebec City, Quebec, Canada, August; Schnabel, R., Wahl, R., Klein, R., Efficient ransac for point-cloud shape detection (2007) Comput. Graphics Forum, 26 (2), pp. 214-226; Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X., DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN (2017) ACM Trans. Database Syst. (TODS), 42 (3), p. 19; She, L., Yang, S., Cheng, Y., Jia, Y., Chai, J.Y., Xi, N., Back to the blocks world: Learning new actions through situated human-robot dialogue (2014) Proceedings of the SIGDIAL 2014 Conference, pp. 89-97. , Philadelphia, U.S.A., June; Siskind, J.M., A computational study of cross-situational techniques for learning word-to-meaning mappings (1996) Cognition, 61, pp. 39-91; Smith, A.D.M., Smith, K., (2012) Cross-Situational Learning, pp. 864-866. , https://doi.org/10.1007/978-1-4419-1428-61712, Springer, Boston; Smith, K., Smith, A.D.M., Blythe, R.A., Cross-situational learning: An experimental study of word-learning mechanisms (2011) Cogn. Sci., 35 (3), pp. 480-498; Steels, L., Loetzsch, M., The grounded naming game (2012) Experiments in Cultural Language Evolution, pp. 41-59. , Steels, L. (ed.) , John Benjamins, Amsterdam; Strom, J., Richardson, A., Olson, E., Graph-based segmentation for colored 3D laser point clouds (2010) International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan; Taniguchi, A., Taniguchi, T., Cangelosi, A., Cross-situational learning with Bayesian generative models for multimodal category and word learning in robots (2017) Front. Neurorobot., 11, p. 66; Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S., Roy, N., Approaching the symbol grounding problem with probabilistic graphical models (2011) AI Mag, 32 (4), pp. 64-76; (2017) HSR Manual, , 2017.4.17 edn., April","Roesler, O.; Artificial Intelligence Lab, Belgium; email: oliver@roesler.co.uk","Silva M.F.Luis Lima J.Reis L.P.Sanfeliu A.Tardioli D.",,"Springer","4th Iberian Robotics Conference, ROBOT 2019","20 November 2019 through 22 November 2019",,236649,21945357,9783030361495,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85079100022
"Yu C.-E.","57206401831;","Emotional contagion in human-robot interaction",2020,"e-Review of Tourism Research","17","5",,"793","798",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078185323&partnerID=40&md5=748c598b04ae4b128092c18790e63b8d","Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg, A-5412, Austria","Yu, C.-E., Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg, A-5412, Austria","Emotional contagion is particularly important in face-to-face interaction, and hospitality industry is no exception. Given that emotion is contagious, its effects in human-robot interaction remain largely unknown. This paper takes Sophia, the first humanoid robot that can display emotions, as a reference point, and investigates the emotional effect on online users' conative reactions. The results demonstrated that the expression of fear and disgust serves as the influential factors in affecting interests and engagements. Nevertheless, emotional contagion still deserves further exploration. Overall, this research contributes to the existing body of literature related to humanlike service robot, emotional contagion, and human-robot interaction. © 2019 Texas A and M University.","Emotional contagion; Engagement; Humanoid robot; Robot service; Social media",,,,,,,,,,,,,,,,,"Choi, S., Liu, S.Q., Mattila, A.S., ""How may I help you?"" Says a robot: Examining language styles in the service encounter (2019) International Journal of Hospitality Management, 82, pp. 32-38; Ekman, P., Friesen, W.V., Hager, J.C., Facial action coding system: The manual on CD ROM (2002) A Human Face, pp. 77-254. , Salt Lake City; Goertzel, B., Mossbridge, J., Monroe, E., Hanson, D., Yu, G., (2017) Humanoid robots as agents of human consciousness expansion, , arXiv preprint; Hatfield, E., Cacioppo, J.T., Rapson, R.L., Emotional contagion (1993) Current directions in psychological science, 2 (3), pp. 96-100; Hennig-Thurau, T., Groth, M., Paul, M., Gremler, D.D., Are all smiles created equal? How emotional contagion and emotional labor affect service relationships (2006) Journal of Marketing, 70 (3), pp. 58-73; Hochschild, A.R., (2012) The managed heart: Commercialization of human feeling, , University of California Press; Houston, L., III, Grandey, A.A., Sawyer, K., Who cares if ""service with a smile"" is authentic? An expectancy-based model of customer race and differential service reactions (2018) Organizational Behavior and Human Decision Processes, 144, pp. 85-96; Huang, P.F., Dai, C.W., The impacts of emotional contagion and emotional labor perception on employee's service performance (2010) International Journal of Electronic Business Management, 8 (1), pp. 68-79; Ngan, H.F.B., Yu, C.E., To smile or not to smile-an eye-tracking study on service recovery (2018) Current Issues in Tourism, pp. 1-6; Otterbring, T., Smile for a while: the effect of employee-displayed smiling on customer affect and satisfaction (2017) Journal of Service Management, 28 (2), pp. 284-304; Schreiner, M., Fischer, T., Riedl, R., Impact of content characteristics and emotion on behavioral engagement in social media: Literature review and research agenda (2019) Electronic Commerce Research, pp. 1-17; Swani, K., Milne, G.R., Brown, B.P., Assaf, A.G., Donthu, N., What messages to post? Evaluating the popularity of social media communications in business versus consumer markets (2017) Industrial Marketing Management, 62, pp. 77-87; Tung, V.W.S., Au, N., Exploring customer experiences with robotics in hospitality (2018) International Journal of Contemporary Hospitality Management, 30 (7), pp. 2680-2697; Valentini, C., Romenti, S., Murtarelli, G., Pizzetti, M., Digital visual engagement: Influencing purchase intentions on Instagram (2018) Journal of Communication Management, 22 (4), pp. 362-381","Yu, C.-E.; Innovation and Management in Tourism, Urstein Süd 1, Austria; email: cyu.imte-m2018@fh-salzburg.ac.at",,,"Texas A and M University",,,,,19415842,,,,"English","e-Review Tour. Res.",Article,"Final","",Scopus,2-s2.0-85078185323
"Galin R.R., Meshcheryakov R.V.","57209776926;23035794100;","Human-Robot Interaction Efficiency and Human-Robot Collaboration",2020,"Studies in Systems, Decision and Control","272",,,"55","63",,21,"10.1007/978-3-030-37841-7_5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078076562&doi=10.1007%2f978-3-030-37841-7_5&partnerID=40&md5=815d7db7fb6b0c635d6140107ca55551","V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Galin, R.R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Meshcheryakov, R.V., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","This paper presents the question about the interaction between human and collaborative robots, their joint actions in the safety-shared workspace. Presented difference between collaborative robots and other robotic solutions. Collaborative robot technical specification reviewed and the efficiency of human-robot interaction and the HRC Index. In safety case, it is important to identify sources of potential harm, to determine which of the persons in the robot’s vicinity may be in peril and to assess the type of injuries the robot may cause to this person. One of the main tasks that we reviewed is to organize effective interaction between a human and a collaborative robot, taking into account their capabilities. © 2020, Springer Nature Switzerland AG.","Collaboration; Efficiency criterion; Human-robot collaboration; Human-robot interaction",,,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331","Acknowledgements The reported study was partially funded by RFBR according to the research project No. 19-08-00331.",,,,,,,,,,"Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; Asimov, I., (1942) Runaround. In: Astounding Science Fiction; Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; ISO. ISO/TS 15066:2016-02 (E) Robots and Robotic devices—Collaborative Robots (2016); Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; Yuschenko, A.S., Human-robot: Compatibility and cooperation (2014) J. Robot. Tech. Cybern., 1 (2), pp. 4-9. , in Russian; Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , https://doi.org/10.1016/j.mechatronics.2018.02.009; Khalid, A., Kirisci, P., Ghrairi, Z., Thoben, K.-D., Pannek, J., Towards implementing safety and security concepts for human (2017) 39Th International MATADOR Conference on Advanced Manufacturing, pp. 0-7. , robot collaboration in the context of Industry 4.0. In, Manchester, UK), pp; Lazarte, M., Robots and humans can work together with new ISO guidance Homepage, , https://www.iso.org/news/2016/03/Ref2057.html, last accessed 2018/04/13; Yanco, H., Drury, J., Classifying human-robot interaction: An updated taxonomy (2004) Proceedings of the IEEE International Conference Systems, Man and Cybernetics (SMC), Vol. 3., Pp. 2841–2846. IEEE; Anandan, T., Robots and humans: Safety collaboration (2017) Control Eng, 6 (72), pp. 46-49; Robla-Gomez, S., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; Chueshev, A., Melekhova, O., Meshcheryakov, R., Cloud robotic platform on basis of fog computing approach International Conference on Interactive Collaborative Robotics, pp. 34-43; Ershova, L., The factors of the effectiveness of introducing robotic complex at Russian enterprises (2018) News of the Ural State Mining University, 2 (50), pp. 130-134","Galin, R.R.; V.A. Trapeznikov Institute of Control Sciences of Russian Academy of SciencesRussian Federation; email: rinat.r.galin@yandex.ru",,,"Springer",,,,,21984182,,,,"English","Stud. Syst. Decis. Control",Book Chapter,"Final","",Scopus,2-s2.0-85078076562
"Chanel C.P.C., Roy R.N., Dehais F., Drougard N.","36681830500;55904560000;13005898500;55936524900;","Towards mixed-initiative human–robot interaction: Assessment of discriminative physiological and behavioral features for performance prediction",2020,"Sensors (Switzerland)","20","1","296","","",,7,"10.3390/s20010296","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077673780&doi=10.3390%2fs20010296&partnerID=40&md5=8fdbacf32e09fe5b4140059f3a5fe173","ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France","Chanel, C.P.C., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France; Roy, R.N., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France; Dehais, F., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France; Drougard, N., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France","The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human; Human behavior; Intelligent sensors; Performance prediction; Physiological computing; Robot interaction","Automation; Behavioral research; Eye tracking; Forecasting; Intelligent robots; Machine design; Physiology; User interfaces; Human; Human behaviors; Intelligent sensors; Performance prediction; Physiological computing; Robot interactions; Human robot interaction; adult; behavior; computer interface; female; genetic procedures; human; male; man machine interaction; physiology; robotics; Adult; Behavior; Biosensing Techniques; Female; Humans; Male; Man-Machine Systems; Robotics; User-Computer Interface",,,,,"2021","Funding: This work has been financially supported by the Dassault Aviation Chair (https://www.isae-supaero. fr/fr/isae-supaero/mecenat-relations-avec-la-fondation-isae-supaero/chaire-dassault-aviation-casac) (CASAC 2016–2021).","This work has been financially supported by the Dassault Aviation Chair (https://www.isae-supaero. fr/fr/isae-supaero/mecenat-relations-avec-la-fondation-isae-supaero/chaire-dassault-aviation-casac) (CASAC 2016?2021). Acknowledgments: Many thanks are given to Corentin Chauffaut for the constructive discussions about this work.",,,,,,,,,"Finzi, A., Orlandini, A., (2005) A Mixed-Initiative Approach to Human-Robot Interaction in Rescue Scenarios; American Association for Artificial Intelligence: Menlo Park, , CA, USA; Nagatani, K., Kiribayashi, S., Okada, Y., Otake, K., Yoshida, K., Tadokoro, S., Nishimura, T., Fukushima, M., Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots (2013) J. 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Factors, 50, pp. 511-520; Dehais, F., Causse, M., Vachon, F., Tremblay, S., Cognitive conflict in human–automation interactions: A psychophysiological study (2012) Appl. Ergon., 43, pp. 588-595; Dehais, F., Peysakhovich, V., Scannella, S., Fongue, J., Gateau, T., Automation surprise in aviation: Real-time solutions (2015) Proceedings of the 33Rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2525-2534. , Seoul, Korea; Lyons, J.B., Being transparent about transparency: A model for human-robot interaction (2013) Proceedings of the 2013 AAAI Spring Symposium Series, pp. 25-27. , Palo Alto, CA, USA, March","Chanel, C.P.C.; ISAE-SUPAERO, France; email: caroline.chanel@isae-supaero.fr",,,"MDPI AG",,,,,14248220,,,"31948046","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85077673780
"Marti P., Iacono I., Parlangeli O., Stienstra J.","57193313740;53879697500;6508237137;53865710500;","Social Coordination in Human–Robot Interaction Through Reciprocal Engagement",2020,"Smart Innovation, Systems and Technologies","158",,,"273","285",,,"10.1007/978-981-13-9652-6_25","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076116736&doi=10.1007%2f978-981-13-9652-6_25&partnerID=40&md5=edf2baa801b2201a666a334d856fd60f","University of Siena, Siena, 53100, Italy; Eindhoven University of Technology, Eindhoven, Netherlands","Marti, P., University of Siena, Siena, 53100, Italy, Eindhoven University of Technology, Eindhoven, Netherlands; Iacono, I., University of Siena, Siena, 53100, Italy; Parlangeli, O., University of Siena, Siena, 53100, Italy; Stienstra, J., Eindhoven University of Technology, Eindhoven, Netherlands","In this paper, we provide a theoretical and experimental investigation on human–robot interaction scenarios where collaborative activities are perceived more meaningful and easier to perform by the human agent in the situation of reciprocal engagement with the robot. Inspired by the perceptual-crossing paradigm (Auvray and Rohde in Front Hum Neurosci, [1]), we defined reciprocal engagement as an interaction concept that seeks to extend perceptual crossing to functional purposes. For instance, when a robot and a person face a door and they both intend to go through it, a designed behaviour negotiates who is to go first in appropriate manner, either the human or the robot. The objective of the study is to explore social coordination mechanisms that are easy and natural for the human agent in order to engage in interaction with a robot. Two experiments were conducted which explore interaction scenarios with and without reciprocal engagement between a person and a robot to appreciate their significance for the human actor. In the first one, interaction scenarios are evaluated on the basis on video clips; in the second one, participants were asked to interact directly with the robot according to the same scenarios. The results show a preference of participants towards reciprocal engagement situations which were considered more meaningful and easier to learn. With this work, we hope to inspire design thinking to shift from discrete, procedural design mechanisms to continuous and action-driven mechanisms of social coordination when addressing the interaction between humans and systems in smart environments. © 2020, Springer Nature Singapore Pte Ltd.","Human-centred design; Human–robot interaction; Interactive systems; Reciprocal engagement; Smart environments; Social coordination","Ecosystems; Machine design; Regional planning; Human centred design; Interactive system; Reciprocal engagement; Robot interactions; Smart environment; Social coordination; Human robot interaction",,,,,"National Aeronautics and Space Administration, NASA: NAG5-10243, NAG5-10708, NAG5-8107, NAG5-9982; Henry Luce Foundation, LUCE","Financial support from NASA Long Term Astrophysics Program grants NAG5-10708 (M. G. and R. M. S.), NAG5-8107 (W. N. B.), and NAG5-9982 (M. E.) is gratefully acknowledged. Funds were also provided by NASA grant NAG5-10243 (M. G. and R. M. S.) and by the Clare Boothe Luce Program of the Henry Luce Foundation (R. M. S.).",,,,,,,,,,"Auvray, M., Rohde, M., Perceptual crossing: The simplest online paradigm (2012) Front. Hum. Neurosci., 6; de Jaegher, H., Social understanding through direct perception? Yes, by interacting (2009) Conscious. Cogn., 18, pp. 535-542; Di Paolo, E., Rohde, M., Iizuka, H., Sensitivity to social contingency or stability of interaction? Modelling the dynamics of perceptual-crossing (2008) New Ideas Psychol, 26, pp. 278-294; Froese, T., Di Paolo, E., Modelling social interaction as perceptual-crossing: An investigation into the dynamics of the interaction process (2010) Connect. Sci., 22, pp. 43-68; Marti, P., Iacono, I., Tittarelli, M., Stienstra, J.T., Exploring movement qualities in a reciprocal engagement (2014) Proceedings of the Icdl-Epirob, pp. 13-16. , Genova, Oct; Merleau-Ponty, M., (1962) Phenomenology of Perception (Trans: Smith, C.), , Humanities Press, New York; Liang, R.H., Chung, W.M., Kao, H.L., Li, T.Y., InTouch: Crossing social interaction with perception (2013) Design, User Experience, and Usability. User Experience in Novel Technological Environments. Lecture Notes in Computer Science, 8014, pp. 306-315. , Marcus, A. (ed.), Springer, Berlin; Auvray, M., Lenay, C., Stewart, J., Perceptual interactions in a minimalist virtual environment (2009) New Ideas Psychol, 27 (1), pp. 32-47; Lenay, C., It’s so touching”: Emotional value in distal contact (2010) Int. J. Des., 4 (2), pp. 15-25; Deckers, E., Wensveen, S.A.G., Ahn, R., Overbeeke, K., Designing for perceptual-crossing to improve user involvement (2011) Proceedings of CHI 2011, pp. 1929-1938. , Vancouver, ACM, New York, NY, USA ©2011. ISBN: 978-1-4503-0228-9; Marti, P., Perceiving while being perceived (2010) Int. J. Des., 4 (2), pp. 27-38; Anas, S.A.B., Qiu, S., Rauterberg, G.W.M., Hu, J., Exploring social interaction with everyday object based on perceptual crossing (2016) Proceedings of HAI 2016, 24–27 Oct 2016, Biopolis, Singapore, pp. 11-18. , ACM, New York. ISBN 978-1-4503-4508-8; Parlangeli, O., Chiantini, T., Guidi, S., A mind in a disk: The attribution of mental state to technological systems (2012) Work, 41, pp. 1118-1123; Amirabdollahian, F., Op Den Akker, F.R., Bedaf, S., Bormann, R., Draper, H., Gelderblom, G.J., Gutierrez Ruiz, C., Dautenhahn, K., Assistive technology design and development for acceptable robotics companions for ageing years (2013) Paladyn J. Behav. Robot., 4 (2), pp. 94-112. , ISSN (Print) 2081-4836","Marti, P.; University of SienaItaly; email: marti@unisi.it","Rehm M.Saldien J.Manca S.",,"Springer","4th International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2019","22 May 2019 through 24 May 2019",,232689,21903018,9789811396519,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85076116736
"Lee H.-S., Kang B.-Y.","56824620800;26643468300;","Continuous emotion estimation of facial expressions on JAFFE and CK+ datasets for human–robot interaction",2020,"Intelligent Service Robotics","13","1",,"15","27",,2,"10.1007/s11370-019-00301-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076101762&doi=10.1007%2fs11370-019-00301-x&partnerID=40&md5=07b121af7ff92dcf9888078ec04e5f17","School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, South Korea","Lee, H.-S., School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, South Korea; Kang, B.-Y., School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, South Korea","Human–robot interaction was always based on estimation of human emotions from human facial expressions, voice and gestures. Human emotions were always categorized in a discretized manner, while we estimate facial images from common datasets for continuous emotions. Linear regression was used in this study which numerically quantizes human emotions as valence and arousal by displaying the raw images on the two-respective coordinate axis. The face image datasets from the Japanese female facial expression (JAFFE) dataset and the extended Cohn–Kanade (CK+) dataset were used in this experiment. Human emotions for the above-mentioned datasets were interpreted by 85 participants who were used in the experimentation. The best result from a series of experiments shows that the minimum of root mean square error for the JAFFE dataset was 0.1661 for valence and 0.1379 for arousal. The proposed method has been compared with previous methods such as songs, sentences, and it is observed that the proposed method for common datasets testing showed an outstanding emotion estimation performance. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Arousal; Emotion estimation; Human–robot interaction; Valence","Mean square error; Arousal; Emotion estimation; Facial Expressions; Human emotion; Human facial expressions; Robot interactions; Root mean square errors; Valence; Human robot interaction",,,,,"National Research Foundation of Korea, NRF: NRF-2019R1A2C1011270","This work was supported by the National Research Foundation of Korea funded by the Korean Government under Grant NRF-2019R1A2C1011270. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",,,,,,,,,,"Ekman, P., Friesen, W.V., Measuring facial movement (1976) Environm Psychol Nonverbal Behav, 1 (1), pp. 56-75; Ekman, P., An argument for basic emotions (1992) Cogn Emot, 6 (3-4), pp. 169-200; Ekman, P., Strong evidence for universals in facial expressions: a reply to Russell’s mistaken critique (1994) Psychol Bull, 115 (2), pp. 268-287; Picard, R.W., (1995) Affective Computing, , MIT Technical report No. 321; Ma, C., Prendinger, H., Ishizuka, M., Emotion estimation and reasoning based on affective textual interaction (2005) International Conference on Affective Computing and Intelligent Interaction, pp. 622-628. , California, USA; Matsumoto, K., Kita, K., Ren, F., Emotion estimation from sentence using relation between Japanese slangs and emotion expressions (2012) Proceedings of the 26Th Pacific Asia Conference on Language, Information, and Computation, pp. 343-350. , Bali, Indonesia; 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Sun, K., Yu, J., Huang, Y., Hu, X., An improved valence-arousal emotion space for video affective content representation and recognition (2009) 2009 IEEE International Conference on Multimedia and Expo, pp. 566-569. , New York, USA; Mehrabian, A., Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament (1996) Curr Psychol, 14 (4), pp. 261-292; Grimm, M., Kroschel, K., Emotion estimation in speech using a 3D emotion space concept (2007) Robust speech recognition and understanding, , InTech, Rijeka; Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J., Coding facial expressions with gabor wavelets (1998) Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 200-205; Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., The extended cohn-kanade dataset (Ck+): A complete dataset for action unit and emotion-specified expression (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 94-101. , CA, USA, San Francisco; Bargiela, A., Pedrycz, W., Nakashima, T., Multiple regression with fuzzy data (2007) Fuzzy Sets Syst, 158 (19), pp. 2169-2188; Viola, P., Jones, M.J., Robust real-time face detection (2004) Int J Comput Vis, 57 (2), pp. 137-154; Lienhart, R., Maydt, J., An extended set of haar-like features for rapid object detection (2002) 2002 International Conference on Image Processing, 1, pp. I-900. , New York, USA; Schwegmann, C.P., Kleynhans, W., Salmon, B.P., Synthetic aperture radar ship detection using Haar-like features (2016) IEEE GeoSci Rem Sens Lett, 14 (2), pp. 154-158","Kang, B.-Y.; School of Mechanical Engineering, South Korea; email: kby09@knu.ac.kr",,,"Springer",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Review,"Final","",Scopus,2-s2.0-85076101762
"Savić S.Ž., Gnjatović M., Stefanović D., Lalić B., Maček N.","55900806500;24481099800;57198355699;35208507300;56364492700;","Automatic domain modeling for human–robot interaction",2020,"Intelligent Service Robotics","13","1",,"99","111",,1,"10.1007/s11370-019-00303-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075933023&doi=10.1007%2fs11370-019-00303-9&partnerID=40&md5=f68ea0232b287960b0dd5632d73480f1","Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, Belgrade, 11000, Serbia; Faculty of Computer Science, Megatrend University, Bulevar maršala Tolbuhina 8, New Belgrade, 11070, Serbia","Savić, S.Ž., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Gnjatović, M., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Stefanović, D., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Lalić, B., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Maček, N., School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, Belgrade, 11000, Serbia, Faculty of Computer Science, Megatrend University, Bulevar maršala Tolbuhina 8, New Belgrade, 11070, Serbia","This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically unconstrained task-oriented human–robot interaction domains. At the specification level, it is cognitively inspired, addressing selected cognitive mechanisms of the human memory system (e.g., integration, semantic categorization, associative learning, etc.) that are relevant for natural language human–robot interaction. We discuss a corpus-based validation of the introduced approach and report on its particular implementation within the conversational agent integrated with a human-like robot. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Cognitive mechanisms; Domain modeling; Focus tree; Human–robot interaction; Robot MARKO","Semantics; Associative learning; Cognitive mechanisms; Conversational agents; Domain model; Focus trees; Human memory systems; Robot interactions; Semantic categorization; Human robot interaction",,,,,"Ministarstvo Prosvete, Nauke i TehnoloÅ¡kog Razvoja: III44008, TR32035","The presented study was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (research Grants III44008 and TR32035), and as part of the project “Collaborative strategies of heterogeneous robot activity at solving agriculture missions controlled via intuitive human–robot interfaces” (ID 99), sponsored within the framework of the ERA.Net RUS Plus program. The responsibility for the content of this article lies with the authors.",,,,,,,,,,"Anderson, J.R., (2007) How can the human mind occur in the physical universe?, , Oxford University Press, Oxford; Anderson, J.R., ACT: a simple theory of complex cognition (1996) Am Psychol, 51 (4), pp. 355-365; Anderson, J.R., (1985) Cognitive psychology and its implications, , 2, Freeman, New York; Anderson, J.R., (1983) The architecture of cognition, , Harvard University Press, Cambridge; Anderson, J.R., Bower, G.H., (1973) Human associative memory, , Wiley, New York; Bothell, D., (2007) ACT-R 6.0 Reference Manual. From the ACT-R Web site, , http://act-r.psy.cmu.edu/actr7.x/reference-manual.pdf, Accessed 06 Jun 2019; Collins, A.M., Loftus, E.F., A spreading activation theory of semantic memory (1975) Psychol Rev, 82 (6), pp. 407-428; Di Nuovo, A., Broz, F., Wang, N., Belpaeme, T., Cangelosi, A., Jones, R., Esposito, R., Dario, P., The multi-modal interface of Robot-Era multi-robot services tailored for the elderly (2018) Intell Serv Robot, 11 (1), pp. 109-126; Fillmore, C.J., The case for case (1968) Universals in Linguistic Theory, pp. 1-89. , Bach E, Harms R (eds), Holt, Rinehart, and Winston, New York, NY; Firth, J., A synopsis of linguistic theory 1930–1955. Studies in linguistic analysis (1968) Selected Papers of J. R. Firth (1952–59), pp. 168-205. , Palmer FR, Longmans, London; Gnjatović, M., Therapist-centered design of a robot’s dialogue behavior (2014) Cogn Comput, 6 (4), pp. 775-788; Gnjatović, M., Delić, V., Cognitively-inspired representational approach to meaning in machine dialogue (2014) Knowl Based Syst, 71, pp. 25-33; Gnjatović, M., Delić, V., Electrophysiologically-inspired evaluation of dialogue act complexity (2013) Proceedings of the 4Th IEEE International Conference on Cognitive Infocommunications, pp. 167-172. , CogInfoCom, Budapest, Hungary; Gnjatović, M., Janev, M., Delić, V., Focus tree: modeling attentional information in task-oriented human–machine interaction (2012) Appl Intell, 37 (3), pp. 305-320; Gnjatović, M., Rösner, D., Inducing genuine emotions in simulated speech-based human–machine interaction: the NIMITEK corpus (2010) IEEE Trans Affect Comput, 1 (2), pp. 132-144; Halliday, M.A.K., (2004) An introduction to functional grammar, , 3, Hodder Arnold, London; Harris, Z., Distributional structure (1954) Word, 10 (23), pp. 146-162; Hernández-García, D., Monje, C., Balaguer, C., A use case of an adaptive cognitive architecture for the operation of humanoid robots in real environments (2016) Int J Adv Robot Syst, 14 (1), pp. 1-15; Johnson, T.R., Wang, H., Zhang, J., Wang, Y., A model of spatio-temporal coding of memory for multidimensional stimuli (2002) Proceedings of the 24Th Annual Meeting of the Cognitive Science Society, , Fairfax, VA: August; Jokinen, K., McTear, M., Spoken Dialogue Systems. Synthesis lectures on human language technologies (2009) Morgan and Claypool, 2 (1), pp. 1-151; Jones, M.N., Mewhort, D.J.K., Representing word meaning and order information in a composite holographic lexicon (2007) Psychol Rev, 114 (1), pp. 1-37; Jurafsky, D., Martin, J.H., (2009) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, , 2, Prentice-Hall, Upper Saddle River; Landauer, T., Dumais, S., A solution to Plato’s problem: the latent semantic analysis theory of the acquisition, induction, and representation of knowledge (1997) Psychol Rev, 104 (2), pp. 211-240; Lund, K., Burgess, C., Producing high-dimensional semantic spaces from lexical co-occurrence (1996) Behav Res Methods Instrum Comput, 28 (2), pp. 203-208; Mišković, D., Gnjatović, M., Štrbac, P., Trenkić, B., Nikša, J., Delić, V., Hybrid methodological approach to context-dependent speech recognition (2017) Int J Adv Robot Syst, 14 (1), pp. 1-12; Pineda, L.A., Rodríguez, A., Fuentes, G., Rascón, C., Meza, I., A light non-monotonic knowledge-base for service robots (2017) Intell Serv Robot, 10 (3), pp. 159-171; Pylyshyn, Z., What the mind’s eye tells the mind’s brain: a critique of mental imagery (1973) Psychol Bull, 80 (1), pp. 1-24; Quillian, M.R., The teachable language comprehender: a simulation program and theory of language (1969) Commun ACM, 12 (8), pp. 459-476; Quillian, M.R., Semantic memory (1968) Semantic information processing, , Minsky MU, (ed), MIT Press, Cambridge; Rogers, T.T., McClelland, J.L., Parallel distributed processing at 25: further explorations in the microstructure of cognition (2014) Cogn Sci, 38 (6), pp. 1024-1077; Rumelhart, D.E., McClelland, J.L., (1986) Parallel distributed processing, explorations in the microstructure of cognition. Volume 1: foundations, , MIT Press, Cambridge; Rutledge-Taylor, M., Lebiere, C., Thomson, R., Staszewski, J., Anderson, J.R., A comparison of rule-based versus exemplar-based categorization using the ACT-R architecture (2012) Proceedings of the 21St Annual Behavior Representation in Modeling and Simulation Conference, pp. 44-50; Savić, S., Gnjatović, M., Mišković, D., Tasevski, J., Maček, N., Cognitively-inspired symbolic framework for knowledge representation (2017) Proceedings of the 8Th IEEE International Conference on Cognitive Infocommunications (Coginfocom), pp. 315-320. , Debrecen, Hungary, September 2017; Sternberg, R.J., Sternberg, K., (2012) Cognitive psychology, , 6, Cengage Learning, Wadsworth; Stowell, T., (1981) Origins of Phrase Structure, , Ph.D. Thesis. Department of Linguistics and Philosophy, Massachusetts Institute of Technology; Tasevski, J., Gnjatović, M., Borovac, B., Assessing the Children’s receptivity to the robot MARKO (2018) Acta Polytech Hung, 15 (5), pp. 47-66; Trafton, G., Hiatt, L., Harrison, A., Tamborello, F., Khemlani, S., Schultz, A., ACT-R/E: an embodied cognitive architecture for human–robot interaction (2013) J Hum Robot Interact, 2 (1), pp. 30-55; Thomson, R., Pyke, A., Trafton, J.G., Hiatt, L.M., An account of associative learning in memory recall (2015) Proceedings of the 37Th Annual Conference of the Cognitive Science Society. Cognitive Science Society. Austin, pp. 2386-2391","Gnjatović, M.; Faculty of Technical Sciences, Trg Dositeja Obradovića 6, Serbia; email: milangnjatovic@uns.ac.rs",,,"Springer",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Article,"Final","",Scopus,2-s2.0-85075933023
"Pereira R., Garrote L., Barros T., Carona C., Bento L.C., Nunes U.J.","57212084884;56841438900;57212082760;26533871200;14070145000;57200710287;","Expressive Robotic Head for Human-Robot Interaction Studies",2020,"IFMBE Proceedings","76",,,"1627","1637",,,"10.1007/978-3-030-31635-8_200","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075875323&doi=10.1007%2f978-3-030-31635-8_200&partnerID=40&md5=4e7c439a31b647ec937b92d55957ea70","Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal; Cerebral Palsy Association of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavioural Intervention, University of Coimbra, Coimbra, Portugal","Pereira, R., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Garrote, L., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal; Barros, T., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Carona, C., Cerebral Palsy Association of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavioural Intervention, University of Coimbra, Coimbra, Portugal; Bento, L.C., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Nunes, U.J., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal","In this paper we present an improvement of the ISR-RobotHead prototype interaction capabilities having in view future Human-Robot Interactions studies. To accomplish that, a new hardware and software architectures and new facial expressions were developed. This new prototype (ISR-RobotHead v2.0) uses LCDs to display six human facial emotional expressions, avoiding the use of interlinked mechanical systems. The prototype also incorporates cameras, microphones, speakers, and LEDs on the robot’s cheeks. To validate the new cartoon facial expressions, an exploratory study was performed with children (4–6 years old) and adults (23–24 years old), who were asked to identify the expression displayed by the ISR-RobotHead v2.0; for children, the evaluation of accuracy in the detection of emotional expressions was performed through a corresponding number of cards picturing child’s emotional facial expressions. Preliminary results successfully validate the proposed robot’s non-verbal communication. © 2020, Springer Nature Switzerland AG.","Facial expressions; Human-robot interaction; LCD-based robotic head; Speech recognition","Biochemical engineering; Liquid crystal displays; Man machine systems; Medical computing; Robotics; Speech recognition; Emotional expressions; Exploratory studies; Facial Expressions; Hardware and software architectures; Mechanical systems; Non-verbal communications; Robotic head; Human robot interaction",,,,,"UID/PSI/00730/2019; UID/EEA/00048/2013; Fundação para a Ciência e a Tecnologia, FCT: AMS-HMI12; European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","Acknowledgements. The authors would like to thank Professor Rosa Roldão for facilitating the conduction of experimental tests with children at “Centro de A¸cão Social do Concelho de Ílhavo”. This work was partially supported by FCT Project AMS-HMI12 (https://sites.google.com/site/amshmi12/), ISR-UC and University of Coimbra, and by UID/EEA/00048/2013 with FEDER funding through programs QREN and COMPETE. It was also supported by Center for Research in Neuropsychology and Cognitive-Behavior Intervention (UID/PSI/00730/2019) and Cerebral Palsy Association of Coimbra.","The authors would like to thank Professor Rosa Rold?o for facilitating the conduction of experimental tests with children at ?Centro de A??o Social do Concelho de ?lhavo?. This work was partially supported by FCT Project AMS-HMI12 (https://sites.google.com/site/amshmi12/), ISR-UC and University of Coimbra, and by UID/EEA/00048/2013 with FEDER funding through programs QREN and COMPETE. It was also supported by Center for Research in Neuropsychol-ogy and Cognitive-Behavior Intervention (UID/PSI/00730/2019) and Cerebral Palsy Association of Coimbra.",,,,,,,,,"Ahn, H.S., Kim, P.J., Choi, J.H., Mansoor, S.B., Kang, W.S., Yoon, S.M., Na, J.H., Ko, H.S., Emotional head robot with behavior decision model and face recognition (2007) 2007 International Conference on Control, Automation and Systems, pp. 2719-2724. , pp., October; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) Int. J. Soc. Robot., 7, pp. 465-478; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot., 3 (21), p. eaat5954; Breazeal, C., Emotion and sociable humanoid robots (2003) Int. J. Hum.-Comput. Stud., 59, pp. 119-155; http://www.bluefrogrobotics.com/en/buddy/; Costa, A., Steffgen, G., Lera, F., Nazarikhorram, A., Ziafati, P., Socially assistive robots for teaching emotional abilities to children with Autism Spectrum Disorder (2017) 3Rd Workshop on Child-Robot Interaction at HRI; Han, J., Jo, M., Park, S., Kim, S., The educational use of home robots for children (2005) ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, pp. 378-383. , pp., August; Kȩdzierski, J., Muszyński, R., Zoll, C., Oleksy, A., Frontkiewicz, M., EMYS– emotive head of a social robot (2013) Int. J. Soc. Robot, 5, pp. 237-249; Këpuska, V., Bohouta, G., Comparing speech recognition systems (Microsoft API, Google API And CMU Sphinx) (2017) Int. J. Eng. Res. Appl., 7, pp. 20-24; Libin, A.V., Libin, E.V., Person-robot interactions from the robopsychologists’ point of view: The robotic psychology and robotherapy approach (2004) Proc. IEEE, 92, pp. 1789-1803; Lobue, V., Thrasher, C., The child affective facial expression (CAFE) set: Validity and reliability from untrained adults (2015) Front. Psychol., 5, p. 1532; Loureiro, R., Lopes, A., Carona, C., Almeida, D., Faria, F., Garrote, L., Premebida, C., Nunes, U.J., ISR-RobotHead: Robotic head with LCD-based emotional expressiveness (2017) 2017 IEEE 5Th Portuguese Meeting on Bioengineering (ENBENG); Lütkebohle, I., Hegel, F., Schulz, S., Hackel, M., Wrede, B., Wachsmuth, S., Sagerer, G., (2010) The Bielefeld Anthropomorphic Robot Head Flobi. In: 2010 IEEE International Conference on Robotics and Automation, pp. 3384-3391. , pp., May; McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A survey of autonomous human affect detection methods for social robots engaged in naturalHRI (2016) J.Intell.Robot.Syst., 82, pp. 101-133; Mori, M., Macdorman, K.F., Kageki, N., The uncanny valley [From the Field] (2012) IEEE Robot. Autom. Mag., 19, pp. 98-100; Orejana, J.R., Macdonald, B.A., Ahn, H.S., Peri, K., Broadbent, E., Healthcare Robots in Homes of Rural Older Adults (2015) Social Robotics. ICSR 2015. Lecture Notes in Computer Science, 9388, pp. 512-521. , Tapus, A., André, E., Martin, JC., Fer-land, F., Ammi, M., pp., Springer, Cham; Pereira, R., (2018) Isr-Robothead V2.0: New Hardware and Software Modules. Master Degree Dissertation, , Coimbra, University; Schulz, T., Tørresen, J., Herstad, J., Animation techniques in human-robot interaction user studies: A systematic literature review (2018) Clinical Orthopaedics and Related Research; Sosnowski, S., Bittermann, A., Kuhnlenz, K., Buss, M., Design and Evaluation of Emotion-Display EDDIE (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3113-3118. , pp., October; Tan, X.Z., Vázquez, M., Carter, E.J., Morales, C.G., Steinfeld, A., Inducing bystander interventions during robot abuse with social mechanisms (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 169-177. , pp","Pereira, R.; Institute of Systems and Robotics, Portugal; email: ricardo.pereira@isr.uc.pt","Henriques J.de Carvalho P.Neves N.",,"Springer","15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019","26 September 2019 through 28 September 2019",,232529,16800737,9783030316341,,,"English","IFMBE Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85075875323
"Mruthyunjaya V., Jankowski C.","57211644266;57212968659;","Human-Augmented Robotic Intelligence (HARI) for Human-Robot Interaction",2020,"Advances in Intelligent Systems and Computing","1070",,,"204","223",,,"10.1007/978-3-030-32523-7_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075669850&doi=10.1007%2f978-3-030-32523-7_14&partnerID=40&md5=b77c676d9aeafc04347a0574baf46dac","Aisera, Palo Alto, United States; Cloudminds Technology, Santa Clara, United States","Mruthyunjaya, V., Aisera, Palo Alto, United States; Jankowski, C., Cloudminds Technology, Santa Clara, United States","This paper provides a system design framework for a human-robot interaction system. The design introduces a human-augmented robotic intelligence embedded in a human-robot interaction system. The motivations behind the system design are spoken dialogue systems, Wizard-of-OZ framework, and existing HRI designs for socially intelligent robots. In this work, we explain how artificial intelligence of human-robot interaction system is enhanced by human intelligence through collaboration. The collaborative artificial intelligence enables the system to learn from demonstration. The main objective and the gradual progression from this paper is to build an iterative interactive system that is capable of achieving human-robot interaction similar to the nuances of human-human interaction. © 2020, Springer Nature Switzerland AG.","Artificial intelligence; Automatic speech; Cloud robot; Human-augmented robotic intelligence; Human-in-the-loop; Human-robot interaction; Natural language processing; Robot control unit; Social robot; Spoken dialogue systems; Virtual Backbone Network","Artificial intelligence; Intelligent robots; Machine design; Man machine systems; Natural language processing systems; Robotics; Speech processing; Systems analysis; Virtual reality; Automatic speech; Human-in-the-loop; NAtural language processing; Robot controls; Robotic intelligence; Social robots; Spoken dialogue system; Virtual backbone network; Human robot interaction",,,,,,,,,,,,,,,,"Dupont, S., Luettin, J., Audio-visual speech modeling for continuous speech recognition (2000) IEEE Trans. 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Secur., 19 (5), pp. 653-659","Jankowski, C.; Cloudminds TechnologyUnited States; email: charles.jankowski@cloudminds.com","Arai K.Bhatia R.Kapoor S.",,"Springer","4th Future Technologies Conference, FTC 2019","24 October 2019 through 25 October 2019",,233129,21945357,9783030325220,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85075669850
"Tunç A.Ö.","57191197268;","Human-Robot Interaction in Organizations",2020,"Contributions to Management Science",,,,"31","45",,,"10.1007/978-3-030-29739-8_2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075067408&doi=10.1007%2f978-3-030-29739-8_2&partnerID=40&md5=5f27296ae7eea27a97fc3cb019b9bded","Department of Business, Faculty of Political Sciences, Istanbul University, Fatih, Istanbul, Turkey","Tunç, A.Ö., Department of Business, Faculty of Political Sciences, Istanbul University, Fatih, Istanbul, Turkey","As advancement in technology is on the rise in organizational setting as observed in other areas, human-robot interaction is getting more focused in both academic and practical studies. Social robots influence business life deeply and hence human life by changing organizational settings. This study aims to explore how the interaction between humans and robots affects the workplace and in what aspects we can explain the nature of sociality and collaboration with robots. It also aims to put forward advantages and disadvantages of human-robot interaction by presenting essential reference points and discussing many aspects of human-robot interaction in organizations. © 2020, Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Arkin, R.C., Fujita, M., Takagi, T., Hasegawa, R., An ethological and emotional basis for human–robot interaction (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 191-201; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction. Proceedings of the Roman 2004 (2004) Kurashiki, pp. 591-594; Breazeal, C.L., (2004) Designing Sociable Robots. Intelligent Robotics and Autonomous Agents, , illustrated ed.). Cambridge: MIT Press; Breazeal, C., Human-robot partnership, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 79-81; Breazeal, C., Scassellati, B., How to build robots that make friends and influence people (1999) Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No. 99CH36289, 2, pp. 858-863. , Vol., pp., IEEE; Brooks, R., Humanoid robots (2002) Communications of the ACM, 45 (3), pp. 33-38; Brynjolfsson, E., McAfee, A., The second machine age: Akıllı Teknolojiler Devrinde Çalışma, İlerleme ve Refah (2015) (Çev., , Levent Göktem). İstanbul: Türk Hava Yolları Yayınları; Carley, K.M., (2002) Smart Agents and Organizations of the Future, pp. 1-2. , In The Handbook of New Media, Citeseer; Cascio, W.F., Montealegre, R., (2016) How Technology is Changing Work and Organizations. the Annual Review of Organizational Psychology and Organizational Behavior, 3, pp. 349-375; Claure, H., Jung, M., (2018) Understanding Social Dynamics in Robot-Human Handovers through the Lens of Expectancy Violations Theory HRI’18; Coovert, M.D., Thompson, L.F., Toward a synergistic relationship between psychology and technology (2014) The Psychology of Workplace Technology, pp. 1-20. , M. D. Coovert & L. F. Thompson (Eds.), (pp., London: Routledge; Dautenhahn, K., Human-robot interaction (2013) The Encyclopedia of Human-Computer Interaction, , 2nd ed.). New York: Springer; Davenport, T.H., Kirby, J., Beyond automation: Strategies for remaining gainfully employed in an era of very smart machines (2015) Harvard Business Review, 93 (6), pp. 58-65; Ford, M., (2018) Robotların yükselişi: Yapay Zeka Ve işsiz Bir Gelecek Tehlikesi (Çev.: Cem Duran), p. 4. , Baskı. İstanbul: Kronik Yayınları; Goodrich, M.A., Schultz, A.C., Human–robot interaction: A survey (2007) Foundation and Trends in Human-Computer Interaction, 1 (3), pp. 203-275; Hinds, P.J., Roberts, T.L., Jones, H., Whose job is it anyway? A study of human-robot interaction in a collaborative task (2004) Human-Computer Interaction, 19 (1), pp. 151-181; Ishiguro, H., Asada, M., Humanoid and android science, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 74-76; Kiesler, S., Goodrich, M.A., (2018); Kiesler, S., Hinds, P., Introduction to this special issue on human-robot interaction, human–computer (2004) Interactions, 19 (1-2), pp. 1-8; Mataric, M.J., Socially assistive robotics, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 81-83; Mutlu, B., Forlizzi, J., (2008) Robots in Organizations: The Role of Workflow, Social, and Environmental Factors in Human-Robot Interaction, p. 36. , Human-Computer Interaction Institute, Paper; Omohundro, S.M., (2007) The Nature of Self-Improving Artificial Intelligence. Talk Presented at the Singularity Summit, pp. 8-9. , San Francisco, CA; Persson, P., Laaksolahti, J., Lonnqvist, P., Understanding social intelligence (2002) Socially Intelligent Agents: Creating Relationships with Computers and Robots, pp. 21-28. , K. Dautenhahn, A. H. Bond, L. Canamero, & B. Edmonds (Eds.), (pp., New York: Springer Science & Business Media; Redden, E.S., Elliott, L.R., Barnes, M.J., Robots: The new teammates (2014) The Psychology of Workplace Technology, pp. 185-208. , M. D. Coovert & L. F. Thompson (Eds.), (pp., London: Routledge; Robert, L.P., You, S., (2014) Human-Robot Interaction in Groups: Theory, Method, and Design for Robots in Groups; Sande, G.N., Ellard, J.H., Ross, M., Effect of arbitrarily assigned labels on self-perceptions and social perceptions: The mere position effect (1986) Journal of Personality and Social Psychology, 50, pp. 684-689; Scassellati, B., How developmental psychology and robotics complement each other (2000) MIT Artificial Intelligence Lab (Report Documentation Pages, pp. 1-8. , Lansing, MI; Shibata, T., An overview of human interactive robots for psychological enrichment (2004) Proceedings of the IEEE, 92 (11), pp. 1749-1758; Shibata, T., Irie, R., Artificial emotional creature for human-robot interaction-a new direction for intelligent systems (1997) Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, p. 47. , (p., IEEE; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Common metrics for human-robot interaction (2006) Proceedings of the 1St ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 33-40. , March, (pp., ACM; Steuer, J., (1995) Vividness and Source of Evaluation as Determinants of Social Responses Toward Mediated Representations of Agency. Unpublished Doctoral Dissertation, , Stanford University, California; Stock, R.M., Merkle, M., Can humanoid service robots perform better than service employees? A comparison of innovative behavior cues (2018) Proceedings of the 51St Hawaii International Conference on System Sciences; Sung, J.Y., Christensen, H.I., Grinter, R.E., Robots in the wild: Understanding long-term use (2009) HRI’09 Proceedings of the 4Th ACM/IEEE International Conference on Human Robot Interaction, pp. 45-52. , March 13, La Jolla, California, USA (pp; Tapus, A., Mataric, M.J., Emulating empathy in socially assistive robotics (2007) AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics, pp. 93-96. , March, (pp; Thielscher, M., Cognitive robotics, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 77-79; van Oost, E., Reed, D., Towards a sociological understanding of robots as companions (2011) Human-Robot Personal Relationships (Vol. LNICTS 59, pp. 11-18. , M. H. Lamers & F. J. Verbeek (Eds.), pp., Heidelberg: Springer; Velasquez, J.D., An emotion-based approach to robotics (1999) Proceedings of IEEE/ RSJ International Conference on Intelligent Robots and Systems, pp. 235-240. , October, Kyongju, Korea (pp","Tunç, A.Ö.; Department of Business, Turkey; email: aozbebek@istanbul.edu.tr",,,"Springer",,,,,14311941,,,,"English","Contrib. Manag. Sci.",Book Chapter,"Final","",Scopus,2-s2.0-85075067408
"Luo J., Liu C., Wang N., Yang C.","57198979642;56255340400;55446268900;57204881979;","A Method of Intention Estimation for Human-Robot Interaction",2020,"Advances in Intelligent Systems and Computing","1043",,,"69","80",,,"10.1007/978-3-030-29933-0_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072866490&doi=10.1007%2f978-3-030-29933-0_6&partnerID=40&md5=78cdbfe24d07a9790564a85f62137e1b","South China University of Technology, Guangzhou, 510640, China; CNRS-University of Montpellier, UMR 5506, 161 Rue Ada, Montpellier, 34095, France; Bristol Robotics Laboratory, UWE, Bristol, BS16 1QY, United Kingdom","Luo, J., South China University of Technology, Guangzhou, 510640, China, CNRS-University of Montpellier, UMR 5506, 161 Rue Ada, Montpellier, 34095, France; Liu, C., CNRS-University of Montpellier, UMR 5506, 161 Rue Ada, Montpellier, 34095, France; Wang, N., Bristol Robotics Laboratory, UWE, Bristol, BS16 1QY, United Kingdom; Yang, C., Bristol Robotics Laboratory, UWE, Bristol, BS16 1QY, United Kingdom","Dynamics of human wrist play an important role in human-robot interaction. In this paper, we develop a novel method to classify the human wrist’s motion and to recognize its stiffness profile. In the proposed method, an integrated framework of linear discriminant analysis and extreme learning machine is developed to evaluate the intention of the wrist. Specifically, linear discriminant analysis is used to classify gestures of the wrist. Based on the result of classification, extreme learning method is use to construct a regression model of the stiffness. The experimental results are demonstrated the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.","Extreme learning machine; Human-robot interaction; Intention estimation; Linear discriminant analysis","Discriminant analysis; Knowledge acquisition; Machine learning; Man machine systems; Regression analysis; Stiffness; Extreme learning machine; Integrated frameworks; Intention estimation; Learning methods; Linear discriminant analysis; Regression model; Stiffness profiles; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/S001913; National Natural Science Foundation of China, NSFC: 61811530281, 61861136009","information. This work was partially supported by National Nature Science Foundation (NSFC) under Grants 61861136009 and 61811530281 Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/S001913.",,,,,,,,,,"Hang, S., Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1447-1453; Hang, S., Sandoval, J., Makhdoomi, M., Safety-enhanced human-robot interaction control of redundant robot for teleoperated minimally invasive surgery (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6611-6616. , pp., IEEE; Luo, J., Enhanced teleoperation performance using hybrid control and virtual fixture (2019) Int. J. Syst. Sci., 50 (3), pp. 451-462; Luo, J., A task learning mechanism for the telerobots (2019) Int. J. Humanoid Robot., 16 (2); Geethanjali, P., Ray, K., A low-cost real-time research platform for EMG pattern recognition-based p°rosthetic hand (2015) IEEE/ASME Trans. Mechatron., 20 (4), pp. 1948-1955; Jahromi, M.G., Parsaei, H., Zamani, A., Stashuk, D.W., Cross comparison of motor unit potential features used in EMG signal decomposition (2018) IEEE Trans. Neural Syst. Rehabil. Eng., 26 (5), pp. 1017-1025; Ryu, J., Lee, B.-H., Kim, D.-H., SEMG signal-based lower limb human motion detection using a top and slope feature extraction algorithm (2017) IEEE Signal Process. Lett., 24 (7), pp. 929-932; Doulah, A.S.U., Fattah, S.A., Zhu, W.-P., Ahmad, M.O., Wavelet domain feature extraction scheme based on dominant motor unit action potential of EMG signal for neuromuscular disease classification (2014) IEEE Trans. Biomed. Circuits Syst., 8 (2), pp. 155-164; Ajoudani, A., Tsagarakis, N., Bicchi, A., Tele-impedance: Teleoperation with impedance regulation using a body–machine interface (2012) Int. J. Robot. Res., 31 (13), pp. 1642-1656; Kiguchi, K., Hayashi, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Trans. Syst., Man, Cybern. Part B (Cybernetics), 42 (4), pp. 1064-1071; Han, J., Ding, Q., Xiong, A., Zhao, X., A state-space EMG model for the estimation of continuous joint movements (2015) IEEE Trans. Ind. Electron., 62 (7), pp. 4267-4275; Tang, Z., Yu, H., Cang, S., Impact of load variation on joint angle estimation from surface EMG signals (2016) IEEE Trans. Neural Syst. Rehabil. Eng., 24 (12), pp. 1342-1350; Yang, C., Haptics electromyography perception and learning enhanced intelligence for teleoperated robot (2018) IEEE Trans. Autom. Sci. Eng., 99, pp. 1-10; Jing, P., Heisterkamp, D.R., Dai, H.K., LDA/SVM driven nearest neighbor classification (2003) IEEE Trans. Neural Netw., 14 (4), pp. 940-942; Huang, G.B., Zhou, H., Ding, X., Zhang, R., Extreme learning machine for regression and multiclass classification (2012) IEEE Trans. Syst. Man Cybern. B Cybern., 42 (2), pp. 513-529","Yang, C.; Bristol Robotics Laboratory, United Kingdom; email: cyang@ieee.org","Ju Z.Zhou D.Gegov A.Yang L.Yang C.",,"Springer Verlag","19th Annual UK Workshop on Computational Intelligence, UKCI 2019","4 September 2019 through 6 September 2019",,231289,21945357,9783030299323,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85072866490
"Chen L., Su W., Feng Y., Wu M., She J., Hirota K.","55774663200;57196287282;57204552414;35231682600;7102576674;7402829702;","Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction",2020,"Information Sciences","509",,,"150","163",,32,"10.1016/j.ins.2019.09.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071968961&doi=10.1016%2fj.ins.2019.09.005&partnerID=40&md5=d4a0e3e853941735783543aa2d46b65f","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; School of Engineering, Tokyo University of Technology, Tokyo, 192-0982, Japan; Tokyo Institute of Technology, Yokohama, 226-8502, Japan; School of Automation, Beijing Institute of Technology, Beijing, 100081, China","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Feng, Y., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; She, J., School of Engineering, Tokyo University of Technology, Tokyo, 192-0982, Japan; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan, School of Automation, Beijing Institute of Technology, Beijing, 100081, China","The two-layer fuzzy multiple random forest (TLFMRF) is proposed for speech emotion recognition. When recognizing speech emotion, there are usually some problems. One is that feature extraction relies on personalized features. The other is that emotion recognition doesn't consider the differences among different categories of people. In the proposal, personalized and non-personalized features are fused for speech emotion recognition. High dimensional emotional features are divided into different subclasses by adopting the fuzzy C-means clustering algorithm, and multiple random forest is used to recognize different emotional states. Finally, a TLFMRF is established. Moreover, a separate classification of certain emotions which are difficult to recognize to some extent is conducted. The results show that the TLFMRF can identify emotions in a stable manner. To demonstrate the effectiveness of the proposal, experiments on CASIA corpus and Berlin EmoDB are conducted. Experimental results show the recognition accuracies of the proposal are 1.39%–7.64% and 4.06%–4.30% higher than that of back propagation neural network and random forest respectively. Meanwhile, preliminary application experiments are also conducted to investigate the emotional social robot system, and application results indicate that mobile robot can real-time track six basic emotions, including angry, fear, happy, neutral, sad, and surprise. © 2019","Fuzzy C-means; Human-robot interaction; Multiple random forest; Speech emotion recognition","Backpropagation; Clustering algorithms; Decision trees; Fuzzy clustering; Fuzzy systems; Human robot interaction; Man machine systems; Neural networks; Speech; Back propagation neural networks; Emotion recognition; Fuzzy C mean; Fuzzy c-means clustering algorithms; High-dimensional; Random forests; Recognition accuracy; Speech emotion recognition; Speech recognition",,,,,"2018039; 2017010201010133; National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61973286; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","This work was supported by the National Natural Science Foundation of China under Grants 61973286 , 61603356 , and 61733016 , the 111 project under Grant B17040 , the Wuhan Science and Technology Project under Grant 2017010201010133 , and the Fundamental Research Funds for the Central Universities , China University of Geosciences (Wuhan) (No. 2018039 ).",,,,,,,,,,"Albornoz, E.M., Milone, D.H., Rufiner, H.L., Feature extraction based on bio-inspired model for robust emotion recognition (2017) Soft Comput., 21 (17), pp. 5145-5158; Albornoz, M.E., Milone, D., Emotion recognition in never-seen languages using a novel ensemble method with emotion profiles (2016) IEEE Trans. Affect. Comput., 8 (99), pp. 1-11; Ayadi, M.E., Kamel, M.S., Karray, F., Survey on speech emotion recognition: features classification schemes, and databases (2011) Pattern Recognit., 44 (3), pp. 572-587; Bezdek, J.C., A physical interpretation of fuzzy ISODATA (1976) IEEE Trans. Syst. Man Cybern., 6 (5), pp. 387-389; Breiman, L., Random forests (2001) Mach. Learn., 45, pp. 5-32; (2005), http://emodb.bilderbar.info/index-1280.html, Berlin database of emotional speech [Online], available:, October 10; (2008), http://www.chineseldc.org/resourceinfo.php?rid=76, CASIA chinese emotion corpus [Online], available:, June 11; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z.T., Hua, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2017) IEEE Trans. Syst. Man Cybern; Chen, L.F., Liu, Z.T., Wu, M., Ding, M., Dong, F.Y., Hirota, K., Emotion-age-gender-nationality based intention understanding in human-robot interaction using two-layer fuzzy support vector regression (2015) Int. J. Soc. Robot., 7 (5), pp. 709-729; Chen, L.F., Wu, M., Zhou, M.T., She, J.H., Dong, F.Y., Hirota, K., Information-driven multi-robot behavior adaptation to emotional intention in human-robot interaction (2018) IEEE Trans. Cognit. Dev.Syst., 10 (3), pp. 647-658; Chen, L.F., Zhou, M.T., Su, W.J., Wu, M., She, J.H., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Inf. Sci., 428, pp. 49-61; Chen, L.F., Zhou, M.T., Wu, M., She, J.H., Liu, Z.T., Dong, F.Y., Hirota, K., Three-layer weighted fuzzy support vector regression for emotional intention understanding in human-robot interaction (2018) IEEE Trans. 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Rob., 24 (1-2), pp. 47-67; Iliou, T., Anagnostopoulos, C.N., Comparison of different classifiers for emotion recognition (2009) Proceedings of Panhellenic Conference on Informatics, Corfu, Greece, pp. 102-106; Kondo, K., Taira, K., Estimation of binaural speech intelligibility using machine learning (2018) Appl. Acoust., 129, pp. 408-416; Kim, J.B., Park, J.S., Multistage data selection-based unsupervised speaker adaptation for personalized speech emotion recognition (2016) Eng. Appl. Artif. Intell., 52 (100), pp. 126-134; Kim, J., André, E., Emotion recognition based on physiological changes in music listening (2018) IEEE Trans. Pattern Anal. Mach.Intell., 30 (12), pp. 2067-2083; Kim, E.H., Hyun, K.H., Kim, S.H., Improved emotion recognition with a novel speaker-independent feature (2009) IEEE/ASME Trans. 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Health Inform.; Oyedotun, O.K., Khashman, A., Prototype-incorporated emotional neural network (2018) IEEE Trans. Neural Netw. Learn. Syst., 29 (8), pp. 3560-3572; Park, J.S., Kim, J.H., Oh, Y.H., Feature vector classification based speech emotion recognition for service robots (2009) IEEE Trans. Consum. Electron., 55 (3), pp. 1590-1596; Pal, N.R., Bezdek, J.C., On cluster validity for the fuzzy c-means model (1995) IEEE Trans. Fuzzy Syst., 3 (3), pp. 370-379; Raposo, F., Ribeiro, R., Matos, D.M.D., Using generic summarization to improve music information retrieval tasks (2015) IEEE/ACM Trans. Audio Speech Lang.Process., 24 (6), pp. 1119-1128; Song, P., Ou, S.F., Du, Z.B., Learning corpus-invariant discriminant feature representations for speech emotion recognition (2017) IEICE Trans. Inf. Syst., E100-D (5), pp. 1136-1139; Schuller, B.W., Batliner, A.M., Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing (2014), John Wiley & Sons Inc; Schuller, B., Steidl, S., Batliner, A., The INTERSPEECH emotion challenge (2009) Proce. INTERSPEECH, pp. 312-315; Sheikhan, M., Bejani, M., Gharavian, D., Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method (2013) Neural Comput. Appl., 23 (1), pp. 215-227; Sun, Y., Wen, G., Ensemble softmax regression model for speech emotion recognition (2016) Multimed. Tools Appl., 76 (6), pp. 8305-8328; Vaiciukynas, E., Verikas, A., Gelzinis, A., Detecting Parkinson's disease from sustained phonation and speech signals (2017) PLoS ONE, 12 (10), pp. 1-16; Wu, C.H., Liang, W.B., Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels (2010) IEEE Trans. Affect. Comput., 2 (1), pp. 10-21; Yuncu, E., Hacihabiboglu, H., Bozsahin, C., Automatic speech emotion recognition using auditory models with binary decision tree and SVM (2014) Proceedings of International Conference on Pattern Recognition, pp. 773-778; Zhou, M.T., Chen, L.F., Xu, J.P., Cheng, X.H., Wu, M., Cao, W.H., She, J.H., Hirota, K., FCM-based multiple random forest for speech emotion recognition (2017) Proceedings of the 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics; Zhang, S., Zhao, X., Lei, B., Speech emotion recognition using an enhanced kernel isomap for human-robot interaction (2013) Int. J. Adv. Rob. Syst., 10 (2), pp. 1-7","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Elsevier Inc.",,,,,00200255,,ISIJB,,"English","Inf Sci",Article,"Final","",Scopus,2-s2.0-85071968961
"Gaifullin R., Ivanou M., Gazizov R.","57210234843;57205614485;57210232892;","Natural human-robot interaction toolkit",2020,"Advances in Intelligent Systems and Computing","1018",,,"196","200",,1,"10.1007/978-3-030-25629-6_31","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070011084&doi=10.1007%2f978-3-030-25629-6_31&partnerID=40&md5=059ab49414e9c429f7f9396861ce9c87","Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation","Gaifullin, R., Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation; Ivanou, M., Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation; Gazizov, R., Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation","Conventional human-robot interaction is usually limited to a set of commands a professional human operator is supposed to use, and/or teleoperation capabilities. The paper explores capabilities a robot could use to communicate more naturally with a human, support for a range of hardware configurations is provided. The capabilities developed for interaction toolkit include speech and face recognition. Finally, a use case for such a toolkit and performance with respect to different hardware configurations are discussed. © 2020, Springer Nature Switzerland AG.","Human-centered design; Human-robot interaction; Interaction infrastructure; Robotics","Face recognition; Machine design; Man machine systems; Robotics; Speech recognition; Hardware configurations; Human operator; Human-centered designs; Interaction infrastructure; Human robot interaction",,,,,"Ministry of Science and Higher Education of the Russian Federation: 075-10-2018-010, 14.606.21.0007, RFMEFI60617X0007","Acknowledgements. This work has been supported by the Ministry of Science and Higher Education of the Russian Federation with the project “Development of anthropomorphic robotic complexes with variable stiffness actuators for movement on the flat and the rugged terrains” (agreement: № 075-10-2018-010 (№ 14.606.21.0007), ID: RFMEFI60617X0007).",,,,,,,,,,"Fong, T., Nourbakhsh, I., Kunz, C., Fluckiger, L., Schreiner, J., Ambrose, R., Burridge, R., Scholtz, J., The peer-to-peer human-robot interaction project (2005) Space 2005, , American Institute of Aeronautics and Astronautics; Abadi, M., Agarwal, A., Barham, P., (2015) Tensorflow: Large-Scale Machine Learning on Heterogeneous Systems, , arXiv e-prints; Openvino Deep Learning Computer Vision Toolkit, , https://software.intel.com/en-us/openvino-toolkit; Povey, D., The Kaldi speech recognition toolkit (2011) Proc. ASRU, pp. 1-4. , Hawaii, US, pp; Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z., FaceBoxes: A CPU real-time face detector with high accuracy (2017) 2017 IEEE International Joint Conference on Biometrics (IJCB), , IEEE; Schroff, F., Kalenichenko, D., Philbin, J., FaceNet: A unified embedding for face recognition and clustering (2015) In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; Gsteamer Multimedia Framework, , https://gstreamer.freedesktop.org/; Tanel, A., Full-duplex speech-to-text system for Estonian (2014) FAIA, 268, pp. 3-10; Introducing the Intel Neural Compute Stick 2, , https://software.intel.com/en-us/neural-compute-stick; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceeding of the 1St ACM SIGCHI/SIGART Conference on Human-Robot Interaction-Hri ’06, , ACM Press","Gaifullin, R.; Innopolis University, Universitetskaya. 1, Russian Federation; email: r.gayfullin@innopolis.ru","Ahram T.Taiar R.Colson S.Choplin A.",,"Springer Verlag","1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019","22 August 2019 through 24 August 2019",,229139,21945357,9783030256289,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85070011084
"Roda-Sanchez L., Olivares T., García A.S., Garrido-Hidalgo C., Fernández-Caballero A.","57202112308;6602909884;16174733600;57193328014;6602230534;","Gesture control system for industry 4.0 human-robot interaction – A usability test",2020,"Advances in Intelligent Systems and Computing","1006",,,"54","61",,2,"10.1007/978-3-030-24097-4_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068619014&doi=10.1007%2f978-3-030-24097-4_7&partnerID=40&md5=128795d90bffa8d150f1982e1ce060b1","Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","Roda-Sanchez, L., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Olivares, T., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; García, A.S., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Garrido-Hidalgo, C., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Fernández-Caballero, A., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","The Industry 4.0 paradigm pursues improvements in production rate, flexibility, efficiency, quality, among others, through the use of technologies like Internet of Things (IoT), ambient intelligence and collaborative robots. Robots developing precision tasks, works in hazardous environments or movements of heavy parts, autonomously or in cooperation with workers, offer great advantages. Although collaboration provides great benefits, these technologies should be appropriate for all kind of workers, independently of their technical skills. If this problem is not addressed properly, irruption of robots could lead to social instability and/or rejection of useful advances. In this work, a gesture control system based on wearables oriented to Industry 4.0 robots is tested with real users to validate a novel gesture control system as an intuitive tool. © Springer Nature Switzerland AG 2020.","Ambient intelligence; Gesture control; Human-robot interaction; Industry 4.0; IoT","Ambient intelligence; Application programs; Artificial intelligence; Control systems; Industry 4.0; Intelligent robots; Internet of things; Man machine systems; Wearable technology; Collaborative robots; Gesture control; Hazardous environment; Internet of Things (IOT); Production rates; Technical skills; Usability tests; Human robot interaction",,,,,"European Regional Development Fund, FEDER: DPI2016-80894-R; Agencia Estatal de Investigación, AEI; University of the East, UE","Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant.","Acknowledgements. This work was partially supported by Spanish Ministerio de",,,,,,,,,"Gascueña, J.M., Navarro, E., Fernández-Sotos, P., Fernández-Caballero, A., Pavón, J., IDK and ICARO to develop multi-agent systems in support of ambient intelligence (2015) J. Intell. Fuzzy Syst., 28 (1), pp. 3-15; Rivas-Casado, A., Martinez-Tomás, R., Fernández-Caballero, A., Multi-agent system for knowledge-based event recognition and composition (2011) Expert Syst, 28 (5), pp. 488-501; Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B., Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0 (2019) Inf. Fusion, 50, pp. 92-111; Peruzzini, M., Grandi, F., Pellicciari, M., Benchmarking of tools for user experience analysis in Industry 4.0 (2017) Procedia Manuf, 11, pp. 806-813; Dajun, Z., Minghui, S., Fei, C., Chih-Min, L., Longzhi, Y., Changjing, S., Changle, Z., Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller (2018) Neurocomputing, 282, pp. 218-231; Hortelano, D., Olivares, T., Ruiz, M.C., Garrido-Hidalgo, C., López, V., From sensor networks to Internet of Things. Bluetooth Low Energy, a standard for this evolution (2017) Sensors, 17 (2), p. 372; Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., Ruiz, M.C., OperaBLE: An IoT-based wearable to improve efficiency and smart worker care services in Industry 4.0 (2018) J. Sens., 2018; Garrido-Hidalgo, C., Hortelano, D., Roda-Sanchez, L., Olivares, T., Ruiz, M.C., López, V., IoT heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable Industry 4.0 (2018) IEEE Access, 6, pp. 28417-28437; Lund, A., Measuring usability with the USE questionnaire (2001) Usability and User Experience Newsletter of the STC Usability SIG, 8. , vol; López-Jaquero, V., Montero, F., Molina, J.P., Fernández-Caballero, A., González, P., Model-based design of adaptive user interfaces through connectors (2003) Interactive Systems-Design, Specification, and Verification, pp. 245-257. , Jorge, J.A., Jardim, N.N., Falcão e Cunha, J. (eds.), pp., Springer, Heidelberg; Neto, P., Pires, J.N., Moreira, A.P., Accelerometer-based control of an industrial robotic arm (2009) The 18Th IEEE International Symposium on Robot and Human Interactive Communication, 11, pp. 1192-1197. , vol., pp; Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human behavior and hand gesture classification for smart human-robot interaction (2017) Procedia Manuf, 11, pp. 91-98","Roda-Sanchez, L.; Instituto de Investigación en Informática de Albacete, Spain; email: luis.roda@alu.uclm.es","Novais P.Carneiro D.Lloret J.Chamoso P.Navarro E.Omatu S.",,"Springer Verlag","10th International Symposium on Ambient Intelligence, ISAmI 2019","26 June 2019 through 28 June 2019",,227779,21945357,9783030240967,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85068619014
"Ye H., Jeong H., Zhong W., Bhatt S., Izzetoglu K., Ayaz H., Suri R.","57202888529;57209418986;57209420778;57196045718;6603128234;6507639765;7103225263;","The effect of anthropomorphization and gender of a robot on human-robot interactions",2020,"Advances in Intelligent Systems and Computing","953",,,"357","362",,4,"10.1007/978-3-030-20473-0_34","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067658410&doi=10.1007%2f978-3-030-20473-0_34&partnerID=40&md5=a33c027cea039a33b96b2a6a6510eac4","Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Drexel Business Solutions Institute, 3220 Market Street, Philadelphia, PA  19104, United States","Ye, H., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Jeong, H., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Zhong, W., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Bhatt, S., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Izzetoglu, K., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Ayaz, H., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States, Drexel Business Solutions Institute, 3220 Market Street, Philadelphia, PA  19104, United States; Suri, R., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States, Drexel Business Solutions Institute, 3220 Market Street, Philadelphia, PA  19104, United States","The popularity of assistant robots has increased in the recent past. Past research has looked at the effect of anthropomorphization of robots and considered the consumers’ gender as an important factor. However, research has not examined the interaction between a robot’s gender and its level of anthropomorphization on human-robot interactions. Our results indicate that males and females perceive a service failure differently depending upon the level of anthropomorphization of the robots involved in the failure. © Springer Nature Switzerland AG 2020.","Automation; Consumer behavior; Gender; Human-robot interaction; Service failure","Automation; Consumer behavior; Ergonomics; Human computer interaction; Man machine systems; Safety engineering; Assistant robot; Gender; Service failure; Human robot interaction",,,,,,,,,,,,,,,,"Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychol. Rev., 114 (4), p. 864; Hinds, P.J., Roberts, T.L., Jones, H., Whose job is it anyway? A study of human-robot interaction in a collaborative task (2004) Hum. Comput. Interact., 19 (1), pp. 151-181; Złotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: Opportunities and challenges in human–robot interaction (2015) Int. J. Social Robot., 7 (3), pp. 347-360; de Visser, E.J., Monfort, S.S., Goodyear, K., Lu, L., O’Hara, M., Lee, M.R., Parasuraman, R., Krueger, F., A little anthropomorphism goes a long way: Effects of oxytocin on trust, compliance, and team performance with automated agents (2017) Hum. Factors, 59 (1), pp. 116-133; Mori, M., The Uncanny Valley (1970) Energy, 7 (4), pp. 33-35; Saygin, A.P., Chaminade, T., Ishiguro, H., Driver, J., Frith, C., The thing that should not be: Predictive coding and the Uncanny Valley in perceiving human and humanoid robot actions (2011) Soc. Cogn. Affect. Neurosci., 7 (4), pp. 413-422; Walters, M.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Avoiding the Uncanny Valley: Robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion (2008) Auton. Robots, 24 (2), pp. 159-178; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human–robot interaction (2014) Comput. Hum. Behav., 38, pp. 75-84; Parasuraman, A., Zeithaml, V.A., Berry, L.L., Servqual: A multiple-item scale for measuring consumer perc (1988) J. Retail., 64 (1), p. 12; Hur, J.D., Koo, M., Hofmann, W., When temptations come alive: How anthropomorphism undermines self-control (2015) J. Consum. Res., 42 (2), pp. 340-358; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots: Concepts, design and applications (2002) Technical Report CMU-RI-TR-02-, 29; Thrun, S., Toward a framework for human-robot interaction (2004) Hum. Comput. Interact., 19 (1), pp. 9-24; Ayaz, H., Onaral, B., Izzetoglu, K., Shewokis, P.A., McKendrick, R., Parasuraman, R., Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: Empirical examples and a technological development (2013) Front. Hum. Neuros., 7, p. 871","Ye, H.; Drexel University, 3141 Chestnut Street, United States; email: hy368@drexel.edu","Ayaz H.",,"Springer Verlag","AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019","24 July 2019 through 28 July 2019",,227099,21945357,9783030204723,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067658410
"Tello J.A., Zapata M., Paillacho D.","57209328960;56818703100;56084736200;","Kinematic optimization of the robot head movements for the evaluation of human-robot interaction in social robotics",2020,"Advances in Intelligent Systems and Computing","975",,,"108","118",,2,"10.1007/978-3-030-20216-3_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067392738&doi=10.1007%2f978-3-030-20216-3_11&partnerID=40&md5=ecbbb79bf2a8fe35c496f36e730a614f","Research Center of Mechatronics and Interactive Systems, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador","Tello, J.A., Research Center of Mechatronics and Interactive Systems, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Zapata, M., Research Center of Mechatronics and Interactive Systems, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Paillacho, D., ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador","This paper presents the simplification of the head movements from the analysis of the biomechanical parameters of the head and neck at the mechanical and structural level through CAD modeling and construction with additive printing in ABS/PLA to implement non-verbal communication strategies and establish behavior patterns in the social interaction. This is using in the denominated MASHI (Multipurpose Assistant robot for Social Human-robot Interaction) experimental robotic telepresence platform, implemented by a display with a fish-eye camera along with the mechanical mechanism, which permits 4 degrees of freedom (DoF). In the development of mathematical-mechanical modeling for the kinematics codification that governs the robot and the autonomy of movement, we have the Pitch, Roll, and Yaw movements, and the combination of all of them to establish an active communication through telepresence. For the computational implementation, it will be show the rotational matrix to describe the movement. © Springer Nature Switzerland AG 2020.","3D additive printing; Biomechanics; Computing engineering; Human robotic interaction; Lightweight mechanical design","3D modeling; 3D printers; Additives; Biomechanics; Computer aided design; Degrees of freedom (mechanics); Human computer interaction; Kinematics; Man machine systems; Robotics; Visual communication; Biomechanical parameters; Computational implementations; Experimental robotics; Human-Robotic interaction; Kinematic optimization; Mechanical design; Non-verbal communications; Social human-robot interactions; Human robot interaction",,,,,,,,,,,,,,,,"Nordin, A.I., Hudson, M., Denisova, A., Beeston, J., (2016) Perceptions of Telepresence Robot Form, 4. , vol; Victornunez, D., Rodriguez, V., México, E.M., Modelo Vrml Interactivo De Un Robot Humanoide Bioloid; Danev, L., Hamann, M., Fricke, N., Hollarek, T., Paillacho, D., Development of animated facial expressions to express emotions in a robot: RobotIcon, 2017 IEEE 2nd Ecuador Tech (2017) Chapters Meet. ETCM, 2017 (Janua), pp. 1-6. , vol., pp; Nourbakhsh, I.R., Robots and Education in the Classroom and in the Museum: On the Study of Robots, and Robots for Study; Sirimanna, S.W.D.T.S., Jayasekara, A.G.B.P., Edirisinghe, E.A.N.S., Lakmal, R.A.P., Lakshitha, T.G.G., (2015) Design and Simulation of a Human-Like Robot Neck Mechanism, 2015 Electr. Eng. Conf. [Eecon], 1893. , December; Pinzón Ríos, I.D., Cabeza hacia adelante: Una mirada desde la biomecánica y sus implicaciones sobre el movimiento corporal humano TT-forward head: A biomechanical perspective and its implications for the human body movement (2015) Rev. Univ. Ind. Santander, Salud, 47 (1), pp. 75-83; Ölçücüoğlu, O., Human-like robot head design a thesis submitted to the Graduate School of Natural and Applied Sciences of (2007) Middle East Technical University; Díaz-Boladeras, M., Paillacho, D., Angulo, C., Torres, O., González-Diéguez, J., Albo-Canals, J., Evaluating group-robot interaction in crowded public spaces: A week-long exploratory study in the wild with a humanoid robot guiding visitors through a science museum (2015) Int. J. Humanoid Robot., 12 (4); Dine, A., Vosniakos, G.-C., On the development of a robot-operated 3D-printer (2018) Procedia Manuf., 17 (Jan.), pp. 6-13; Eisenberg, M., 3D printing for children: What to build next? (2013) Int. J. Child-Computer Interact., 1, pp. 7-13; Avilés Arriaga, H.H., Herrera Rivas, H., Nuño Maganda, M.A., Un Esquema 3D para la Descripción Visual de Gestos Dinámicos, no (2014) December; Hernández, X.R., (2017) Rediscovering the Experimental Robotic Platform MASHI THESIS, No. January, P., p. 49; Diaz, M., Paillacho, D., Angulo, C., An exploratory study of group-robot social interactions in a cultural center (2015) IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 44-48. , IROS 2015","Tello, J.A.; Research Center of Mechatronics and Interactive Systems, Machala y Sabanilla, Ecuador; email: jorgealvarez@uti.edu.ec","Alexander T.Di Nicolantonio M.Rossi E.",,"Springer Verlag","AHFE International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, 2019","24 July 2019 through 28 July 2019",,226919,21945357,9783030202156,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067392738
"Amanatidis T., Langdon P., Clarkson P.J.","57200382670;55781400200;7103180311;","An Inclusive, Fully Autonomous Vehicle Simulator for the Introduction of Human-Robot Interaction Technologies",2020,"Advances in Intelligent Systems and Computing","964",,,"157","165",,,"10.1007/978-3-030-20503-4_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067339081&doi=10.1007%2f978-3-030-20503-4_14&partnerID=40&md5=97e022eb3ea296cedb67166a95fdc506","Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom","Amanatidis, T., Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom; Langdon, P., Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom; Clarkson, P.J., Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom","As fully autonomous - SAE level 5 - vehicles approach commercialisation, there is need to design and test user interfaces specifically for such use. However, testing in real environments is currently limited or, in many cases, impossible. In this paper, as a solution, we present a dedicated simulator for fully autonomous vehicles. First, we outline the design requirements for this simulator. Inclusive design principles were used to accommodate a large range of diversity in our population. It can thus be used by individuals with visual, auditory and certain physical and cognitive impairments. Second, we describe the capabilities of the simulator in terms of human-robot interaction technologies. We aim to assess both performance and non-performance characteristics of the resulting systems and how they can be integrated in the transportation experience. Third, we collate the knowledge obtained during this project to provide a deeper understanding of human factors in operating fully autonomous vehicles. We hope these results provide a basis for further research and improve the experience of users. &#x00A9; 2020, Springer Nature Switzerland AG.","Automotive simulators; Autonomous vehicles; Human factors and ergonomics; Human-robot interaction; Inclusive design","Autonomous vehicles; Ergonomics; Human computer interaction; Machine design; Man machine systems; Simulators; User interfaces; Cognitive impairment; Commercialisation; Design and tests; Fully-autonomous vehicles; Human factors and ergonomics; Inclusive design; Performance characteristics; Real environments; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/N011899/1",,,,,,,,,,,"Fernandez-Duque, D., Johnson, M.L., Cause and effect theories of attention: The role of conceptual metaphors (2002) Review of General Psychology, pp. 153-165. , pp; Dikmen, M., Burns, C.M., Autonomous driving in the real world: Experiences with tesla autopilot and summon (2016) Proceedings of the 8Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 225-228. , https://doi.org/10.1145/3003715.3005465, pp., ACM, New York; Waymo, , https://waymo.com/; (2019) GM Will Make an Autonomous Car without Steering Wheel Or Pedals By, , https://www.theverge.com/2018/1/12/16880978/gm-autonomous-car-2019-detroit-auto-show-2018; Mitchell, W.J., Borroni-Bird, C.E., Burns, L.D., (2010) Reinventing the Automobile: Personal Urban Mobility for the 21St Century, , MIT Press, Cambridge; Fernandez-Duque, D., Johnson, M.L., Cause and effect theories of attention: The role of conceptual metaphors (2002) Review of General Psychology, pp. 153-165. , pp; Politis, I., Langdon, P., Adebayo, D., Bradley, M., Clarkson, P.J., Skrypchuk, L., Mouzakitis, A., Stanton, N., An evaluation of inclusive dialogue-based interfaces for the takeover of control in autonomous cars (2018) 23Rd International Conference on Intelligent User Interfaces, pp. 601-606. , https://doi.org/10.1145/3172944.3172990, pp., ACM, New York; Wintersberger, P., Riener, A., Frison, A.-K., Automated driving system, male, or female driver: Who’d you prefer? Comparative analysis of passengers’ mental conditions, emotional states & qualitative feedback (2016) Proceedings of the 8Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 51-58. , https://doi.org/10.1145/3003715.3005410, pp., ACM, New York; Wang, P., Sibi, S., Mok, B., Ju, W., Marionette: Enabling on-road wizard-of-oz autonomous driving studies (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 234-243. , https://doi.org/10.1145/2909824.3020256, pp., ACM, New York; Baltodano, S., Sibi, S., Martelaro, N., Gowda, N., Ju, W., The RRADS platform: A real road autonomous driving simulator (2015) Proceedings of the 7Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 281-288. , https://doi.org/10.1145/2799250.2799288, pp., ACM, New York; Oliveira, L., Luton, J., Iyer, S., Burns, C., Mouzakitis, A., Jennings, P., Birrell, S., Evaluating how interfaces influence the user interaction with fully autonomous vehicles (2018) Proceedings of the 10Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 320-331. , https://doi.org/10.1145/3239060.3239065, pp., ACM, New York; Braun, M., Weiser, S., Pfleging, B., Alt, F., A comparison of emotion elicitation methods for affective driving studies (2018) Adjunct Proceedings of the 10Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 77-81. , https://doi.org/10.1145/3239092.3265945, pp., ACM, New York; Amanatidis, T., Langdon, P., Clarkson, P.J., Needs and expectations for fully autonomous vehicle interfaces (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 51-52. , https://doi.org/10.1145/3173386.3177054, pp., ACM, New York; (2015) Transportation Systems Catapult: Intelligent Mobility: Traveller Needs and U.K. Capability Study; Amanatidis, T., Langdon, P.M., Clarkson, P.J., Inclusivity considerations for fully autonomous vehicle user interfaces (2018) Breaking down Barriers, pp. 207-214. , https://doi.org/10.1007/978-3-319-75028-6_18, pp., Springer, Cham; Clarkson, P.J., Coleman, R., Keates, S., Lebbon, C., (2013) Inclusive Design: Design for the Whole Population, , Springer, London; Langdon, P., Johnson, D., Huppert, F., Clarkson, P.J., A framework for collecting inclusive design data for the UK population (2015) Appl. Ergon. 46(Part B), pp. 318-324. , https://doi.org/10.1016/j.apergo.2013.03.011; Politis, I., Langdon, P., Bradley, M., Skrypchuk, L., Mouzakitis, A., Clarkson, P.J., Designing autonomy in cars: A survey and two focus groups on driving habits of an inclusive user group, and group attitudes towards autonomous cars (2017) Advances in Design for Inclusion, pp. 161-173. , https://doi.org/10.1007/978-3-319-60597-5_15, pp., Springer, Cham","Amanatidis, T.; Engineering Design Centre, Trumpington Street, United Kingdom; email: ta323@cam.ac.uk","Stanton N.",,"Springer Verlag","AHFE International Conference on Human Factors in Transportation, 2019","24 July 2019 through 28 July 2019",,226949,21945357,9783030205027,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067339081
"Agrigoroaie R., Tapus A.","57189047432;23010519200;","Cognitive Performance and Physiological Response Analysis: Analysis of the Variation of Physiological Parameters Based on User’s Personality, Sensory Profile, and Morningness–Eveningness Type in a Human–Robot Interaction Scenario",2020,"International Journal of Social Robotics","12","1",,"47","64",,1,"10.1007/s12369-019-00532-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065118423&doi=10.1007%2fs12369-019-00532-z&partnerID=40&md5=b22eec5ffeeadf4c780790ca64fe8071","Autonomous Systems and Robotics Laboratory, U2IS, ENSTA ParisTech, 828 Boulevard des Marechaux, Palaiseau, France","Agrigoroaie, R., Autonomous Systems and Robotics Laboratory, U2IS, ENSTA ParisTech, 828 Boulevard des Marechaux, Palaiseau, France; Tapus, A., Autonomous Systems and Robotics Laboratory, U2IS, ENSTA ParisTech, 828 Boulevard des Marechaux, Palaiseau, France","For more socially natural interactions between robots and humans there is a need for the robots to detect and understand the current internal state of the individuals they interact with. In this paper, we investigate the impact of personality, user sensory profile, and morningness–eveningness type of an individual on the variation of different physiological parameters (i.e., blinking, galvanic skin response, facial temperature variation), as well as the performance in a non-verbal word-color Stroop task. The TIAGo humanoid robot displayed two interaction styles that are based on visual and auditory stimuli. The visual and auditory stimuli used by the robot are in accordance with the user sensory profile. We report the results of a within participants study carried out with 24 participants. Our results show that personality, user sensory profile, and morningness–eveningness type have an impact on the variation of the chosen physiological parameters. Furthermore, the interaction style of the robot has an influence on the variation of the physiological parameters. © 2019, Springer Nature B.V.","HRI; Internal state; Physiological parameters; User profile","Agricultural robots; Anthropomorphic robots; Electrophysiology; Psychophysiology; Sensory analysis; Social robots; Cognitive performance; Galvanic skin response; Internal state; Natural interactions; Physiological parameters; Physiological response; Temperature variation; User profile; Physiological models",,,,,"H2020 Health: 643691",,,,,,,,,,,"Abdelrahman, Y., Velloso, E., Dingler, T., Schmidt, A., Vetere, F., Cognitive heat: exploring the usage of thermal imaging to unobtrusively estimate cognitive load (2017) Proc ACM Interact Mob Wearable Ubiquitous Technol, 1 (3), p. 33; Adan, A., Influence of morningness-eveningness preference in the relationship between body temperature and performance: a diurnal study (1991) Pers Individ Dif, 12 (11), pp. 1159-1169; Adan, A., Archer, S.N., Hidalgo, M.P., Di Milia, L., Natale, V., Randler, C., Circadian typology: a comprehensive review (2012) Chronobiol Int, 29 (9), pp. 1153-1175; Agrigoroaie, R., Tapus, A., Contactless physiological data analysis for user quality of life improving by using a humanoid social robot (2017) International Conference on Image Analysis and Processing, pp. 696-706; Agrigoroaie, R., Tapus, A., Influence of robot’s interaction style on performance in a stroop task (2017) International Conference on Social Robotics; Agrigoroaie, R., Tapus, A., ’oh! i am so sorry!’: Understanding user physiological variation while spoiling a game task (2018) IROS; Bandura, A., Social cognitive theory of personality (1999) Handbook of Personality: Theory and Research, pp. 154-196. , Pervin LA, John OP, Elsevier, Amsterdam; Bandura, A., Jourden, F.J., Self-regulatory mechanisms governing the impact of social comparison on complex decision making (1991) J Pers Soc Psychol, 60 (6), p. 941; Bellotto, N., Fernandez-Carmona, M., Cosar, S., Enrichme integration of ambient intelligence and robotics for aal (2017) AAAI Spring Symposium Series, , AAAI; Bentivoglio, A.R., Analysis of blink rate patterns in normal subjects (1997) Mov Disord, 12 (6), pp. 1028-1034; Brown, C., Dunn, W., (2002) Adult/adolescent sensory profile: self questionnaire, , Pearson Clinical, Bengaluru; Brown, C., Tollefson, N., Dunn, W., Cromwell, R., Filion, D., The adult sensory profile: measuring patterns of sensory processing (2001) Am J Occup Ther, 55 (1), pp. 75-82; Campagne, A., Pebayle, T., Muzet, A., Oculomotor changes due to road events during prolonged monotonous simulated driving (2005) Biol Psychol, 68 (3), pp. 353-368; Cassell, J., Bickmore, T., External manifestations of trustworthiness in the interface (2000) Commun ACM, 43 (12), pp. 50-56; Charles, R.L., Nixon, J., Measuring mental workload using physiological measures: a systematic review (2019) Appl Ergon, 74, pp. 221-232; Corr, P., Cooper, A., The reinforcement sensitivity theory of personality questionnaire: development and validation (2016) Psychol Assess, 28 (11), p. 1427; Cruz-Maya, A., Tapus, A., Negotiating with a robot: Analysis of regulatory focus behavior (2018) Proceedings of the IEEE International Conference on Robotics and Automation; Dunn, W., The impact of sensory processing abilities on the daily lives of young children and their families: a conceptual model (1997) Infants Young Child, 9, pp. 23-35; Ehrlich, S., Wykowska, A., Ramirez-Amaro, K., Cheng, G., When to engage in interaction and how? Eeg-based enhancement of robot’s ability to sense social signals in hri (2014) 2014 14Th IEEE-RAS International Conference on Humanoid Robots (Humanoids), IEEE, pp. 1104-1109; Eysenck, H., (1967) The biological basis of personality, , Taylor & Francis Springfield; Eysenck, H., Psychophysiology and personality: extraversion, neuroticism and psychoticism (1983) Physiol Correl Hum behav, 3, pp. 13-30; Eysenck, H.J., Dimensions of personality: 16, 5 or 3?–criteria for a taxonomic paradigm (1991) Pers Individ Dif, 12 (8), pp. 773-790; Eysenck, H.J., Eysenck, S.B.G., (1975) Manual of the eysenck personality questionnaire (junior and adult), , Hodder and Stoughton, London; Fukuda, K., Eye blinks: new indices for the detection of deception (2001) Int J Psychophysiol, 40 (3), pp. 239-245; Goldberg, L., An alternative ‘description of personality’: the big-five factor structure (1990) J Pers Soc Psychol, 59 (6), p. 1216; Gray, J., McNaughton, N., (2000) The neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system, , 2, Oxford University Press, New York; Hall, E.T., (1990) The Hidden Dimension, p. 217. , Bantam Doubleday Dell Publishing Group Inc; Horne, J., Ostberg, O., A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms (1976) Int J Chronobiol, 4 (2), pp. 97-110; Ioannou, S., Morris, P., Mercer, H., Baker, M., Gallese, V., Reddy, V., Proximity and gaze influences facial temperature: a thermal infrared imaging study (2014) Front Psychol, 5, p. 845; Kahneman, D., (1973) Attention and effort, , Prentice-Hall, New Jersey; Kramer, A.F., Physiological metrics of mental workload: A review of recent progress (1991) Mult Task Perform, pp. 279-328; Liu, C., Online affect detection and robot behavior adaptation for intervention of children with autism (2008) IEEE Transactions on Robotics; MacLeod, C., Half a century of research on the stroop effect: an integrative review (1991) Psychol Bull, 109 (2), p. 163; Mitsunaga, N., Robot behavior adaptation for hri based on policy gradient reinforcement learning (2006) J Robot Soc Jpn, 24 (7), pp. 820-829; Mower, E., Feil-Seifer, D.J., Mataric, M.J., Narayanan, S., Investigating implicit cues for user state estimation in human–robot interaction using physiological measurements. In: RO-MAN 2007-The 16th IEEE international symposium on robot and human interactive communication, IEEE (2007) Pp 1125–1130; Nakatsu, R., Toward the creation of a new medium for the multimedia era (1998) Proc IEEE, 86 (5), pp. 825-836; Nourbakhsh, N., Chen, F., Wang, Y., Calvo, R.A., Detecting users cognitive load by galvanic skin response with affective interference (2017) ACM Trans Interact Intell Syst, 7 (3), pp. 12:1-12:20; Nourbakhsh, N., Wang, Y., Chen, F., Gsr and blink features for cognitive load classification (2013) IFIP Conference on human–computer Interaction. Springer, pp. 159-166; Nourbakhsh, N., Wang, Y., Chen, F., Calvo, R.A., Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks (2012) Proceedings of the 24Th Australian computer–human Interaction Conference, , ACM; Or, C.K., Duffy, V.G., Development of a facial skin temperature-based methodology for non-intrusive mental workload measurement (2007) Occup Ergon, 7 (2), pp. 83-94; Paas, F.G., Van Merriënboer, J.J., The efficiency of instructional conditions: an approach to combine mental effort and performance measures (1993) Hum Factors, 35 (4), pp. 737-743; Pages, J., Marchionni, L., Ferro, F., Tiago: The modular robot that adapts to different research needs (2016) International Workshop on Robot Modularity, IROS, , In; Randler, C., Differences between smokers and nonsmokers in morningness–eveningness (2008) Soc Behav Pers, 36 (5), pp. 673-680; Reeves, B., Nass, C.I., (1996) The media equation: how people treat computers, television, and new media like real people and places, , Cambridge University Press, Cambridge; Renaud, P., Blondin, J.P., The stress of stroop performance: physiological and emotional responses to color-word interference, task pacing, and pacing speed (1997) Int J Psychophysiol, 27 (2), pp. 87-97; Setz, C., Arnrich, B., Schumm, J., La Marca, R., Tröster, G., Ehlert, U., Discriminating stress from cognitive load using a wearable eda device (2010) IEEE Trans Inf Technol Biomed, 14 (2), pp. 410-417; Shi, Y., Ruiz, N., Taib, R., Choi, E., Chen, F., Galvanic skin response (Gsr) as an index of cognitive load (2007) CHI’07 Extended Abstracts on Human Factors in Computing Systems, pp. 2651-2656. , Begole B, Payne S, Churchill E, Amant RS, Gilmore D, Rosson MB, ACM, NY; Sorostinean, M., Ferland, F., Tapus, A., Reliable stress measurement using face temperature variation with a thermal camera in human–robot interaction (2015) 15Th IEEE-RAS International Conference on Humanoid Robots; Sprent, P., Smeeton, N.C., (2000) Applied nonparametric statistical methods, , Chapman and Hall/CRC, Santa Clarita; Stelmack, R.M., (1981) The psychophysiology of extraversion and neuroticism, pp. 38-64. , Springer, Berlin; Stern, J.A., Walrath, L.C., Goldstein, R., The endogenous eyeblink (1984) Psychophysiology, 21 (1), pp. 22-33; Stroop, J., Studies of interference in serial verbal reactions (1935) J Explor Psychol, 18 (6), p. 643; Svetlak, M., Electrodermal complexity during the stroop colour word test (2010) Auton Neurosci, 152 (1-2), pp. 101-107; Tanaka, Y., Yamaoka, K., Blink activity and task difficulty (1993) Percept Mot Skills, 77 (1), pp. 55-66; Tapus, A., Tapus, C., Mataric, M.J., Hands-off therapist robot behavior adaptation to user personality for post-stroke rehabilitation therapy (2007) 2007 IEEE International Conference on Robotics and Automation; Tulen, J., Characterization of stress reactions to the stroop color word test (1989) Pharmacol Biochem Behav, 32 (1), pp. 9-15; Walters, M., The influence of subjects’ personality traits on personal spatial zones in a human–robot interaction experiment (2005) IEEE International Workshop on Robot and Human Interactive Communication; Yanco, H.A., Drury, J.L., Scholtz, J., Beyond usability evaluation: analysis of human–robot interaction at a major robotics competition (2004) Hum Comput Interact, 19 (1-2), pp. 117-149","Agrigoroaie, R.; Autonomous Systems and Robotics Laboratory, ENSTA ParisTech, 828 Boulevard des Marechaux, France; email: roxana.agrigoroaie@ensta-paristech.fr",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85065118423
"Carfì A., Villalobos J., Coronado E., Bruno B., Mastrogiovanni F.","57204949335;57195425336;57188864138;36922823500;56962745700;","Can Human-Inspired Learning Behaviour Facilitate Human–Robot Interaction?",2020,"International Journal of Social Robotics","12","1",,"173","186",,4,"10.1007/s12369-019-00548-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064672169&doi=10.1007%2fs12369-019-00548-5&partnerID=40&md5=e08b3c9bdfcaf75bc8de44ab9557ceb8","Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy","Carfì, A., Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Villalobos, J., Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Coronado, E., Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Bruno, B., Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy; Mastrogiovanni, F., Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16145, Italy","The evolution of production systems for smart factories foresees a tight relation between human operators and robots. Specifically, when robot task reconfiguration is needed, the operator must be provided with an easy and intuitive way to do it. A useful tool for robot task reconfiguration is Programming by Demonstration (PbD). PbD allows human operators to teach a robot new tasks by showing it a number of examples. The article presents two studies investigating the role of the robot in PbD. A preliminary study compares standard PbD with human–human teaching and suggests that a collaborative robot should actively participate in the teaching process as human practitioners typically do. The main study uses a wizard of oz approach to determine the effects of having a robot actively participating in the teaching process, specifically by controlling the end-effector. The results suggest that active behaviour inspired by humans can lead to a more intuitive PbD. © 2019, Springer Nature B.V.","Human robot interaction; Industry 4.0; Kinesthetic teaching; Programming by demonstration","Agricultural robots; Behavioral research; End effectors; Human robot interaction; Industry 4.0; Robot programming; Active behaviours; Collaborative robots; Human operator; Kinesthetic teachings; Production system; Programming by demon-stration; Robot interactions; Teaching process; Social robots",,,,,,"The authors would like to thank the teachers and students of the vocational education and training schools ?Centro Oratorio Votivo, Casa di Carit?, Arti e Mestieri, Ovada? and ?Istituto Tecnico Industriale Statale Italo Calvino, Genova? for their contribution to the drafting and execution of the experiments.",,,,,,,,,,"Akgun, B., Cakmak, M., Yoo, J.W., Thomaz, A.L., Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human–Robot Interaction, ACM, pp. 391-398; Alexandrova, S., Cakmak, M., Hsiao, K., Takayama, L., Robot programming by demonstration with interactive action visualizations (2014) Proceedings of Robotics: Science and Systems, , RSS 2014, Berkeley, USA; Antonsson, E., Mann, R., The frequency content of gait (1985) J Biomech, 18 (1), pp. 39-47; Argall, B., Sauser, E., Billard, A., Policy adaptation through tactile correction (2010) Proceedings of the 2010 Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB 2010), , Leicester, United Kingdom; Biggs, G., Macdonald, B., A survey of robot programming systems (2003) Proceedings of the Australasian Conference on Robotics and Automation (ACRA 2003), , Brisbane, Australia; Billard, A., Calinon, S., Dillmann, R., Schaal, S., Robot programming by demonstration (2008) Springer Handbook of Robotics, Springer, pp. 1371-1394; Calinon, S., Guenter, F., Billard, A., On learning, representing, and generalizing a task in a humanoid robot (2007) Neural Netw, 37 (2), pp. 286-298; Chaudhuri, B., A new definition of neighborhood of a point in multi-dimensional space (1996) Pattern Recogn Lett, 17 (1), pp. 11-17; Dillmann, R., Kaiser, M., Ude, A., Acquisition of elementary robot skills from human demonstration (1995) Proceedings of the International Symposium on Intelligent Robotics Systems (SIRS 1995), , Pisa, Italy; H Kagermann, W.W., Helbig, J., (2013) Recommendations for Implementing the Strategic Initiative, , Industrie 4.0: Final report of the Industrie 4.0 working group. Produktion, Automatisierung und Logistik; Halbert, D.C., (1984) Programming by Example, , Ph.D. thesis, University of California, Berkeley, USA; Inamura, T., Kojo, N., Inaba, M., Situation recognition and behavior induction based on geometric symbol representation of multimodal sensorimotor patterns (2006) Proceeding of the 2006 IEEE/RSJ Internationl Conference on Intelligent Robots and Systems (IROS 2006), , Beijing, China; Ito, M., Noda, K., Hoshino, Y., Tani, J., Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model (2006) Neural Netw, 19 (3), pp. 323-337; Kang, S.B., Ikeuchi, K., A robot system that observes and replicates grasping tasks (1995) Proceedings of the 1995 IEEE International Conference on Computer Vision (ICCV 1995), , Boston, USA; Kormushev, P., Calinon, S., Caldwell, D.G., Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input (2011) Adv Robot, 25 (5), pp. 581-603; Lambrecht, J., Kleinsorge, M., Rosenstrauch, M., Krüger, J., Spatial programming for industrial robots through task demonstration (2013) Int J Adv Robot Syst, 10 (5), p. 254; Liu, S., Asada, H., Teaching and learning of deburring robots using neural networks (1993) Proceedings of the 1993 IEEE International Conference on Robotics and Automation (ICRA 1993), , Atlanta, USA; Lucke, D., Constantinescu, C., Westkämper, E., Smart factory—a step towards the next generation of manufacturing (2008) Manufacturing Systems and Technologies for The New Frontier: The 41St CIRP Conference on Manufacturing Systems, , Tokyo, Japan; Massa, D., Callegari, M., Cristalli, C., Manual guidance for industrial robot programming (2015) Ind Robot Int J, 42 (5), pp. 457-465; Pais, A.L., Argall, B.D., Billard, A.G., Assessing interaction dynamics in the context of robot programming by demonstration (2013) Int J Soc Robot, 5 (4), pp. 477-490; Suay, H.B., Toris, R., Chernova, S., A practical comparison of three robot learning from demonstration algorithm (2012) Int J Soc Robot, 4 (4), pp. 319-330; Tung, C.P., Kak, A.C., Automatic learning of assembly tasks using a DataGlove system (1995) Proceeding of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, 1995, p. 1. , Pittsburgh, USA, vol; Yang, J., Xu, Y., Chen, C.S., Hidden Markov model approach to skill learning and its application to telerobotics (1994) IEEE Trans Robot Autom, 10 (5), pp. 621-631","Carfì, A.; Department of Informatics, Via Opera Pia 13, Italy; email: alessandro.carfi@dibris.unige.it",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85064672169
