Article published In: Interaction Studies
Vol. 17:3 (2016) ► pp.321–347
Do relative positions and proxemics affect the engagement in a Human-Robot collaborative scenario?
Published online: 30 March 2017
https://doi.org/10.1075/is.17.3.01pap
https://doi.org/10.1075/is.17.3.01pap
Abstract
This paper investigates the effects of relative position and proxemics in the engagement process involved in Human-Robot collaboration. We evaluate the differences between two experimental placement conditions (frontal vs. lateral) for an autonomous robot in a collaborative task with a user across two different types of robot behaviours (helpful vs. neutral). The study evaluated placement and behaviour types around a touch table with 80 participants by measuring gaze, smiling behaviour, distance from the task, and finally electrodermal activity. Results suggest an overall user preference and higher engagement rates with the helpful robot in the frontal position. We discuss how behaviours and position of the robot relative to a user may affect user engagement and collaboration, in particular when the robot aims to provide help via socio-emotional bonding.
Keywords: human-robot interaction, sensors, proxemics, position, robot tutor, engagement
Article outline
- Introduction
- Background research
- Task and social engagement
- Proxemics and positions in a framework of socio-emotional learning
- Affect recognition
- System description
- Methodology
- Participants
- Scenario and robot behaviours
- Helpful, Bonding-oriented, and Instructive
- Neutral and partially instructive
- Experimental setup
- Procedure
- Data collection
- Data analysis
- Cameras and video processing
- Electrodermal activity analysis
- Gaze estimation procedure
- Hypotheses
- Results
- Electrodermal activity
- Gaze behaviour
- Distance from the sensor
- Smile estimation
- Discussion and conclusions
- Acknowledgments
- Notes
References
References (50)
Ahlberg, J. (2001). CANDIDE-3 – an updated parameterized face (Report No. LiTH-ISY-R-2326). Dept. of Electrical Engineering, Linköping University, Sweden.
Ba, S. O., & Odobez, J. M. (2006). Head pose tracking and focus of attention recognition algorithms in meeting rooms. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 41221, pp. 345–357. Springer, Heidelberg.
Bargh, J. A. (1988). Automatic information processing: Implications for communication and affect. In L. Donohew, H. Sypher, & E. T. Higgins (Eds.), Communication, social cognition and affect (pp. 9–32). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc
Belpaeme, T., Baxter, P., Read, R., Wood, R., Cuayáhuitl, H., Kiefer, B., … Humbert., R., (2012). Multimodal Child-Robot Interaction: Building Social Bonds. Journal of Human-Robot Interaction, 11, 33–55.
Ben-Shakhar, G. (1985). Standardization within individuals: A simple method to neutralize individual differences in skin conductance. Psychophysiology, 221, 292–299.
Boucsein, W., Fowles, D.C., Grimnes, S., Ben-Shakhar, G., Roth, W.T., & Filion, D.L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 491, 1017–1034.
Bowlby, J. (1970). Disruption of affectional bonds and its effects on behavior. Journal of Contemporary Psychotherapy, 21, 75–86.
Brush, T. A. (1997). The effects of group composition on achievement and time on task for students completing ILS activities in cooperative pairs. Journal of Research on Computing in Education, 30(1), 2–17.
Castellano, G., Paiva, A., Kappas, A., Aylett, R., Hastie, H., Barendregt, W., Nabais, F., & Bull, S. (2013). Towards empathic virtual and robotic tutors. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 79261, pp. 733–736. Springer, Heidelberg
Chanel, G., Rebetez, C., Bétrancourt, M., & Pun, T. (2008). Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. In Proceedings of the 12th international conference on Entertainment and media in the ubiquitous era (pp. 13–17). ACM.
Conati, C. (2002). Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence, 161, 555–575.
Corrigan, L.J., Basedow, C., Küster, D., Kappas, A., Peters, C., & Castellano, G. (2015). Perception matters! Engagement in task orientated social robotics, in Robot and Human Interactive Communication (RO-MAN), 2015 24th IEEE International Symposium, KOBE, Aug. 31 2015-Sept. 4, pp. 375–380.
Corrigan, L.J., Peters, C., Küster, D., & Castellano, G. (2016). Engagement Perception and Generation for Social Robots and Virtual Agents, in Esposito, A., Jain, L.C., (Eds.), Toward Robotic Socially Believable Behaving Systems – Modelling Emotions – Intelligent Systems Reference Library, Vol. 1051 – In Print
Cruickshank, D. R., Jenkins, D. B., & Metcalf, K. K. (2009). The act of teaching. (5th ed.), Boston: McGraw-Hill Higher Education.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New York: Harper Perennial, (5th ed.), Boston: McGraw-Hill Higher Education.
D’Mello, S., Chipman, P., & Graesser, A. C. (2007). Posture as a predictor of learner’s affective engagement. In Proceedings of the 29th annual cognitive science society (Vol. 11, pp. 905–910). Cognitive Science Society, Austin, TX.
D’Mello, S. K., & Graesser, A. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction, 201, 147–187.
Dawson, M. E., Schell, A. M., & Filion, D. L. (2007). The electrodermal system. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd ed., pp. 159–181). New York: Cambridge University Press.
Feldman, R. (2007). Parent–infant synchrony and the construction of shared timing; physiological precursors, developmental outcomes, and risk conditions. Journal of Child psychology and Psychiatry, 481, 329–354.
Fiore S.M., Wiltshire T.J., Lobato E.J. C., Jentsch F.G., Huang W.H., & Axelrod B. (2013) Towards understanding social cues and signals in human-robot interaction: Effects of robot gaze and proxemic behavior, Frontiers in Psychology, Volume 41
Eresha, G., Haring, M., Endrass, B., Andre, E., & Obaid, M. (2013). Investigating the influence of culture on proxemic behaviors for humanoid robots. In Proceedings of RO-MAN, 2013 IEEE (pp. 430–435). IEEE.
Fong, T., Thorpe, C., & Baur, C. (2002). Robot as partner: Vehicle teleoperation with collaborative control. In Proceedings from the 2002 NRL Workshop on MultiRobot Systems, Washington, D. C.
Ford, A. D., Olmi, D. J., Edwards, R. P., & Tingstrom, D. H. (2001). The sequential introduction of compliance training components with elementary-aged children in general education classroom settings. School Psychology Quarterly, 161, 142–157.
Fridlund, A. J. (1994). Human facial expression: An evolutionary view. San Diego, CA: Academic Press.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York, Routledge.
Hollenstein, T., & Lanteigne, D. (2014). Models and methods of emotional concordance. Biological psychology, 981, 1–5.
Horiguch, Y., Sawaragi, T., & Akashi, G. (2000). Naturalistic human-robot collaboration based upon mixed-initiative interactions in teleoperating environment. In Systems, Man, and Cybernetics, 2000 IEEE International Conference on (Vol. 21, pp. 876–881). IEEE.
Kennedy, J., Baxter, P., & Belpaeme, T. (2014). Comparing robot embodiments in a guided discovery learning interaction with children. International Journal of Social Robotics, 71, 293–308.
Kim, Y., & Mutlu, B. (2014). How social distance shapes human–robot interaction. International Journal of Human-Computer Studies, 721, 783–795.
Koay K.L., Syrdal D.S., Ashagari-Oskoei, M., Walters, M.L., & Dautenhahn K. (2014). Social Roles and Baseline Proxemic Preferences for a Domestic Service Robot. International Journal of Social Robotics 61: 469–488.
Küster, D., & Kappas, A. (2014). What could a body tell a social robot that it does not know? In A. Holzinger, S. H. Fairclough, D. Majoe, & H. P. da Silva (Eds.), In Proceedings of the International Conference on Physiological Computing Systems (pp. 358–367). SciTePress Digital Library.
Leite, I., Henriques, R., Martinho, C., & Paiva, A. (2013). Sensors in the wild: exploring electrodermal activity in child-robot interaction. In Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction (pp. 41–48). IEEE Press.
Malta, L., Miyajima, C. & Takeda, K. (2008) Multimodal estimation of a driver’s affective state. In Workshop on Affective Interaction in Natural Environments (AFFINE), ACM International Conference on Multimodal Interfaces (ICMI’08), Chania, Crete, Greece.
Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and emotion, 231, 209–237.
Mead, R., Atrash, M., & Matarić, M.J., (2013). “Automated Proxemic Feature Extraction and Behavior Recognition: Applications in Human-Robot Interaction”, International Journal of Social Robotics, (123691): 1–12.
Papadopoulos, F., Dautenhahn, K., & Ho, W. C. (2012). Exploring the use of robots as social mediators in a remote human-human collaborative communication experiment. Paladyn, 31, 1–10.
(2013). AIBOStory – Autonomous Robots supporting Interactive, Collaborative Story-telling. Paladyn, Journal of Behavioral Robotics, 41, 10–22. Chicago.
Picard, W., & Healey, J. A. (2000). Wearable and automotive systems for affect recognition from physiology, MIT, Tech. Rev.
Picard, R. W., Fedor, S., & Ayzenberg, Y. (2016). Multiple arousal theory and daily-life electrodermal activity asymmetry. Emotion Review, 81, 62–75.
Prendinger, H., Mayer, S., Mori, J., & Ishizuka, M. (2003). Persona effect revisited: Using bio-signals to measure and reflect the impact of character-based interfaces. In Proceedings of the 4th International Working Conference on Intelligent Virtual Agents, (IVA-031), pages 283–291, Kloster Irsee, Germany
Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the 1993 IEEE International Conference on Neural Networks (ICNN 93), vol. 11, San Francisco, pp. 586–591.
Robison, J. L., Mcquiggan, S. W. & Lester, J. C. (2009). Modeling Task-Based vs. Affect-based Feedback Behavior in Pedagogical Agents: An Inductive Approach, In Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, Amsterdam, The Netherlands, The Netherlands, pp. 25–32.
Sidner, C. L., Kidd, C. D., Lee, C., & Lesh, N. (2004). Where to look: a study of human-robot engagement. In Proceedings of the 9th international conference on Intelligent user interfaces (pp. 78–84). ACM.
Takayama, L., & Pantofaru, C. (2009). Influences on proxemic behaviors in human-robot interaction. In Intelligent Robots and Systems. IROS 2009. IEEE/RSJ International Conference on (pp. 5495–5502). IEEE.
Tassinary, L. G., Cacioppo, J. T. and Vanman, E. J. (2007). The skeletomotor system: Surface electromyography. In J. T. Cacioppo, L. G. Tassinary and G. G. Berntson (Ed.), Handbook of Psychophysiology 3rd ed. (pp. 267–299). New York: Cambridge University Press.
Wall, A. (1993). How Teacher Location in the Classroom Can Improve Students’ Behavior, The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 66 (5), 299–301.
Walters, M.L., Oskoei, M.A., Syrdal, D.S., & Dautenhahn, K. (2011). A Long-Term Human-Robot Proxemic Study. Proceedings RO-MAN 2011, 20th IEEE International Symposium on Robot and Human Interactive Communication, Atlanta, Georgia, USA – 31 July – 3 August 2011, pp. 137–142.
Zaga, C., Truong, K. P., Lohse, M., & Evers, V. (2014). Exploring child-robot engagement in a collaborative task. In: Proceedings of the Child-Robot Interaction Workshop: Social Bonding, Learning and Ethics, 17 Jun 2014, Aarhus, Denmark. Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID).
Cited by (16)
Cited by 16 other publications
Li, Xiaodong, Jingwen Duan, Shanshan Zhang & Ai Ren
Yao, Minghui, Jiyu Li & Zhenyuan Wang
Yuan, Yunyun, Pingqing Liu, Bin Liu & Zunkang Cui
Fiorini, Laura, Luigi Coviello, Alessandra Sorrentino, Daniele Sancarlo, Filomena Ciccone, Grazia D’Onofrio, Gianmaria Mancioppi, Erika Rovini & Filippo Cavallo
Koller, Michael, Astrid Weiss, Matthias Hirschmanner & Markus Vincze
Terzioğlu, Yunus, Keith Rebello & Timothy Bickmore
XIE, Binfu, Da TAO, Shilong LI & Xinyuan REN
Sen, Wang, Zhao Hong & Zhu Xiaomei
Perugia, Giulia, Maike Paetzel-Prüsmann, Madelene Alanenpää & Ginevra Castellano
Oertel, Catharine, Ginevra Castellano, Mohamed Chetouani, Jauwairia Nasir, Mohammad Obaid, Catherine Pelachaud & Christopher Peters
Sirithunge, Chapa, H. M. Ravindu T. Bandara, A. G. Buddhika P. Jayasekara & D. P. Chandima
Chapa Sirithunge, H. P., H. M. Ravindu T. Bandara, A. G. Buddhika P. Jayasekara, D. P. Chandima & H. M. Harsha S. Abeykoon
de la Puente, Paloma, Markus Bajones, Christian Reuther, Daniel Wolf, David Fischinger & Markus Vincze
Gracia, Luis, J. Ernesto Solanes, Pau Muñoz-Benavent, Jaime Valls Miro, Carlos Perez-Vidal & Josep Tornero
2019. Human-robot collaboration for surface treatment tasks. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems 20:1 ► pp. 148 ff.
Saunderson, Shane & Goldie Nejat
Obaid, Mohammad, Ruth Aylett, Wolmet Barendregt, Christina Basedow, Lee J. Corrigan, Lynne Hall, Aidan Jones, Arvid Kappas, Dennis Küster, Ana Paiva, Fotios Papadopoulos, Sofia Serholt & Ginevra Castellano
This list is based on CrossRef data as of 17 march 2026. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.
