Volume 17, Issue 3
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
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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.


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  1. Ahlberg, J.
    (2001) CANDIDE-3 – an updated parameterized face (Report No. LiTH-ISY-R-2326). Dept. of Electrical Engineering, Linköping University, Sweden.
  2. 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.4122, pp.345–357. Springer, Heidelberg.
    [Google Scholar]
  3. 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
    [Google Scholar]
  4. 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, 1, 33–55.
    [Google Scholar]
  5. Ben-Shakhar, G.
    (1985) Standardization within individuals: A simple method to neutralize individual differences in skin conductance. Psychophysiology, 22, 292–299. doi: 10.1111/j.1469‑8986.1985.tb01603.x
    https://doi.org/10.1111/j.1469-8986.1985.tb01603.x [Google Scholar]
  6. Boucsein, W. , Fowles, D.C. , Grimnes, S. , Ben-Shakhar, G. , Roth, W.T. , & Filion, D.L.
    (2012) Publication recommendations for electrodermal measurements. Psychophysiology, 49, 1017–1034. doi: 10.1111/j.1469‑8986.2012.01384.x
    https://doi.org/10.1111/j.1469-8986.2012.01384.x [Google Scholar]
  7. Bowlby, J.
    (1970) Disruption of affectional bonds and its effects on behavior. Journal of Contemporary Psychotherapy, 2, 75–86. doi: 10.1007/BF02118173
    https://doi.org/10.1007/BF02118173 [Google Scholar]
  8. 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. doi: 10.1080/08886504.1997.10782210
    https://doi.org/10.1080/08886504.1997.10782210 [Google Scholar]
  9. 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.7926, pp.733–736. Springer, Heidelberg
    [Google Scholar]
  10. Chanel, G. , Rebetez, C. , Bétrancourt, M. , & Pun, T.
    (2008) Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. InProceedings of the 12th international conference on Entertainment and media in the ubiquitous era (pp.13–17). ACM.
    [Google Scholar]
  11. Conati, C.
    (2002) Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence, 16, 555–575. doi: 10.1080/08839510290030390
    https://doi.org/10.1080/08839510290030390 [Google Scholar]
  12. Corrigan, L.J. , Basedow, C. , Küster, D. , Kappas, A. , Peters, C. , & Castellano, G.
    (2015) Perception matters! Engagement in task orientated social robotics, inRobot and Human Interactive Communication (RO-MAN) 2015 24th IEEE International Symposium, KOBE, Aug. 31 2015-Sept. 4, pp.375–380.
    [Google Scholar]
  13. 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.105 – In Print doi: 10.1007/978‑3‑319‑31056‑5_4
    https://doi.org/10.1007/978-3-319-31056-5_4 [Google Scholar]
  14. Cruickshank, D. R. , Jenkins, D. B. , & Metcalf, K. K.
    (2009) The act of teaching. (5th ed.), Boston: McGraw-Hill Higher Education.
    [Google Scholar]
  15. Csikszentmihalyi, M.
    (1990) Flow: The Psychology of Optimal Experience. New York: Harper Perennial, (5th ed.), Boston: McGraw-Hill Higher Education.
    [Google Scholar]
  16. D’Mello, S. , Chipman, P. , & Graesser, A. C.
    (2007) Posture as a predictor of learner’s affective engagement. InProceedings of the 29th annual cognitive science society (Vol.1, pp.905–910). Cognitive Science Society, Austin, TX.
    [Google Scholar]
  17. 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, 20, 147–187. doi: 10.1007/s11257‑010‑9074‑4
    https://doi.org/10.1007/s11257-010-9074-4 [Google Scholar]
  18. 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. doi: 10.1017/CBO9780511546396.007
    https://doi.org/10.1017/CBO9780511546396.007 [Google Scholar]
  19. 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, 48, 329–354. doi: 10.1111/j.1469‑7610.2006.01701.x
    https://doi.org/10.1111/j.1469-7610.2006.01701.x [Google Scholar]
  20. 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, Volume4 doi: 10.3389/fpsyg.2013.00859
    https://doi.org/10.3389/fpsyg.2013.00859 [Google Scholar]
  21. Eresha, G. , Haring, M. , Endrass, B. , Andre, E. , & Obaid, M.
    (2013) Investigating the influence of culture on proxemic behaviors for humanoid robots. InProceedings of RO-MAN 2013 IEEE (pp.430–435). IEEE.
    [Google Scholar]
  22. Fong, T. , Thorpe, C. , & Baur, C.
    (2002) Robot as partner: Vehicle teleoperation with collaborative control. InProceedings from the 2002 NRL Workshop on MultiRobot Systems, Washington, D. C. doi: 10.1007/978‑94‑017‑2376‑3_21
    https://doi.org/10.1007/978-94-017-2376-3_21 [Google Scholar]
  23. 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, 16, 142–157. doi: 10.1521/scpq.
    https://doi.org/10.1521/scpq. [Google Scholar]
  24. Fridlund, A. J.
    (1994) Human facial expression: An evolutionary view. San Diego, CA: Academic Press.
    [Google Scholar]
  25. Hall, E. T.
    (1966) The Hidden Dimension. New York: Doubleday
    [Google Scholar]
  26. Hattie, J.
    (2009) Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York, Routledge.
    [Google Scholar]
  27. Hollenstein, T. , & Lanteigne, D.
    (2014) Models and methods of emotional concordance. Biological psychology, 98, 1–5. doi: 10.1016/j.biopsycho.2013.12.012
    https://doi.org/10.1016/j.biopsycho.2013.12.012 [Google Scholar]
  28. 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.2, pp.876–881). IEEE.
    [Google Scholar]
  29. Jones, V. , & Jones, L.
    (1995), Comprehensive classroom management (4th ed.) Boston: Allyn & Bacon
    [Google Scholar]
  30. Kennedy, J. , Baxter, P. , & Belpaeme, T.
    (2014) Comparing robot embodiments in a guided discovery learning interaction with children. International Journal of Social Robotics, 7, 293–308. doi: 10.1007/s12369‑014‑0277‑4
    https://doi.org/10.1007/s12369-014-0277-4 [Google Scholar]
  31. Kim, Y. , & Mutlu, B.
    (2014) How social distance shapes human–robot interaction. International Journal of Human-Computer Studies, 72, 783–795. doi: 10.1016/j.ijhcs.2014.05.005
    https://doi.org/10.1016/j.ijhcs.2014.05.005 [Google Scholar]
  32. 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 Robotics6: 469–488. doi: 10.1007/s12369‑014‑0232‑4
    https://doi.org/10.1007/s12369-014-0232-4 [Google Scholar]
  33. 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.), InProceedings of the International Conference on Physiological Computing Systems (pp.358–367). SciTePress Digital Library. doi: 10.5220/0004892503580367
    https://doi.org/10.5220/0004892503580367 [Google Scholar]
  34. Leite, I. , Henriques, R. , Martinho, C. , & Paiva, A.
    (2013) Sensors in the wild: exploring electrodermal activity in child-robot interaction. InProceedings of the 8th ACM/IEEE international conference on Human-robot interaction (pp.41–48). IEEE Press.
    [Google Scholar]
  35. Malta, L. , Miyajima, C. & Takeda, K.
    (2008) Multimodal estimation of a driver’s affective state. InWorkshop on Affective Interaction in Natural Environments (AFFINE), ACM International Conference on Multimodal Interfaces (ICMI’08), Chania, Crete, Greece.
    [Google Scholar]
  36. Mauss, I. B. , & Robinson, M. D.
    (2009) Measures of emotion: A review. Cognition and emotion, 23, 209–237.
    [Google Scholar]
  37. 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, (12369): 1–12.
    [Google Scholar]
  38. 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, 3, 1–10. doi: 10.2478/s13230‑012‑0018‑z
    https://doi.org/10.2478/s13230-012-0018-z [Google Scholar]
  39. (2013) AIBOStory – Autonomous Robots supporting Interactive, Collaborative Story-telling. Paladyn, Journal of Behavioral Robotics, 4, 10–22. Chicago.
    [Google Scholar]
  40. Picard, W. , & Healey, J. A.
    (2000) Wearable and automotive systems for affect recognition from physiology, MIT, Tech. Rev.
    [Google Scholar]
  41. Picard, R. W. , Fedor, S. , & Ayzenberg, Y.
    (2016) Multiple arousal theory and daily-life electrodermal activity asymmetry. Emotion Review, 8, 62–75. doi: 10.1177/1754073914565517
    https://doi.org/10.1177/1754073914565517 [Google Scholar]
  42. 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. InProceedings of the 4th International Working Conference on Intelligent Virtual Agents, (IVA-03), pages283–291, Kloster Irsee, Germany
    [Google Scholar]
  43. 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.1, San Francisco, pp.586–591.
    [Google Scholar]
  44. Robison, J. L. , Mcquiggan, S. W. & Lester, J. C.
    (2009) Modeling Task-Based vs. Affect-based Feedback Behavior in Pedagogical Agents: An Inductive Approach, InProceedings 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.
    [Google Scholar]
  45. Sidner, C. L. , Kidd, C. D. , Lee, C. , & Lesh, N.
    (2004) Where to look: a study of human-robot engagement. InProceedings of the 9th international conference on Intelligent user interfaces (pp.78–84). ACM.
    [Google Scholar]
  46. Takayama, L. , & Pantofaru, C.
    (2009) Influences on proxemic behaviors in human-robot interaction. InIntelligent Robots and Systems. IROS 2009. IEEE/RSJ International Conferenceon (pp.5495–5502). IEEE.
    [Google Scholar]
  47. 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 Psychophysiology3rd ed. (pp.267–299). New York: Cambridge University Press. doi: 10.1017/CBO9780511546396.012
    https://doi.org/10.1017/CBO9780511546396.012 [Google Scholar]
  48. 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. doi: 10.1080/00098655.1993.9955998
    https://doi.org/10.1080/00098655.1993.9955998 [Google Scholar]
  49. 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. doi: 10.1109/ROMAN.2011.6005274
    https://doi.org/10.1109/ROMAN.2011.6005274 [Google Scholar]
  50. 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).
    [Google Scholar]

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  • Article Type: Research Article
Keyword(s): engagement; human-robot interaction; position; proxemics; robot tutor; sensors
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