Article published in:
Interaction Studies
Vol. 20:2 (2019) ► pp. 234–255

The perception of a robot partner’s effort elicits a sense of commitment to human-robot interaction

| Central European University
| University of Warwick
| Università di Genova | Istituto Italiano di Tecnologia
| Istituto Italiano di Tecnologia
| Istituto Italiano di Tecnologia
| University of Warwick
Previous research has shown that the perception that one’s partner is investing effort in a joint action can generate a sense of commitment, leading participants to persist longer despite increasing boredom. The current research extends this finding to human-robot interaction. We implemented a 2-player version of the classic snake game which became increasingly boring over the course of each round, and operationalized commitment in terms of how long participants persisted before pressing a ‘finish’ button to conclude each round. Participants were informed that they would be linked via internet with their partner, a humanoid robot. Our results reveal that participants persisted longer when they perceived what they believed to be cues of their robot partner’s effortful contribution to the joint action. This provides evidence that the perception of a robot partner’s effort can elicit a sense of commitment to human-robot interaction.
Keywords: commitment, effort, human-robot interaction, joint action
Published online: 07 October 2019
https://doi.org/10.1075/is.18001.sze
References

References

Bainbridge, W. A., Hart, J., Kim, E. S., & Scassellati, B.
(2008, August). The effect of presence on human-robot interaction. In Robot and Human Interactive Communication 2008 RO-MAN 2008 The 17th IEEE International Symposium on (pp. 701–706). IEEE.Google Scholar
Breazeal, C., Brooks, A., Gray, J., Hoffman, G., Kidd, C., Lee, H., … & Mulanda, D.
(2004) Humanoid robots as cooperative partners for people. Int. Journal of Humanoid Robots, 1(2), 1–34.Google Scholar
Brooks, D. J., Begum, M., and Yanco, H. A.
(2016) “Analysis of reactions towards failures and recovery strategies for autonomous robots,” in Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2016) (New York, NY: IEEE), 487–492.Google Scholar
Chennells, M., Michael, J.
(2018) Effort and performance in a cooperative activity are boosted by the perception of a partner’s effort, Nature: Scientific Reports (2018) 8:15692 | CrossrefGoogle Scholar
Clodic, A., Cao, H., Alili, S., Montreuil, V., Alami, R., & Chatila, R.
(2009) Shary: a supervision system adapted to human-robot interaction. In Experimental Robotics (pp. 229–238). Springer Berlin/Heidelberg. CrossrefGoogle Scholar
Clodic, A., Fleury, S., Alami, R., Chatila, R., Bailly, G., Brethes, L.
, …others (2006) Rackham: An interactive robot-guide. In ROMAN 2006-the 15th IEEE International Symposium on Robot and Human Interactive Communication, Hatfield (pp. 502–509). CrossrefGoogle Scholar
Cousineau, D.
(2005) Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. Tutorials in Quantitative Methods for Psychology, 1(1), 42–45. CrossrefGoogle Scholar
Cully, A., Clune, J., Tarapore, D., & Mouret, J. B.
(2015) Robots that can adapt like animals. Nature, 521(7553), 503–507. CrossrefGoogle Scholar
DeSteno, D., Breazeal, C., Frank, R. H., Pizarro, D., Baumann, J., Dickens, L., & Lee, J. J.
(2012) Detecting the trustworthiness of novel partners in economic exchange. Psychological science, 23(12), 1549–1556. CrossrefGoogle Scholar
Dragan, A. D., Lee, K. C., & Srinivasa, S. S.
(2013, March). Legibility and predictability of robot motion. In Human-Robot Interaction (HRI), 2013 8th ACM/IEEE International Conference on (pp. 301–308). IEEE.Google Scholar
Fasola, J., and Matarić, M. J.
A socially assistive robot exercise coach for the elderly. Journal of Human-Robot Interaction 2.2 (2013): 3–32. CrossrefGoogle Scholar
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G.
(2009) Statistical power analyses using G∗ Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. CrossrefGoogle Scholar
Feltz, D. L., Forlenza, S. T., Winn, B., & Kerr, N. L.
(2014) Cyber buddy is better than no buddy: A test of the Köhler motivation effect in exergames. GAMES FOR HEALTH: Research, Development, and Clinical Applications, 3(2), 98–105. CrossrefGoogle Scholar
Ferrari, F., Eyssel, F.
2016 Toward a Hybrid Society. In: Springer Handbook of Robotics. Springer International Publishing, p. 909–918.Google Scholar
Fischer, K.
(2011) How people talk with robots: Designing dialog to reduce user uncertainty. AI Magazine, 32(4), 31–38. CrossrefGoogle Scholar
Grigore, E. C., Eder, K., Pipe, A. G., Melhuish, C., & Leonards, U.
(2013, November). Joint action understanding improves robot-to-human object handover. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on (pp. 4622–4629). IEEE. CrossrefGoogle Scholar
Hamacher, A., Bianchi-Berthouze, N., Pipe, A. G., & Eder, K.
(2016, August). Believing in BERT: Using expressive communication to enhance trust and counteract operational error in physical Human-Robot Interaction. In Robot and Human Interactive Communication (RO-MAN), 2016 25th IEEE International Symposium on (pp. 493–500). IEEE.Google Scholar
Kahn Jr, P. H., Kanda, T., Ishiguro, H., Freier, N. G., Severson, R. L., Gill, B. T., … & Shen, S.
(2012) “Robovie, you’ll have to go into the closet now”: Children’s social and moral relationships with a humanoid robot. Developmental psychology, 48(2), 303. CrossrefGoogle Scholar
Lee, M. K., Kiesler, S., Forlizzi, J.
Receptionist or information 
kiosk: How do people talk with a robot? Presented at the Conference on Computer-Supported Cooperative Work, New York, 2010. Crossref
Lee, J. J., Knox, B., Baumann, J., Breazeal, C., & DeSteno, D.
(2013) Computationally modeling interpersonal trust. Frontiers in psychology, 4, 893. CrossrefGoogle Scholar
Lenz, C., Nair, S., Rickert, M., Knoll, A., Rosel, W., Gast, J., … & Wallhoff, F.
(2008, August). Joint-action for humans and industrial robots for assembly tasks. In Robot and Human Interactive Communication, 2008. RO-MAN 2008. The 17th IEEE International Symposium on (pp. 130–135). IEEE. CrossrefGoogle Scholar
Loftus, G. R., & Masson, M. E.
(1994) Using confidence intervals in within-subject designs. Psychonomic bulletin & review, 1(4), 476–490. CrossrefGoogle Scholar
Michael, J., & Salice, A.
(2017) The Sense of Commitment in Human–Robot Interaction. International journal of social robotics, 9(5), 755–763. CrossrefGoogle Scholar
Michael, J., Sebanz, N., & Knoblich, G.
(2015) The sense of commitment: A minimal approach. Frontiers in psychology, 6 1968.Google Scholar
(2016) Observing joint action: Coordination creates commitment. Cognition, 157, 106–113. CrossrefGoogle Scholar
Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., & Tscheligi, M.
(2017) To err is robot: How humans assess and act toward an erroneous social robot. Frontiers in Robotics and AI, 4, 21.Google Scholar
Palinko, O., Sciutti, A., Wakita, Y., Matsumoto, Y., & Sandini, G.
(2016, November). If looks could kill: Humanoid robots play a gaze-based social game with humans. In Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on (pp. 905–910). IEEE. CrossrefGoogle Scholar
Peirce, J. W.
(2007) PsychoPy – Psychophysics software in Python. Journal of Neuroscience Methods, 162(1–2), 8–13. CrossrefGoogle Scholar
Schneider, S. & Kümmert, F.
Exercising with a humanoid companion are more effective than exercising alone, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun 2016, pp. 495–501. CrossrefGoogle Scholar
Sciutti, A., Bisio, A., Nori, F., Metta, G., Fadiga, L., & Sandini, G.
(2012) Anticipatory gaze in human-robot interactions. In Gaze in HRI from modeling to communication” workshop at the 7th ACM/IEEE international conference on human-robot interaction, Boston, Massachusetts, USA.Google Scholar
(2013) Robots can be perceived as goal-oriented agents. Interaction Studies, 14(3), 329–350.Google Scholar
Sciutti, A., Bisio, A., Nori, F., Metta, G., Fadiga, L., Pozzo, T., & Sandini, G.
(2012) Measuring human-robot interaction through motor resonance. International Journal of Social Robotics, 4(3), 223–234. CrossrefGoogle Scholar
Sciutti, A., Schillingmann, L., Palinko, O., Nagai, Y., & Sandini, G.
(2015, March). A gaze-contingent dictating robot to study turn-taking. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (pp. 137–138). ACM.Google Scholar
Sebanz, N., Knoblich, G., & Prinz, W.
(2003) Representing others’ actions: Just like one’s own? Cognition, 88, B11–B21. CrossrefGoogle Scholar
Stenzel, A., Chinellato, E., Bou, M. A. T., del Pobil, Á. P., Lappe, M., & Liepelt, R.
(2012) When humanoid robots become human-like interaction partners: corepresentation of robotic actions. Journal of Experimental Psychology: Human Perception and Performance, 38(5), 1073.Google Scholar
Stulp, F., Grizou, J., Busch, B., & Lopes, M.
(2015, September). Facilitating intention prediction for humans by optimizing robot motions. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (pp. 1249–1255). IEEE. CrossrefGoogle Scholar
Székely, M., & Michael, J.
(2018) Investing in commitment: Persistence in a joint action is enhanced by the perception of a partner’s effort. Cognition, 174, 37–42. CrossrefGoogle Scholar
Wainer, J., Feil-Seifer, D. J., Shell, D. A., & Mataric, M. J.
(2006, September). The role of physical embodiment in human-robot interaction. In Robot and Human Interactive Communication 2006 ROMAN 2006. The 15th IEEE International Symposium on (pp. 117–122). IEEE.Google Scholar

video