Spatiotemporal Coordination Supports a Sense of Commitment in Human-Robot Interaction

  • Alessia VignoloEmail author
  • Alessandra Sciutti
  • Francesco Rea
  • John Michael
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11876)


In the current study, we presented participants with videos in which a humanoid robot (iCub) and a human agent were tidying up by moving toys from a table into a container. In the High Coordination condition, the two agents worked together in a coordinated manner, with the human picking up the toys and passing them to the robot. In the Low Coordination condition, they worked in parallel without coordinating. Participants were asked to imagine themselves in the position of the human agent and to respond to a battery of questions to probe the extent to which they felt committed to the joint action. While we did not observe a main effect of our coordination manipulation, the results do reveal that participants who perceived a higher degree of coordination also indicated a greater sense of commitment to the joint action. Moreover, the results show that participants’ sensitivity to the coordination manipulation was contingent on their prior attitudes towards the robot: participants in the High Coordination condition reported a greater sense of commitment than participants in the Low Coordination condition, except among those participants who were a priori least inclined to experience a close sense of relationship with the robot.


Cognitive human-robot interaction Sense of commitment iCub 



This research was supported by a Starting Grant from the European Research Council (nr. 679092, SENSE OF COMMITMENT).


  1. 1.
    Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)CrossRefGoogle Scholar
  2. 2.
    Breazeal, C., Brooks, A., Gray, J., Hoffman, G., Kidd, C., Lee, H.: Humanoid robots as cooperative partners for people. J. Hum. Robot. 1, 34 (2004)Google Scholar
  3. 3.
    Cakmak, M., Thomaz, A.L.: Designing robot learners that ask good questions. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2012, pp. 17–24. ACM, New York (2012).
  4. 4.
    Clodic, A., Cao, H., Alili, S., Montreuil, V., Alami, R., Chatila, R.: SHARY: a supervision system adapted to human-robot interaction. In: Khatib, O., Kumar, V., Pappas, G.J. (eds.) Experimental Robotics, vol. 54, pp. 229–38. Springer, Heidelberg (2009). Scholar
  5. 5.
    Fischer, K., Weigelin, H., Bodenhagen, L.: Increasing trust in human-robot medical interactions: effects of transparency and adaptability. Paladyn J. Behav. Robot. 9(1), 95–109 (2018)CrossRefGoogle Scholar
  6. 6.
    Grigore, E., Eder, K., Pipe, A., Melhuish, C., Leonards, U.: Joint action understanding improves robot-to-human object handover. IEEE/RSJ International Conference on Intelligent Robots and Systems,pp. 4622–9 (2013)Google Scholar
  7. 7.
    Iqbal, T., Rack, S., Riek, L.D.: Movement coordination in human - robot teams: a dynamical system approach. IEEE Trans. Robot. 34, 909–919 (2016)CrossRefGoogle Scholar
  8. 8.
    Lenz, C., Nair, S., Rickert, M., Knoll, A., Rosel, W., Gast, J.: Joint-action for humans and industrial robots for assembly tasks, pp. 130–135. IEEE (2008)Google Scholar
  9. 9.
    Metta, G., et al.: The iCub humanoid robot: an open-systems platform for research in cognitive development. Neural Netw. 23(8–9), 1125–1134 (2010)CrossRefGoogle Scholar
  10. 10.
    Michael, J., Powell, H.: Feeling committed to a robot: why, what, when, and how? Philos. Trans. R. Soc. B: Biol. Sci. 374(1771), 20180039 (2019)CrossRefGoogle Scholar
  11. 11.
    Michael, J., Salice, A.: The sense of commitment in human-robot interaction. Int. J. Soc. Robot. 9(5), 755–63 (2017)CrossRefGoogle Scholar
  12. 12.
    Michael, J., Sebanz, N., Knoblich, G.: Observing joint action: coordination creates commitment. Cognition 157, 106–113 (2016)CrossRefGoogle Scholar
  13. 13.
    Michael, J., Sebanz, N., Knoblich, G.: The sense of commitment: a minimal approach. Front. Psychol. 6, 1968 (2016)CrossRefGoogle Scholar
  14. 14.
    Rea, F., Vignolo, A., Sciutti, A., Noceti, N.: Human motion understanding for selecting action timing in collaborative human-robot interaction. Front. Robot. AI 6, 58 (2019)CrossRefGoogle Scholar
  15. 15.
    Sciutti, A., Bisio, A., Nori, F., Metta, G., Fadiga, L., Pozzo, T.: Measuring human-robot interaction through motor resonance. Int. J. Soc. Robot. 4(3), 223–34 (2012)CrossRefGoogle Scholar
  16. 16.
    Sciutti, A., Mara, M., Tagliasco, V., Sandini, G.: Humanizing human-robot interaction: on the importance of mutual understanding. IEEE Technol. Soc. Mag. 37(1), 22–29 (2018)CrossRefGoogle Scholar
  17. 17.
    Vignolo, A., Powell, H., McEllin, L., Rea, F., Sciutti, A., Michael, J.: An adaptive robot teacher boosts a human partner’s learning performance in joint action. In: The 28th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN 2019), New Delhi, India (2019)Google Scholar
  18. 18.
    Vignolo, A., Noceti, N., Rea, F., Sciutti, A., Odone, F., Sandini, G.: Detecting biological motion for human-robot interaction: a link between perception and action. Front. Robot. AI 4, 14 (2017)CrossRefGoogle Scholar
  19. 19.
    Woosnam, K.M.: The inclusion of other in the self (IOS) scale. Ann. Tour. Res. 37, 857–60 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alessia Vignolo
    • 1
    • 2
    Email author
  • Alessandra Sciutti
    • 3
  • Francesco Rea
    • 2
  • John Michael
    • 1
  1. 1.Department of Philosophy, Social Sciences BuildingUniversity of WarwickCoventryUK
  2. 2.Robotics Brain and Cognitive Sciences Unit, Istituto Italiano di TecnologiaGenoaItaly
  3. 3.CONTACT Unit, Istituto Italiano di TecnologiaGenoaItaly

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