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Gesture Cues in Navigational Robots

Investigating the Effects of Honesty on People’s Perceptions and Performance in a Navigational Game
  • Joey A. F. Verhoeven
  • Peter A. M. RuijtenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11876)

Abstract

As robots have become better in tasks such as motion planning and obstacle avoidance, they will soon face a new challenge: sharing a physical space with humans. This challenge means that robots and humans need to be able to interpret what the other is doing at the moment, and predict what will happen in the near future. In the current study we tested whether people would learn from a robot’s navigation behavior while playing a navigational game. The robot was either honest or dishonest in showing its navigational intentions. Results showed differences in people’s understanding of the robot’s behavior, the perceived human-likeness of the robot, and performance in the game. People also improved their performance throughout the dishonest rounds. These findings can be used in the design of robots that need to function effectively in mixed human-robot environments.

Keywords

Perceived message understanding Anthropomorphism Mental models Robot navigation 

References

  1. 1.
    Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O.: Progress and prospects of the human-robot collaboration. Auton. Robots 42(5), 957–975 (2018).  https://doi.org/10.1007/s10514-017-9677-2CrossRefGoogle Scholar
  2. 2.
    Bauer, A., Wollherr, D., Buss, M.: Human-robot collaboration: a survey. Int. J. Humanoid Rob. 05(01), 47–66 (2008).  https://doi.org/10.1142/S0219843608001303CrossRefGoogle Scholar
  3. 3.
    Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M.: Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708–713, August 2005.  https://doi.org/10.1109/IROS.2005.1545011
  4. 4.
    Campo, S., Cameron, K.A., Brossard, D., Frazer, M.S.: Social norms and expectancy violation theories: assessing the effectiveness of health communication campaigns. Commun. Monogr. 71(4), 448–470 (2004).  https://doi.org/10.1080/0363452042000307498CrossRefGoogle Scholar
  5. 5.
    Christensen, H.I., Pacchierotti, E.: Embodied social interaction for robots. In: AISB 2005, pp. 40–45 (2005)Google Scholar
  6. 6.
    Duffy, B.R.: Anthropomorphism and the social robot. Robot. Auton. Syst. 42(3), 177–190 (2003).  https://doi.org/10.1016/S0921-8890(02)00374-3CrossRefzbMATHGoogle Scholar
  7. 7.
    Fong, T., Nourbakhsh, I., Dautenhahn, K.: A survey of socially interactive robots. Robot. Auton. Syst. 42(3), 143–166 (2003).  https://doi.org/10.1016/S0921-8890(02)00372-XCrossRefzbMATHGoogle Scholar
  8. 8.
    Harms, P.C., Biocca, P.F.: Internal consistency and reliability of the networked minds measure of social presence (2004). http://cogprints.org/7026/1/Harms_04_reliability_validity_social_presence_(Biocca).pdf
  9. 9.
    Kiesler, S., Goetz, J.: Mental models of robotic assistants (2002).  https://doi.org/10.1145/506443.506491CrossRefGoogle Scholar
  10. 10.
    Klein, G., Feltovich, P.J., Bradshaw, J.M., Woods, D.D.: Common ground and coordination in joint activity. Organ. Simul. 53, 139–184 (2005).  https://doi.org/10.1002/0471739448.ch6CrossRefGoogle Scholar
  11. 11.
    Kruse, T., Pandey, A.K., Alami, R., Kirsch, A.: Human-aware robot navigation: a survey. Robot. Auton. Syst. 61(12), 1726–1743 (2013).  https://doi.org/10.1016/j.robot.2013.05.007CrossRefGoogle Scholar
  12. 12.
    Law, E., et al.: A wizard-of-oz study of curiosity in human-robot interaction. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 607–614. IEEE (2017).  https://doi.org/10.1109/ROMAN.2017.8172365
  13. 13.
    Lemaignan, S., Fink, J., Dillenbourg, P.: The dynamics of anthropomorphism in robotics. In: Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2014, pp. 226–227. ACM, New York (2014).  https://doi.org/10.1145/2559636.2559814
  14. 14.
    Mumm, J., Mutlu, B.: Human-robot proxemics: physical and psychological distancing in human-robot interaction. In: Proceedings of the 6th International Conference on Human-Robot Interaction, HRI 2011, pp. 331–338. ACM, New York (2011).  https://doi.org/10.1145/1957656.1957786
  15. 15.
    Neggers, M.M.E., Ruijten, P.A.M., Cuijpers, R.H., IJsselsteijn, W.A.: Investigating the efficiency and understandability of directional cues in robot navigation. In: RO-MAN 2019 (2019, submitted)Google Scholar
  16. 16.
    Rios-Martinez, J., Spalanzani, A., Laugier, C.: From proxemics theory to socially-aware navigation: a survey. Int. J. Soc. Robot. 7(2), 137–153 (2015).  https://doi.org/10.1007/s12369-014-0251-1CrossRefGoogle Scholar
  17. 17.
    Ruijten, P.A.M., Cuijpers, R.H.: Dynamic perceptions of human-likeness while interacting with a social robot. In: Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017, pp. 273–274. ACM, New York (2017).  https://doi.org/10.1145/3029798.3038361
  18. 18.
    Saulnier, P., Sharlin, E., Greenberg, S.: Exploring minimal nonverbal interruption in HRI. In: 2011 RO-MAN, pp. 79–86. IEEE (2011).  https://doi.org/10.1109/ROMAN.2011.6005257
  19. 19.
    Waytz, A., Morewedge, C.K., Epley, N., Monteleone, G., Gao, J.H., Cacioppo, J.T.: Making sense by making sentient: effectance motivation increases anthropomorphism. J. Pers. Soc. Psychol. 99(3), 410–435 (2010).  https://doi.org/10.1037/a0020240CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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