skip to main content
10.1145/2441776.2441954acmconferencesArticle/Chapter ViewAbstractPublication PagescscwConference Proceedingsconference-collections
research-article

Engaging robots: easing complex human-robot teamwork using backchanneling

Authors Info & Claims
Published:23 February 2013Publication History

ABSTRACT

People are increasingly working with robots in teams and recent research has focused on how human-robot teams function, but little attention has yet been paid to the role of social signaling behavior in human-robot teams. In a controlled experiment, we examined the role of backchanneling and task complexity on team functioning and perceptions of the robots' engagement and competence. Based on results from 73 participants interacting with autonomous humanoid robots as part of a human-robot team (one participant, one confederate, and three robots), we found that when robots used backchanneling team functioning improved and the robots were seen as more engaged. Ironically, the robots using backchanneling were perceived as less competent than those that did not. Our results suggest that backchanneling plays an important role in human-robot teams and that the design and implementation of robots for human-robot teams may be more effective if backchanneling capability is provided.

Skip Supplemental Material Section

Supplemental Material

cscw0586-file3.mov

mov

23.2 MB

References

  1. ARToolKit. {Software}. Available from: http://www.hitl.washington.edu/artoolkit/.Google ScholarGoogle Scholar
  2. Aviezer, H., et al., Angry, Disgusted, or Afraid? Psychological Science, 2008. 19(7): p. 724--732.Google ScholarGoogle Scholar
  3. Barsade, S. G., The Ripple Effect: Emotional Contagion and its Influence on Group Behavior. Administrative Science Quarterly, 2002. 47(4): p. 644--675.Google ScholarGoogle Scholar
  4. Barsade, S. G. and D. E. Gibson, Why does affect matter in organizations? The Academy of Management Perspectives, 2007. 21(1): p. 36--59.Google ScholarGoogle Scholar
  5. Bethel, C. L. and R. R. Murphy, Affective expression in appearance constrained robots, in Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction. 2006, ACM: Salt Lake City, Utah, USA. p. 327--328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bethel, C. L. and R. R. Murphy, Non-facial/non-verbal methods of affective expression as applied to robot-assisted victim assessment, in Proceedings of the ACM/IEEE international conference on Human-robot interaction. 2007, ACM: Arlington, Virginia, USA. p. 287--294. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bickmore, T. W. and R. W. Picard, Towards caring machines, in CHI '04 extended abstracts on Human factors in computing systems. 2004, ACM: Vienna, Austria. p. 1489--1492. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Breazeal, C., et al. Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. in Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on. 2005.Google ScholarGoogle ScholarCross RefCross Ref
  9. Breazeal, C. and B. Scassellati. How to build robots that make friends and influence people. in Intelligent Robots and Systems, 1999. IROS '99. Proceedings. 1999 IEEE/RSJ International Conference on. 1999.Google ScholarGoogle ScholarCross RefCross Ref
  10. Breazeal, C. L., Designing sociable robots. 2004: The MIT Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Bruce, A., I. Nourbakhsh, and R. Simmons. The role of expressiveness and attention in human-robot interaction. in Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on. 2002.Google ScholarGoogle ScholarCross RefCross Ref
  12. Brunken, R., J. L. Plass, and D. Leutner, Direct Measurement of Cognitive Load in Multimedia Learning. Educational Psychologist, 2003. 38(1): p. 53--61.Google ScholarGoogle Scholar
  13. Campbell, D. J., Task complexity: A review and analysis. The Academy of Management Review, 1988. 13(1): p. 40--52.Google ScholarGoogle Scholar
  14. CARMEN. Robot Navigation Toolkit. Available from: http://carmen.sourceforge.net/.Google ScholarGoogle Scholar
  15. Casper, J. and R. R. Murphy, Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2003. 33(3): p. 367--385. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Cereproc. {Software}. Available from http://www.cereproc.com/.Google ScholarGoogle Scholar
  17. Clark, H. H. and S. A. Brennan, Grounding in Communication, in Perspectives on socially shared cognition, L. B. Resnick, J. M. Levine, and S. D. Teasley, Editors. 1991, APA Book.: Washington.Google ScholarGoogle Scholar
  18. Coan, J. A. and J. M. Gottman, The Specific Affect Coding System (SPAFF), in Handbook of emotion elicitation and assessment., J. A. Coan and J. J. B. Allen, Editors. 2007, New York, NY, US: Oxford University Press. p. 267--285.Google ScholarGoogle Scholar
  19. Dennis, A. R. and S. T. Kinney, Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality. Information Systems Research, 1998. 9(3): p. 256--274. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Earley, P. C., Influence of information, choice and task complexity upon goal acceptance, performance, and personal goals. Journal of Applied Psychology, 1985. 70(3): p. 481--491.Google ScholarGoogle Scholar
  21. Felps, W., T. R. Mitchell, and E. Byington, How, when, and why bad apples spoil the barrel: Negative group members and dysfunctional groups. Research in organizational behavior, 2006. 27: p. 175--222.Google ScholarGoogle Scholar
  22. Fincannon, T., et al. Evidence of the need for social intelligence in rescue robots. in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2004. Sendai, Japan: IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  23. Fong, T., I. Nourbakhsh, and K. Dautenhahn, A survey of socially interactive robots. Robotics and Autonomous Systems, 2003. 42(3): p. 143--166.Google ScholarGoogle Scholar
  24. Gockley, R., R. Simmons, and J. Forlizzi. Modeling Affect in Socially Interactive Robots. in Robot and Human Interactive Communication, 2006. ROMAN 2006. The 15th IEEE International Symposium on. 2006.Google ScholarGoogle ScholarCross RefCross Ref
  25. Jacob, M., et al., Gestonurse: a robotic surgical nurse for handling surgical instruments in the operating room. Journal of Robotic Surgery, 2012. 6(1): p. 53--63.Google ScholarGoogle Scholar
  26. Johnson, C., Gender, Legitimate Authority, and Leader-Subordinate Conversations. American Sociological Review, 1994. 59(1): p. 122--135.Google ScholarGoogle Scholar
  27. Jones, H. and P. Hinds. Extreme work teams: using swat teams as a model for coordinating distributed robots. in ACM conference on Computer supported cooperative work, CSCW'02. 2002. New Orleans, Louisiana, USA.: ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Jung, M. F., J. Chong, and L. J. Leifer. Group Hedonic Balance and Pair Programming Performance: Affective Interaction Dynamics as indicators of Performance. in ACM SIGCHI Conference on Human Factors in Computing Systems (CHI'12). 2012. Austin, Texas, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Kahn, W. A., Psychological Conditions of Personal Engagement and Disengagement at Work. Academy of Management Journal, 1990. 33(4): p. 692--724.Google ScholarGoogle Scholar
  30. Keltner, D. and J. Haidt, Social functions of emotions, in Emotions: Currrent issues and future directions, T. J. M. G. A. Bonanno, Editor. 2001, Guilford Press: New York, NY, US. p. 192--213.Google ScholarGoogle Scholar
  31. Knutson, B., Facial expressions of emotion influence interpersonal trait inferences. Journal of Nonverbal Behavior, 1996. 20(3): p. 165--182.Google ScholarGoogle Scholar
  32. Lee, K. M., et al., Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human-Robot Interaction. Journal of Communication, 2006. 56(4): p. 754--772.Google ScholarGoogle Scholar
  33. Lewis, K., Measuring transactive memory systems in the field: Scale development and validation. Journal of Applied Psychology, 2003. 88(4): p. 587--604.Google ScholarGoogle Scholar
  34. Liu, C., et al. Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction. in 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 2012. Kyoto, Japan: IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Maynard, D. C. and M. D. Hakel, Effects of objective and subjective task complexity on performance. Human Performance; Human Performance, 1997. 10(4): p. 303--330.Google ScholarGoogle Scholar
  36. Murphy, R. R., Human-robot interaction in rescue robotics. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2004. 34(2): p. 138--153. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Murphy, R. R. and J. L. Burke, Up from the Rubble: Lessons Learned about HRI from Search and Rescue. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2005. 49(3): p. 437--441.Google ScholarGoogle ScholarCross RefCross Ref
  38. Murphy, R. R., et al., Emotion-based control of cooperating heterogeneous mobile robots. Robotics and Automation, IEEE Transactions on, 2002. 18(5): p. 744--757.Google ScholarGoogle Scholar
  39. Nass, C. and B. Reeves, The media equation: How people treat computers, televisions, and new media as real people and places. 1996, Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Parasuraman, R. and C. A. Miller, Trust and etiquette in high-criticality automated systems. Commun. ACM, 2004. 47(4): p. 51--55. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Pasupathi, M., et al., Responsive Listening in Long- Married Couples: A Psycholinguistic Perspective. Journal of Nonverbal Behavior, 1999. 23(2): p. 173--193.Google ScholarGoogle Scholar
  42. Rose, R., M. Scheutz, and P. Schermerhorn. Empirical investigations into the believability of robot affect. in Proceedings of the AAAI Spring Symposium. 2008.Google ScholarGoogle Scholar
  43. Schegloff, E. A., Sequence organization in interaction: A primer in conversation analysis I. Vol. 1. 2007: Cambridge Univ Pr.Google ScholarGoogle Scholar
  44. Shah, J., et al., Improved human-robot team performance using chaski, a human-inspired plan execution system, in Proceedings of the 6th international conference on Human-robot interaction. 2011, ACM: Lausanne, Switzerland. p. 29--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Sidner, C. L., et al., Explorations in engagement for humans and robots. Artificial Intelligence, 2005. 166(1): p. 140--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Sidner, C. L., et al., The effect of head-nod recognition in human-robot conversation, in Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction. 2006, ACM: Salt Lake City, Utah, USA. p. 290--296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Sphinx, C. {Software}. Available from: http://cmusphinx.sourceforge.net/.Google ScholarGoogle Scholar
  48. Stubbs, K., P. J. Hinds, and D. Wettergreen, Autonomy and Common Ground in Human-Robot Interaction: A Field Study. Intelligent Systems, IEEE, 2007. 22(2): p. 42--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Takayama, L., V. Groom, and C. Nass, I'm sorry, Dave: i'm afraid i won't do that: social aspects of human-agent conflict, in Proceedings of the 27th international conference on Human factors in computing systems. 2009, ACM: Boston, MA, USA. p. 2099--2108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. van Gerven, P. W. M., et al., Modality and variability as factors in training the elderly. Applied Cognitive Psychology, 2006. 20(3): p. 311--320.Google ScholarGoogle Scholar
  51. Vicon. {Software}. Available from: http://www.vicon.com/.Google ScholarGoogle Scholar
  52. Wang, E., et al., Effects of head movement on perceptions of humanoid robot behavior, in Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction. 2006, ACM: Salt Lake City, Utah, USA. p. 180--185. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Wang, L. and C. Chen, A Combined Optimization Method for Solving the Inverse Kinematics Problem of Mechanical Manipulators. IEEE Trans. On Robotics and Applications, 1991. 7(4): p. 489--499.Google ScholarGoogle Scholar
  54. Watson, D., L. A. Clark, and A. Tellegen, Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 1988. 54(6): p. 1063--1070.Google ScholarGoogle Scholar
  55. Weick, K. E., The Vulnerable System: An Analysis of the Tenerife Air Disaster. Journal of Management, 1990. 16(3): p. 571--593.Google ScholarGoogle Scholar
  56. Yamazaki, A., et al., Precision timing in human-robot interaction: coordination of head movement and utterance, in Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems CHI'08. 2008, ACM: Florence, Italy. p. 131--140. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Engaging robots: easing complex human-robot teamwork using backchanneling

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            CSCW '13: Proceedings of the 2013 conference on Computer supported cooperative work
            February 2013
            1594 pages
            ISBN:9781450313315
            DOI:10.1145/2441776

            Copyright © 2013 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 23 February 2013

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            Overall Acceptance Rate2,235of8,521submissions,26%

            Upcoming Conference

            CSCW '24

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader