Abstract
From teaching in labs to training for assembly, a role that robots are expected to play is to instruct their users in completing physical tasks. While instruction requires a range of capabilities, such as use of verbal and nonverbal language, a fundamental requirement for an instructional robot is to provide its students with instructions in a way that maximizes their task performance. In this paper, we present an autonomous instructional robot and investigate how different instructional strategies affect user performance and experience. Our analysis of human instructor–trainee interactions identified two key instructional strategies: (1) grouping instructions together and (2) summarizing the outcome of subsequent instructions. We implemented these strategies into a humanlike robot that autonomously instructed its users in a pipe-assembly task. To achieve autonomous instruction, we also developed a repair mechanism that enabled the robot to correct mistakes and misunderstandings. An evaluation of the instructional strategies in a human–robot interaction study showed that employing the grouping strategy resulted in faster task completion and increased rapport with the robot, although it also increased the number of task breakdowns. A comparison of our results with the human instructor–trainee interactions revealed many similarities, areas where our model for robot instructors could be improved, and the nuanced ways in which human instructors use training strategies such as summarization. Our findings offer strong implications for the design of instructional robots and directions of future research.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
References
Alfieri, L., Brooks, P., Aldrich, N., & Tenenbaum, H. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1–18.
Andrist, S., Spannan, E., & Mutlu, B. (2013). Rhetorical robots: Making robots more effective speakers using linguistic cues of expertise. In Proc. HRI’13 (pp. 341–348).
Bicho, E., Erlhagen, W., Louro, L., & Costa e Silva, E. (2011). Neuro-cognitive mechanisms of decision making in joint action: A human-robot interaction study. Human Movement Science, 30(5), 846–868.
Blaylock, N., Allen, J., & Ferguson, G. (2003). Managing communicative intentions with collaborative problem solving. In K. JCJ & R. Smith (Eds.), Current and new directions in discourse and dialogue (pp. 63–84). Berlin: Springer.
Boucher, J. D., Pattacini, U., Lelong, A., Bailly, G., Elisei, F., Fagel, S., et al. (2012). I reach faster when I see you look: Gaze effects in human–human and human–robot face-to-face cooperation. Frontiers in Neurorobotics, 6, 1–11.
Brooks, A. G., & Breazeal, C. (2006). Working with robots and objects: Revisiting deictic reference for achieving spatial common ground. In Proceedings of the 1st ACM SIGCHI/SIGART conference on human-robot interaction (pp. 297–304). New York: ACM.
Clark, H. H. (1996). Using language (Vol. 1996). Cambridge: Cambridge University Press.
Clark, H. H. (2005). Coordinating with each other in a material world. Discourse Studies, 7(4–5), 507–525.
Foster, M., Giuliani, M., Isard, A., Matheson, C., Oberlander, J., & Knoll, A. (2009). Evaluating description and reference strategies in a cooperative human-robot dialogue system. In Proc. IJCAI’09 (pp. 1818–1823).
Gallese, V., & Goldman, A. (1998). Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences, 2(12), 493–501.
Gonsior, B., Wollherr, D., & Buss, M. (2010). Towards a dialog strategy for handling miscommunication in human-robot dialog. In Proc. RO-MAN’10
Gray, J., Breazeal, C., Berlin, M., Brooks, A., & Lieberman, J. (2005). Action parsing and goal inference using self as simulator. In Proc. RO-MAN’05
Grosz, B., & Kraus, S. (1996). Collaborative plans for complex group action. Artificial Intelligence, 86(2), 269–357.
Grosz, B., & Sidner, C. (1986). Attention, intentions, and the structure of discourse. Computational Linguistics, 12(3), 175–204.
Hato, Y., Satake, S., Kanda, T., Imai, M., & Hagita, N. (2010). Pointing to space: Modeling of deictic interaction referring to regions. In Proceedings of the 5th ACM/IEEE international conference on human-robot interaction (pp. 301–308). New York: IEEE Press
Hirst, G., McRoy, S., Heeman, P., Edmonds, P., & Horton, D. (1994). Repairing conversational misunderstandings and non-understandings. Speech Communication, 15(3), 213–229.
Hoey, J., Poupart, P., Boutilier, C., & Mihailidis, A. (2005). POMDP models for assistive technology. In Proceedings of the AAAI 2005 fall symposium.
Huang, C. M., & Mutlu, B. (2012). Robot behavior toolkit: Generating effective social behaviors for robots. In Proc. HRI’12 (pp. 25–32).
Kanda, T., Sato, R., Saiwaki, N., & Ishiguro, H. (2007). A two-month field trial in an elementary school for long-term human-robot interaction. IEEE Transactions on Robotics, 23(5), 962–971.
Koulouri, T., & Lauria, S. (2009). Exploring miscommunication and collaborative behaviour in HRI. In Proc. SIGDIAL’09.
Landis, J., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
Lee, M., Kiesler, S., Forlizzi, J., Srinivasa, S., & Rybski, P. (2010). Gracefully mitigating breakdowns in robotic services. In Proc. HRI’10.
Markowitz, J. (2015). Robots that talk and listen. Boston: Walter de Gruyter.
McNeill, D. (1992). Hand and mind: What gestures reveal about thought. Chicago: University of Chicago Press.
Nicolescu, M., & Mataric, M. (2003). Linking perception and action in a control architecture for human-robot domains. In Proc. HICSS’03.
Norrick, N. (1991). On the organization of corrective exchanges in conversation. Journal of Pragmatics, 16(1), 59–83.
Reigeluth, C., Merrill, M., Wilson, B., & Spiller, R. (1980). The elaboration theory of instruction: A model for sequencing and synthesizing instruction. Instructional Science, 9(3), 195–219.
Sakita, K., Ogawara, K., Murakami, S., Kawamura, K., & Ikeuchi, K. (2004). Flexible cooperation between human and robot by interpreting human intention from gaze information. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004 (IROS 2004) Proceedings (Vol. 1, pp. 846–851). New York: IEEE
Sauppé, A., & Mutlu, B. (2014a). Effective task training strategies for instructional robots. In Proceedings of the 10th annual robotics: science and systems conference.
Sauppé, A., & Mutlu, B. (2014b). Robot deictics: How gesture and context shape referential communication. In Proceedings of the 2014 ACM/IEEE international conference on human-robot interaction (pp. 342–349). ACM.
Schegloff, E., Jefferson, G., & Sacks, H. (1977). The preference for self-correction in the organization of repair in conversation. Language, 53, 361–382.
Seedhouse, P. (1999). The relationship between context and the organization of repair in the l2 classroom. International Review of Applied Linguistics in Language Teaching, 37(1), 59–80.
Staudte, M., & Crocker, M. (2009). Visual attention in spoken human-robot interaction. In Proc. HRI’09 (pp. 77–84).
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
Tanaka, F., & Movellan, J. (2006). Behavior analysis of children’s touch on a small humanoid robot: Long-term observation at a daily classroom over three months. In Proc. RO-MAN’06.
Tanaka, R., & Kimura, T. (2009) The use of robots in early education: A scenario based on ethical consideration. In Proc. RO-MAN’09.
Tomasello, M., Conti-Ramsden, G., & Ewert, B. (1990). Young children’s conversations with their mothers and fathers: Differences in breakdown and repair. Journal of Child Language, 17(01), 115–130.
Torrey, C., Powers, A., Marge, M., Fussell, S., & Kiesler, S. (2006). Effects of adaptive robot dialogue on information exchange and social relations. In Proc. HRI’06 (pp. 126–133).
Torrey, C., Powers, A., Fussell, S., & Kiesler, S. (2007). Exploring adaptive dialogue based on a robot’s awareness of human gaze and task progress. In Proc. HRI’07.
Trafton, J., Cassimatis, N., Bugajska, M., Brock, D., Mintz, F., & Schultz, A. (2005). Enabling effective human-robot interaction using perspective-taking in robots. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 35(4), 460–470.
Zahn, C. (1984). A reexamination of conversational repair. Communications Monographs, 51(1), 56–66.
Acknowledgments
We thank Brandi Hefty, Jilana Boston, Ross Luo, Chien-Ming Huang, and Catherine Steffel for their contributions to and National Science Foundation Awards 1149970 and 1426824 and Mitsubishi Heavy Industries, Ltd. for their support of this work. Some of the findings from the human–human and human–robot data presented here have been published in the Proceeding of Robotics: Science and Systems (Sauppé and Mutlu 2014a) and included in a book chapter in Robots that Talk and Listen (Markowitz 2015).
Author information
Authors and Affiliations
Corresponding author
Additional information
This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.
Rights and permissions
About this article
Cite this article
Sauppé, A., Mutlu, B. Effective task training strategies for human and robot instructors. Auton Robot 39, 313–329 (2015). https://doi.org/10.1007/s10514-015-9461-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-015-9461-0