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19 - Task learning through imitation and human–robot interaction

Published online by Cambridge University Press:  10 December 2009

By
Monica N. Nicolescu  and
Maja J. Matarić
Edited by
Chrystopher L. Nehaniv  and
Kerstin Dautenhahn
Monica N. Nicolescu
Affiliation:
Department of Computer Science and Engineering, University of Nevada, USA
Maja J. Matarić
Affiliation:
Computer Science Department, University of Southern California, USA
Chrystopher L. Nehaniv
Affiliation:
University of Hertfordshire
Kerstin Dautenhahn
Affiliation:
University of Hertfordshire
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Summary

Introduction

Human–robot interaction is a rapidly growing area of robotics. Environments that feature the interaction of humans and robots present a number of challenges involving robot learning (imitative) and interactive capabilities. The two problems are tightly related since, on the one hand, social interaction is often an important aspect of imitation learning and, on the other, imitative behavior enhances a robot's social and interactive capabilities. In this work we present a framework that unifies these issues, providing a natural means for robots to interact with people and to learn from interactive experiences.

We focus on two major challenges. The first is the design of robot social capabilities that allow for engagement in various types of interactions. Examples include robot teachers (David and Ball, 1986), workers, team members (Matsui et al., 1997), museum tour-guides (Thrun et al., 1999), toys (Michaud and Caron, 2002), and emotional companions (Breazeal, 2002; Cañamero and Fredslund, 2000). Designing control architectures for such robots presents various, often domain-specific, challenges.

The second challenge we address is endowing robots with the ability to learn through social interaction with humans or other robots, in order to improve their performance and expand their capabilities. Learning by imitation (Dautenhahn and Nehaniv, 2002; Hayes and Demiris, 1994; Schaal, 1997) provides a most natural approach to this problem; methods using gestures (Voyles and Khosla, 1998), natural language (Lauria et al., 2002), and animal “clicker training” (Kaplan et al., 2002) have also been successfully applied.

Type
Chapter
Information
Imitation and Social Learning in Robots, Humans and Animals
Behavioural, Social and Communicative Dimensions
 , pp. 407 - 424
Publisher: Cambridge University Press
Print publication year: 2007

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References

Arkin, R. C. (1998). Behavior-Based Robotics. Cambridge, MA: MIT Press.Google Scholar
Billard, A. and Dautenhahn, K. (1999). Experiments in learning by imitation – grounding and use of communication in robotic agents. Adaptive Behavior, 7(3/4), 415–38.CrossRefGoogle Scholar
Breazeal, C. (2002). Designing Sociable Robots. Cambridge, MA: MIT Press.Google Scholar
Breazeal, C. and Scassellati, B. (1999). How to build robots that make friends and influence people. Proceedings of the IROS, Kyonju, Korea, 858–63.Google Scholar
Cañamero, L. D. and Fredslund, J. (2000). How does it feel? Emotional interaction with a humanoid lego robot. Tech Report FS-00-04, AAAI Fall Symposium, Menlo Park, CA.
Dautenhahn, K. and Nehaniv, C. L. (eds.) (2002). Imitation in Animals and Artifacts. Cambridge, MA: MIT Press.Google Scholar
David, A. and Ball, M. P. (1986). The video game: a model for teacher-student collaboration. Momentum, 17(1), 24–6.Google Scholar
Kortenkamp, D., Hubet, E. and Bonasso, R. P. (1996). Recognizing and interpreting gestures on a mobile robot. Proceedings of the AAAI, 915–21.Google Scholar
Dennett, D. C. (1987). The Intentional Stance. Cambridge, MA:MIT Press.Google Scholar
E., Jenkins, O. C. and Matarić, M. J. (2004). Exemplar-based primitives for humanoid movement classification and control. In IEEE International Conference on Robotics and Automation, 140–5.Google Scholar
Kaplan, F., Oudeyer, P.-Y., Kubinyi, E. and Miklûsi, A. (2002). Robotic clicker training. Robotics and Autonomous Systems, 38, 197–206.CrossRefGoogle Scholar
Friedrich, H. and Dillmann, R. (1995). Robot programming based on a single demonstration and user intentions. Proceedings of the 3rd European Workshop on Learning Robots (EWLR-3), Heraklion, Crete, Greece, April 1995.Google Scholar
Hayes, G. and Demiris, J. (1994). A robot controller using learning by imitation. Proceeding of the International Symposium on Intelligent Robotic Systems, 198–204.Google Scholar
Kaiser, M. (1997). Transfer of elementary skills via human–robot interaction. Adaptive Behavior, 5(3–4), 249–80.CrossRefGoogle Scholar
Matarić, M. J. (1997). Behavior-based control: examples from navigation, learning, and group behavior. Journal of Experimental and Theoretical Artificial Intelligence, 9(2–3), 323–36.CrossRefGoogle Scholar
Matarić, M. J. (2002). Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics. In Nehaniv, C. and Dautenhahn, K. (eds.), Imitation in Animals and Artifacts. Cambridge, MA: MIT Press, 392–422.Google Scholar
, Matsui T., Asoh, H., Hara, I., Motomura, Y., Akaho, S., Kurita, T., Asano, F. and Yamaski, N. (1997). An office conversation mobile robot that learns by navigation and conversation. Proceedings of the Real World Computing Symposium, 59–62.Google Scholar
Michaud, F. and Caron, S. (2002). Roball, the rolling robot. Autonomous Robots, 12(2), 211–22.CrossRefGoogle Scholar
Nicolescu, M. (2003). A Framework for Learning from Demonstration, Generalization and Practice in Human-Robot Domains. Ph.D. thesis, University of Southern California.Google Scholar
Nicolescu, M. N. and Matarić, M. J. (2001a). Experience-based representation construction: learning from human and robot teachers. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 740–5.Google Scholar
Nicolescu, M. N. and Matarić, M. J. (2001b). Learning and interacting in human–robot domain. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Special Issue on Socially Intelligent Agents – The Human in the Loop, 31(5), 419–30.CrossRef
Nicolescu, M. N. and Matarić, M. J. (2002). A hierarchical architecture for behavior-based robots. In Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, 227–33.Google Scholar
Nicolescu, M. N. and Matarić, M. J. (2003). Natural methods for robot task learning: instructive demonstration, generalization and practice. Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multi-Agent Systems, 241–8.Google Scholar
Jenkins, O. C., Matarić, M. J. and Weber, S. (2000). Primitive-based movement classification for humanoid imitation. Proceedings of the First IEEE-RAS International Conference on Humanoid Robotics, 858–63.Google Scholar
Gaussier, P., Moga, S., Banquet, J. and Quoy, M. (1998). From perception–action loops to imitation processes: a bottom-up approach of learning by imitation. Applied Artificial Intelligence Journal, 12(78), 701–29.CrossRefGoogle Scholar
Ramesh, A. and Matarić, M. J. (2002a). Learning movement sequences from demonstration. In Proceedings of the International Conference on Development and Learning, 203–8.Google Scholar
Ramesh, A. and Matarić, M. J. (2002b). Parametric primitives for motor representation and control. In IEEE International Conference on Robotics and Automation, 863–8.Google Scholar
Schaal, S. (1997). Learning from demonstration. In Mozer, M. C., Jordan, M. I. and Petsche, T. (eds.), Advances in Neural Information Processing Systems 9. Cambridge, MA: MIT Press, 1040–6.Google Scholar
Lauria, S., Bugmann, G., Kyriacou, T. and Klein, E. (2002). Mobile robot programming using natural language. Robotics and Autonomous Systems, 38, 171–81.CrossRefGoogle Scholar
Thrun, S., Bennewitz, M., Burgard, W., Dellaert, F., Fox, D., Haehnel, D., Rosenberg, C., Roy, N., Schulte, J. and Schulz, D. (1999). A second generation mobile tour-guide robot. In Proceeding of the IEEE International Conference on Robotics and Automation, 14–26.Google Scholar
Voyles, R. and Khosla, P. (1998). A multi-agent system for programming robotic agents by human demonstration. Proceedings of the AI and Manufacturing Research Planning Workshop. Menlo Park, CA: AAAI Press, 184–90.Google Scholar
Werger, B. B. (2000). Ayllu: distributed port-arbitrated behavior-based control. Proceeding of the The 5th International Symposium on Distributed Autonomous Robotic Systems, 25–34.Google Scholar

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