Abstract
Humanoid robots are increasingly popular due to their flexible nature and how easy it is for humans to relate to them. Imitation learning has been used to transfer skills and experience between robots, however, transferring skills from a humanoid demonstrator to a non-humanoid such as a wheeled robot is no simple task. The imitator must be able to abstract the behaviour it observes and in addition translate this behaviour to actions that it is able to perform. Since humanoid robots’ have much larger ranges of motion than most robots, this is can be quite difficult. This work describes an approach for a non-humanoid robot to learn a task by observing demonstrations of a humanoid robot using global vision. We use a combination of tracking sequences of primitives and predicting future primitive actions from existing combinations using forward models to construct more abstract behaviours that bridge the differences between the different robot types. To evaluate the success of learning from a humanoid demonstrator, we also evaluate how well a wheeled robot can learn from a physically identical wheeled robot, as well as a much smaller wheeled robot.
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References
Demiris, J., Hayes, G.: Imitation as a dual-route process featuring predictive and learning components: A biologically plausible computational model. In: Dautenhahn, K., Nehaniv, C. (eds.) Imitation in Animals and Artifacts, pp. 327–361. MIT Press, Cambridge (2002)
Matarić, M.J.: Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics. In: Dautenhahn, K., Nehaniv, C. (eds.) Imitation in Animals and Artifacts, pp. 391–422. MIT Press, Cambridge (2002)
Billard, A., Matarić, M.J.: A biologically inspired robotic model for learning by imitation. In: Proceedings, Autonomous Agents 2000, Barcelona, Spain, pp. 373–380 (2000)
Weber, S., Matarić, M.J., Jenkins, O.: Experiments in imitation using perceptuo-motor primitives. In: Sierra, C., Gini, M., Rosenschein, J.S. (eds.) Proceedings of the fourth international conference on Autonomous agents, Barcelona, Catalonia, Spain, June 2000, pp. 136–137. ACM Press, New York (2000)
Matarić, M.J.: Getting humanoids to move and imitate. IEEE Intelligent Systems, pp. 18–24 (July 2000)
Yamaguchi, T., Tanaka, Y., Yachida, M.: Speed up reinforcement learning between two agents with adaptive mimetism. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1997. IROS 1997., vol. 2, pp. 594–600 (1997)
Price, B., Boutilier, C.: Accelerating reinforcement learning through implicit imitation. Journal of Artificial Intelligence Research 19, 569–629 (2003)
Calinon, S., Billard, A.: Learning of Gestures by Imitation in a Humanoid Robot. In: Dautenhahn, K., Nehaniv, C.L. (eds.) Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions, pp. 153–177. Cambridge University Press, Cambridge (2007)
Rabiner, L., Juang, B.: An introduction to hidden markov models. IEEE ASSP Magazine, 4–16 (1986)
Allen, J., Anderson, J.: A vision-based approach to imitation using heterogeneous demonstrators. In: Geib, C., Pynadath, D. (eds.) Proceedings of the AAAI Workshop on Plan and Intent Recognition, Vancouver, Canada, July 2007, pp. 9–16. AAAI Press, Menlo Park (2007)
Furgale, P., Anderson, J., Baltes, J.: Real-time vision-based pattern tracking without predefined colors. In: Proceedings of the Third International Conference on Computational Intelligence, Robotics, and Autonomous Systems (CIRAS), Singapore (December 2005)
Dearden, A., Demiris, Y.: Learning forward models for robots. In: Proceedings of IJCAI 2005, Edinburgh Scotland, pp. 1440–1445 (August 2005)
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Allen, J., Anderson, J., Baltes, J. (2010). Imitation Learning from Humanoids in a Heterogeneous Setting. In: Vadakkepat, P., et al. Trends in Intelligent Robotics. FIRA 2010. Communications in Computer and Information Science, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15810-0_14
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DOI: https://doi.org/10.1007/978-3-642-15810-0_14
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