Abstract
The traditional approach to implement motor behaviour in a robot required a programmer to carefully decide the joint velocities at each timestep. By using the principle of learning by imitation, the robot can instead be taught simply by showing it what to do. This paper investigates the self-organization of a connectionist modular architecture for motor learning and control that is used to imitate human dancing. We have observed that the internal representation of a motion behaviour tends to be captured by more than one module. This supports the hypothesis that a modular architecture for motor learning is capable of self-organizing the decomposition of a movement.
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References
Schaal, S.: Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences 3(6), 233–242 (1999)
Piaget, J.: Play, dreams and imitation in childhood. W. W. Norton, New York (1962)
Meltzoff, A.N., Moore, M.K.: Explaining facial imitation: A theoretical model. Early Development and Parenting 6, 179–192 (1997)
Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L.: Premotor cortex and the recognition of motor actions. Cognitive Brain Research 3, 131–141 (1996)
Arbib, M.: The Mirror System, Imitation, and the Evolution of Language. In: Imitation in animals and artifacts, pp. 229–280. MIT Press, Cambridge (2002)
Gallese, V., Goldman, A.: Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences 2(12) (1998)
Demiris, Y., Hayes, G.: Imitation as a dual-route process featuring predictive and learning components: a biologically-plausible computational model. In: Imitation in animals and artifacts, pp. 327–361. MIT Press, Cambridge (2002)
Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems 54, 361–369 (2006)
Wolpert, D.M., Doya, K., Kawato, M.: A unifying computational framework for motor control and social interaction. Philosophical Transactions: Biological Sciences 358(1431), 593–602 (2003)
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)
Wolpert, D.M., Miall, R.C., Kawato, M.: Internal models in the cerebellum. Trends in Cognitive Sciences 2(9) (1998)
Matarić, M.J.: Sensory-Motor Primitives as a Basis for Learning by Imitation: Linking Perception to Action and Biology to Robotics. In: Imitation in animals and artifacts, pp. 392–422. MIT Press, Cambridge (2002)
Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11, 1317–1329 (1998)
Jordan, M.I., Rumelhart, D.E.: Forward models: Supervised learning with a distal teacher. Cognitive Science 16, 307–354 (1992)
Ans, B., Rousset, S., French, R.M., Musca, S.: Self-refreshing memory in artificial neural networks: learning temporal structures without catastrophic forgetting. Connection Science 16(2), 71–99 (2004)
Pfeifer, R., Scheier, C.: Understanding Intelligence (Illustrator-Isabelle Follath). MIT Press, Cambridge (2001)
Kawato, M.: Feedback-error-learning neural network for supervised motor learning. In: Eckmiller, R. (ed.) Advanced neural computers, pp. 365–372 (1990)
Nehaniv, C.L., Dautenhahn, K.: The Correspondence Problem. In: Imitation in Animals and Artifacts, pp. 41–63. MIT Press, Cambridge (2002)
Torres, E.B., Zipser, D.: Simultaneous control of hand displacements and rotations in orientation-matching experiments. J Appl Physiol 96(5), 1978–1987 (2004)
Desmurget, M., Prablanc, C.: Postural Control of Three-Dimensional Prehension Movements. J Neurophysiol 77(1), 452–464 (1997)
Ijspeert, A., Nakanishi, J., Schaal, S.: Trajectory formation for imitation with nonlinear dynamical systems. In: Proceedings of the IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS2001), pp. 752–757 (2001)
Haruno, M., Wolpert, D.M., Kawato, M.: MOSAIC model for sensorimotor learning and control. Neural Comp. 13(10), 2201–2220 (2001)
Kuniyoshi, Y., Yorozu, Y., Ohmura, Y., Terada, K., Otani, T., Nagakubo, A., Yamamoto, T.: From humanoid embodiment to theory of mind. In: Pierre, S., Barbeau, M., Kranakis, E. (eds.) ADHOC-NOW 2003. LNCS, vol. 2865, pp. 202–218. Springer, Heidelberg (2003)
Diedrichsen, J., Hazeltine, E., Kennerley, S., Ivry, R.B.: Moving to directly cued locations abolishes spatial interference during bimanual actions. Psychological Science 12(6), 493–498 (2001)
d’Avella, A., Bizzi, E.: Shared and specific muscle synergies in natural motor behaviors. PNAS 102(8), 3076–3081 (2005)
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Tidemann, A., Öztürk, P. (2007). Self-organizing Multiple Models for Imitation: Teaching a Robot to Dance the YMCA. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_29
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DOI: https://doi.org/10.1007/978-3-540-73325-6_29
Publisher Name: Springer, Berlin, Heidelberg
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