Abstract
Understanding mechanisms of imitation is a complex task in both human sciences and robotics. On the one hand, one can build systems that analyze observed motion, map it to their own body, and produce the motor commands to needed to achieve the inferred motion using engineering techniques. On the other hand, one can model the neural circuits involved in action observation and production in minute detail and hope that imitation will be an emergent property of the system. However if the goal is to build robots capable of skillful actions, midway solutions appear to be more appropriate. In this direction, we first introduce a conceptually biologically realistic neural network that can learn to imitate hand postures, either with the help of a teacher or by self-observation. Then we move to a paradigm we have recently proposed, where robot skill synthesis is achieved by exploiting the human capacity to learn novel control tasks.
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References
Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philos. Trans. R Soc. Lond. B Biol. Sci. 358(1431), 537–547 (2003)
Billard, A., Epars, Y., Calinon, S., Schaal, S., Cheng, G.: Discovering optimal imitation strategies. Robotics and Autonomous Systems 47(2-3), 69–77 (2004)
Meltzoff, A.N., Decety, J.: What imitation tells us about social cognition: a rapprochement between developmental psychology and cognitive neuroscience. Philos. Trans. R Soc. Lond. B Biol. Sci. 358(1431), 491–500 (2003)
Chaminade, T., Meltzoff, A.N., Decety, J.: An fMRI study of imitation: action representation and body schema. Neuropsychologia 43(1), 115–127 (2005)
Oztop, E., Chaminade, T., Cheng, G., Kawato, M.: Imitation Bootstrapping: Experiments on a Robotic Hand. In: IEEE-RAS International Conference on Humanoid Robots, Tsukuba, Japan (2005)
Hassoun, M.: Associative Neural Memories: Theory and Implementation. Oxford University Press, Oxford (1993)
Kuniyoshi, Y., Yorozu, Y., Inaba, M., Inoue, H.: From Visuo-Motor Self Learning to Early Imitation - A Neural Architecture for Humanoid Learning. In: International Conference on Robotics & Automation, IEEE, Taipei, Taiwan (2003)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79(8), 2554–2558 (1982)
Oztop, E.: A New Content Addresable Memory Model Utilizing High Order Neurons, in Computer Engineering, Master Thesis, Middle East Technical University, Ankara (1996)
Iriki, A., Tanaka, M., Iwamura, Y.: Coding of modified body schema during tool use by macaque postcentral neurones. Neuroreport 7(14), 2325–2330 (1996)
Obayashi, S., Suhara, T., Kawabe, K., Okauchi, T., Maeda, J., Akine, Y., Onoe, H., Iriki, A.: Functional brain mapping of monkey tool use. Neuroimage 14(4), 853–861 (2001)
Oztop, E., Lin, L.H., Kawato, M., Cheng, G.: Extensive Human Training for Robot Skill Synthesis: Validation on a Robotic Hand. In: IEEE International Conference on Robotics and Automation, Roma, Italy (2007)
Cheng, G., Hyon, S., Morimoto, J., Ude, A., Jacobsen, S.: CB: A humanoid research platform for exploring neuroscience. In: IEEE-RAS International Conference on Humanoid Robots, Genova, Italy (2006)
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© 2008 Springer-Verlag Berlin Heidelberg
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Oztop, E., Babic, J., Hale, J., Cheng, G., Kawato, M. (2008). From Biologically Realistic Imitation to Robot Teaching Via Human Motor Learning. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_23
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DOI: https://doi.org/10.1007/978-3-540-69162-4_23
Publisher Name: Springer, Berlin, Heidelberg
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