Abstract
Learning by imitation enables people to program robots simply by showing them what to do, instead of having to specify the motor commands of the robot. To achieve imitative behaviour in a simulated robot, a modular connectionist architecture for motor learning and control was implemented. The architecture was used to imitate human dance movements. The architecture self-organizes the decomposition of the movement to be imitated across different modules. The results show that the decomposition of the movement tends to be both competitive (i.e. one module dominates the others for a part of the movement) and collaborative (i.e. modules cooperate in controlling the robot).
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Tidemann, A. (2008). Using Multiple Models to Imitate the YMCA. In: Nguyen, N.T., Jo, G.S., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2008. Lecture Notes in Computer Science(), vol 4953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78582-8_79
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DOI: https://doi.org/10.1007/978-3-540-78582-8_79
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