Learning Motor Primitives for Robotics

Kober, J; Peters, J; Oztop, E

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Learning Motor Primitives for Robotics

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Kober, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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引用

Kober, J., Peters, J., & Oztop, E. (2009). Learning Motor Primitives for Robotics. Talk presented at Advanced Telecommunications Research Center ATR. Kyoto, Japan. 2009-06-11.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-C452-F

要旨

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.