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Learning Motor Primitives for Robotics

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84021

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Kober, J., & Peters, J. (2010). Learning Motor Primitives for Robotics. Talk presented at EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality. Barcelona, Spain.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C15A-8
Zusammenfassung
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.