Proceedings of Autonome Mobile Systeme (2009)
Obtaining novel skills is one of the most important problems in robotics. Machine learning techniques may be a promising approach for automatic and autonomous acquisition of movement policies. How- ever, this requires both an appropriate policy representation and suitable learning algorithms. Employing the most recent form of the dynami- cal systems motor primitives originally introduced by Ijspeert et al. , we show how both discrete and rhythmic tasks can be learned using a concerted approach of both imitation and reinforcement learning, and present our current best performing learning algorithms. Finally, we show that it is possible to include a start-up phase in rhythmic primitives.We apply our approach to two elementary movements, i.e., Ball-in-a-Cup and Ball-Paddling, which can be learned on a real Barrett WAM robot arm at a pace similar to human learning.
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