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Towards Robot Skill Learning: From Simple Skills to Table Tennis

Jan Peters; Jens Kober; Katharina Mülling; Oliver Krömer; Gerhard Neumann
In: Hendrik Blockeel; Kristian Kersting; Siegfried Nijssen; Filip Zelezný (Hrsg.). Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings, Part III. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2013), September 23-27, Prague, Czech Republic, Pages 627-631, Lecture Notes in Artificial Intelligence (LNAI), Vol. 8190, Springer, 2013.


Learning robots that can acquire new motor skills and refine existing ones have been a long-standing vision of both robotics, and machine learning. However, off-the-shelf machine learning appears not to be adequate for robot skill learning, as it neither scales to anthropomorphic robotics nor do fulfills the crucial real-time requirements. As an alternative, we propose to divide the generic skill learning problem into parts that can be well-understood from a robotics point of view. In this context, we have developed machine learning methods applicable to robot skill learning. This paper discusses recent progress ranging from simple skill learning problems to a game of robot table tennis.

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