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  Policy Search for Motor Primitives

Peters, J., & Kober, J. (2009). Policy Search for Motor Primitives. KI - Zeitschrift Künstliche Intelligenz, 23(3), 38-40.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C369-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BE4B-5
Genre: Journal Article

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 Creators:
Peters, J1, 2, Author              
Kober, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Many motor skills in humanoid robotics can be learned using parametrized motor primitives from demonstrations. However, most interesting motor learning problems require self-improvement often beyond the reach of current reinforcement learning methods due to the high dimensionality of the state-space. We develop an EM-inspired algorithm applicable to complex motor learning tasks. We compare this algorithm to several well-known parametrized policy search methods and show that it outperforms them. We apply it to motor learning problems and show that it can learn the complex Ball-in-a-Cup task using a real Barrett WAM robot arm.

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 Dates: 2009-08
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 6871
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Title: KI - Zeitschrift Künstliche Intelligenz
Source Genre: Journal
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Pages: - Volume / Issue: 23 (3) Sequence Number: - Start / End Page: 38 - 40 Identifier: -