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  Policy Search for Motor Primitives in Robotics

Kober, J., & Peters, J. (2011). Policy Search for Motor Primitives in Robotics. Machine Learning, 84(1-2), 171-203. doi:10.1007/s10994-010-5223-6.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB0C-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B568-E
Genre: Journal Article

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Kober, J1, 2, Author              
Peters, 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. While successful applications to date have been achieved with imitation learning, most of the interesting motor learning problems are high-dimensional reinforcement learning problems. These problems are often beyond the reach of current reinforcement learning methods. In this paper, we study parametrized policy search methods and apply these to benchmark problems of motor primitive learning in robotics. We show that many well-known parametrized policy search methods can be derived from a general, common framework. This framework yields both policy gradient methods and expectation-maximization (EM) inspired algorithms. We introduce a novel EM-inspired algorithm for policy learning that is particularly well-suited for dynamical system motor primitives. We compare this algorithm, both in simulation and on a real robot, to several well-known parametrized policy search methods such as episodic REINFORCE, ‘Vanilla’ Policy Gradients with optimal baselines, episodic Natural Actor Critic, and episodic Reward-Weighted Regression. We show that the proposed method out-performs them on an empirical benchmark of learning dynamical system motor primitives both in simulation and on a real robot. We apply it in the context of motor learning and show that it can learn a complex Ball-in-a-Cup task on a real Barrett WAM™ robot arm.

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 Dates: 2011-07
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1007/s10994-010-5223-6
BibTex Citekey: 6802
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Title: Machine Learning
Source Genre: Journal
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Publ. Info: Dordrecht : Springer
Pages: - Volume / Issue: 84 (1-2) Sequence Number: - Start / End Page: 171 - 203 Identifier: ISSN: 0885-6125
CoNE: https://pure.mpg.de/cone/journals/resource/08856125