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  Policy Gradient Methods for Robotics

Peters, J., & Schaal, S. (2006). Policy Gradient Methods for Robotics. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2219-2225). Los Alamitos, CA, USA: IEEE Computer Society.

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 Creators:
Peters, J1, Author           
Schaal, S1, Author           
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1External Organizations, ou_persistent22              

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 Abstract: The acquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of high-dimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm.

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 Dates: 2006-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/IROS.2006.282564
BibTex Citekey: 5047
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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006)
Place of Event: Beijing, China
Start-/End Date: 2006-10-09 - 2006-10-15

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Title: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
Source Genre: Proceedings
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Publ. Info: Los Alamitos, CA, USA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2219 - 2225 Identifier: ISBN: 1-4244-0259-X