Policy Learning for Motor Skills

Peters, J; Schaal, S

doi:10.1007/978-3-540-69162-4_25

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Policy Learning for Motor Skills

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Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-69162-4_25.pdf
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Citation

Peters, J., & Schaal, S. (2008). Policy Learning for Motor Skills. In M. Ishikawa, K. Doya, H. Miyamoto, & T. yamakawa (Eds.), Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16 (pp. 233-242). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C909-7

Abstract

Policy learning which allows autonomous robots to adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, we study policy learning algorithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structures for task representation and execution.