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Poster

Policy Learning: A Unified Perspective With Applications In Robotics

MPS-Authors

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Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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

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Nguyen-Tuong, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Peters, J., Kober, J., & Nguyen-Tuong, D. (2008). Policy Learning: A Unified Perspective With Applications In Robotics.

Abstract

Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning al- gorithms from a common point of view, i.e, policy gradient algorithms, natural- gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.