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Policy Search for Motor Primitives
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.
@article{6871, title = {Policy Search for Motor Primitives}, journal = {KI - Zeitschrift K{\"u}nstliche Intelligenz}, 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.}, volume = {23}, number = {3}, pages = {38-40}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = aug, year = {2009}, slug = {6871}, author = {Peters, J. and Kober, J.}, month_numeric = {8} }