Reinforcement Learning for Parameterized Motor Primitives

Abstract— One of the major challenges in both action generation for robotics and in the understanding of human motor control is to learn the “building blocks of movement generation”, called motor primitives. Motor primitives, as used in this paper, are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties. While a lot of progress has been made in teaching parameterized motor primitives using supervised or imitation learning, the selfimprovement by interaction of the system with the environment remains a challenging problem. In this paper, we evaluate different reinforcement learning approaches for improving the performance of parameterized motor primitives. For pursuing this goal, we highlight the difficulties with current reinforcement learning methods, and outline both established and novel algorithms for the gradientbased improvement of parameterized policies. We compare these algorithms in the context of motor pr...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Jan Peters, Stefan Schaal
• Keywords: IJCNN 2006, Motor Primitive, Parameterized Motor Primitives, Reinforcement Learning