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Reinforcement learning of dynamic motor sequence: learning to stand up

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2 Author(s)
J. Morimoto ; Graduate Sch. of Inf. Sci., Nara Inst. of Sci. & Technol., Japan ; K. Doya

We propose a learning method for implementing human-like sequential movements in robots. As an example of dynamic sequential movement, we consider the “stand-up” task for a two-joint, three-link robot. In contrast to the case of steady walking or standing, the desired trajectory for such a transient behavior is very difficult to derive. The goal of the task is to find a path that links a lying state to an upright state under the constraints of the system dynamics. The geometry of the robot is such that there is no static solution; the robot has to stand up dynamically utilizing the momentum of its body. We use reinforcement learning, in particular, a continuous time and state temporal difference (TD) learning method. For successful results, we use 1) an efficient method of value function approximation in a high-dimensional state space, and 2) a hierarchical architecture which divides a large state space into a few smaller pieces

Published in:

Intelligent Robots and Systems, 1998. Proceedings., 1998 IEEE/RSJ International Conference on  (Volume:3 )

Date of Conference:

13-17 Oct 1998