Presentation 1997/7/24
Learning "stand-up" trajectories using reinforcement learning
Jun Morimoto, Kenji Doya,
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Abstract(in English) In this study we consideris the "stand-up" task for a two-joint, three-link robot. In contrast to the case of steady walking or standing, the optimal 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. 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, continuous time and state temporal difference (TD) learning method. Successful results were obtained by combining continuous TD with an efficient method of function approximation in high dimentional state space.
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Keyword(in English) reinfocement learning / motor control / robotics / stand up
Paper # NC97-28
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Committee NC
Conference Date 1997/7/24(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Learning "stand-up" trajectories using reinforcement learning
Sub Title (in English)
Keyword(1) reinfocement learning
Keyword(2) motor control
Keyword(3) robotics
Keyword(4) stand up
1st Author's Name Jun Morimoto
1st Author's Affiliation Graduate School of Infomation Science, Nara Institute of Science and Technology : ATR Human Information Processing Res. Labs.()
2nd Author's Name Kenji Doya
2nd Author's Affiliation Kawato Dynamic Brain Project, JST : Graduate School of Infomation Science, Nara Institute of Science and Technology
Date 1997/7/24
Paper # NC97-28
Volume (vol) vol.97
Number (no) 201
Page pp.pp.-
#Pages 8
Date of Issue