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JRM Vol.17 No.6 pp. 636-644
doi: 10.20965/jrm.2005.p0636
(2005)

Paper:

Off-Policy Natural Policy Gradient Method for a Biped Walking Using a CPG Controller

Yutaka Nakamura, Takeshi Mori, Yoichi Tokita,
Tomohiro Shibata, and Shin Ishii

Theoretical Life Science Lab., Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan

Received:
February 10, 2005
Accepted:
June 21, 2005
Published:
December 20, 2005
Keywords:
reinforcement learning, off-policy learning, biped walking, central pattern generator (CPG)
Abstract

Referring to the mechanism of animals’ rhythmic movements, motor control schemes using a central pattern generator (CPG) controller have been studied. We previously proposed reinforcement learning (RL) called the CPG-actor-critic model, as an autonomous learning framework for a CPG controller. Here, we propose an off-policy natural policy gradient RL algorithm for the CPG-actor-critic model, to solve the “exploration-exploitation” problem by meta-controlling “behavior policy.” We apply this RL algorithm to an automatic control problem using a biped robot simulator. Computer simulation demonstrated that the CPG controller enables the biped robot to walk stably and efficiently based on our new algorithm.

Cite this article as:
Yutaka Nakamura, Takeshi Mori, Yoichi Tokita,
Tomohiro Shibata, and Shin Ishii, “Off-Policy Natural Policy Gradient Method for a Biped Walking Using a CPG Controller,” J. Robot. Mechatron., Vol.17, No.6, pp. 636-644, 2005.
Data files:
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