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JRM Vol.20 No.3 pp. 350-357
doi: 10.20965/jrm.2008.p0350
(2008)

Paper:

Hardware and Numerical Experiments of Autonomous Robust Skill Generation Using Reinforcement Learning

Kei Senda, Takayuki Kondo, Yoshimitsu Iwasaki, Shinji Fujii,
Naofumi Fujiwara, and Naoki Suganuma

Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan

Received:
September 29, 2007
Accepted:
February 5, 2008
Published:
June 20, 2008
Keywords:
skill generation, reinforcement learning, space robot, robustness, autonomy
Abstract

It is difficult for robots to achieve tasks contacting environment due to error between the controller models and the real environment. To solve this problem, we propose having a robot autonomously obtains proficient robust skills against model error. Numerical simulation and experiments using an autonomous space robot demonstrate the feasibility of our proposal in the real environment.

Cite this article as:
Kei Senda, Takayuki Kondo, Yoshimitsu Iwasaki, Shinji Fujii,
Naofumi Fujiwara, and Naoki Suganuma, “Hardware and Numerical Experiments of Autonomous Robust Skill Generation Using Reinforcement Learning,” J. Robot. Mechatron., Vol.20, No.3, pp. 350-357, 2008.
Data files:
References
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