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
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.
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.
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