MISC

査読有り
2003年

Extended QDSEGA for controlling real robots - Acquisition of locomotion patterns for snake-like robot

2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-3, PROCEEDINGS
  • K Ito
  • ,
  • T Kamegawa
  • ,
  • F Matsuno

1
開始ページ
791
終了ページ
796
記述言語
英語
掲載種別
出版者・発行元
IEEE

Reinforcement learning is very effective for robot learning. Because it does not need prior knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: "Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)". It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. However the application of QDSEGA is restricted to static systems.
A snake-like robot has many redundant degrees of freedom and the dynamics of the system are very important to complete the locomotion task. So application of usual reinforcement learning is very difficult.
In this paper, we extend layered structure of QDSEGA so that it becomes possible to apply it to real robots that have complexities and dynamics. We apply it to acquisition of locomotion pattern of the snake-like robot and demonstrate the effectiveness and the validity of QDSEGA with the extended layered structure by simulation and experiment.

リンク情報
CiNii Articles
http://ci.nii.ac.jp/naid/120002309391
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000187419900127&DestApp=WOS_CPL
ID情報
  • ISSN : 1050-4729
  • CiNii Articles ID : 120002309391
  • Web of Science ID : WOS:000187419900127

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