2006年
Fast and stable learning of Quasi-Passive Dynamic Walking by an unstable biped robot based on off-policy natural actor-critic
IEEE International Conference on Intelligent Robots and Systems
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- 開始ページ
- 5226
- 終了ページ
- 5231
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/IROS.2006.281663
Recently, many researchers on humanoid robotics are interested in Quasi-Passive-Dynamic Walking (Quasi-PDW) which is similar to human walking. It is desirable that control parameters in Quasi-PDW are automatically adjusted because robots often suffer from changes in their physical parameters and the surrounding environment. Reinforcement learning (RL) can be a key technology to this adaptability, and it has been shown that RL realizes Quasi-PDW in a simulation study. To apply the existing method to controlling real robots, however, requires further improvement to accelerate its learning, otherwise the robots will break down before acquiring appropriate controls. To accelerate the learning, this study employs off-policy natural actor-critic (off-NAC), and applies it to an acquisition problem of Quasi-PDW. The most important feature of the off-NAC is that it reuses the samples that has already been obtained by previous controllers. This study also shows an adaptive method of the learning rate. Simulation as well as real experiments demonstrate that fast and stable learning of Quasi-PDW of an unstable biped robot can be realized by our modified off-NAC. © 2006 IEEE.
- リンク情報
- ID情報
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- DOI : 10.1109/IROS.2006.281663
- SCOPUS ID : 34250613580