Presentation 2005/7/20
Learning of Qusasi-Passive Dynamic Walking by a Stochastic Policy Gradient Method
Kentarou HITOMI, Tomohiro SHIBATA, Yutaka NAKAMURA, Shin ISHII,
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Abstract(in English) A class of biped locomotion called Passive Dynamic Walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although PDW is sensitive to the initial condition and disturbances, some studies of Quasi-PDW, which incorporates supplemental actuators, have been reported to overcome the sensitivity. In this article, we propose a reinforcement learning scheme designed in particular for Quasi-PDW walking. The keys of our approach are a reward function and a learning method of a simple intermittent feedback controller, both of which utilize the robot's passive dynamics as much as possible. They successfully make the action selection problem for walking significantly reduced. Computer simulations show that the parameter in a Quasi-PDW controller is quickly learned after only 180 episodes, and that the obtained controller is robust against sudden perturbations and variations in the slope gradient.
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Keyword(in English) passive dynamic walking / reinforcement learning / adaptive control
Paper # NC2005-35
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Committee NC
Conference Date 2005/7/20(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Learning of Qusasi-Passive Dynamic Walking by a Stochastic Policy Gradient Method
Sub Title (in English)
Keyword(1) passive dynamic walking
Keyword(2) reinforcement learning
Keyword(3) adaptive control
1st Author's Name Kentarou HITOMI
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name Tomohiro SHIBATA
2nd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology:Computational Neuroscience Laboratories, ATR
3rd Author's Name Yutaka NAKAMURA
3rd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
4th Author's Name Shin ISHII
4th Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
Date 2005/7/20
Paper # NC2005-35
Volume (vol) vol.105
Number (no) 211
Page pp.pp.-
#Pages 6
Date of Issue