By Topic

Fast and Stable Learning of Quasi-Passive Dynamic Walking by an Unstable Biped Robot based on Off-Policy Natural Actor-Critic

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Ueno, T. ; Graduate Sch. of Inf. Sci., Nara Inst. of Sci. & Technol. ; Nakamura, Y. ; Takuma, T. ; Shibata, T.
more authors

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

Published in:

Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on

Date of Conference:

9-15 Oct. 2006