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Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
Authors Gen Endo, Jun Morimoto, Takamitsu Matsubara, Jun Nakanishi, Gordon Cheng 
Title:
Learning cpg sensory feedback with policy gradient for biped locomotion for a full-body humanoid 
Abstract:
This paper describes a learning framework for a central pattern generator based biped locomotion controller using a policy gradient method. Our goals in this study are to achieve biped walking with a 3D hardware humanoid, and to develop an efficient learning algorithm with CPG by reducing the dimensionality of the state space used for learning. We demonstrate that an appropriate feedback controller can be acquired within a thousand trials by numerical simulations and the obtained controller in n...    »
 
Book / Congress title:
Proceedings of the Twentieth National Conference on Artificial Intelligence 
Volume:
20 
Edition:
Date of congress:
July 9-13, 2005 
Publisher:
Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press 
Year:
2005 
Print-ISBN:
978-1-57735-236-5