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Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
Jun Morimoto, Jun Nakanishi, Gen Endo, Gordon Cheng, Christopher G Atkeson, Garth Zeglin 
Title:
Poincare-map-based reinforcement learning for biped walking 
Pages contribution:
2381-2386 
Abstract:
We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately modulate an observed walking pattern. Via-points are detected from the observed walking trajectories using the minimum jerk criterion. The learning algorithm modulates the via-points as control actions to improve walking trajectories. This decision is based on a learned model of the Poincaré map of the periodic walking pattern. The model maps from a state in the single support p...    »
 
Editor:
IEEE 
Book / Congress title:
Proceedings of the 2005 IEEE International Conference on Robotics and Automation 
Organization:
IEEE 
Year:
2005 
Year / month:
2005-04 
Reviewed:
ja 
Language:
en