User: Guest  Login
Title:

Poincare-map-based reinforcement learning for biped walking

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Jun Morimoto, Jun Nakanishi, Gen Endo, Gordon Cheng, Christopher G Atkeson, Garth Zeglin
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
 BibTeX