A Simple Reinforcement Learning Algorithm For Biped Walking
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, Vol. 3, pp. 3030 - 3035, April, 2004
Abstract
We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately place the swing leg. This decision is based on a learned model of the Poincare map of the periodic walking pattern. The model maps from a state at the middle of a step and foot placement to a state at next middle of a step. We also modify the desired walking cycle frequency based on online measurements. We present simulation results, and are currently implementing this approach on an actual biped robot.
BibTeX
@conference{Morimoto-2004-8910,author = {Jun Morimoto and Gordon Cheng and Chris Atkeson and Garth Zeglin},
title = {A Simple Reinforcement Learning Algorithm For Biped Walking},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2004},
month = {April},
volume = {3},
pages = {3030 - 3035},
}
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