A Simple Reinforcement Learning Algorithm For Biped Walking

Jun Morimoto, Gordon Cheng, Chris Atkeson, and Garth Zeglin
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), April, 2004, pp. 3030 - 3035.


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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.

Notes
Associated Project(s): Dynamic Biped
Number of pages: 6

Text Reference
Jun Morimoto, Gordon Cheng, Chris Atkeson, and Garth Zeglin, "A Simple Reinforcement Learning Algorithm For Biped Walking," Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), April, 2004, pp. 3030 - 3035.

BibTeX Reference
@inproceedings{Morimoto_2004_5595,
   author = "Jun Morimoto and Gordon Cheng and Chris Atkeson and Garth Zeglin",
   title = "A Simple Reinforcement Learning Algorithm For Biped Walking",
   booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04)",
   pages = "3030 - 3035",
   month = "April",
   year = "2004",
}