Quadruped robot obstacle negotiation via reinforcement learning (2006)
Download From
IEEE
Download Links
- [ai.stanford.edu]
- [www.robotics.stanford.edu]
- [cs.stanford.edu]
- [www.stanford.edu]
- [www.cs.stanford.edu]
- [www.eecs.umich.edu]
- [www.robotics.stanford.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | in In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA |
| Citations: | 9 - 2 self |
BibTeX
@INPROCEEDINGS{Lee06quadrupedrobot,
author = {Honglak Lee and Yirong Shen and Chih-han Yu and Gurjeet Singh and Andrew Y. Ng},
title = {Quadruped robot obstacle negotiation via reinforcement learning},
booktitle = {in In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA},
year = {2006}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Abstract — Legged robots can, in principle, traverse a large variety of obstacles and terrains. In this paper, we describe a successful application of reinforcement learning to the problem of negotiating obstacles with a quadruped robot. Our algorithm is based on a two-level hierarchical decomposition of the task, in which the high-level controller selects the sequence of footplacement positions, and the low-level controller generates the continuous motions to move each foot to the specified positions. The high-level controller uses an estimate of the value function to guide its search; this estimate is learned partially from supervised data. The low-level controller is obtained via policy search. We demonstrate that our robot can successfully climb over a variety of obstacles which were not seen at training time. I.
