For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents a machine learning approach to legged locomotion, with all training done on the physical robots. The main contributions are a specification of our fully automated learning environment and a detailed empirical comparison of four different machine learning algorithms for learning quadrupedal locomotion. The resulting learned walk is considerably faster than all previously reported hand-coded walks for the same robot platform.

PDF, PS, HTML

In Nineteenth National Conference on Artificial Intelligence, pp. 611-616, July 2004.

@InProceedings{kohl:aaai04, title={Machine Learning for Fast Quadrupedal Locomotion}, author={Nate Kohl and Peter Stone}, booktitle={Nineteenth National Conference on Artificial Intelligence}, month={July}, pages={611-616}, url="http://www.cs.utexas.edu/users/ai-lab?kohl:aaai04", year={2004} }