Machine Learning for Fast Quadrupedal Locomotion (2004)
Nate Kohl and Peter Stone
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.
In Nineteenth National Conference on Artificial Intelligence, pp. 611-616, July 2004.

Peter Stone Faculty pstone [at] cs utexas edu