Machine Learning for Fast Quadrupedal Locomotion

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.

• Type: Conference
• Year: 2004
• Where: AAAI
• Authors: Nate Kohl, Peter Stone
• Keywords: AAAI 2004, Intelligent Agents, Machine Learning, Machine Learning Algorithms, Machine Learning Approach