This paper presents a machine learning approach to optimizing a quadrupedal trot gait for forward speed. Given a parameterized walk designed for a specific robot, we propose using a form of policy gradient reinforcement learning to automatically search the set of possible parameters with the goal of finding the fastest possible walk. We implement and test our approach on a commercially available quadrupedal robot platform, namely the Sony Aibo robot. After about three hours of learning, all on the physical robots and with no human intervention other than to change the batteries, the robots achieved the fastest walk known for the Aibo, significantly outperforming a variety of existing hand-coded and learned solutions.

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In Proceedings of the {IEEE} International Conference on Robotics and Automation, pp. 2619-2624, May 2004.

@InProceedings{kohl:icra04, title={Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion}, author={Nate Kohl and Peter Stone}, booktitle={Proceedings of the {IEEE} International Conference on Robotics and Automation}, month={May}, pages={2619-2624}, url="http://www.cs.utexas.edu/users/ai-lab?kohl:icra04", year={2004} }