Policy gradient reinforcement learning for fast quadrupedal locomotion

Abstract

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… (More)
DOI: 10.1109/ROBOT.2004.1307456

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@article{Kohl2004PolicyGR, title={Policy gradient reinforcement learning for fast quadrupedal locomotion}, author={Nate Kohl and Peter Stone}, journal={IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004}, year={2004}, volume={3}, pages={2619-2624 Vol.3} }