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arXiv · 2107.03741

Adaptation of Quadruped Robot Locomotion with Meta-Learning

Arsen Kuzhamuratov·Dmitry Sorokin·Alexander Ulanov·A. I. Lvovsky
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Abstract

Animals have remarkable abilities to adapt locomotion to different terrains and tasks. However, robots trained by means of reinforcement learning are typically able to solve only a single task and a transferred policy is usually inferior to that trained from scratch. In this work, we demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks. The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.

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Arsen Kuzhamuratov, Dmitry Sorokin, Alexander Ulanov, A. I. Lvovsky. 2021-07-08. Adaptation of Quadruped Robot Locomotion with Meta-Learning. https://arxiv.org/abs/2107.03741

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