Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

• URL: http://arxiv.org/abs/2112.04741v1
• Date: Thu, 9 Dec 2021 07:45:25 GMT
• Title: Learning multiple gaits of quadruped robot using hierarchical reinforcement learning
• Authors: Yunho Kim, Bukun Son, and Dongjun Lee
• Abstract summary: We propose a hierarchical controller for quadruped robot that could generate multiple gaits while tracking velocity command. Experiment results show 1) the existence of optimal gait for specific velocity range 2) the efficiency of our hierarchical controller compared to a controller composed of a single policy.
• Score: 9.60618440185329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a growing interest in learning a velocity command tracking controller of quadruped robot using reinforcement learning due to its robustness and scalability. However, a single policy, trained end-to-end, usually shows a single gait regardless of the command velocity. This could be a suboptimal solution considering the existence of optimal gait according to the velocity for quadruped animals. In this work, we propose a hierarchical controller for quadruped robot that could generate multiple gaits (i.e. pace, trot, bound) while tracking velocity command. Our controller is composed of two policies, each working as a central pattern generator and local feedback controller, and trained with hierarchical reinforcement learning. Experiment results show 1) the existence of optimal gait for specific velocity range 2) the efficiency of our hierarchical controller compared to a controller composed of a single policy, which usually shows a single gait. Codes are publicly available.

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