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There is a growing interest in learning a velocity command trackingcontroller of quadruped robot using reinforcement learning due to itsrobustness and scalability. However, a single policy, trained end-to-end,usually shows a single gait regardless of the command velocity. This could be asuboptimal solution considering the existence of optimal gait according to thevelocity for quadruped animals. In this work, we propose a hierarchicalcontroller for quadruped robot that could generate multiple gaits (i.e. pace,trot, bound) while tracking velocity command. Our controller is composed of twopolicies, each working as a central pattern generator and local feedbackcontroller, and trained with hierarchical reinforcement learning. Experimentresults show 1) the existence of optimal gait for specific velocity range 2)the efficiency of our hierarchical controller compared to a controller composedof a single policy, which usually shows a single gait. Codes are publiclyavailable.

Learning multiple gaits of quadruped robot using hierarchical reinforcement learning