  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.
