  We focus on the problem of developing efficient controllers for quadrupedal
robots. Animals can actively switch gaits at different speeds to lower their
energy consumption. In this paper, we devise a hierarchical learning framework,
in which distinctive locomotion gaits and natural gait transitions emerge
automatically with a simple reward of energy minimization. We use reinforcement
learning to train a high-level gait policy that specifies the contact schedules
of each foot, while the low-level Model Predictive Controller (MPC) optimizes
the motor torques so that the robot can walk at a desired velocity using that
gait pattern. We test our learning framework on a quadruped robot and
demonstrate automatic gait transitions, from walking to trotting and to
fly-trotting, as the robot increases its speed up to 2.5m/s (5 body lengths/s).
We show that the learned hierarchical controller consumes much less energy
across a wide range of locomotion speed than baseline controllers.
