  We present a hierarchical framework that combines model-based control and
reinforcement learning (RL) to synthesize robust controllers for a quadruped
(the Unitree Laikago). The system consists of a high-level controller that
learns to choose from a set of primitives in response to changes in the
environment and a low-level controller that utilizes an established control
method to robustly execute the primitives. Our framework learns a controller
that can adapt to challenging environmental changes on the fly, including novel
scenarios not seen during training. The learned controller is up to 85~percent
more energy efficient and is more robust compared to baseline methods. We also
deploy the controller on a physical robot without any randomization or
adaptation scheme.

Robotics;Machine Learning