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

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion