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

NVIDIA researchers present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped robot (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 environme