Hierarchical Deep Reinforcement Learning for Legged Robot Navigation

ANYmal C with Target Pose


High-level tasks such as pose tracking for legged systems are important yet challenging topics in mobile navigation. In scenarios where only high-level objectives are specified, obtained skills can be directly used as independent low-level components that break away from the high-level controller design. In this work, the effectiveness of an RL-based controller is proved with a proposed hierarchical control structure for a quadrupedal system where a high-level target-commanded policy learns to utilize existing low-level locomotion skills to continuously navigate the robot to track given pose trajectories on open and flat terrain. Experiments on a locomotion alignment task on ANYmal show that the functionality of the proposed RL-based controller is able to yield comparable tracking behaviors as a fine-tuned PD controller while providing additional safety guarantees and naturalness.

Chenhao Li
Chenhao Li
Reinforcement Learning for Robotics

My research interests focus on the general field of robot learning, including reinforcement learning, developmental robotics and legged intelligence.