Meta-Reinforcement Learning for Universal Quadrupedal Locomotion Control

This work presents a deep reinforcement learning-based approach to develop a policy for robot-agnostic locomotion control. Our method involves training an agent equipped with memory, implemented as a recurrent policy, on a diverse set of procedurally generated quadruped robots. We demonstrate that the policies trained by our framework transfer seamlessly to both simulated and real-world quadrupeds not encountered during training, maintaining high-quality motion across platforms. Through a series of simulation and hardware experiments, we highlight the critical role of the recurrent unit in enabling generalization, rapid adaptation to changes in the robot's dynamic properties, and sample efficiency.