Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion
This work addresses the simulation-to-reality gap for legged robots, providing clarity on design decisions for robust locomotion, though it is incremental in refining existing methods.
The study investigated the role of dynamics randomization in training robust locomotion policies for the Laikago quadruped robot, finding that direct sim-to-real transfer is possible without it, contrary to prior work, and conducted extensive ablation studies to identify key factors for successful policy transfer.
Understanding the gap between simulation and reality is critical for reinforcement learning with legged robots, which are largely trained in simulation. However, recent work has resulted in sometimes conflicting conclusions with regard to which factors are important for success, including the role of dynamics randomization. In this paper, we aim to provide clarity and understanding on the role of dynamics randomization in learning robust locomotion policies for the Laikago quadruped robot. Surprisingly, in contrast to prior work with the same robot model, we find that direct sim-to-real transfer is possible without dynamics randomization or on-robot adaptation schemes. We conduct extensive ablation studies in a sim-to-sim setting to understand the key issues underlying successful policy transfer, including other design decisions that can impact policy robustness. We further ground our conclusions via sim-to-real experiments with various gaits, speeds, and stepping frequencies. Additional Details: https://www.pair.toronto.edu/understanding-dr/.