ダイナミクスランダム化の再検討：四足歩行の事例研究
Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion
Laikago四足ロボットの歩行方策学習において、ダイナミクスランダム化なしでもsim-to-real転移が可能であることを示し、成功を左右する設計要因をアブレーション研究で分析した論文。
著者: Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, Animesh Garg
分類: cs.RO
原文アブストラクト
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/.
