ハイブリッドゼロダイナミクスに着想を得た強化学習による3D二足歩行のフィードバック制御方策設計
Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning
歩行ダイナミクスのハイブリッド性とハイブリッドゼロダイナミクス理論を方策構造に組み込み、軽量で短時間学習かつ外乱に頑健な3D二足歩行の強化学習制御をCassieで実現した。
著者: Guillermo A. Castillo, Bowen Weng, Wei Zhang, Ayonga Hereid
分類: cs.RO, cs.LG, cs.NE
原文アブストラクト
This paper presents a novel model-free reinforcement learning (RL) framework to design feedback control policies for 3D bipedal walking. Existing RL algorithms are often trained in an end-to-end manner or rely on prior knowledge of some reference joint trajectories. Different from these studies, we propose a novel policy structure that appropriately incorporates physical insights gained from the hybrid nature of the walking dynamics and the well-established hybrid zero dynamics approach for 3D bipedal walking. As a result, the overall RL framework has several key advantages, including lightweight network structure, short training time, and less dependence on prior knowledge. We demonstrate the effectiveness of the proposed method on Cassie, a challenging 3D bipedal robot. The proposed solution produces stable limit walking cycles that can track various walking speed in different directions. Surprisingly, without specifically trained with disturbances to achieve robustness, it also performs robustly against various adversarial forces applied to the torso towards both the forward and the backward directions.
