Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning

TL;DR

This paper introduces a hybrid zero dynamics-inspired reinforcement learning framework for 3D bipedal robot walking, achieving stable, adaptable, and robust locomotion with less training time and prior knowledge.

Contribution

It proposes a novel RL policy structure that integrates hybrid zero dynamics principles, improving efficiency and robustness in 3D bipedal walking control.

Findings

Successfully stabilizes walking cycles on Cassie robot

Achieves speed tracking in multiple directions

Demonstrates robustness against adversarial forces

Abstract

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.