Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

TL;DR

This paper introduces a new robustness metric for bipedal locomotion, using discrete-time barrier functions and simulation-in-the-loop learning to synthesize stable walking gaits, validated on a real exoskeleton.

Contribution

It proposes a novel robustness metric based on hybrid forward invariant sets and applies discrete-time barrier functions for gait synthesis in exoskeletons.

Findings

Successful flat-foot and multi-contact walking demonstrated on Atalante exoskeleton.

The robustness metric correlates with improved locomotion stability.

Simulation-in-the-loop approach effectively synthesizes robust gaits.

Abstract

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness -- the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.