Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics

TL;DR

This paper introduces a novel approach combining musculoskeletal models with hybrid zero dynamics to generate stable, natural multicontact walking gaits for robotic assistive devices, demonstrated on a prosthesis with user preference validation.

Contribution

It integrates musculoskeletal models into the gait generation process using hybrid zero dynamics, enabling more natural and stable locomotion in robotic assistive devices.

Findings

Generated gaits closely match natural motion capture data.

Users preferred gaits produced by the new framework over traditional methods.

The approach successfully achieves multicontact locomotion on a prosthesis.

Abstract

Generating stable walking gaits that yield natural locomotion when executed on robotic-assistive devices is a challenging task that often requires hand-tuning by domain experts. This paper presents an alternative methodology, where we propose the addition of musculoskeletal models directly into the gait generation process to intuitively shape the resulting behavior. In particular, we construct a multi-domain hybrid system model that combines the system dynamics with muscle models to represent natural multicontact walking. Provably stable walking gaits can then be generated for this model via the hybrid zero dynamics (HZD) method. We experimentally apply our integrated framework towards achieving multicontact locomotion on a dual-actuated transfemoral prosthesis, AMPRO3, for two subjects. The results demonstrate that enforcing muscle model constraints produces gaits that yield natural locomotion (as analyzed via comparison to motion capture data and electromyography). Moreover, gaits generated with our framework were strongly preferred by the non-disabled prosthetic users as compared to gaits generated with the nominal HZD method, even with the use of systematic tuning methods. We conclude that the novel approach of combining robotic walking methods (specifically HZD) with muscle models successfully generates anthropomorphic robotic-assisted locomotion.