Experiment-free exoskeleton assistance via learning in simulation

Document Type

Article

Publication Date

6-13-2024

Abstract

Exoskeletons have enormous potential to improve human locomotive performance1–3. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.

Identifier

85195972059 (Scopus)

Publication Title

Nature

External Full Text Location

https://doi.org/10.1038/s41586-024-07382-4

e-ISSN

14764687

ISSN

00280836

PubMed ID

38867127

First Page

353

Last Page

359

Issue

8016

Volume

630

Grant

DRRP 90DPGE0019

Fund Ref

National Science Foundation

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