Experiment-free exoskeleton assistance via learning in simulation.

ABSTRACT: Exoskeletons have enormous potential to improve human locomotive performance^1-3. 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

SUBMITTER: Luo S 

PROVIDER: S-EPMC11344585 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature