Experiment-free exoskeleton assistance via learning in simulation.
Nature
; 630(8016): 353-359, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38867127
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.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Robótica
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Exoesqueleto Energizado
/
Quadril
Limite:
Humans
Idioma:
En
Revista:
Nature
/
Nature (Lond.)
/
Nature (London)
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos