Experiment-free exoskeleton assistance via learning in simulation.
Nature
; 630(8016): 353-359, 2024 Jun.
Article
en 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
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Robótica
/
Dispositivo Exoesqueleto
/
Cadera
Límite:
Humans
Idioma:
En
Revista:
Nature
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos