Login / Signup

Experiment-free exoskeleton assistance via learning in simulation.

Shuzhen LuoMenghan JiangSainan ZhangJunxi ZhuShuangyue YuIsrael Dominguez SilvaTian WangElliott J RouseBolei ZhouHyunwoo YukXianlian Alex ZhouHao Su
Published in: Nature (2024)
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 laws 2 . 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.
Keyphrases
  • endothelial cells
  • induced pluripotent stem cells
  • virtual reality
  • pluripotent stem cells
  • healthcare
  • public health
  • mental health
  • high intensity
  • total hip arthroplasty
  • loop mediated isothermal amplification