AIを活用したシミュレーション・トレーニングが、ロボット外骨格における人間のパフォーマンスを向上させる(AI-Powered Simulation Training Improves Human Performance in Robotic Exoskeletons)

2024-06-12 ノースカロライナ州立大学(NCState)

<関連情報>

• https://news.ncsu.edu/2024/06/ai-training-robotic-exoskeletons/
• https://www.nature.com/articles/s41586-024-07382-4

シミュレーション学習による実験不要の外骨格補助 Experiment-free exoskeleton assistance via learning in simulation

Shuzhen Luo,Menghan Jiang,Sainan Zhang,Junxi Zhu,Shuangyue Yu,Israel Dominguez Silva,Tian Wang,Elliott Rouse,Bolei Zhou,Hyunwoo Yuk,Xianlian Zhou & Hao Su
Nature  Published:12 June 2024
DOI:https://doi.org/10.1038/s41586-024-07382-4

Abstract

Exoskeletons have enormous potential to improve human locomotive performance^1,2,3. 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.