美国北卡罗莱纳州立大学苏浩团队通过模拟学习实现免实验外骨骼辅助。2024年6月12日，国际知名学术期刊《自然》在线发表了这一成果。

研究人员展示了一种在模拟中学习多功能控制策略的免实验方法。研究人员的仿真学习框架利用动态感知的肌肉骨骼和外骨骼模型，以及数据驱动的强化学习来弥合仿真与现实之间的差距，而无需进行人体实验。将学习到的控制器部署在定制的髋关节外骨骼上，可在不同的活动中自动产生帮助，在行走、跑步和爬楼梯时，代谢率分别降低了24.3%、13.1%和15.4%。

这个框架可以提供一种可推广和可扩展的策略，用于快速开发和广泛采用各种辅助机器人，既适用于健全者，也适用于行动不便者。

据介绍，外骨骼在提高人类机动性能方面潜力巨大。然而，由于需要长时间的人体试验和手工制定控制法则，外骨骼的发展和广泛推广受到了限制。

附：英文原文

Title: Experiment-free exoskeleton assistance via learning in simulation

Author: Luo, Shuzhen, Jiang, Menghan, Zhang, Sainan, Zhu, Junxi, Yu, Shuangyue, Dominguez Silva, Israel, Wang, Tian, Rouse, Elliott, Zhou, Bolei, Yuk, Hyunwoo, Zhou, Xianlian, Su, Hao

Issue&Volume: 2024-06-12

Abstract: Exoskeletons have enormous potential to improve human locomotive performance1,2,3. 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.

DOI: 10.1038/s41586-024-07382-4

Source: https://www.nature.com/articles/s41586-024-07382-4