发表机构
New Jersey Institute of Technology; Kessler Foundation（新泽西理工学院; 凯斯勒基金会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UniExo通过多技能人体策略蒸馏与多智能体协同强化学习，训练统一髋部外骨骼控制器，实现行走、转弯、跑步和倒走及其转换的无标签控制，提升跟踪成功率与鲁棒性。
AI 中文摘要
日常运动涵盖了多种活动以及活动之间的频繁转换，然而大多数外骨骼控制器是为单一活动或一组有限的相关动作而设计的。因此，活动的变化通常需要显式的模式切换以及单独调优或重新训练的控制器。基于仿真的学习减少了对硬件调优的需求，但通常仍保留这一局限性。在此，我们提出UniExo框架，该框架首先构建一个多技能肌肉骨骼人体策略，然后与之联合训练一个外骨骼控制策略。四个针对行走、转弯、跑步和倒走的单技能模仿专家被蒸馏到一个由技能潜变量结构化的单一网络中，随后通过强化学习在转换序列上进行微调。由此产生的统一人体策略在四个技能的未见片段上实现了94.7%的平均跟踪成功率，并且比其组成专家对扰动具有更强的鲁棒性。一个单一的髋部外骨骼控制器（UniExo）从人体策略的髋部力矩预测中初始化，并通过多智能体强化学习在四个技能上与之协同适应。这种协同适应改变了辅助力矩的时序，并提高了传递给髋部的正功比例。当部署在定制髋部外骨骼上时，该控制器在六名参与者中泛化到四种跑步机速度，并协助一名参与者完成包含所有四种技能及其转换的连续路线，无需技能标签或显式模式切换。因此，UniExo朝着用统一的、用户特定的控制器取代特定活动的控制器迈出了一步，这些控制器支持多样化的运动活动及其之间的转换。
英文摘要
Daily locomotion encompasses diverse activities and frequent transitions between them, yet most exoskeleton controllers are designed for a single activity or a narrow set of related movements. Changes in activity therefore typically require explicit mode switching and separately tuned or retrained controllers. Simulation-based learning reduces the need for hardware-based tuning but generally retains this limitation. Here we present UniExo, a framework that first constructs a multi-skill musculoskeletal human policy and then jointly trains an exoskeleton control policy with it. Four single-skill imitation experts for walking, turning, running and backward walking are distilled into a single network structured by a skill latent and subsequently fine-tuned through reinforcement learning on transition sequences. The resultant unified human policy achieves a mean tracking success rate of 94.7% on unseen clips of the four skills and exhibits greater robustness to perturbations than its constituent experts. A single hip exoskeleton controller (UniExo) is initialized from hip moment prediction of the human policy and co-adapted with it through multi-agent reinforcement learning across the four skills. This co-adaptation shifts the timing of the assistance torque and raises the fraction of positive work delivered to the hip. When deployed on a custom hip exoskeleton, the controller generalizes across four treadmill speeds in six participants and assists one participant through a continuous route of all four skills and their transitions, without skill labels or explicit mode switching. UniExo thus provides a step towards replacing activity-specific controllers with unified, user-specific controllers that support diverse locomotor activities and the transitions between them.
Comments9 pages, 8 figures
