用仿真代替真人实验，优化外骨骼助行控制策略。
Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation
- 融合神经肌肉模型与强化学习，模拟人类对助力的适应过程。
- 在不依赖真人实验情况下，仍能稳定生成可靠优化结果。
- 可推广至步态异常人群，精准匹配病情严重程度与助力需求。
外骨骼在提升行动能力方面潜力巨大，但适配辅助力仍面临挑战，因人体对额外作用力的适应机制复杂。当前最优控制器优化方法需大量真人实验，参与者需持续行走数小时，导致最需要帮助的行动障碍者难以参与。本文提出Exo-Plore仿真框架，结合神经肌肉仿真与深度强化学习，无需真实人体实验即可优化髋部外骨骼辅助控制。该框架可（1）生成反映人体对助力适应的逼真步态数据；（2）在步态随机性强的条件下仍获得可靠优化结果；（3）泛化至病理步态，表现出病理严重程度与最优助力之间强线性关系。
原文摘要 · Abstract (English)
Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adaptation to external forces. Current state-of-the-art approaches for optimizing exoskeleton controllers require extensive human experiments in which participants must walk for hours, creating a paradox: those who could benefit most from exoskeleton assistance, such as individuals with mobility impairments, are rarely able to participate in such demanding procedures. We present Exo-plore, a simulation framework that combines neuromechanical simulation with deep reinforcement learning to optimize hip exoskeleton assistance without requiring real human experiments. Exo-plore can (1) generate realistic gait data that captures human adaptation to assistive forces, (2) produce reliable optimization results despite the stochastic nature of human gait, and (3) generalize to pathological gaits, showing strong linear relationships between pathology severity and optimal assistance.
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