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.

翻译：外骨骼在增强人体移动能力方面展现出巨大潜力，但由于人体对外部力的适应机制极为复杂，如何提供适宜的辅助仍面临挑战。当前最先进的外骨骼控制器优化方法需要大量人体实验，参与者必须持续行走数小时，这形成了一个悖论：那些最需要外骨骼辅助的人群（如行动障碍者）往往无法承受如此严苛的实验流程。本文提出Exo-Plore仿真框架，该框架将神经力学仿真与深度强化学习相结合，可在无需真人实验的情况下优化髋关节外骨骼辅助策略。Exo-Plore具备以下能力：（1）生成能捕捉人体对辅助力适应过程的真实步态数据；（2）在人体步态随机性的影响下仍能产生可靠的优化结果；（3）可泛化至病理步态，其优化结果在病理严重程度与最佳辅助力度之间呈现显著的线性关系。