Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which limit accessibility to clinical populations. To address this challenge, we introduce a device-agnostic framework that combines physiologically plausible musculoskeletal simulation with reinforcement learning to enable scalable personalized exoskeleton assistance for both able-bodied and clinical populations. Our control policies not only generate physiologically plausible locomotion dynamics but also capture clinically observed compensatory strategies under targeted muscular deficits, providing a unified computational model of both healthy and pathological gait. Without task-specific tuning, the resulting exoskeleton control policies produce assistive torque profiles at the hip and ankle that align with state-of-the-art profiles validated in human experiments, while consistently reducing metabolic cost across walking speeds. For simulated impaired-gait models, the learned control policies yield asymmetric, deficit-specific exoskeleton assistance that improves both energetic efficiency and bilateral kinematic symmetry without explicit prescription of the target gait pattern. These results demonstrate that physiologically plausible musculoskeletal simulation via reinforcement learning can serve as a scalable foundation for personalized exoskeleton control across both able-bodied and clinical populations, eliminating the need for extensive physical trials.

翻译：设计下肢外骨骼的通用控制策略仍受到大量数据采集或迭代优化程序的根本性限制，这降低了其在临床人群中的可用性。为应对这一挑战，我们提出一种设备无关的框架，将生理可信的肌肉骨骼模拟与强化学习相结合，实现对健全人群和临床人群的可扩展个性化外骨骼辅助。我们的控制策略不仅生成生理可信的运动动力学特性，还能在目标肌肉功能缺陷下捕获临床观察到的代偿策略，为健康步态与病理步态提供统一的计算模型。无需特定任务调优，所生成的外骨骼控制策略在髋关节和踝关节处产生的辅助力矩曲线与经人体实验验证的最新成果一致，并在不同行走速度下持续降低代谢消耗。针对模拟的步态障碍模型，学习到的控制策略产生非对称、缺陷特异性的外骨骼辅助，在无需显式指定目标步态模式的情况下，同时提升能量效率和双侧运动对称性。这些结果表明，通过强化学习实现生理可信的肌肉骨骼模拟，可作为面向健全人群和临床人群的个性化外骨骼控制的可扩展基础，从而避免大量物理试验的需求。