用于动力膝盖-脚踝假肢控制器个性化的重放约束模拟框架

中文摘要 AI 辅助

动力假肢腿的阻抗控制器个性化对于适应个体步态生物力学至关重要，但仍具挑战性。现有方法依赖耗时的人工参与探索和/或将优化限制在低维单关节参数子空间。模拟到现实的转移已实现有腿机器人的高维运动控制，但在辅助设备控制中人类伙伴难以建模。我们提出了一个重放约束模拟框架：基于MuJoCo的模拟器在重放记录的髋关节运动学和个体行走数据中基于反馈的地面反作用力时再现假肢膝盖-脚踝动力学，无需对复杂的人类神经肌肉控制机制进行建模。我们用深度强化学习策略展示了该框架，该策略同时个性化两个关节的相位相关刚度、阻尼和平衡角，最大化仅从板载假肢测量计算的基于生物模仿的奖励。对三名经股截肢参与者在0.8米/秒的平地行走实验中证明了强大的模拟到硬件预测有效性（Pearson r = 0.96 - 0.997）。硬件上表现最佳的策略在所有参与者的模拟策略中始终在前五名被预测到。相对于未个性化的基线，学习到的控制器将整体生物模仿奖励提高了42 - 59%。该框架支持动力假肢腿的可扩展高维个性化，并且适合扩展到更高维的控制器参数化，如神经网络控制器。

英文摘要

Personalization of impedance controllers for powered prosthetic legs is critical to accommodating individual gait biomechanics but remains challenging. Existing methods rely on time-intensive human-in-the-loop exploration and/or constrain optimization to low-dimensional, single-joint parameter subspaces. Sim-to-real transfer has enabled high-dimensional locomotion control for legged robots, but in assistive device control the human partner remains un-modelable. We present a replay-constrained simulation framework: a MuJoCo-based simulator reproduces prosthetic knee-ankle dynamics while replaying recorded hip kinematics and feedback-based ground reaction forces from individual walking data, bypassing the need to model complex human neuromuscular control mechanisms. We demonstrate the framework with a deep reinforcement learning policy that personalizes phase-dependent stiffness, damping, and equilibrium angle at both joints simultaneously, maximizing a biomimicry-based reward computed solely from onboard prosthesis measurements. Experiments with three participants with transfemoral amputation during level-ground walking at 0.8~m/s demonstrate strong simulation-to-hardware predictive validity (Pearson $r=0.96$--$0.997$). The best-performing policy on hardware was consistently predicted within the top five simulation policies for all participants. The learned controllers improved overall biomimicry rewards by 42--59\% relative to the unpersonalized baseline. The framework supports scalable high-dimensional personalization of powered prosthetic legs and is amenable to extension to higher-dimensional controller parameterizations such as neural-network controllers.

发表机构

• College of Engineering, University of Michigan（密歇根大学工程学院）

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