发表机构
Tsinghua University; Shenzhen MileBot Robotics Co., Ltd（清华大学; 深圳迈宝机器人有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出ATP无源控制框架，通过仿真训练的强化学习控制器等实现上肢外骨骼安全辅助，可降低目标肌肉活动最高达48%。
AI 中文摘要
为不同运动提供辅助是外骨骼的核心目标，解剖学知识可实现对不同任务具有泛化性的响应式支持。然而，解剖学辅助主要在下肢外骨骼中被研究，这类周期性、负重运动对力矩精度要求较低。将此类辅助扩展到复杂、非周期性的上肢运动仍具挑战性。本文提出基于无源控制的解剖学力矩（ATP）用于安全上肢外骨骼辅助：首先，可扩展的肌肉骨骼仿真框架训练统一的强化学习肌肉控制器，该控制器可泛化至上肢各类运动，生成解剖学参考力矩且无需复杂生物力学计算；其次，在线力矩优化方案可适配不同运动、抑制肌腱诱发的峰值，并结合学习到的异常分数以实现安全舒适的辅助；第三，交互力矩控制器通过缆线驱动的柔顺外骨骼传递辅助，无需将运动限制在预定义轨迹内，同时能量罐模块保障无源特性，具备力矩跟踪和系统无源的理论保证。仿真与真实实验表明，该控制器可准确跟踪长时间运动序列，泛化至实时人体运动，实现精准力矩跟踪并保持无源特性，在能量罐补充后可恢复跟踪。针对5名参与者的肌电图研究进一步显示，与重力补偿和开环辅助相比，该方案在静态和动态任务中可降低目标肌肉活动，在动态多关节任务中，与无外骨骼辅助的情况相比，肌肉活动降低幅度最高达48%。
英文摘要
Providing assistance across diverse movements is a central objective of exoskeletons, and anatomical knowledge can enable responsive support that generalizes across tasks. However, anatomical assistance has mainly been studied for lower-limb exoskeletons, where periodic, weight-bearing motions impose lower demands on torque precision. Extending such assistance to complex, nonperiodic upper-limb movements remains challenging. This paper proposes Anatomical Torque with Passivity-Based Control (ATP) for safe upper-limb exoskeleton assistance. First, a scalable musculoskeletal simulation framework trains a unified reinforcement-learning muscle controller that generalizes across upper-limb movements and generates anatomical reference torques without complex biomechanical computations. Second, an online torque-refinement scheme adapts the reference to diverse movements, suppresses tendon-induced spikes, and incorporates a learned anomaly score for safe and comfortable assistance. Third, an interaction torque controller delivers assistance through a cable-driven compliant exoskeleton without constraining motion to predefined trajectories, while an energy tank preserves passivity with theoretical guarantees on torque tracking and system passivity. Simulations and real-world experiments show accurate tracking of long-duration motion sequences and generalization to real-time human movements. The controller achieves accurate torque tracking while preserving passivity and resumes tracking after energy-tank replenishment. An EMG study with five participants further shows reduced target-muscle activity during static and dynamic tasks compared with gravity compensation and open-loop assistance, with reductions of up to 48% relative to movement without the exoskeleton in a dynamic multi-joint task.
