让康复机器人实时优化动作，减少肩部肌肉负荷。
Biomechanics-Aware Trajectory Optimization for Online Navigation during Robotic Physiotherapy
- 用高精度肩关节模型嵌入控制框架，生成低应变轨迹。
- 实测响应速度达标，运动平稳，人机交互力小。
- 适合需要动态适应患者突发动作的康复场景。
机器人设备为物理治疗与康复训练提供了巨大机遇，但现有方法难以融入确保安全有效的生物力学指标。本文提出BATON（Biomechanics-Aware Trajectory Optimization），一种面向肩袖康复的在线机器人导航方法，通过将高保真度人体肩关节OpenSim模型嵌入最优控制框架，实现实时生成最小化肌肉应变的治疗动作轨迹。其核心优势在于可基于机器人感知的位姿与力/力矩数据，实时在线调整轨迹以应对患者不可预测的自主动作或反射反应。该能力由一个基于模型的实时估计算法实现，利用快速冗余求解器推断肌群活动变化。我们在真实人机交互实验中验证了BATON，评估了响应速度、运动平滑性及交互力，结果表明其具备快速响应与稳定控制能力。
原文摘要 · Abstract (English)
Robotic devices provide a great opportunity to assist in delivering physical therapy and rehabilitation movements, yet current robot-assisted methods struggle to incorporate biomechanical metrics essential for safe and effective therapy. We introduce BATON, a Biomechanics-Aware Trajectory Optimization approach to online robotic Navigation of human musculoskeletal loads for rotator cuff rehabilitation. BATON embeds a high-fidelity OpenSim model of the human shoulder into an optimal control framework, generating strain-minimizing trajectories for real-time control of therapeutic movements. \addedText{Its core strength lies in the ability to adapt biomechanics-informed trajectories online to unpredictable volitional human actions or reflexive reactions during physical human-robot interaction based on robot-sensed motion and forces. BATON's adaptability is enabled by a real-time, model-based estimator that infers changes in muscle activity via a rapid redundancy solver driven by robot pose and force/torque sensor data. We validated BATON through physical human-robot interaction experiments, assessing response speed, motion smoothness, and interaction forces.
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