针对手外骨骼追踪不准问题，提出用户自适应标定方法提升操作精度。
Human-Exoskeleton Kinematic Calibration to Improve Hand Tracking for Dexterous Teleoperation
- 基于残差加权优化估计虚拟连杆参数，实现个体化标定。
- 7名参与者实验显示关节与指尖追踪误差显著降低。
- 适用于闭环结构和传感少的外骨骼，适合高精度遥操作场景。
手部外骨骼是灵巧遥操作与沉浸式操控的重要工具，但因个体解剖差异和穿戴不一致，常导致运动学错位，影响追踪精度，限制在精密任务中的应用。本文提出一种面向个体的外骨骼手部追踪标定框架，通过残差加权优化估计虚拟连杆参数，并引入数据驱动方法，利用动作捕捉真值调整代价函数权重，实现跨用户的准确一致标定。在Maestro手部外骨骼上对7名健康参与者进行验证，结果表明在不同手部形态下，关节与指尖追踪误差均显著下降。基于Unity的虚拟手可视化进一步展示了运动保真度提升。该框架可推广至具有闭合回路运动学和有限感知的外骨骼系统，为高保真遥操作与机器人学习应用奠定基础。
原文摘要 · Abstract (English)
Hand exoskeletons are critical tools for dexterous teleoperation and immersive manipulation interfaces, but achieving accurate hand tracking remains a challenge due to user-specific anatomical variability and donning inconsistencies. These issues lead to kinematic misalignments that degrade tracking performance and limit applicability in precision tasks. We propose a subject-specific calibration framework for exoskeleton-based hand tracking that estimates virtual link parameters through residual-weighted optimization. A data-driven approach is introduced to empirically tune cost function weights using motion capture ground truth, enabling accurate and consistent calibration across users. Implemented on the Maestro hand exoskeleton with seven healthy participants, the method achieved substantial reductions in joint and fingertip tracking errors across diverse hand geometries. Qualitative visualizations using a Unity-based virtual hand further demonstrate improved motion fidelity. The proposed framework generalizes to exoskeletons with closed-loop kinematics and minimal sensing, laying the foundation for high-fidelity teleoperation and robot learning applications.
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