Fiber Optic Sensing Glove for High Performance Dexterous Manipulation Capture

2026-08-25

AI Summary

为解决视觉方法在遮挡和复杂光照下手部姿态捕捉困难及传感器手套精度低的问题，提出一种光纤传感手套，利用多芯形状感知纤维实现高精度手部姿态跟踪。

Abstract

Capturing hand pose during dexterous manipulation remains difficult: vision-based methods degrade under occlusion and challenging lighting, while sensorized gloves, though occlusion-free, are prone to drift and magnetic interference and rarely match motion-capture accuracy. We introduce a fiber optic sensing glove for full hand pose tracking that targets these failure modes, using multi-core shape-sensing fibers that capture each fiber's full 3D shape rather than curvature alone. A novel pipeline registers each reconstructed fiber shape to a common hand reference frame, and a new inverse-kinematics solver reconstructs full hand pose at 60 Hz using curve constraints. Benchmarked on a 2-hour dataset of dexterous object manipulation tasks across 5 subjects, the glove achieves 7.2 mm mean fingertip position error against motion capture ground truth, reduced to 4.9 mm by a one-time factory calibration of the fiber routing hub that transfers across users and sessions. These capabilities enable high-fidelity data capture and bimanual virtual teleoperation - both essential to advancing the robotics field.

Problem

• hand pose
• dexterous manipulation
• occlusion
• sensorized gloves
• motion capture

Innovation

• Fiber Optic Sensing
• Shape-Sensing Fibers
• Inverse-Kinematics Solver
• High-Fidelity Data Capture
• Virtual Teleoperation