Human-Exoskeleton Kinematic Calibration to Improve Hand Tracking for Dexterous Teleoperation

TL;DR

This paper presents a subject-specific calibration method for hand exoskeletons that significantly improves tracking accuracy by estimating virtual link parameters, enabling more precise teleoperation and manipulation.

Contribution

A novel data-driven calibration framework that estimates virtual link parameters for exoskeletons, improving hand tracking accuracy across users with minimal sensing.

Findings

Substantial reduction in joint and fingertip tracking errors.

Enhanced motion fidelity demonstrated through virtual hand visualizations.

Method generalizes to various exoskeleton designs with limited sensors.

Abstract

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.