Human-Exoskeleton Kinematic Calibration to Improve Hand Tracking for Dexterous Teleoperation
Haiyun Zhang, Stefano Dalla Gasperina, Saad N. Yousaf, Job D. Ramirez, Toshimitsu Tsuboi, Tetsuya Narita, Ashish D. Deshpande
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. While demonstrated on the MAESTRO exoskeleton, the framework may be extended in principle to other hand exoskeleton systems that provide comparable kinematic constraints and sensing capabilities.
BibTeX
@inproceedings{ral2026_humanexoskeleton,
title = {Human-Exoskeleton Kinematic Calibration to Improve Hand Tracking for Dexterous Teleoperation},
author = {Haiyun Zhang and Stefano Dalla Gasperina and Saad N. Yousaf and Job D. Ramirez and Toshimitsu Tsuboi and Tetsuya Narita and Ashish D. Deshpande},
booktitle = {RA-L 2026},
year = {2026}
}