Human-Exoskeleton Kinematic Calibration toImprove Hand Tracking for Dexterous Teleoperation

Описание: Hand exoskeletons are critical tools for dexterous
teleoperation and immersive manipulation interfaces, but achiev-
ing 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. This paper
presents a subject-specific calibration framework for exoskeleton-
based hand tracking that leverages redundant joint sensing and
a residual-weighted optimization strategy to estimate virtual link
parameters. Implemented on the MAESTRO exoskeleton, our
method improves joint angle and fingertip position estimation
across users with diverse hand geometries. We introduce a
data-driven approach to empirically tune cost function weights
using motion capture ground truth, enabling more accurate and
consistent calibration across participants. Quantitative results
from seven subjects show substantial reductions in joint and
fingertip tracking errors compared to uncalibrated and evenly
weighted models. Qualitative visualizations using a Unity-based
virtual hand further confirm improvements in motion fidelity.
The proposed framework generalizes across exoskeleton designs
with closed-loop kinematics and minimal sensing, and lays the
foundation for high-fidelity teleoperation and learning-from-
demonstration applications.