Vision-Language Model-Guided Online Calibration of an Electromagnetic Digital Twin
Overview
A VLM-guided framework improves calibration accuracy for an electromagnetic digital twin using a Unitree G1 robot and NVIDIA Sionna, achieving 1.74e-4 conductivity error within 20 meters of travel.
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Reported quantity · travel distance: 20 other · Basis not reported
“within 20 m of travel; random initialization never converges, while random waypoints require over twice the travel.”
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- arXiv Robotics — research abstractsDemo: Vision-Language Model-Guided Online Calibration of an Electromagnetic Digital Twin
This research introduces a vision-language model (VLM)-guided framework for online calibration of an electromagnetic digital twin, using a Unitree G1 robot and NVIDIA Sionna. The framework employs material classification and waypoint planning to enhance calibration accuracy and reduce travel costs, achieving a normalized mean absolute conductivity error of $1.74 imes10^{-4}$ within 20 meters of travel.
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