AnyDexRT: Calibration-Efficient Dexterous Hand Retargeting with Few-Shot Human Guidance
Overview
This research presents AnyDexRT, a novel approach for calibrating dexterous robotic hands that leverages self-supervised learning and minimal human guidance to map operator motions to robot actions. By focusing on task-relevant regions and refining pinch poses via a contact classifier, the method aims to enhance teleoperation intuitiveness and efficiency across diverse hand models without extensive manual tuning.
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Control: teleoperated
“arXiv:2607.08341v2 Announce Type: replace Abstract: Teleoperation is a key interface for controlling dexterous robotic hands and collecting demonstrations for imitation learning. Its effectiveness largely depends on kinematic retargeting, which maps operator hand motions to feasible and intuitive robot hand motions. Existing methods often require hand-crafted objectives, precise calibration, or global shape matching”
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- arXiv Robotics — research abstractsSignalAnyDexRT: Calibration-Efficient Dexterous Hand Retargeting with Few-Shot Human Guidance
This research presents AnyDexRT, a novel approach for calibrating dexterous robotic hands that leverages self-supervised learning and minimal human guidance to map operator motions to robot actions. By focusing on task-relevant regions and refining pinch poses via a contact classifier, the method aims to enhance teleoperation intuitiveness and efficiency across diverse hand models without extensive manual tuning.
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