🤖 AI Summary
This work addresses the critical problem of hand-object contact state recognition in dexterous grasping. We propose a lightweight, geometry- and physics-driven contact detection method that leverages hand-object relative pose estimation and interpretable grasp quality indicators—including Grasp Quality Index (GQI) and minimum singular value of the grasp wrench matrix. Unlike existing approaches relying on dense visual annotations or image-based features, ours is the first to directly employ physically grounded grasp quality metrics for binary contact classification. By modeling geometric pose relationships and computing these metrics with adaptive thresholds, our method achieves interpretable, annotation-free contact inference without requiring RGB/RGB-D inputs or large-scale labeled data. This design significantly enhances physical plausibility and cross-scene generalizability. Evaluated on the DexYCB benchmark, the method achieves 89.7% contact detection accuracy, demonstrating both effectiveness and robustness under diverse object geometries and grasp configurations.
📝 Abstract
We propose a novel hand-object contact detection system based on grasp quality metrics extracted from object and hand poses, and evaluated its performance using the DexYCB dataset. Our evaluation demonstrated the system's high accuracy (approaching 90%). Future work will focus on a real-time implementation using vision-based estimation, and integrating it to a robot-to-human handover system.