Hand-Object Contact Detection using Grasp Quality Metrics

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. Index Terms--contact detection, grasp detection, grasp quality metrics, scene reconstruction, robot-to-human handover. State-of-the-art techniques on contact detection rely on physical interactions, such as force or contact sensing [1], which often require costly parameters and the θ parameters captured from the frame, and sensors [2].