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Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Zhengtong Xu, Yeping Wang, Ben Abbatematteo, Jom Preechayasomboon, Sonny Chan, Nick Colonnese, Amirhossein H. Memar
Abstract: Contact-Grounded Policy (CGP) enables fine-grained, contact-rich dexterous manipulation by grounding multi-point contacts through predicting the actual robot state and tactile feedback, and by using a learned contact-consistency mapping to convert these predictions into controller-executable targets for a compliance controller. CGP supports both dense tactile arrays and vision-based tactile sensors mounted on the hand. We collect demonstrations via teleoperation in both simulation and on a physical robot, and evaluate CGP across multiple dexterous manipulation tasks.