SOURCE-LINKED INTELLIGENCE
ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation
Reliable robotic grasping benefits from estimating the evolving physical interaction and selecting a grasp-dependent compression target. Tactile sensors provide direct interaction measurements but require dedicated hardware at deployment. We introduce ZeroTouch, a tactile-supervised framework that predicts dense contact deformation, the instantaneous six-axis wrench, and a grasp-dependent desired compression target from wrist RGB observations, gripper state, and local gravity direction. Tactile measurements are used only as privileged supervision during training and are not required at deploym
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T13:03:10.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.