ZeroTouch: Tactile-Supervised Visual Contact Estimation for Contact-Rich Manipulation

2026-09-18

AI Summary

为解决机器人抓取中的物理交互估计问题，ZeroTouch通过视觉监督而非触觉硬件预测接触变形和力矩，提高抓取成功率。

Abstract

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 deployment. On the full validation set, the complete architecture reduces normal-force MAE from 2.017 N for a state-only baseline to 0.531 N. In physical evaluation with 20 trials per condition, ZeroTouch achieves 95% success on an unseen object, 80% in a seen-object/unseen-grasp condition, and 90% under a content/load shift. Under the same evaluation protocol, OpenVLA achieves 25%, 40%, and 55%, while SmolVLA achieves 10%, 25%, and 35%, respectively.

Problem

• tactile-supervised
• contact estimation
• robotic grasping
• physical interaction
• compression target

Innovation

• tactile-supervised
• visual contact estimation
• dense contact deformation
• six-axis wrench
• grasp-dependent compression