NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation

2026-06-23

AI Summary

This study addresses the challenge of accurately perceiving in-hand object contact states for robotic manipulation without dedicated tactile sensors. Inspired by human multimodal perception, the work proposes the first contact-sensing-free framework that fuses RGB-D visual inputs with proprioceptive data to generate tactile-like binary contact signals for contact state estimation. The approach employs a Transformer-based multimodal fusion architecture trained end-to-end to jointly process image and joint state information. Experimental results demonstrate that the model performs effectively in both simulated and real-world environments, generalizes to unseen objects, and successfully supports reinforcement learning tasks such as in-hand object reorientation. The method offers a low-cost, highly generalizable solution for contact-rich manipulation without requiring specialized tactile hardware.

Abstract

Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a remarkable ability to infer contact by integrating visual information with an innate sense of their body's pose and movement. Inspired by this embodied perceptual skill, we investigate whether a robot can learn to infer contact from vision, an approach that also offers a scalable alternative to tactile hardware specifically for binary contact estimation, which faces practical challenges in cost, fragility, and integration. We present NoContactNoWorries, a transformer-based multimodal framework that fuses RGB-D vision with the robot's proprioception to infer binary contact states as a pseudo-tactile signal for hand-object interactions. We validate by training a single contact prediction model on multiple objects and show that the inferred contact signal supports downstream reinforcement learning agents for in-hand object reorientation, generalizing to novel objects. Experiments in both simulation and on a real-world robot validate our approach, highlighting the feasibility of inferring contact from vision and proprioception. Project Page: https://soham2560.github.io/no-contact-no-worries/

Problem

• contact estimation
• dexterous manipulation
• vision-based perception
• proprioception
• tactile sensing

Innovation

• vision-proprioception fusion
• contact estimation
• transformer-based multimodal learning
• tactile-free manipulation
• in-hand reorientation