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

• URL: http://arxiv.org/abs/2606.24450v1
• Date: Tue, 23 Jun 2026 11:36:46 GMT
• Title: NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation
• Abstract summary: NoContactNoWorries is a framework that fuses RGB-D vision with the robot's proprioception to infer binary contact states.<n>We show that the inferred contact signal supports downstream reinforcement learning agents for in-hand object reorientation.
• Score: 0.7378853859331618
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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/

Related papers

• AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion [60.79507611737292]
  Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback.<n>Existing robotic grasping approaches rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact.<n>We propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement.
  arXiv  Detail & Related papers  (2026-08-06T16:29:11Z)
• FingerEye: Continuous and Unified Vision-Tactile Sensing for Dexterous Manipulation [8.809778152652724]
  Dexterous robotic manipulation requires comprehensive perception across all phases of interaction.<n>We introduce FingerEye, a compact and cost-effective sensor that provides continuous vision-tactile feedback throughout the interaction process.<n>We develop a vision-tactile imitation learning policy that fuses signals from multiple FingerEye sensors to learn dexterous manipulation behaviors from limited real-world data.
  arXiv  Detail & Related papers  (2026-04-22T15:37:34Z)
• Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation [82.63833405368159]
  Generalizing tool manipulation requires both semantic planning and precise physical control.<n>We propose Semantic-Contact Fields (SCFields), a unified 3D representation fusing visual semantics with dense contact estimates.<n> Experiments on scraping, crayon drawing, and peeling demonstrate robust category-level generalization.
  arXiv  Detail & Related papers  (2026-02-14T16:05:08Z)
• Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation [14.221542785249524]
  We introduce DreamTacVLA, a framework that grounds VLA models in contact physics by learning to feel the future.<n>Our model adopts a hierarchical perception scheme in which high-resolution tactile images serve as micro-vision inputs.<n>To further deepen the model's understanding of fine-grained contact dynamics, we finetune the system with a tactile world model that predicts future tactile signals.
  arXiv  Detail & Related papers  (2025-12-29T21:06:33Z)
• OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction [93.88239833545623]
  We present OpenTouch, the first in-the-wild egocentric full-hand tactile dataset.<n>We show that tactile signals provide a compact yet powerful cue for grasp understanding.<n>We aim to advance multimodal egocentric perception, embodied learning, and contact-rich robotic manipulation.
  arXiv  Detail & Related papers  (2025-12-18T18:18:17Z)
• Grasp Like Humans: Learning Generalizable Multi-Fingered Grasping from Human Proprioceptive Sensorimotor Integration [26.351720551267846]
  Tactile and kinesthetic perceptions are crucial for human dexterous manipulation, enabling reliable grasping of objects via sensorimotor integration.<n>We propose a novel glove-mediated tactile-kinematic perception-prediction framework for grasp skill transfer from human intuitive and natural operation to robotic execution based on imitation learning.
  arXiv  Detail & Related papers  (2025-09-10T07:44:12Z)
• Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing [15.970078821894758]
  We introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation. Robot Synesthesia is a novel point cloud-based tactile representation inspired by human tactile-visual synesthesia.
  arXiv  Detail & Related papers  (2023-12-04T12:35:43Z)
• Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control [12.302685367517718]
  High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. We study a new concept: textittactile saliency for robot touch, inspired by the human touch attention mechanism from neuroscience.
  arXiv  Detail & Related papers  (2023-07-26T21:19:45Z)
• Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
  Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. vision-based tactile sensors are being widely used for various robotic perception and control tasks. We present a method for interactive perception using vision-based tactile sensors for a part mating task.
  arXiv  Detail & Related papers  (2023-03-10T16:27:37Z)
• See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation [49.925499720323806]
  We study how visual, auditory, and tactile perception can jointly help robots to solve complex manipulation tasks. We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor.
  arXiv  Detail & Related papers  (2022-12-07T18:55:53Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.