Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations

• URL: http://arxiv.org/abs/2607.01684v1
• Date: Thu, 02 Jul 2026 04:27:11 GMT
• Title: Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations
• Abstract summary: We present TacImag, a tactile imagination framework that predicts tactile observations from vision and proprioception and uses the generated signals to guide manipulation policies.<n>We evaluate TacImag on six simulated and four real-world manipulation tasks.<n>In real-world experiments, imagined force fields improve contact-sensitive tasks by 44.4% on average, whereas imagined tactile images improve texture-sensitive tasks by 23.3%.
• Score: 4.954401349140998
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tactile sensing can substantially improve contact-rich robotic manipulation, yet its practical deployment remains limited by the fragility, calibration requirements, and maintenance burden of tactile hardware. This raises a fundamental question: can robots benefit from tactile knowledge without requiring tactile sensors at deployment? We present TacImag, a tactile imagination framework that predicts tactile observations from vision and proprioception and uses the generated signals to guide manipulation policies. Trained from paired visuotactile demonstrations, TacImag enables touch-informed manipulation using only visual observations at test time. We evaluate TacImag on six simulated and four real-world manipulation tasks. Across simulation and real-world experiments, imagined tactile observations consistently improve manipulation performance without requiring tactile hardware. In real-world experiments, imagined force fields improve contact-sensitive tasks by 44.4% on average, whereas imagined tactile images improve texture-sensitive tasks by 23.3%, revealing that the effectiveness of tactile imagination depends strongly on the relationship between tactile representation and task requirements. Our results further suggest that tactile imagination does not simply recover missing tactile measurements. Instead, it acts as a form of contact-aware supervision that transforms subtle visual interaction cues into representations that are easier for manipulation policies to exploit.

Related papers

• FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation [19.661583133282477]
  We present Feature-Extracted Latent Tactile (FELT), a learning-based framework that synthesizes per-finger pressure tactile images from RGB observations.<n>FELT uses a large frozen visual encoder and a lightweight query decoder to predict tactile signals in a single feed-forward pass.<n>Experiments on four contact-rich manipulation tasks demonstrate that both generated tactile images and latent tactile features improve policy success.
  arXiv  Detail & Related papers  (2026-07-22T19:32:42Z)
• Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation [74.87716678195099]
  Visuo-Tactile policies leveraging optical tactile sensors have shown great promise in contact-rich manipulation.<n>Despite their advantages, extracting fine-grained contact states necessary for contact-rich manipulation remains an open challenge.<n>We propose a motion-aware tactile representation to facilitate contact-rich manipulation.
  arXiv  Detail & Related papers  (2026-06-29T08:20:18Z)
• Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning [6.509572742369941]
  We propose a framework for tactile-only blind grasping that is deployable on a physical multi-fingered robotic hand.<n>We introduce a Real2Sim tactile calibration pipeline that constructs a contact-calibrated digital-twin simulator capable of reproducing real tactile signals.<n>Second, we improve the expressiveness of sparse tactile observations using a layout-aware tactile encoder, which incorporates sensor-geometry priors through self-supervised pretraining.<n>Third, to improve generalization to unseen objects, we train object-specific reinforcement-learning experts in the calibrated simulator and aggregate their successful grasp trajectories into a tactile-conditioned Diffusion Policy.
  arXiv  Detail & Related papers  (2026-06-10T07:46:38Z)
• TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation [50.608989079323784]
  We propose TouchThinker, a tactile-language framework that scales tactile commonsense reasoning to the open world from both data and representation perspectives.<n>First, we construct TouchThinker-1M, a million-scale, multi-source tactile reasoning dataset covering textbf415 objects, textbf8 scenarios, and textbf7 sensor types.<n>Then, we propose action-aware modeling mechanism to improve tactile representation efficiency and enable efficient reasoning.
  arXiv  Detail & Related papers  (2026-06-10T03:58:32Z)
• AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning [56.20593626288444]
  AetheRock is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip.<n>We propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning.
  arXiv  Detail & Related papers  (2026-06-08T17:38:57Z)
• TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance [53.35296919674763]
  TouchGuide is a novel cross-policy visuo-tactile fusion paradigm that fuses modalities within a low-dimensional action space.<n>touchGuide operates in two stages to guide a pre-trained diffusion or flow-matching visuomotor policy.<n>To facilitate TouchGuide training with high-quality and cost-effective data, we introduce TacUMI, a data collection system.
  arXiv  Detail & Related papers  (2026-01-28T04:22:47Z)
• Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper [7.618517580705364]
  We present a portable, lightweight gripper with integrated tactile sensors.<n>We propose a cross-modal representation learning framework that integrates visual and tactile signals.<n>We validate our approach on fine-grained tasks such as test tube insertion and pipette-based fluid transfer.
  arXiv  Detail & Related papers  (2025-07-20T17:53:59Z)
• DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity [11.450027373581019]
  We introduce a multi-finger robot system designed to manipulate objects using the sense of touch, without relying on vision. For tasks that mimic daily life, the robot uses its sense of touch to manipulate randomly placed objects in dark.
  arXiv  Detail & Related papers  (2024-01-23T05:37:32Z)
• 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)
• Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor [14.492202828369127]
  We leverage a multimodal visuotactile sensor within the framework of imitation learning (IL) to perform contact-rich tasks.<n>We introduce two algorithmic contributions, tactile force matching and learned mode switching, as complimentary methods for improving IL.<n>Our results show that the inclusion of force matching raises average policy success rates by 62.5%, visuotactile mode switching by 30.3%, and visuotactile data as a policy input by 42.5%.
  arXiv  Detail & Related papers  (2023-11-02T14:02:42Z)
• 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)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)

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.