A Low-Cost Vision-Based Tactile Gripper with Pretraining Learning for Contact-Rich Manipulation

• URL: http://arxiv.org/abs/2602.00514v2
• Date: Tue, 03 Feb 2026 08:06:28 GMT
• Title: A Low-Cost Vision-Based Tactile Gripper with Pretraining Learning for Contact-Rich Manipulation
• Abstract summary: We present LVTG, a low-cost visuo-tactile gripper designed for stable, robust, and efficient physical interaction.<n>Unlike existing visuo-tactile sensors, LVTG enables more effective and stable grasping of larger and heavier everyday objects.<n>The integration of vision and tactile feedback allows LVTG to provide rich, high-fidelity sensory data.
• Score: 5.419573979314354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic manipulation in contact-rich environments remains challenging, particularly when relying on conventional tactile sensors that suffer from limited sensing range, reliability, and cost-effectiveness. In this work, we present LVTG, a low-cost visuo-tactile gripper designed for stable, robust, and efficient physical interaction. Unlike existing visuo-tactile sensors, LVTG enables more effective and stable grasping of larger and heavier everyday objects, thanks to its enhanced tactile sensing area and greater opening angle. Its surface skin is made of highly wear-resistant material, significantly improving durability and extending operational lifespan. The integration of vision and tactile feedback allows LVTG to provide rich, high-fidelity sensory data, facilitating reliable perception during complex manipulation tasks. Furthermore, LVTG features a modular design that supports rapid maintenance and replacement. To effectively fuse vision and touch, We adopt a CLIP-inspired contrastive learning objective to align tactile embeddings with their corresponding visual observations, enabling a shared cross-modal representation space for visuo-tactile perception. This alignment improves the performance of an Action Chunking Transformer (ACT) policy in contact-rich manipulation, leading to more efficient data collection and more effective policy learning. Compared to the original ACT method, the proposed LVTG with pretraining achieves significantly higher success rates in manipulation tasks.

Related papers

• CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation [22.8919569384701]
  We propose a Control-aware Compliance VLA framework for reactive control.<n>The CC-VLA model employs a multimodal mixture-of-experts (MoE) to encode force signal sequences and vision-language fused feature.<n>We show that CC-VLA significantly improves success rates in challenging force-perception tasks and enhances force-control precision.
  arXiv  Detail & Related papers  (2026-09-05T02:54:51Z)
• UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models [32.87879913150572]
  We propose a unified tactile learning framework for contact-rich manipulation.<n>We show that our method improves success rate, manipulation accuracy, and contact robustness over existing methods.
  arXiv  Detail & Related papers  (2026-06-30T14:24:00Z)
• 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)
• T-Rex: Tactile-Reactive Dexterous Manipulation [139.71263755530654]
  We introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder.<n>We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation.
  arXiv  Detail & Related papers  (2026-06-15T17:59:55Z)
• 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)
• Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation [29.061513339025026]
  We present TAG, a low-cost glove system that integrates precise hand motion capture with high-resolution tactile feedback.<n>For motion capture, TAG employs a non-contact magnetic sensing design that provides drift-free, electromagnetically robust 21-DoF joint tracking.<n>We show that TAG enables reliable real-time perception of contact geometry and dynamic force, improves success rates in contact-rich teleoperation tasks, and increases the reliability of demonstration data collection for learning-based manipulation.
  arXiv  Detail & Related papers  (2026-03-30T15:04:13Z)
• HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing [1.5861606459586157]
  We argue that tactile-aware manipulation can be learned offline and deployed without direct haptic feedback at inference.<n>We present HapticVLA, which proceeds in two tightly coupled stages: Safety-Aware Reward-Weighted Flow Matching (SA-RWFM) and Tactile Distillation (TD)
  arXiv  Detail & Related papers  (2026-03-16T13:24:58Z)
• TEGA: A Tactile-Enhanced Grasping Assistant for Assistive Robotics via Sensor Fusion and Closed-Loop Haptic Feedback [1.3861223044153617]
  We present the tactile enhanced grasping assistant (TEGA)<n>A closed loop assistive teleoperation framework fuses EMG based intent2force inference with visuotactile sensing mapped into real time vibrotactile feedback.<n>A wearable haptic vest delivers real time tactile feedback, allowing users to dynamically refine grasp force during manipulation.
  arXiv  Detail & Related papers  (2026-03-05T04:12:55Z)
• UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking [47.900334665380115]
  We propose UniVTAC, a simulation-based visuo-tactile data platform.<n>We introduce the UniVTAC encoder, a visuo-tactile encoder trained on large-scale simulation-synthesized data.<n>We present the UniVTAC Benchmark, which consists of eight representative visuo-tactile manipulation tasks.
  arXiv  Detail & Related papers  (2026-02-10T18:57:00Z)
• UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
  We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
  arXiv  Detail & Related papers  (2026-02-01T11:03:01Z)
• 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)
• Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation [58.95799126311524]
  Humans can accomplish contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces.<n>Existing visual imitation learning approaches rely on action chunking to model complex behaviors.<n>We introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality.
  arXiv  Detail & Related papers  (2025-03-04T18:58:21Z)
• Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins [17.412763585521688]
  We present the Visuo-Skin (ViSk) framework, a simple approach that uses a transformer-based policy and treats skin sensor data as additional tokens alongside visual information. ViSk significantly outperforms both vision-only and optical tactile sensing based policies. Further analysis reveals that combining tactile and visual modalities enhances policy performance and spatial generalization, achieving an average improvement of 27.5% across tasks.
  arXiv  Detail & Related papers  (2024-10-22T17:59:49Z)

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.