UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking

• URL: http://arxiv.org/abs/2602.10093v1
• Date: Tue, 10 Feb 2026 18:57:00 GMT
• Title: UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking
• Abstract summary: 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.
• Score: 47.900334665380115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic manipulation has seen rapid progress with vision-language-action (VLA) policies. However, visuo-tactile perception is critical for contact-rich manipulation, as tasks such as insertion are difficult to complete robustly using vision alone. At the same time, acquiring large-scale and reliable tactile data in the physical world remains costly and challenging, and the lack of a unified evaluation platform further limits policy learning and systematic analysis. To address these challenges, we propose UniVTAC, a simulation-based visuo-tactile data synthesis platform that supports three commonly used visuo-tactile sensors and enables scalable and controllable generation of informative contact interactions. Based on this platform, we introduce the UniVTAC Encoder, a visuo-tactile encoder trained on large-scale simulation-synthesized data with designed supervisory signals, providing tactile-centric visuo-tactile representations for downstream manipulation tasks. In addition, we present the UniVTAC Benchmark, which consists of eight representative visuo-tactile manipulation tasks for evaluating tactile-driven policies. Experimental results show that integrating the UniVTAC Encoder improves average success rates by 17.1% on the UniVTAC Benchmark, while real-world robotic experiments further demonstrate a 25% improvement in task success. Our webpage is available at https://univtac.github.io/.

Related papers

• $\mathcal{N}_0$-Foundation: Towards the Age of Tactile Intelligence [0.0]
  $mathcalN$-Foundation is a paradigm for tactile-enabled embodied manipulation.<n>We engineer the infrastructure for scalable data collection, including a vision-based tactile sensor, a tactile Universal Manipulation Interface (UMI), and a synchronized visuo-tactile data collection system.<n>We construct NeoData, which contains more than 30000 hours of synchronized visual and tactile demonstrations, spanning six embodiments, 450 tasks, and billions of paired RGB and tactile frames collected through a mixture of real-robot teleoperation and UMI-based demonstrations.
  arXiv  Detail & Related papers  (2026-08-30T06:46:18Z)
• Vision-Based Tactile Intelligence for Robotics: Sensing, Learning, and Embodied Manipulation [45.09609338527887]
  Vision-based tactile sensors (VBTSs) offer a powerful alternative by con-verting contact-induced deformation of a soft interface into im-ages.<n>This review surveys the full VBTS pipeline and treats sensing hardware, learning methods, simulation, and datasets as an integrated sensing-and-learning system.
  arXiv  Detail & Related papers  (2026-08-16T02:39:12Z)
• 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)
• FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation [66.22092641373067]
  FTP-1 is the first generalist foundation tactile policy pretrained to acquire transferable tactile manipulation abilities.<n>It supports varied tactile inputs, including image-, array-, and state-based signals.<n>It learns tactile skills that transfer beyond the sensors seen during pretraining.
  arXiv  Detail & Related papers  (2026-06-11T09:30:09Z)
• HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning [24.64460457808404]
  HapTile is a contact-grounded visuotactile manipulation dataset that advances beyond vision-only trajectory datasets.<n>The data collection platform integrates haptic feedback directly into the teleoperation controller, enabling the operator to perceive contact interactions in real time.<n>The dataset comprises everyday manipulation tasks spanning a broad range of contact-rich skills, including pick-and-place, folding, pressing, stacking, and other routine activities.
  arXiv  Detail & Related papers  (2026-06-03T12:48:17Z)
• VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation [10.08235353271524]
  Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging.<n>This is largely due to the lack of datasets with rich physical interaction signals, systematic task organization, and sufficient scale.<n>We introduce the VTOUCH dataset to provide high-fidelity physical interaction signals, adopt a matrix-style task design, and employ automated data collection pipelines covering real-world, demand-driven scenarios.
  arXiv  Detail & Related papers  (2026-04-22T11:08:08Z)
• OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation [60.609604885961716]
  We present textbfOmniViTac, a large-scale visuo-tactile-action dataset comprising $21,000+$ trajectories across $86$ tasks and $100+$ objects.<n>We propose textbf OmniVTA, a world-model-based visuo-tactile manipulation framework that integrates four tightly coupled modules.
  arXiv  Detail & Related papers  (2026-03-19T17:52:42Z)
• DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation [4.463599051210204]
  Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation.<n>We present DexViTac, a portable, human-centric data collection system tailored for contact-rich dexterous manipulation.
  arXiv  Detail & Related papers  (2026-03-18T15:39:58Z)
• 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)
• 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)
• Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting [25.355424080824996]
  Tactile sensing is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning.<n>Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements.<n>We demonstrate that overlapping training and test samples in commonly used datasets inflate performance metrics, obscuring the true generalizability of tactile models.
  arXiv  Detail & Related papers  (2025-03-10T02:37:22Z)
• 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)
• Learning Visuotactile Skills with Two Multifingered Hands [80.99370364907278]
  We explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Our results mark a promising step forward in bimanual multifingered manipulation from visuotactile data.
  arXiv  Detail & Related papers  (2024-04-25T17:59:41Z)
• 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)

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.