# VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation

arXiv: https://arxiv.org/abs/2604.20444 · HTML: https://mlsanity.com/paper/2604.20444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [RoboNet: Large-Scale Multi-Robot Learning](https://mlsanity.com/paper/1910.11215/) (2019)
- [ContactPose: A Dataset of Grasps with Object Contact and Hand Pose](https://mlsanity.com/paper/2007.09545/) (2020)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video](https://mlsanity.com/paper/2311.18448/) (2023)
- [DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions](https://mlsanity.com/paper/2403.17827/) (2024)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System](https://mlsanity.com/paper/2511.02832/) (2025)
- [Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation](https://mlsanity.com/paper/2312.13469/) (2023)
- [FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation](https://mlsanity.com/paper/2506.01941/) (2025)
- [Digitizing Touch with an Artificial Multimodal Fingertip](https://mlsanity.com/paper/2411.02479/) (2024)
- [V-HOP: Visuo-Haptic 6D Object Pose Tracking](https://mlsanity.com/paper/2502.17434/) (2025)

## Built on by (most cited first, 2 of 2)

- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [ViTaR: Visuo-Tactile Residual Adaptation for Foundation VLA Manipulation](https://mlsanity.com/paper/2608.15816/) (2026)

## Abstract (arXiv)

Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging. This is largely due to the lack of datasets with rich physical interaction signals, systematic task organization, and sufficient scale. To address these limitations, we introduce the VTOUCH dataset. It leverages vision based tactile sensing to provide high-fidelity physical interaction signals, adopts a matrix-style task design to enable systematic learning, and employs automated data collection pipelines covering real-world, demand-driven scenarios to ensure scalability. To further validate the effectiveness of the dataset, we conduct extensive quantitative experiments on cross-modal retrieval as well as real-robot evaluation. Finally, we demonstrate real-world performance through generalizable inference across multiple robots, policies, and tasks.