# ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface

arXiv: https://arxiv.org/abs/2504.06156 · HTML: https://mlsanity.com/paper/2504.06156/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2025.

## Built on by (most cited first, 23 of 23)

- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [Cosmos 3: Omnimodal World Models for Physical AI](https://mlsanity.com/paper/2606.02800/) (2026)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper](https://mlsanity.com/paper/2507.15062/) (2025)
- [Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories](https://mlsanity.com/paper/2607.15330/) (2026)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning](https://mlsanity.com/paper/2510.14930/) (2025)
- [End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection](https://mlsanity.com/paper/2511.00139/) (2025)
- [Feel the Force: Contact-Driven Learning from Humans](https://mlsanity.com/paper/2506.01944/) (2025)
- [Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective](https://mlsanity.com/paper/2509.09154/) (2025)
- [Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation](https://mlsanity.com/paper/2512.09851/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale](https://mlsanity.com/paper/2606.10244/) (2026)
- [ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13877/) (2026)
- [Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation](https://mlsanity.com/paper/2606.29941/) (2026)
- [HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2608.19574/) (2026)
- [Gentle Object Retraction in Dense Clutter Using Multimodal Force Sensing and Imitation Learning](https://mlsanity.com/paper/2508.19476/) (2025)
- [OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies](https://mlsanity.com/paper/2607.03723/) (2026)
- [TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation](https://mlsanity.com/paper/2609.20414/) (2026)
- [NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation](https://mlsanity.com/paper/2606.24450/) (2026)
- [S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information](https://mlsanity.com/paper/2607.26047/) (2026)
- [VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation](https://mlsanity.com/paper/2608.21290/) (2026)
- [ForeTac-VLA: A Forecasting-Based Tactile-Vision-Language-Action Model for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2609.20980/) (2026)

## Abstract (arXiv)

Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact properties. Solving such dexterous manipulation tasks often relies on imitation learning from demonstration datasets, which are typically collected via teleoperation systems and often demand substantial time and effort. To address these challenges, we present ViTaMIn, an embodiment-free manipulation interface that seamlessly integrates visual and tactile sensing into a hand-held gripper, enabling data collection without the need for teleoperation. Our design employs a compliant Fin Ray gripper with tactile sensing, allowing operators to perceive force feedback during manipulation for more intuitive operation. Additionally, we propose a multimodal representation learning strategy to obtain pre-trained tactile representations, improving data efficiency and policy robustness. Experiments on seven contact-rich manipulation tasks demonstrate that ViTaMIn significantly outperforms baseline methods, demonstrating its effectiveness for complex manipulation tasks.