# UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects

arXiv: https://arxiv.org/abs/2408.06481 · HTML: https://mlsanity.com/paper/2408.06481/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2024.

## Built on by (most cited first, 15 of 15)

- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation](https://mlsanity.com/paper/2603.12665/) (2026)
- [VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs](https://mlsanity.com/paper/2603.23481/) (2026)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention](https://mlsanity.com/paper/2606.26663/) (2026)
- [Sensor-Invariant Tactile Representation](https://mlsanity.com/paper/2502.19638/) (2025)
- [VILP: Imitation Learning with Latent Video Planning](https://mlsanity.com/paper/2502.01784/) (2025)
- [Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation](https://mlsanity.com/paper/2512.09851/) (2025)
- [RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments](https://mlsanity.com/paper/2509.17057/) (2025)
- [TacCoRL: Integrating Tactile Feedback into VLA via Simulation](https://mlsanity.com/paper/2606.11743/) (2026)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)
- [SARL: Spatially-Aware Self-Supervised Representation Learning for Visuo-Tactile Perception](https://mlsanity.com/paper/2512.01908/) (2025)
- [UNIC: Learning Unified Multimodal Extrinsic Contact Estimation](https://mlsanity.com/paper/2601.04356/) (2026)
- [Zero-Shot Transfer of Force Map Estimation Across GelSight Mini Sensors](https://mlsanity.com/paper/2608.18240/) (2026)
- [VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation](https://mlsanity.com/paper/2608.21290/) (2026)

## Abstract (arXiv)

UniT is an approach to tactile representation learning, using VQGAN to learn a compact latent space and serve as the tactile representation. It uses tactile images obtained from a single simple object to train the representation with generalizability. This tactile representation can be zero-shot transferred to various downstream tasks, including perception tasks and manipulation policy learning. Our benchmarkings on in-hand 3D pose and 6D pose estimation tasks and a tactile classification task show that UniT outperforms existing visual and tactile representation learning methods. Additionally, UniT's effectiveness in policy learning is demonstrated across three real-world tasks involving diverse manipulated objects and complex robot-object-environment interactions. Through extensive experimentation, UniT is shown to be a simple-to-train, plug-and-play, yet widely effective method for tactile representation learning. For more details, please refer to our open-source repository https://github.com/ZhengtongXu/UniT and the project website https://zhengtongxu.github.io/unit-website/.