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/.
@article{arxiv.2408.06481,
title = {UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects},
author = {Zhengtong Xu and Raghava Uppuluri and Xinwei Zhang and Cael Fitch and Philip Glen Crandall and Wan Shou and Dongyi Wang and Yu She},
journal= {arXiv preprint arXiv:2408.06481},
year = {2025}
}