UniT: Data Efficient Tactile Representation With Generalization to Unseen Objects
Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip G. Crandall, Wan Shou, Dongyi Wang, Yu She
Abstract
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.
BibTeX
@inproceedings{ral2025_unitdataefficien,
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 G. Crandall and Wan Shou and Dongyi Wang and Yu She},
booktitle = {RA-L 2025},
year = {2025}
}