UniT: Unified Tactile Representation for Robot Learning
Authors: name: Zhengtong Xu, name: Raghava Uppuluri, name: Xinwei Zhang, name: Cael Fitch, name: Philip Glen Crandall, name: Wan Shou, name: Dongyi Wang, name: Yu She

UniT is a novel approach to tactile representation learning, using VQVAE 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 transferability and generalizability. This tactile representation can be
zero-shot transferred to various downstream tasks, including perception tasks
and manipulation policy learning. Our benchmarking on an in-hand 3D pose
estimation task shows 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/unifiedtactile.github.io/.