UniT: Unified Tactile Representation for Robot Learning
<|reference_start|>UniT: Unified Tactile Representation for Robot Learning: 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/.<|reference_end|>
@article{xu2024unit:,
    title={UniT: Unified Tactile Representation for Robot Learning},
    author={Zhengtong Xu, Raghava Uppuluri, Xinwei Zhang, Cael Fitch, Philip Glen
  Crandall, Wan Shou, Dongyi Wang, Yu She},
    journal={arXiv preprint arXiv:2408.06481},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06481},
    primaryClass={cs.RO}
}