The ability to associate touch with other modalities has huge implications for humans and computational systems. However, multimodal learning with touch remains challenging due to the expensive data collection process and non-standardized sensor outputs. We introduce UniTouch, a unified tactile model for vision-based touch sensors connected to multiple modalities, including vision, language, and sound. We achieve this by aligning our UniTouch embeddings to pretrained image embeddings already associated with a variety of other modalities. We further propose learnable sensor-specific tokens, allowing the model to learn from a set of heterogeneous tactile sensors, all at the same time. UniTouch is capable of conducting various touch sensing tasks in the zero-shot setting, from robot grasping prediction to touch image question answering. To the best of our knowledge, UniTouch is the first to demonstrate such capabilities. Project page: https://cfeng16.github.io/UniTouch/
@article{arxiv.2401.18084,
title = {Binding Touch to Everything: Learning Unified Multimodal Tactile Representations},
author = {Fengyu Yang and Chao Feng and Ziyang Chen and Hyoungseob Park and Daniel Wang and Yiming Dou and Ziyao Zeng and Xien Chen and Rit Gangopadhyay and Andrew Owens and Alex Wong},
journal= {arXiv preprint arXiv:2401.18084},
year = {2024}
}