# Contrastive Touch-to-Touch Pretraining

arXiv: https://arxiv.org/abs/2410.11834 · HTML: https://mlsanity.com/paper/2410.11834/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2024.

## Built on by (most cited first, 5 of 5)

- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Cross-Sensor Touch Generation](https://mlsanity.com/paper/2510.09817/) (2025)
- [RETRO: REthinking Tactile Representation Learning with Material PriOrs](https://mlsanity.com/paper/2505.14319/) (2025)
- [Tac-DINO: Learning Vision-Tactile Features with Patch Alignment](https://mlsanity.com/paper/2606.12069/) (2026)
- [Tac2Pix: Image-Space Visuo-Tactile Fusion for Dexterous Manipulation](https://mlsanity.com/paper/2606.08765/) (2026)

## Abstract (arXiv)

Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between different tactile sensors. Unlike current approaches that focus on reconstruction or task-specific supervision, we leverage contrastive learning to integrate tactile signals from two different sensors into a shared embedding space, using a dataset in which the same objects are probed with multiple sensors. We apply this approach to paired touch signals from GelSlim and Soft Bubble sensors. We show that our learned features provide strong pretraining for downstream pose estimation and classification tasks. We also show that our embedding enables models trained using one touch sensor to be deployed using another without additional training. Project details can be found at https://www.mmintlab.com/research/cttp/.