# Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation

arXiv: https://arxiv.org/abs/2409.08269 · HTML: https://mlsanity.com/paper/2409.08269/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

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

- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [Universal Visuo-Tactile Video Understanding for Embodied Interaction](https://mlsanity.com/paper/2505.22566/) (2025)
- [UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation](https://mlsanity.com/paper/2606.31451/) (2026)
- [Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting](https://mlsanity.com/paper/2503.06860/) (2025)
- [ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation](https://mlsanity.com/paper/2505.20498/) (2025)

## Abstract (arXiv)

Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one sensor, we use a generative model to estimate how the same physical contact would be perceived by another sensor. This allows us to apply sensor-specific methods to the generated signal. We implement this idea by training a diffusion model to translate between the popular GelSlim and Soft Bubble sensors. As a downstream task, we perform in-hand object pose estimation using GelSlim sensors while using an algorithm that operates only on Soft Bubble signals. The dataset, the code, and additional details can be found at https://www.mmintlab.com/research/touch2touch/.