# Transferring Tactile Data Across Sensors

arXiv: https://arxiv.org/abs/2410.14310 · HTML: https://mlsanity.com/paper/2410.14310/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Taxim: An Example-based Simulation Model for GelSight Tactile Sensors](https://mlsanity.com/paper/2109.04027/) (2021)
- [Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections](https://mlsanity.com/paper/2103.16747/) (2021)
- [ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition](https://mlsanity.com/paper/1802.07490/) (2018)
- [TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors](https://mlsanity.com/paper/1901.06181/) (2019)
- ["Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception](https://mlsanity.com/paper/1902.06273/) (2019)
- [Visuo-Haptic Object Perception for Robots: An Overview](https://mlsanity.com/paper/2203.11544/) (2022)
- [Optimizing BioTac Simulation for Realistic Tactile Perception](https://mlsanity.com/paper/2404.10425/) (2024)

## Built on by (most cited first, 2 of 2)

- [ACROSS: A Deformation-Based Cross-Modal Representation for Robotic Tactile Perception](https://mlsanity.com/paper/2411.08533/) (2024)
- [Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors](https://mlsanity.com/paper/2506.14980/) (2025)

## Abstract (arXiv)

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datasets obsolete. This article introduces a novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of three steps: first, converting signal data into corresponding 3D deformation meshes; second, translating these 3D deformation meshes from one sensor to another; and third, generating output images using the converted meshes. Our approach enables the continued use of valuable datasets.