# Sensor-Invariant Tactile Representation

arXiv: https://arxiv.org/abs/2502.19638 · HTML: https://mlsanity.com/paper/2502.19638/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 34 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [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)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [Connecting Touch and Vision via Cross-Modal Prediction](https://mlsanity.com/paper/1906.06322/) (2019)
- [Sparsh: Self-supervised touch representations for vision-based tactile sensing](https://mlsanity.com/paper/2410.24090/) (2024)
- [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023)
- [Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry](https://mlsanity.com/paper/2104.01167/) (2021)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [GelSlim3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger](https://mlsanity.com/paper/2103.12269/) (2021)
- [Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features](https://mlsanity.com/paper/2209.13042/) (2022)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [Active Clothing Material Perception using Tactile Sensing and Deep Learning](https://mlsanity.com/paper/1711.00574/) (2017)
- [Tactile Dexterity: Manipulation Primitives with Tactile Feedback](https://mlsanity.com/paper/2002.03236/) (2020)
- [Connecting Look and Feel: Associating the visual and tactile properties of physical materials](https://mlsanity.com/paper/1704.03822/) (2017)
- [Digitizing Touch with an Artificial Multimodal Fingertip](https://mlsanity.com/paper/2411.02479/) (2024)
- [Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip](https://mlsanity.com/paper/1810.13381/) (2018)
- [Simulation of Vision-based Tactile Sensors using Physics based Rendering](https://mlsanity.com/paper/2012.13184/) (2020)
- [UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects](https://mlsanity.com/paper/2408.06481/) (2024)
- [Learning rich touch representations through cross-modal self-supervision](https://mlsanity.com/paper/2101.08616/) (2021)
- [GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing](https://mlsanity.com/paper/2309.10885/) (2023)
- [GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper](https://mlsanity.com/paper/2204.07146/) (2022)
- [Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders](https://mlsanity.com/paper/2307.07358/) (2023)
- [Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation](https://mlsanity.com/paper/2303.02646/) (2023)

## Built on by (most cited first, 8 of 8)

- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Tactile Modality Fusion for Vision-Language-Action Models](https://mlsanity.com/paper/2603.14604/) (2026)
- [Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control](https://mlsanity.com/paper/2609.20761/) (2026)
- [ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation](https://mlsanity.com/paper/2505.20498/) (2025)
- [OmniVaT: Single Domain Generalization for Multimodal Visual-Tactile Learning](https://mlsanity.com/paper/2601.00352/) (2026)
- [N0-Foundation: Towards the Age of Tactile Intelligence](https://mlsanity.com/paper/2608.29601/) (2026)
- [BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensors](https://mlsanity.com/paper/2609.08673/) (2026)
- [Language-Guided Representation Learning for Robust Cross-Sensor Material Recognition](https://mlsanity.com/paper/2609.14783/) (2026)

## Abstract (arXiv)

High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. This limitation hinders the ability to transfer models or knowledge learned from one sensor to another. To address this, we introduce a novel method for extracting Sensor-Invariant Tactile Representations (SITR), enabling zero-shot transfer across optical tactile sensors. Our approach utilizes a transformer-based architecture trained on a diverse dataset of simulated sensor designs, allowing it to generalize to new sensors in the real world with minimal calibration. Experimental results demonstrate the method's effectiveness across various tactile sensing applications, facilitating data and model transferability for future advancements in the field.