mlsanityData to 7 Oct 2026

Sensor-Invariant Tactile Representation

arXiv 2502.19638 ↗First posted 27 Feb 2025cs.RO cs.CV cs.LGcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 28 of its 34 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

28 of 34 references.

References · 28

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  3. 2020Supervised Contrastive Learningcited by 2,803
  4. 2019Contrastive Multiview Codingcited by 1,297
  5. 2020DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulationcited by 164
  6. 2018More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touchcited by 100
  7. 2024Binding Touch to Everything: Learning Unified Multimodal Tactile Representationscited by 94
  8. 2022Touch and Go: Learning from Human-Collected Vision and Touchcited by 84
  9. 2019Connecting Touch and Vision via Cross-Modal Predictioncited by 66
  10. 2024Sparsh: Self-supervised touch representations for vision-based tactile sensingcited by 61
  11. 2023Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Playcited by 60
  12. 2021Tactile-RL for Insertion: Generalization to Objects of Unknown Geometrycited by 45
16 more
  1. 2024Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Taskscited by 44
  2. 2021GelSlim3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Fingercited by 39
  3. 2022Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Featurescited by 37
  4. 2021GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Fingercited by 34
  5. 2017Active Clothing Material Perception using Tactile Sensing and Deep Learningcited by 29
  6. 2017Connecting Look and Feel: Associating the visual and tactile properties of physical materialscited by 22
  7. 2020Tactile Dexterity: Manipulation Primitives with Tactile Feedbackcited by 21
  8. 2024Digitizing Touch with an Artificial Multimodal Fingertipcited by 17
  9. 2018Maintaining Grasps within Slipping Bound by Monitoring Incipient Slipcited by 16
  10. 2020Simulation of Vision-based Tactile Sensors using Physics based Renderingcited by 15
  11. 2024UniT: Data Efficient Tactile Representation with Generalization to Unseen Objectscited by 15
  12. 2021Learning rich touch representations through cross-modal self-supervisioncited by 13
  13. 2023GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensingcited by 10
  14. 2022GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Grippercited by 9
  15. 2023Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoderscited by 8
  16. 2023Seq2Seq Imitation Learning for Tactile Feedback-based Manipulationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 7.

8 papers.

  1. 2026AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perceptioncited by 11
  2. 2026Tactile Modality Fusion for Vision-Language-Action Modelscited by 2
  3. 2026Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Controlcited by 1
  4. 2025ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generationcited by 0
  5. 2026OmniVaT: Single Domain Generalization for Multimodal Visual-Tactile Learningcited by 0
  6. 2026N0-Foundation: Towards the Age of Tactile Intelligencecited by 0
  7. 2026BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensorscited by 0
  8. 2026Language-Guided Representation Learning for Robust Cross-Sensor Material Recognitioncited by 0