# Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies

arXiv: https://arxiv.org/abs/2511.16596 · HTML: https://mlsanity.com/paper/2511.16596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [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)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [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)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback](https://mlsanity.com/paper/2303.07997/) (2023)
- [Taming the Panda with Python: A Powerful Duo for Seamless Robotics Programming and Integration](https://mlsanity.com/paper/2307.07633/) (2023)
- [Deep Learning for Accelerated and Robust MRI Reconstruction: a Review](https://mlsanity.com/paper/2404.15692/) (2024)
- [Using Fiber Optic Bundles to Miniaturize Vision-Based Tactile Sensors](https://mlsanity.com/paper/2403.05500/) (2024)
- [Deep Movement Primitives: toward Breast Cancer Examination Robot](https://mlsanity.com/paper/2202.09265/) (2022)
- [Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning](https://mlsanity.com/paper/2502.15767/) (2025)

## Built on by (most cited first, 1 of 1)

- [More with LESS -- Local Scene Representations for Tactile Imaging](https://mlsanity.com/paper/2606.14344/) (2026)

## Abstract (arXiv)

Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a textitrepresentation from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces -- the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.