# More with LESS -- Local Scene Representations for Tactile Imaging

arXiv: https://arxiv.org/abs/2606.14344 · HTML: https://mlsanity.com/paper/2606.14344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [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)
- [SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2011.07215/) (2020)
- [EGO-TOPO: Environment Affordances from Egocentric Video](https://mlsanity.com/paper/2001.04583/) (2020)
- [General In-Hand Object Rotation with Vision and Touch](https://mlsanity.com/paper/2309.09979/) (2023)
- [The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?](https://mlsanity.com/paper/1710.05512/) (2017)
- [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)
- [Active Clothing Material Perception using Tactile Sensing and Deep Learning](https://mlsanity.com/paper/1711.00574/) (2017)
- [ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition](https://mlsanity.com/paper/1802.07490/) (2018)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [Unsupervised Image Representation Learning with Deep Latent Particles](https://mlsanity.com/paper/2205.15821/) (2022)
- [DDLP: Unsupervised Object-Centric Video Prediction with Deep Dynamic Latent Particles](https://mlsanity.com/paper/2306.05957/) (2023)
- [Deep Learning for Tactile Understanding From Visual and Haptic Data](https://mlsanity.com/paper/1511.06065/) (2015)
- [Visual-Tactile Sensing for In-Hand Object Reconstruction](https://mlsanity.com/paper/2303.14498/) (2023)
- [From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor](https://mlsanity.com/paper/1812.02941/) (2018)
- [Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces](https://mlsanity.com/paper/2403.20275/) (2024)
- [Taming the Panda with Python: A Powerful Duo for Seamless Robotics Programming and Integration](https://mlsanity.com/paper/2307.07633/) (2023)
- [Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach](https://mlsanity.com/paper/2206.10716/) (2022)
- [Minsight: A Fingertip-Sized Vision-Based Tactile Sensor for Robotic Manipulation](https://mlsanity.com/paper/2304.10990/) (2023)
- [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)
- [Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies](https://mlsanity.com/paper/2511.16596/) (2025)

## Abstract (arXiv)

Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation. Recent self-supervised learning approaches have shown promising results, but rely on global, unstructured representations and robot-controlled sensing, limiting generalization and practical use. We propose Local Encoder for Spatial Sensing (LESS), an object-centric tactile representation that exploits the local nature of touch. The tactile scene is modeled as a grid of recurrent encoders with local receptive fields, whose states are fused to reconstruct 2D or 3D images of internal structure. This compositional design enables strong generalization: models trained on single-inclusion phantoms accurately image objects with multiple inclusions and varying sizes. The local structure further supports spatial uncertainty estimation. In addition, we enable hand-held tactile imaging via external pose tracking and human-like palpation data, and extend tactile imaging to full 3D reconstruction.