# Active Clothing Material Perception using Tactile Sensing and Deep Learning

arXiv: https://arxiv.org/abs/1711.00574 · HTML: https://mlsanity.com/paper/1711.00574/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 30 of 30)

- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [A Touch, Vision, and Language Dataset for Multimodal Alignment](https://mlsanity.com/paper/2402.13232/) (2024)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper](https://mlsanity.com/paper/2507.15062/) (2025)
- [Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models](https://mlsanity.com/paper/2304.01182/) (2023)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Sensor-Invariant Tactile Representation](https://mlsanity.com/paper/2502.19638/) (2025)
- [Universal Visuo-Tactile Video Understanding for Embodied Interaction](https://mlsanity.com/paper/2505.22566/) (2025)
- [What Matters for Active Texture Recognition With Vision-Based Tactile Sensors](https://mlsanity.com/paper/2403.13701/) (2024)
- [TactoFind: A Tactile Only System for Object Retrieval](https://mlsanity.com/paper/2303.13482/) (2023)
- [Tactile-Filter: Interactive Tactile Perception for Part Mating](https://mlsanity.com/paper/2303.06034/) (2023)
- [LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping](https://mlsanity.com/paper/2403.04934/) (2024)
- [TouchFormer: A Robust Transformer-based Framework for Multimodal Material Perception](https://mlsanity.com/paper/2511.19509/) (2025)
- [Vision-Guided Active Tactile Perception for Crack Detection and Reconstruction](https://mlsanity.com/paper/2105.06325/) (2021)
- [Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation](https://mlsanity.com/paper/2405.03363/) (2024)
- [Tactile MNIST: Benchmarking Active Tactile Perception](https://mlsanity.com/paper/2506.06361/) (2025)
- [Visuo-Tactile Zero-Shot Object Recognition with Vision-Language Model](https://mlsanity.com/paper/2409.09276/) (2024)
- [TaCo: A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data](https://mlsanity.com/paper/2602.09893/) (2026)
- [Material Classification Using Active Temperature Controllable Robotic Gripper](https://mlsanity.com/paper/2111.15344/) (2021)
- [Learning Tactile Insertion in the Real World](https://mlsanity.com/paper/2405.00383/) (2024)
- [Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting](https://mlsanity.com/paper/2503.06860/) (2025)
- [Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects](https://mlsanity.com/paper/2502.02663/) (2025)
- [MLLM-Fabric: Multimodal Large Language Model-Driven Robotic Framework for Fabric Sorting and Selection](https://mlsanity.com/paper/2507.04351/) (2025)
- [SemanticFeels: Semantic Labeling during In-Hand Manipulation](https://mlsanity.com/paper/2602.14099/) (2026)
- [More with LESS -- Local Scene Representations for Tactile Imaging](https://mlsanity.com/paper/2606.14344/) (2026)
- [RCT: A Robot-Collected Touch-Vision-Language Dataset for Tactile Generalization](https://mlsanity.com/paper/2606.31694/) (2026)
- [VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios](https://mlsanity.com/paper/2607.14728/) (2026)
- [Language-Guided Representation Learning for Robust Cross-Sensor Material Recognition](https://mlsanity.com/paper/2609.14783/) (2026)

## Abstract (arXiv)

Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.