# PyTouch: A Machine Learning Library for Touch Processing

arXiv: https://arxiv.org/abs/2105.12791 · HTML: https://mlsanity.com/paper/2105.12791/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (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)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [OmniTact: A Multi-Directional High Resolution Touch Sensor](https://mlsanity.com/paper/2003.06965/) (2020)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [Deep Learning for Tactile Understanding From Visual and Haptic Data](https://mlsanity.com/paper/1511.06065/) (2015)
- [TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors](https://mlsanity.com/paper/1901.06181/) (2019)
- [Building a Library of Tactile Skills Based on FingerVision](https://mlsanity.com/paper/1909.09669/) (2019)

## Built on by (most cited first, 3 of 3)

- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [RETRO: REthinking Tactile Representation Learning with Material PriOrs](https://mlsanity.com/paper/2505.14319/) (2025)
- [Lightweight Estimation of Hand Mesh and Biomechanically Feasible Kinematic Parameters](https://mlsanity.com/paper/2303.14838/) (2023)

## Abstract (arXiv)

With the increased availability of rich tactile sensors, there is an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch -- the first machine learning library dedicated to the processing of touch sensing signals. PyTouch, is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service with the goal of unifying the tactile sensing community by providing a library for building scalable, proven, and performance-validated modules over which applications and research can be built upon. We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations. PyTouch is open-sourced at https://github.com/facebookresearch/pytouch .