PyTouch: A Machine Learning Library for Touch Processing
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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 .
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
10 of 19 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 288
- 2020DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulationcited by 167
- 2018More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touchcited by 102
- 2020OmniTact: A Multi-Directional High Resolution Touch Sensorcited by 28
- 2018Slip Detection with Combined Tactile and Visual Informationcited by 23
- 2015Deep Learning for Tactile Understanding From Visual and Haptic Datacited by 18
- 2019TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensorscited by 17
- 2019Building a Library of Tactile Skills Based on FingerVisioncited by 1
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.