PyTouch: A Machine Learning Library for Touch Processing

With the increased availability of rich tactile sensors, there is an 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.

Authors

• Mike Lambeta
• Huazhe Xu
• Jingwei Xu 0005
• Po-Wei Chou
• Shaoxiong Wang
• Trevor Darrell
• Roberto Calandra

Keywords

• Software libraries
• Software architecture
• Pose estimation
• Tactile sensors
• Machine learning
• Libraries
• Sensors
• Touch Processing
• Tactile Sensor
• Raw Measurements
• Touch Sensor
• Availability Of Sensors
• Touching Objects
• Computer Vision
• Input Image
• Use In Applications
• Transfer Learning
• PyTorch
• Design Choices
• Video Analysis
• Static Images
• Barriers To Entry
• Multiple Sensors
• Software Library
• Joint Model
• Open-source Library
• Tactile Input
• Processing Library
• Digital Sensor
• User Power