PyTouch: A Machine Learning Library for Touch Processing
Mike Lambeta, Huazhe Xu, Jingwei Xu, Po-Wei Chou, Shaoxiong Wang, Trevor Darrell, Roberto Calandra
Abstract
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.
BibTeX
@inproceedings{icra2021_pytouchamachinel,
title = {PyTouch: A Machine Learning Library for Touch Processing},
author = {Mike Lambeta and Huazhe Xu and Jingwei Xu and Po-Wei Chou and Shaoxiong Wang and Trevor Darrell and Roberto Calandra},
booktitle = {ICRA 2021},
year = {2021}
}