PyTouch: A Machine Learning Library for Touch Processing

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Mike Lambeta - , Meta Platforms, Inc. (Author)
  • Huazhe Xu - , University of California at Berkeley (Author)
  • Jingwei Xu - , Shanghai Jiao Tong University (Author)
  • Po-Wei Chou - , Meta Platforms, Inc. (Author)
  • Shaoxiong Wang - , Massachusetts Institute of Technology (MIT) (Author)
  • Trevor Darrell - , University of California at Berkeley (Author)
  • Roberto Calandra - , Meta Platforms, Inc. (Author)

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.

Details

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
Pages13208-13214
Number of pages7
ISBN (electronic)9781728190778
Publication statusPublished - 2021
Peer-reviewedYes
Externally publishedYes

External IDs

ORCID /0000-0001-9430-8433/work/146646297
Scopus 85125470260