PyTouch: A Machine Learning Library for Touch Processing

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

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

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

OriginalspracheEnglisch
Titel2021 IEEE International Conference on Robotics and Automation (ICRA)
Seiten13208-13214
Seitenumfang7
ISBN (elektronisch)9781728190778
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusJa
Extern publiziertJa

Externe IDs

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