Bayesian tactile object recognition: learning and recognising objects using a new inexpensive tactile sensor

Tadeo Corradi, Peter Hall, Pejman Iravani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)
158 Downloads (Pure)

Abstract

We present a Bayesian approach to tactile object recognition that improves on state-of-the-art in using single-touch events in two ways. First by improving recognition accuracy from about 90\% to about 95\%, using about half the number of touches. Second by reducing the number of touches needed for training from about 200 to about 60. In addition, we use a new tactile sensor that is less than one tenth of the cost of widely available sensors. The paper describes the sensor, the likelihood function used with the Naive Bayes classifier, and experiments on a set of ten real objects. We also provide preliminary results to test our approach for its ability to generalise to previously unencountered objects.
Original languageEnglish
Title of host publication 2015 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages3909-3914
Number of pages6
ISBN (Electronic) 978-1-4799-6923-4
DOIs
Publication statusPublished - 2 Jul 2015
EventIEEE Interational Conference on Robotics and Automation (ICRA) 2015 - Washington, Seattle, USA United States
Duration: 26 May 201530 May 2015

Publication series

NameProceedings / IEEE International Conference on Robotics and Automation.
PublisherIEEE
ISSN (Print)1050-4729

Conference

ConferenceIEEE Interational Conference on Robotics and Automation (ICRA) 2015
CountryUSA United States
CitySeattle
Period26/05/1530/05/15

Keywords

  • tactile sensing
  • object recognition
  • Bayesian
  • Robotics

Cite this

Corradi, T., Hall, P., & Iravani, P. (2015). Bayesian tactile object recognition: learning and recognising objects using a new inexpensive tactile sensor. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3909-3914). (Proceedings / IEEE International Conference on Robotics and Automation.). IEEE. https://doi.org/10.1109/ICRA.2015.7139744