@inproceedings{24ccfde93b2e42109c8c29509997043c,
title = "Bayesian tactile object recognition: learning and recognising objects using a new inexpensive tactile sensor",
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. ",
keywords = "tactile sensing, object recognition, Bayesian, Robotics",
author = "Tadeo Corradi and Peter Hall and Pejman Iravani",
year = "2015",
month = jul,
day = "2",
doi = "10.1109/ICRA.2015.7139744",
language = "English",
series = "Proceedings / IEEE International Conference on Robotics and Automation.",
publisher = "IEEE",
pages = "3909--3914",
booktitle = "2015 IEEE International Conference on Robotics and Automation (ICRA)",
address = "USA United States",
note = "IEEE Interational Conference on Robotics and Automation (ICRA) 2015 ; Conference date: 26-05-2015 Through 30-05-2015",
}
