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Active Bayesian perception for angle and position discrimination with a biomimetic fingertip

Martinez-Hernandez, U., DODD, Tony, Prescott, T.J. and Lepora, N.F. (2014) Active Bayesian perception for angle and position discrimination with a biomimetic fingertip. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. 03-07 Nov 2013. IEEE, Tokyo, Japan, pp. 5968-5973. ISBN 9781467363587

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Abstract or description

In this work, we apply active Bayesian perception to angle and position discrimination and extend the method to perform actions in a sensorimotor task using a biomimetic fingertip. The first part of this study tests active perception off-line with a large dataset of edge orientations and positions, using a Monte Carlo validation to ascertain the classification accuracy. We observe a significant improvement over passive methods that lack a sensorimotor loop for actively repositioning the sensor. The second part of this study then applies these findings about active perception to an example sensorimotor task in real-time. Using an appropriate online sensorimotor control architecture, the robot made decisions about what to do next and where to move next, which was applied to a contour-following task around several objects. The successful outcome of this simple but illustrative task demonstrates that active perception can be of practical benefit for tactile robotics.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Faculty: School of Creative Arts and Engineering > Engineering
Depositing User: Library STORE team
Date Deposited: 15 Jul 2020 15:13
Last Modified: 15 Jul 2020 15:13
URI: http://eprints.staffs.ac.uk/id/eprint/6246

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