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

Uriel Martinez-Hernandez, Tony Dodd, Tony J. Prescott, Nathan F. Lepora

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

20 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Pages5968-5973
Number of pages6
DOIs
Publication statusPublished - 2013
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan
Duration: 3 Nov 20138 Nov 2013

Conference

Conference2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
CountryJapan
CityTokyo
Period3/11/138/11/13

Fingerprint Dive into the research topics of 'Active Bayesian perception for angle and position discrimination with a biomimetic fingertip'. Together they form a unique fingerprint.

Cite this