Martinez Hernandez, U orcid.org/0000-0002-9922-7912 and Prescott, TJ (2017) Adaptive perception: learning from sensory predictions to extract object shape with a biomimetic fingertip. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ IROS 2017, 24-28 Sep 2017, Vancouver, BC, Canada. IEEE ISBN 978-1-5386-2682-5
Abstract
In this work, we present an adaptive perception method to improve the performance in accuracy and speed of a tactile exploration task. This work extends our previous studies on sensorimotor control strategies for active tactile perception in robotics. First, we present the active Bayesian perception method to actively reposition a robot to accumulate evidence from better locations to reduce uncertainty. Second, we describe the adaptive perception method that, based on a forward model and a predicted information gain approach, allows to the robot to analyse `what would have happened' if a different decision `would have been made' at previous decision time. This approach permits to adapt the active Bayesian perception process to improve the performance in accuracy and reaction time of an exploration task. Our methods are validated with a contour following exploratory procedure with a touch sensor. The results show that the adaptive perception method allows the robot to make sensory predictions and autonomously adapt, improving the performance of the exploration task.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. https://doi.org/10.1109/IROS.2017.8206590 |
Keywords: | Bayes methods,; Tactile sensors; Adaptation models; Predictive models |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Sep 2017 09:34 |
Last Modified: | 20 Mar 2018 11:09 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS.2017.8206590 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121318 |