Martinez-Hernandez, U, Rubio-Solis, A and Prescott, TJ (2016) Bayesian perception of touch for control of robot emotion. In: Proceedings of the International Joint Conference on Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN), 24-29 Jul 2016, Vancouver, Canada. IEEE , pp. 4927-4933. ISBN 978-1-5090-0620-5
Abstract
In this paper, we present a Bayesian approach for perception of touch and control of robot emotion. Touch is an important sensing modality for the development of social robots, and it is used in this work as stimulus through a human-robot interaction. A Bayesian framework is proposed for perception of various types of touch. This method together with a sequential analysis approach allow the robot to accumulate evidence from the interaction with humans to achieve accurate touch perception for adaptable control of robot emotions. Facial expressions are used to represent the emotions of the iCub humanoid. Emotions in the robotic platform, based on facial expressions, are handled by a control architecture that works with the output from the touch perception process. We validate the accuracy of our system with simulated and real robot touch experiments. Results from this work show that our method is suitable and accurate for perception of touch to control robot emotions, which is essential for the development of sociable robots.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. This is an author produced version of a paper published in 2016 International Joint Conference on Neural Networks (IJCNN). 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. Uploaded in accordance with the publisher's self-archiving policy. |
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: | 07 Feb 2017 11:04 |
Last Modified: | 19 Jan 2018 19:47 |
Published Version: | https://doi.org/10.1109/IJCNN.2016.7727848 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IJCNN.2016.7727848 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:111949 |