Salazar, P.J. and Prescott, T.J. orcid.org/0000-0003-4927-5390 (2022) Deep gaussian processes for angle and position discrimination in active touch sensing. In: Cañamero, L., Gaussier, P., Wilson, M., Boucenna, S. and Cuperlier, N., (eds.) From Animals to Animats 16: 16th International Conference on Simulation of Adaptive Behavior, SAB 2022, Cergy-Pontoise, France, September 20–23, 2022, Proceedings. 16th International Conference on Simulation of Adaptive Behavior, SAB 2022, 20-23 Sep 2022, Cergy-Pontoise, France. Lecture Notes in Computer Science, LNAI 13499 . Springer International Publishing , pp. 41-51. ISBN 9783031167690
Abstract
Active touch sensing can benefit from the representation of uncertainty in order to guide sensing movements and to drive sensing strategies that operate to reduce uncertainty with respect to the task at hand. Here we explore learning approaches that can acquire task knowledge quickly and with relatively small datasets and with the potential to be exploited for active sensing in robots and as models of biological sensory systems. Specifically, we explore the utility of deep (hierarchical) Gaussian Process models (Deep GPs) that have shown promise as models of episodic memory processes due to their low-dimensionality (compactness), generative capability, and ability to explicitly represent uncertainty. Using data obtained in a robotic active touch task (contour following), we show that both single-layer and Deep GP models are capable of providing robust function approximations from tactile data to angle and sensor position, with Deep GPs showing some advantages in terms of accuracy and uncertainty quantification in angle discrimination.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in From Animals to Animats 16. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Neurosciences; Neurodegenerative; Generic health relevance; Active touch; Deep Gaussian process; Contour following; Tactile sensing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 813713 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Jan 2023 14:54 |
Last Modified: | 09 Sep 2023 00:13 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-16770-6_4 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195287 |