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Investigating deep learning approaches for human-robot proxemics
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Social Robotics)ORCID iD: 0000-0003-3324-4418
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Social Robotics)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Social Robotics)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. (Social Robotics)
2018 (English)In: Proc. 27th International Symposium on Robot and Human Interactive Communication, IEEE, 2018, p. 1093-1098Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we investigate the applicability of deep learning methods to adapt and predict comfortable human-robot proxemics. Proposing a network architecture, we experiment with three different layer configurations, obtaining three different end-to-end trainable models. Using these, we compare their predictive performances on data obtained during a human-robot interaction study. We find that our long short-term memory based model outperforms a gated recurrent unit based model and a feed-forward model. Further, we demonstrate how the created model can be used to create customized comfort zones that can help create a personalized experience for individual users.

Place, publisher, year, edition, pages
IEEE, 2018. p. 1093-1098
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:uu:diva-366204DOI: 10.1109/ROMAN.2018.8525731ISI: 000494315600172ISBN: 978-1-5386-7981-4 (electronic)OAI: oai:DiVA.org:uu-366204DiVA, id: diva2:1263872
Conference
RO-MAN 2018, August 27–31, Nanjing, China
Funder
Swedish Foundation for Strategic Research , RIT15-0133Swedish Research Council, 2015-04378Available from: 2018-11-17 Created: 2018-11-17 Last updated: 2019-12-10Bibliographically approved

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Gao, YuanWallkötter, SebastianObaid, MohammadCastellano, Ginevra

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