Logo: to the web site of Uppsala University

uu.sePublications from Uppsala University
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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: 2021-04-12Bibliographically approved
In thesis
1. Machine Behavior Development and Analysis using Reinforcement Learning
Open this publication in new window or tab >>Machine Behavior Development and Analysis using Reinforcement Learning
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

We are approaching a future where robots and humans will co-exist and co-adapt. To understand how can a robot co-adapt with humans, we need to understand and develop efficient algorithms suitable for our interactive purposes. Not only it can help us to advance the field of robotics but also it can help us to understand ourselves. A subject Machine Behavior, proposed by Iyad Rahwan in a recent Science article, studies algorithms and the social environments in which algorithms operate. What this paper's view tells us is that, when we would like to study any artificial robot we create, like natural science, a two-step method based on logical positivism should be applied. That is, we need to, on one hand, provide a complicated theory based on logical deduction, and on another hand, empirically setup experiments to conduct.

Reinforcement learning (RL) is a computational model that helps us to build a theory to explain the interactive process. Integrated with neural networks and statistics, the current RL is able to obtain a reliable learning representation and adapt over interactive processes. It might be one of the first times that we are able to use a theoretical framework to capture uncertainty and adapt automatically during interactions between humans and robots. Though some limitations are observed in different studies, many positive aspects have also been revealed. Additionally, considering the potentials of these methods people observed from related fields e.g. image recognition, physical human-robot interaction and manipulation, we hope this framework will bring more insights to the field of robotics. The main challenge in applying Deep RL to the field of social robotics is the volume of data. In traditional robotics problems such as body control, simultaneous localization and mapping and grasping, deep reinforcement learning often takes place only in a non-human environment. In such an environment, the robot can learn infinitely in the environment to optimize its strategies. However, applications in social robotics tend to be in a complex environment of human-robot interaction. Social robots require human involvement every time they learn in such an environment, which leads to very expensive data collection. In this thesis, we will discuss several ways to deal with this challenge, mainly in terms of two aspects, namely, evaluation of learning algorithms and the development of learning methods for human-robot co-adaptation.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2020. p. 43
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1983
Keywords
reinforcement learning, robotics, human robot interaction
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:uu:diva-423434 (URN)978-91-513-1053-4 (ISBN)
Public defence
2020-12-11, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2020-11-20 Created: 2020-10-25 Last updated: 2021-01-25

Open Access in DiVA

fulltext(505 kB)283 downloads
File information
File name FULLTEXT02.pdfFile size 505 kBChecksum SHA-512
51a1f9f4b035540e57401d4078833f2a622a721ca16859c9fe56051c4de784a8b0a2566d62773481907ba15faae942b62843660ff8fe72181136137701733de4
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Gao, YuanWallkötter, SebastianObaid, MohammadCastellano, Ginevra

Search in DiVA

By author/editor
Gao, YuanWallkötter, SebastianObaid, MohammadCastellano, Ginevra
By organisation
Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer Interaction
Human Computer Interaction

Search outside of DiVA

GoogleGoogle Scholar
Total: 283 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 209 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf