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Social robot learning with deep reinforcement learning and realistic reward shaping
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Deep reinforcement learning has been applied successfully to numerous robotic control tasks, but its applicability to social robot tasks has been comparatively limited. This work combines a spatial autoencoder and state-of-the-art deep reinforcement learning to train a simulated autonomous robot to perform group joining behavior. The resulting control policy uses only first-person camera images and the robot's speed as input. The behavior of the control policy was evaluated in a perceptual study, and was shown to be less rude, more polite, and more sociable when compared to the reference model. We believe this methodology is generalizable to other social robot tasks.

Place, publisher, year, edition, pages
2019. , p. 61
Series
IT ; 19027
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-395918OAI: oai:DiVA.org:uu-395918DiVA, id: diva2:1365641
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2019-10-25 Created: 2019-10-25 Last updated: 2019-10-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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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