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When robot personalisation does not help: Insights from a robot-supported learning study
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
Gothenburg Univ, Dept Appl IT, Gothenburg, Sweden.
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. 705-712Conference paper, Published paper (Refereed)
Abstract [en]

In the domain of robotic tutors, personalised tutoring has started to receive scientists' attention, but is still relatively underexplored. Previous work using reinforcement learning (RL) has addressed personalised tutoring from the perspective of affective policy learning. However, little is known about the effects of robot behaviour personalisation on user's task performance. Moreover, it is also unclear if and when personalisation may be more beneficial than a robot that adapts to its users and the context of the interaction without personalising its behaviour. In this paper we build on previous work on affective policy learning that used RL to learn what robot's supportive behaviours are preferred by users in an educational scenario. We build a RL framework for personalisation that allows a robot to select verbal supportive behaviours to maximise the user's task progress and positive reactions in a learning scenario where a Pepper robot acts as a tutor and helps people to learn how to solve grid-based logic puzzles. A between-subjects design user study showed that participants were more efficient at solving logic puzzles and preferred a robot that exhibits more varied behaviours compared with a robot that personalises its behaviour by converging on a specific one over time. We discuss insights on negative effects of personalisation and report lessons learned together with design implications for personalised robots.

Place, publisher, year, edition, pages
IEEE, 2018. p. 705-712
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:uu:diva-366205DOI: 10.1109/ROMAN.2018.8525832ISI: 000494315600112ISBN: 978-1-5386-7981-4 (electronic)OAI: oai:DiVA.org:uu-366205DiVA, id: diva2:1263873
Conference
RO-MAN 2018, August 27–31, Nanjing, China
Funder
Swedish Research Council, 2015-04378Swedish Foundation for Strategic Research , RIT15-0133Available from: 2018-11-17 Created: 2018-11-17 Last updated: 2019-12-10Bibliographically approved

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Gao, YuanObaid, MohammadCastellano, Ginevra

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