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Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human–Robot Interaction
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
Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology, Stockholm, 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.
Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology, Stockholm, Sweden.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.

Place, publisher, year, edition, pages
2019.
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:uu:diva-398405OAI: oai:DiVA.org:uu-398405DiVA, id: diva2:1375736
Conference
2019 International Conference on Intelligent Robots and Systems, November 3 – 8, 2019, Macau
Note

Yuan Gao and Elena Sibirtseva contributed equally to this work.

Available from: 2019-12-05 Created: 2019-12-05 Last updated: 2019-12-09Bibliographically approved

Open Access in DiVA

fulltext(1518 kB)4 downloads
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Type fulltextMimetype application/pdf

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https://arxiv.org/abs/1908.04087

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