Enabling Embodied Human-Robot Co-Learning

Requirements, Method, and Test With Handover Task

Journal Article (2024)
Authors

E.M. Van Zoelen (TU Delft - Interactive Intelligence, TU Delft - BUS/TNO STAFF)

Hugo Veldman-Loopik (TNO)

Karel van den Bosch (TNO)

Mark Neerincx (TU Delft - Interactive Intelligence)

David A. Abbink (TU Delft - Human-Robot Interaction)

L. Peternel (TU Delft - Human-Robot Interaction)

Research Group
Interactive Intelligence
To reference this document use:
https://doi.org/10.1109/LRA.2024.3519875
More Info
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Publication Year
2024
Language
English
Research Group
Interactive Intelligence
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
2
Volume number
10
Pages (from-to)
1425 - 1432
DOI:
https://doi.org/10.1109/LRA.2024.3519875
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Abstract

Despite a large body of research on robot learning, it has not yet been thoroughly studied how collaborating humans and robots learn reciprocally. In such situations, both humans and robots continuously learn about each other and the task through interaction. This letter addresses the research question: "How can human-robot co-learning be facilitated in physically embodied collaborative tasks?". First, we derived five requirements for successful human-robot co-learning from literature: shared goal, synchrony, interdependence, adaptability, and transparency. Based on these requirements, we designed a collaborative human-robot handover task and a robot Q-learning method. In an evaluation with six human participants co-learning was indeed found to emerge in the hand-over task. Particularly, for three of the human-robot dyads, our designed setup proved to facilitate co-learning in a way that met all five requirements. The task and robot learning method presented in this letter demonstrate how human-robot co-learning can be enabled in physically embodied tasks.

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