Enabling Embodied Human-Robot Co-Learning: Requirements, Method, and Test With Handover Task

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: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“How can human-robot co-learning be facilitated in physically embodied collaborative tasks?”</i>. 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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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-12-18
DOI
https://doi.org/10.1109/lra.2024.3519875
Citations
1
Primary Topic
Social Robot Interaction and HRI
Type
article
Field-Weighted Citation Impact
0.64
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article

Enabling Embodied Human-Robot Co-Learning: Requirements, Method, and Test With Handover Task

Emma M. van Zoelen, Luka Peternel, Mark A. Neerincx, David A. Abbink et al.
1 citations
IEEE Robotics and Automation Letters
Social Robot Interaction and HRI
0.64
article

Enabling Embodied Human-Robot Co-Learning: Requirements, Method, and Test With Handover Task

Emma M. van Zoelen, Luka Peternel, Mark A. Neerincx, David A. Abbink, Karel Van den Bosch, Hugo Veldman-Loopik
article en
1 citations

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: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“How can human-robot co-learning be facilitated in physically embodied collaborative tasks?”</i>. 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.

IEEE Robotics and Automation LettersVol. 10(2)
Netherlands Organisation for Applied Scientific Research (NL), University of Toronto (CA), Ministry of Defence (NL), Delft University of Technology (NL)
Openalex Percentile: Top 24%
Social Robot Interaction and HRI
0.64
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