Investigating LLM-Driven Curiosity in Human-Robot Interaction

要旨

Integrating curious behavior traits into robots is essential for them to learn and adapt to new tasks over their lifetime and to enhance human-robot interaction. However, the effects of robots expressing curiosity on user perception, user interaction, and user experience in collaborative tasks are unclear. In this work, we present a Multimodal Large Language Model-based system that equips a robot with non-verbal and verbal curiosity traits. We conducted a user study ($N=20$) to investigate how these traits modulate the robot's behavior and the users' impressions of sociability and quality of interaction. Participants prepared cocktails or pizzas with a robot, which was either curious or non-curious. Our results show that we could create user-centric curiosity, which users perceived as more human-like, inquisitive, and autonomous while resulting in a longer interaction time. We contribute a set of design recommendations allowing system designers to take advantage of curiosity in collaborative tasks.

著者
Jan Leusmann
LMU Munich, Munich, Germany
Anna Belardinelli
Honda Research Institute Europe, Offenbach, Germany
Luke Haliburton
LMU Munich, Munich, Germany
Stephan Hasler
Honda Research Institute Europe, Offenbach am Main, Germany
Albrecht Schmidt
LMU Munich, Munich, Germany
Sven Mayer
LMU Munich, Munich, Germany
Michael Gienger
Honda Research Institute Europe, Offenbach/Main, Germany
Chao Wang
Honda Research Institute Europe, Offenbach/Main, Germany
DOI

10.1145/3706598.3713923

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713923

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Interacting with Robots

G318+G319
7 件の発表
2025-04-30 20:10:00
2025-04-30 21:40:00
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