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Jan Leusmann, Anna Belardinelli, Luke Haliburton, Stephan Hasler, Albrecht Schmidt, Sven Mayer, Michael Gienger, Chao Wang
Investigating LLM-Driven Curiosity in Human-Robot Interaction
In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, (CHI '25). ACM. 2023. https://doi.org/10.1145/3706598.3713923 (bib)

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.