@inproceedings{leusmann2024comparing,
	title = {Investigating LLM-Driven Curiosity in Human-Robot Interaction},
	author = {Jan Leusmann and Ann Belardinelli and Luke Haliburton and Stephan Hasler and Albrecht Schmidt and Sven Mayer and Michael Gienger and Chao Wang},
	year = {2025},
	booktitle    = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
	publisher = {ACM},
	date = {2025-04-26},
	doi = {10.1145/3706598.3713923},
	abstract = {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.},
	keywords = {human-robot interaction, LLM, curiosity}
}