Investigating LLM-Driven Curiosity in Human-Robot Interaction

MCML Authors

Luke Haliburton

Dr.

* Former Member

→ Group Albrecht Schmidt
 Human-Centered Ubiquitous Media

Albrecht Schmidt

Prof. Dr.

Core PI

Human-Centered Ubiquitous Media

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.

inproceedings LHS+25

---

CHI 2025

ACM CHI Conference on Human Factors in Computing Systems. Yokohama, Japan, Apr 26-May 01, 2025.

---

Authors

J. Leusmann • A. Belardinelli • L. Haliburton • S. Hasler • A. Schmidt • S. Mayer • M. Gienger • C. Wang

---

Links

DOI

---

In Collaboration

 Honda

---

Research Area

 C5 | Humane AI