Understanding Large-Language Model (LLM)-powered Human-Robot Interaction

Published in HRI 24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

Recommended citation: Callie Y. Kim*, Christine P. Lee*, and Bilge Mutlu. 2024. Understanding Large-Language Model (LLM)-powered Human-Robot Interaction. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI 24). Association for Computing Machinery, New York, NY, USA, 371–380.

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Abstract: While social robots are increasingly introduced into domestic settings, few have explored the utility of the robots’ packaging. Here we highlight the potential of product packaging in human-robot interaction to facilitate, expand, and enrich user experience with the robot. We present a social robot’s box as interactive product packaging, designed to be reused as a “home’’ for the robot. Through co-design sessions with children, an narrative-driven and socially engaging box was developed to support initial interactions between the child and the robot. Our findings emphasize the importance of packaging design to produce positive outcomes towards successful human-robot interaction.