  This paper presents an innovative large language model (LLM)-based robotic
system for enhancing multi-modal human-robot interaction (HRI). Traditional HRI
systems relied on complex designs for intent estimation, reasoning, and
behavior generation, which were resource-intensive. In contrast, our system
empowers researchers and practitioners to regulate robot behavior through three
key aspects: providing high-level linguistic guidance, creating "atomic
actions" and expressions the robot can use, and offering a set of examples.
Implemented on a physical robot, it demonstrates proficiency in adapting to
multi-modal inputs and determining the appropriate manner of action to assist
humans with its arms, following researchers' defined guidelines.
Simultaneously, it coordinates the robot's lid, neck, and ear movements with
speech output to produce dynamic, multi-modal expressions. This showcases the
system's potential to revolutionize HRI by shifting from conventional, manual
state-and-flow design methods to an intuitive, guidance-based, and
example-driven approach. Supplementary material can be found at
https://hri-eu.github.io/Lami/
