  Intention-based Human-Robot Interaction (HRI) systems allow robots to
perceive and interpret user actions to proactively interact with humans and
adapt to their behavior. Therefore, intention prediction is pivotal in creating
a natural interactive collaboration between humans and robots. In this paper,
we examine the use of Large Language Models (LLMs) for inferring human
intention during a collaborative object categorization task with a physical
robot. We introduce a hierarchical approach for interpreting user non-verbal
cues, like hand gestures, body poses, and facial expressions and combining them
with environment states and user verbal cues captured using an existing
Automatic Speech Recognition (ASR) system. Our evaluation demonstrates the
potential of LLMs to interpret non-verbal cues and to combine them with their
context-understanding capabilities and real-world knowledge to support
intention prediction during human-robot interaction.
