Human intention-based systems enable robots to perceive and interpret user actions to interact with humans and adapt to their behavior proactively. Therefore, intention prediction is pivotal in creating a natural interaction with social robots in human-designed environments. In this paper, we examine using Large Language Models (LLMs) to infer human intention in a collaborative object categorization task with a physical robot. We propose a novel multimodal approach that integrates user non-verbal cues, like hand gestures, body poses, and facial expressions, with environment states and user verbal cues to predict user intentions in a hierarchical architecture. Our evaluation of five LLMs shows the potential for reasoning about verbal and non-verbal user cues, leveraging their context-understanding and real-world knowledge to support intention prediction while collaborating on a task with a social robot. Video:this https URL
View on arXiv@article{ali2025_2404.08424,
title={ Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task },
author={ Hassan Ali and Philipp Allgeuer and Stefan Wermter },
journal={arXiv preprint arXiv:2404.08424},
year={ 2025 }
}