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arXiv · 2404.08424

Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task

Hassan Ali·Philipp Allgeuer·Stefan Wermter
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Abstract

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: https://youtu.be/tBJHfAuzohI

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Hassan Ali, Philipp Allgeuer, Stefan Wermter. 2024-04-12. Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task. https://doi.org/10.1007/978-981-96-3525-2_25

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