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

• URL: http://arxiv.org/abs/2404.08424v2
• Date: Fri, 27 Sep 2024 10:05:56 GMT
• Title: Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task
• Authors: Hassan Ali, Philipp Allgeuer, Stefan Wermter,
• Abstract summary: In this paper, we examine using Large Language Models 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.
• Score: 17.190635800969456
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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