Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task
<|reference_start|>Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task: 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.<|reference_end|>
@article{ali2024comparing,
    title={Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction
  in an Object Categorization Task},
    author={Hassan Ali, Philipp Allgeuer, Stefan Wermter},
    journal={arXiv preprint arXiv:2404.08424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08424},
    primaryClass={cs.RO cs.AI cs.HC}
}