LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application
<|reference_start|>LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application: Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions to achieve tasks in an open world. However, when applied to a long-horizon collaborative task, this formulation results in excessive prompting for initiating or clarifying robot actions at every step of the task. We propose Language-driven Intention Tracking (LIT), leveraging LLMs and VLMs to model the human user's long-term behavior and to predict the next human intention to guide the robot for proactive collaboration. We demonstrate smooth coordination between a LIT-based collaborative robot and the human user in collaborative cooking tasks.<|reference_end|>
@article{huang2024lit:,
    title={LIT: Large Language Model Driven Intention Tracking for Proactive
  Human-Robot Collaboration -- A Robot Sous-Chef Application},
    author={Zhe Huang, John Pohovey, Ananya Yammanuru, Katherine Driggs-Campbell},
    journal={arXiv preprint arXiv:2406.13787},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13787},
    primaryClass={cs.RO cs.CV}
}