FLAME: Learning to Navigate with Multimodal LLM in Urban Environments
<|reference_start|>FLAME: Learning to Navigate with Multimodal LLM in Urban Environments: Large Language Models (LLMs) have demonstrated potential in Vision-and-Language Navigation (VLN) tasks, yet current applications face challenges. While LLMs excel in general conversation scenarios, they struggle with specialized navigation tasks, yielding suboptimal performance compared to specialized VLN models. We introduce FLAME (FLAMingo-Architected Embodied Agent), a novel Multimodal LLM-based agent and architecture designed for urban VLN tasks that efficiently handles multiple observations. Our approach implements a three-phase tuning technique for effective adaptation to navigation tasks, including single perception tuning for street view description, multiple perception tuning for trajectory summarization, and end-to-end training on VLN datasets. The augmented datasets are synthesized automatically. Experimental results demonstrate FLAME's superiority over existing methods, surpassing state-of-the-art methods by a 7.3% increase in task completion rate on Touchdown dataset. This work showcases the potential of Multimodal LLMs (MLLMs) in complex navigation tasks, representing an advancement towards practical applications of MLLMs in embodied AI. Project page: https://flame-sjtu.github.io<|reference_end|>
@article{xu2024flame:,
    title={FLAME: Learning to Navigate with Multimodal LLM in Urban Environments},
    author={Yunzhe Xu, Yiyuan Pan, Zhe Liu, Hesheng Wang},
    journal={arXiv preprint arXiv:2408.11051},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11051},
    primaryClass={cs.CV cs.AI cs.CL cs.RO}
}