Summary of Flame: Learning to Navigate with Multimodal Llm in Urban Environments, by Yunzhe Xu et al.
FLAME: Learning to Navigate with Multimodal LLM in Urban Environments
by Yunzhe Xu, Yiyuan Pan, Zhe Liu, Hesheng Wang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces FLAME, a novel multimodal Large Language Model (LLM)-based agent and architecture designed for urban Vision-and-Language Navigation (VLN) tasks. It tackles challenges faced by current LLM-based VLN models, which excel in general conversation scenarios but struggle with specialized navigation tasks. The approach uses a three-phase tuning technique, implementing single perception tuning for street view description, multiple perception tuning for route summarization, and end-to-end training on VLN datasets. Experimental results demonstrate FLAME’s superiority over existing methods, surpassing state-of-the-art methods by a 7.3% increase in task completion on the Touchdown dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re lost in a new city and need help finding your way around. This paper talks about how to use special language models called Large Language Models (LLMs) to help with that kind of problem. These LLMs are good at understanding general conversations, but they struggle when it comes to navigating through a place like a city. The researchers introduce a new approach called FLAME that helps these LLMs do better in this type of task. They use a special way of training the models to make them more effective. This work shows how LLMs can be used in real-life navigation tasks and is an important step towards using them for embodied intelligence. |
Keywords
» Artificial intelligence » Large language model » Summarization
