Summary of Mapgpt: Map-guided Prompting with Adaptive Path Planning For Vision-and-language Navigation, by Jiaqi Chen et al.
MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation
by Jiaqi Chen, Bingqian Lin, Ran Xu, Zhenhua Chai, Xiaodan Liang, Kwan-Yee K. Wong
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 presents a novel agent, MapGPT, which leverages Generative Pre-Trained Transformer (GPT) as its brain to excel in vision-and-language navigation tasks. Unlike previous agents, MapGPT constructs an online linguistic-formed map to facilitate global exploration and multi-step path planning. This design enables the agent to understand spatial environments and perform zero-shot tasks with state-of-the-art performance on R2R and REVERIE datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re a robot trying to navigate through a maze! This paper helps us create better robots by giving them “brains” that can learn from experience. The new brain, called MapGPT, lets the robot see its surroundings and make smart decisions about where to go next. It’s like having a map of the whole maze, not just little parts of it! |
Keywords
» Artificial intelligence » Gpt » Transformer » Zero shot
