Summary of Mc-gpt: Empowering Vision-and-language Navigation with Memory Map and Reasoning Chains, by Zhaohuan Zhan et al.
MC-GPT: Empowering Vision-and-Language Navigation with Memory Map and Reasoning Chains
by Zhaohuan Zhan, Lisha Yu, Sijie Yu, Guang Tan
First submitted to arxiv on: 17 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a suite of techniques to enhance the Vision-and-Language Navigation (VLN) task, where an agent must navigate to a destination following natural language instructions. The approach uses Large Language Models (LLMs), which have shown strong generalization capabilities in VLN. However, existing LLM-based methods suffer from limitations in memory construction and diversity of navigation strategies. To address these challenges, the authors introduce a topological map that stores navigation history, as well as a Navigation Chain of Thoughts module that leverages human navigation examples to enrich navigation strategy diversity. The proposed pipeline integrates navigational memory and strategies with perception and action prediction modules. Experimental results on the REVERIE and R2R datasets demonstrate that this method effectively enhances the navigation ability of LLMs and improves the interpretability of navigation reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find your way around a new city using just verbal directions like “Head north for 10 minutes.” This is basically what computers are trying to do in the Vision-and-Language Navigation (VLN) task. Right now, the best way to solve this problem involves training special computer programs called Large Language Models (LLMs). However, these LLMs have some major limitations when it comes to remembering where they’ve been and coming up with different ways to get somewhere. To fix these problems, researchers have developed a new set of techniques that use maps to store navigation history and draw inspiration from human navigation strategies. By combining all this information, the computers can do a much better job of following verbal directions and understanding why they’re making certain choices. |
Keywords
» Artificial intelligence » Generalization
