Summary of Navcot: Boosting Llm-based Vision-and-language Navigation Via Learning Disentangled Reasoning, by Bingqian Lin et al.
NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning
by Bingqian Lin, Yunshuang Nie, Ziming Wei, Jiaqi Chen, Shikui Ma, Jianhua Han, Hang Xu, Xiaojun Chang, Xiaodan Liang
First submitted to arxiv on: 12 Mar 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 This paper introduces Navigational Chain-of-Thought (NavCoT), a novel strategy for Vision-and-Language Navigation (VLN) that leverages large language models (LLMs) to improve navigational decision-making. The approach involves prompting the LLM to forecast a chain-of-thought by imagining the next observation, selecting the best candidate observation, and determining the action based on prior reasoning. This process enables self-guided navigation and mitigates the domain gap between the VLN task and the LLM training corpus. Experimental results demonstrate NavCoT’s superiority over direct action prediction variants, achieving a ~7% relative improvement on the Room-to-Room (R2R) dataset. The paper believes that NavCoT will unlock more task-adaptive and scalable LLM-based embodied agents for real-world robotics applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make robots smarter by using language models. Right now, these models are great at understanding language but not so good at helping robots navigate through spaces. The authors came up with a new way called NavCoT that makes the language model imagine what’s next and then decide what action to take. This makes the robot more independent and can reduce errors. They tested it on different scenarios and found that it worked better than other methods. This could lead to more robots being able to understand language and do tasks like us. |
Keywords
» Artificial intelligence » Language model » Prompting
