LangNav: Language as a Perceptual Representation for Navigation (2024.findings-naacl)
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| Challenge: | Existing approaches to vision-and-language navigation use visual features as the perceptual representation of a visual representation of an agent's egocentric panoramic view. |
| Approach: | They propose to use off-the-shelf vision systems to convert an agent’s egocentric panoramic view into natural language descriptions. |
| Outcome: | The proposed approach improves on the R2R VLN benchmark by using synthetic trajectories from a prompted language model and domain transfer where a policy learned on one simulated environment (ALFRED) is transferred to another (more realistic) environment and combining both vision- and language-based representations. |
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| Challenge: | Vision-and-Language Navigation (VLN) is a subfield of embodied AI that integrates natural language understanding, visual perception, and sequential decision-making to allow autonomous agents to navigate and interact within visual environments. |
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| Challenge: | Using multilingual instructions to learn a better cross-lingual representation is challenging for multilingual agents. |
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| Challenge: | We observe two kinds of instructions that make the grounding in the vision-and-language navigation task quite challenging. |
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| Challenge: | Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence. |
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| Challenge: | Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. |
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| Challenge: | Vision and language navigation (VLN) is a challenging task towards the creation of embodied agents. |
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| Challenge: | Existing vision-and-language navigation models are brittle to multi-level language underspecification. |
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