Generative World Explorer Article Swipe

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Taiming Lu , Tianmin Shu , Alan Yuille , Daniel Khashabi , Jieneng Chen ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2411.11844

Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. In contrast, humans can $\textit{imagine}$ unseen parts of the world through a mental exploration and $\textit{revise}$ their beliefs with imagined observations. Such updated beliefs can allow them to make more informed decisions, without necessitating the physical exploration of the world at all times. To achieve this human-like ability, we introduce the $\textit{Generative World Explorer (Genex)}$, an egocentric world exploration framework that allows an agent to mentally explore a large-scale 3D world (e.g., urban scenes) and acquire imagined observations to update its belief. This updated belief will then help the agent to make a more informed decision at the current step. To train $\textit{Genex}$, we create a synthetic urban scene dataset, Genex-DB. Our experimental results demonstrate that (1) $\textit{Genex}$ can generate high-quality and consistent observations during long-horizon exploration of a large virtual physical world and (2) the beliefs updated with the generated observations can inform an existing decision-making model (e.g., an LLM agent) to make better plans.

Related Topics

Generative Grammar

Computer Science

Artificial Intelligence

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Generative grammar Computer science Artificial intelligence

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2411.11844
PDF: https://arxiv.org/pdf/2411.11844
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4404648165

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OpenAlex ID: https://openalex.org/W4404648165

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DOI: https://doi.org/10.48550/arxiv.2411.11844

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Title: Generative World Explorer

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-11-18

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Authors: Taiming Lu, Tianmin Shu, Alan Yuille, Daniel Khashabi, Jieneng Chen

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Landing page: https://arxiv.org/abs/2411.11844

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PDF URL: https://arxiv.org/pdf/2411.11844

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OA status: green

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OA URL: https://arxiv.org/pdf/2411.11844

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Concepts: Generative grammar, Computer science, Artificial intelligence

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Cited by: 0

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Related works (count): 10

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