GenEx: Generating an Explorable World

TaiMing Lu, Tianmin Shu, Alan Yuille, Daniel Khashabi, Jieneng Chen

Keywords: Generative Models, Video Generation, Embodied AI

TL;DR: We propose Genex to allow the agent for imaginatively exploration in a physical world, and acquire imagined observations to update its belief.

Abstract: Understanding, navigating, and exploring the 3D physical real world has long been a central challenge in the development of artificial intelligence. In this work, we take a step toward this goal by introducing *GenEx*, a system capable of planning complex embodied world exploration, guided by its generative imagination that forms expectations about the surrounding environments. *GenEx* generates high-quality, continuous 360-degree virtual environments, achieving robust loop consistency and active 3D mapping over extended trajectories. Leveraging generative imagination, GPT-assisted agents can undertake complex embodied tasks, including goal-agnostic exploration and goal-driven navigation. Agents utilize imagined observations to update their beliefs, simulate potential outcomes, and enhance their decision-making. Training on the synthetic urban dataset *GenEx-DB* and evaluation on *GenEx-EQA* demonstrate that our approach significantly improves agents' planning capabilities, providing a transformative platform toward intelligent, imaginative embodied exploration.

Primary Area: generative models

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Submission Number: 1658