Generative World Explorer
Authors: name: Taiming Lu, name: Tianmin Shu, name: Alan Yuille, name: Daniel Khashabi, name: Jieneng Chen

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 imagine unseen parts of the world
through a mental exploration and 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 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 Genex, we create a synthetic urban scene dataset, Genex-DB.
Our experimental results demonstrate that (1) 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.