Holodeck: Language Guided Generation of 3D Embodied AI Environments
Authors: name: Yue Yang, name: Fan-Yun Sun, name: Luca Weihs, name: Eli VanderBilt, name: Alvaro Herrasti, name: Winson Han, name: Jiajun Wu, name: Nick Haber, name: Ranjay Krishna, name: Lingjie Liu, name: Chris Callison-Burch, name: Mark Yatskar, name: Aniruddha Kembhavi, name: Christopher Clark

3D simulated environments play a critical role in Embodied AI, but their
creation requires expertise and extensive manual effort, restricting their
diversity and scope. To mitigate this limitation, we present Holodeck, a system
that generates 3D environments to match a user-supplied prompt fully
automatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, and
museums, adjust the designs for styles, and can capture the semantics of
complex queries such as "apartment for a researcher with a cat" and "office of
a professor who is a fan of Star Wars". Holodeck leverages a large language
model (GPT-4) for common sense knowledge about what the scene might look like
and uses a large collection of 3D assets from Objaverse to populate the scene
with diverse objects. To address the challenge of positioning objects
correctly, we prompt GPT-4 to generate spatial relational constraints between
objects and then optimize the layout to satisfy those constraints. Our
large-scale human evaluation shows that annotators prefer Holodeck over
manually designed procedural baselines in residential scenes and that Holodeck
can produce high-quality outputs for diverse scene types. We also demonstrate
an exciting application of Holodeck in Embodied AI, training agents to navigate
in novel scenes like music rooms and daycares without human-constructed data,
which is a significant step forward in developing general-purpose embodied
agents.