  Massive datasets and high-capacity models have driven many recent
advancements in computer vision and natural language understanding. This work
presents a platform to enable similar success stories in Embodied AI. We
propose ProcTHOR, a framework for procedural generation of Embodied AI
environments. ProcTHOR enables us to sample arbitrarily large datasets of
diverse, interactive, customizable, and performant virtual environments to
train and evaluate embodied agents across navigation, interaction, and
manipulation tasks. We demonstrate the power and potential of ProcTHOR via a
sample of 10,000 generated houses and a simple neural model. Models trained
using only RGB images on ProcTHOR, with no explicit mapping and no human task
supervision produce state-of-the-art results across 6 embodied AI benchmarks
for navigation, rearrangement, and arm manipulation, including the presently
running Habitat 2022, AI2-THOR Rearrangement 2022, and RoboTHOR challenges. We
also demonstrate strong 0-shot results on these benchmarks, via pre-training on
ProcTHOR with no fine-tuning on the downstream benchmark, often beating
previous state-of-the-art systems that access the downstream training data.

Artificial Intelligence;Computer Vision and Pattern Recognition;Robotics