ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
This addresses the data bottleneck for Embodied AI researchers by providing scalable environments for training and evaluation, representing a significant advancement rather than an incremental improvement.
The authors tackled the lack of large-scale datasets for Embodied AI by introducing ProcTHOR, a framework for procedurally generating diverse virtual environments, which enabled training models that achieved state-of-the-art results across 6 benchmarks, including 0-shot performance that often beat previous systems.
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.