  We present Habitat, a platform for research in embodied artificial
intelligence (AI). Habitat enables training embodied agents (virtual robots) in
highly efficient photorealistic 3D simulation. Specifically, Habitat consists
of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with
configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is
fast -- when rendering a scene from Matterport3D, it achieves several thousand
frames per second (fps) running single-threaded, and can reach over 10,000 fps
multi-process on a single GPU. (ii) Habitat-API: a modular high-level library
for end-to-end development of embodied AI algorithms -- defining tasks (e.g.,
navigation, instruction following, question answering), configuring, training,
and benchmarking embodied agents.
  These large-scale engineering contributions enable us to answer scientific
questions requiring experiments that were till now impracticable or 'merely'
impractical. Specifically, in the context of point-goal navigation: (1) we
revisit the comparison between learning and SLAM approaches from two recent
works and find evidence for the opposite conclusion -- that learning
outperforms SLAM if scaled to an order of magnitude more experience than
previous investigations, and (2) we conduct the first cross-dataset
generalization experiments {train, test} x {Matterport3D, Gibson} for multiple
sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors
generalize across datasets. We hope that our open-source platform and these
findings will advance research in embodied AI.
