  Developing visual perception models for active agents and sensorimotor
control are cumbersome to be done in the physical world, as existing algorithms
are too slow to efficiently learn in real-time and robots are fragile and
costly. This has given rise to learning-in-simulation which consequently casts
a question on whether the results transfer to real-world. In this paper, we are
concerned with the problem of developing real-world perception for active
agents, propose Gibson Virtual Environment for this purpose, and showcase
sample perceptual tasks learned therein. Gibson is based on virtualizing real
spaces, rather than using artificially designed ones, and currently includes
over 1400 floor spaces from 572 full buildings. The main characteristics of
Gibson are: I. being from the real-world and reflecting its semantic
complexity, II. having an internal synthesis mechanism, "Goggles", enabling
deploying the trained models in real-world without needing further domain
adaptation, III. embodiment of agents and making them subject to constraints of
physics and space.
