  Realistic simulators are critical for training and verifying robotics
systems. While most of the contemporary simulators are hand-crafted, a
scaleable way to build simulators is to use machine learning to learn how the
environment behaves in response to an action, directly from data. In this work,
we aim to learn to simulate a dynamic environment directly in pixel-space, by
watching unannotated sequences of frames and their associated action pairs. We
introduce a novel high-quality neural simulator referred to as DriveGAN that
achieves controllability by disentangling different components without
supervision. In addition to steering controls, it also includes controls for
sampling features of a scene, such as the weather as well as the location of
non-player objects. Since DriveGAN is a fully differentiable simulator, it
further allows for re-simulation of a given video sequence, offering an agent
to drive through a recorded scene again, possibly taking different actions. We
train DriveGAN on multiple datasets, including 160 hours of real-world driving
data. We showcase that our approach greatly surpasses the performance of
previous data-driven simulators, and allows for new features not explored
before.
