DriveGAN: Towards a Controllable High-Quality Neural Simulation
This work addresses the need for scalable, realistic simulators for robotics training and verification, offering a novel unsupervised approach with broad applications in autonomous systems.
The authors tackled the problem of learning a controllable, high-quality neural simulator for dynamic environments directly from unannotated video-action pairs, resulting in DriveGAN which surpasses previous data-driven simulators and enables new features like scene resimulation.
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.