Abstract: Simulation is a crucial component of any robotic system.
Authors: Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler. Learning to Simulate Dynamic Environments with GameGAN. Learn more from the NVIDIA blog and this research paper. Title:Learning to Simulate Dynamic Environments with GameGAN. So when can we play it? NVIDIA will be making this AI tribute to the game available later this year on their AI Playground, where anyone can experience research demos first-hand.
Gamegan simulator#
Another application for the GameGAN is for training autonomous machines in a simulator thus reducing the need of physical environments for training. It simplifies the game development process by reducing the time in planning/creating a new game level over time. It allows people to doodle, and the AI will convert it into a photo realistic image.Īlso Read: NVIDIA Releases new Minecraft with RTX Worlds and a Raffleįor its application in real life, NVIDIA GameGAN can be used to prototype level layouts for developers to try out. GauGAN is a deep learning model created by NVIDIA research. An earlier example of GANs can be seen through NVIDIA GauGAN. These GAN-based models learn to create new content that is convincing enough to pass for the original.
Gamegan generator#
It’s made up of two neural networks that compete with each other: a generator and discriminator. It further disentangles action-dependent and action-independent content.GameGAN is a neural network model that mimics a game engine by utilizing what its developer call “ generative adversarial networks” or GANs. The Dynamics Engine then learns the latent space dynamics. It disentangles themes and content while achieving high-quality generation by leveraging a Variational Auto-Encoder (VAE) and Generative Adversarial Networks (GAN). We propose our encoder-decoder architecture that is pre-trained to produce the latent space for images. Rather than generating a sequence of frames directly, we split the learning process into two steps, motivated by World Model. Generating high-quality temporally-consistent image sequences is a challenging problem. We aim to achieve controllability in two aspects:ġ) We assume there is an egocentric agent that can be controlled by a given action.Ģ) We want to control different aspects of the current scene, for example, by modifying an object or changing the background color. Our objective is to learn a high-quality controllable neural simulator by watching sequences of video frames and their associated actions. We showcase that our approach greatly surpasses the performance of previous data-driven simulators, and allows for new features not explored before. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data. 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. 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. We introduce a novel high-quality neural simulator referred to as DriveGAN that achieves controllability by disentangling different components without supervision. 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. 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. Realistic simulators are critical for training and verifying robotics systems.
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