NVIDIA Isaac Sim on Omniverse Now Available in Open Beta
The Isaac simulation engine creates photorealistic environments and streamlines synthetic data generation and domain randomization to build ground-truth datasets to train robots in various applications.
July 6, 2021
NVIDIA Omniverse is the foundation for NVIDIA’s simulators, including the Isaac platform—which now includes several new features. Discover the next level in simulation capabilities for robots with NVIDIA Isaac Sim open beta, available now.
Built on the Omniverse platform, Isaac Sim is a robotics simulation application and synthetic data generation tool. It allows roboticists to train and test their robots more efficiently by providing a realistic simulation of the robot interacting with environments that can expand coverage beyond what is possible in the real world, NVIDIA reports.
This release of Isaac Sim also adds improved multi-camera support and sensor capabilities, and a PTC OnShape CAD importer to make it easier to bring in 3D assets. These new features will expand the breadth of robots and environments that can be successfully modeled and deployed in every aspect: from design and development of the physical robot, then training the robot, to deploying in a “digital twin” in which the robot is simulated and tested in an accurate and photorealistic virtual environment.
Summary of Key New Features
- Multi-Camera Support
- Fisheye Camera with Synthetic Data
- ROS2 Support
- PTC OnShape Importer
- Improved Sensor Support
- Ultrasonic Sensor
- Force Sensor
- Custom Lidar Patterns
- Downloadable from NVIDIA Omniverse Launcher
Isaac Sim Enables Realistic Simulation
To deliver realistic robotics simulations, Isaac Sim leverages the Omniverse platform’s technologies including advanced GPU-enabled physics simulation with PhysX 5, photorealism with real-time ray and path tracing, and Material Definition Language (MDL) support for physically-based rendering.
Modular, Breadth of Applications
Isaac Sim is built to address many of the most common robotics use cases including manipulation, autonomous navigation and synthetic data generation for training data. Its modular design allows users to easily customize and extend the toolset to accommodate many applications and and environments.
Connectivity and Interoperability
Isaac Sim benefits from Omniverse Nucleus and Omniverse Connectors, enabling collaborative building, sharing and importing of environments and robot models in Universal Scene Description (USD). Easily connect the robot’s brain to a virtual world through Isaac SDK and ROS/ROS2 interface, fully-featured Python scripting, plugins for importing robot and environment models.
Synthetic Data Generation in Isaac Sim Bootstraps Machine Learning
Synthetic Data Generation is a tool that is used to train the perception models found in today’s robots. Isaac Sim has built-in support for a variety of sensor types that are important in training perception models. These sensors include RGB, depth, bounding boxes and segmentation.
Sources: Press materials received from the company and additional information gleaned from the company’s website.