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NVIDIA Isaac Sim on Omniverse Now in Open Beta

New enhanced simulation platform with robotics specific extensions provides essential features for building virtual robotic worlds and experiments.

NVIDIA has just announced their new Isaac simulation engine is now in open Beta. The Isaac Sim creates photorealistic environments, streamlining synthetic data generation and domain randomization to build ground-truth datasets to train robots in applications from logistics and warehouses to factories of the future. Omniverse, the underlying foundation for NVIDIA’s simulators such as this new platform, now includes several new features that allow roboticists to train and test their robots more efficiently by providing a realistic simulation of the robot interacting with compelling environments that can expand coverage beyond what is possible in the real world.

This Isaac Sim release 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 expand the breadth of robots and environments that can be 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 More Robotics Simulation:

Realistic Simulation

  • To deliver realistic robotics simulations, Isaac Sim leverages the Omniverse platform’s powerful 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 environments.

Seamless 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). The robot’s brain is easily connected 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 an important tool that is increasingly used to train the perception models found in today’s robots. Getting real-world, properly labeled data is difficult and in the case of robots operating near people it is particularly challenging. 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.

In the open beta, users can output synthetic data in the KITTI format. This data can then be used directly with the NVIDIA Transfer Learning Toolkit to enhance model performance with use case-specific data.

Domain Randomization:

This varies the parameters that define a simulated scene, such as the lighting, color, and texture of materials in the scene. One of the main objectives is to enhance the training of machine learning (ML) models by exposing the neural network to a variety of domain parameters in simulation. This helps the model to generalize when it encounters real world scenarios. In effect, this technique helps teach models what to ignore.

Isaac Sim supports the randomization of many different attributes that help define a given scene. With these capabilities, ML engineers can ensure that the synthetic dataset contains sufficient diversity to drive robust model performance.

In Isaac Sim open beta, domain randomization capabilities have been enhanced allowing the user to define a region for randomization. Developers can now draw a box around the region in the scene that is to be randomized and the rest of the scene will remain static.

NVIDIA Isaac Sim oOn Omniverse – Synthetic Data for Perception Model Training:

Additional information on the Isaac Sim is available here.

Source: Nvidia

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