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NVIDIA Isaac Sim

NVIDIA Isaac Sim, powered by Omniverse, is a scalable robotics simulation application and synthetic data generation tool that powers photorealistic, physically-accurate virtual environments to develop, test, and manage AI-based robots.

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With Isaac Sim, developers and researchers around the world are able to train and optimize AI robots for a breadth of tasks. In this demo, we showcase three incredible and very different robots developed in simulation and proven in the real world

Isaac Sim Diagram

What’s new in 2022.1

Cortex

Programming behaviors for complex robots like cobots has always been a challenge. Cortex is a decision framework in Isaac Sim that greatly simplifies this programming task. By building a belief representation of the world in simulation, the robot can be aware of its environment and then make decisions about the tasks it is designed to complete.

Learn More About Isaac Cortex >

Gym

Reinforcement Learning (RL) is a powerful machine learning method in robotics for performing complex tasks. Robots can be trained in “gyms” to complete tasks and acquire skills. Isaac Gym is a GPU accelerated RL tool that significantly reduces the amount of time to train robots.

Learn More About Omniverse Isaac Gym >

Omnigraph

Visual programming can simplify application development and debugging. Omnigraph is the Omniverse framework for visual scripting and programming. Robotics applications are often represented as different compute nodes connected to each other which makes them particularly well-suited for visual programming.

Learn More About Omnigraph in Isaac Sim >

Gazebo - Isaac Sim Connector

ROS Developers can move simulations from Gazebo to Isaac Sim and vice-versa with the Ignition-Omniverse Connector. This allows developers to choose the right simulator for the right simulation task

Learn More About using Isaac and Gazebo together >

Realistic Simulation

Isaac Sim leverages the Omniverse platform’s powerful simulation technologies including advanced GPU-enabled physics simulation with PhysX 5, photorealism with real-time ray and path tracing, and MDL material definition support for physically-based rendering.

Modular Architecture Covers Breadth of Applications

No simulator can address every robotics simulation challenge. But Isaac Sim is built to address many of the most common robotics use cases including manipulation, navigation, synthetic data generation for training data. Furthermore, due to its modular design, the tool can be customized and extended to many new use cases.

Seamless Connectivity and Interoperability

With NVIDIA Omniverse, Isaac Sim benefits from Omniverse Nucleus and Omniverse Connectors, enabling collaborative building, sharing, and importing of environments and robot models in USD. Easily connect the robot’s brain to a virtual world through Isaac SDK & ROS/ROS2 interface, fully-featured Python scripting, plugins for importing robot and environment models.

Generating Synthetic Data for Training Perception Models

Training perception models requires large and diverse datasets. Assembling these datasets can be costly, time-consuming, dangerous and even impossible for certain corner cases. By leveraging Omniverse Replicator for Isaac Sim, developers can bootstrap the training task. In the early phases of a project, synthetic data can accelerate proof of concepts or validate ML workflows. In later stages of a development cycle, real data can be augmented with synthetic data to reduce the time for training a production model. Isaac Sim has built in support for domain randomization allowing for changes in texture, colors, lighting, and placement. It also features support for different types of data including bounding boxes, depth, and segmentation. Developers can output the datasets in KITTI format making it easier to leverage NVIDIA’s TAO Toolkit.

Explore using Replicator Composer in Isaac Sim ›

Simulating Manipulation

One of the key application areas for robots is for manipulators which can identify objects, pick them up and move them. In a modern factory or warehouse environment, manipulators can greatly increase the efficiency and throughput of handling and sorting materials.Programming them has always been a challenge and to address this we’ve introduced Isaac Cortex. Isaac Sim has built in examples of common tasks like filling bins , stacking bins, and stacking blocks. These python based examples can be modified to work for your custom tasks. The UR10, Frank Emika Panda (Leonardo), and the low-cost DofBot are manipulator robots supported in Isaac and can jumpstart your projects.

Learn More About Leonardo and UR10 Interactive Demos ›

Simulating Navigation

Autonomous mobile robots must be able to move in their environments from point A to point B. This capability is enabled by the navigation stack. Isaac Sim supports the development and testing of your robots navigation capabilities. Isaac Sim provides a complete example of how to use the ROS Navigation stack. In this example, the NVIDIA Carter robot moves its way autonomously through a warehouse environment. Similarly, the Isaac Navigation Stack running on Carter can be easily exercised in one the examples.

More on ROS 2 Navigation Stack on Carter ›

More on Isaac Navigation Stack on Carter ›

Importing Robots/Assets into Isaac Sim

Importing robot models and other assets into robotics simulators is of critical importance and oftentimes a significant challenge when setting up a training or testing scenario. Leveraging the powerful connector capabilities built into Omniverse, Isaac Sim has built-in support for popular product design formats. The advanced URDF importer has been tested on multiple robot models. Additionally, CAD files can be imported directly from Onshape and from STEP files with minimal post-processing.

To make it easier to add assets to different environment, Isaac Sim supports Shapenet. The Shapenet importer provides access to a massive amount of 3D assets.

More information on using the OnShape Importer ›

More information on using the STEP Importer ›