End-to-End SDG Workflows with NVIDIA Isaac Sim

As robots increasingly undertake complex mobility tasks, developers require accurate simulations that can be applied across various environments and workloads. Collecting high-quality data in the physical world is often costly and time-consuming, making synthetic data generation at scale essential for advancing physical AI. NVIDIA Isaac Sim and NVIDIA OSMO provide a comprehensive solution for building simulated environments and orchestrating end-to-end synthetic data generation workflows. These tools allow developers to create physics-accurate simulations, generate diverse datasets using MobilityGen, and enhance data with visual diversity through Cosmos Transfer. By leveraging cloud technology and open-source frameworks, developers can efficiently train robot policies and models, bridging the gap between simulated and real-world data. This matters because it accelerates the development and deployment of advanced robotics systems, making them more adaptable and efficient in real-world applications.

As robotics technology advances, the demand for physics-accurate simulations that can be easily adapted across various environments and workloads becomes increasingly crucial. The creation of synthetic data in simulated environments is a game-changer for training robotic models, as it provides a cost-effective and scalable solution compared to collecting data in the physical world. By leveraging NVIDIA Isaac Sim and NVIDIA OSMO, developers can generate high-quality synthetic data at scale, which is vital for the development of robust robotic policies and models. This approach not only accelerates the development of physical AI but also bridges the gap between simulated and real-world data, ensuring that the models perform well in real-world scenarios.

Building a simulated environment is the first step in this process. Developers can use NVIDIA Isaac Sim to create detailed and realistic environments, either locally or in the cloud. The use of Omniverse NuRec technology allows for the reconstruction of 3D digital twins from real-world sensor data, providing a rich source of synthetic data for training AI models. By incorporating SimReady assets, developers can ensure that the simulated environments are populated with accurate 3D models that include semantic labeling and physics properties. This setup is essential for collecting synthetic data that is representative of real-world conditions, which is crucial for training effective robotic models.

Once the simulated environment is established, synthetic data generation can be enhanced using tools like MobilityGen and Cosmos Transfer. MobilityGen provides a workflow for generating data for mobile robots, supporting both manual and automated data collection methods. This data can then be augmented with visual diversity using Cosmos Transfer, which generates photorealistic videos from synthetic data. This augmentation process is key to reducing the sim-to-real gap, as it introduces visual variations that improve the performance of robotic policies when deployed in real-world environments. The ability to scale these processes using NVIDIA OSMO ensures that developers can efficiently manage and execute large-scale data generation and augmentation workflows.

Scaling the data generation pipeline in the cloud is essential for handling the extensive computational and storage demands of synthetic data generation (SDG) workloads. NVIDIA OSMO provides a cloud-native orchestrator that allows developers to define, run, and monitor multistage physical AI pipelines across diverse compute environments. By deploying OSMO on platforms like Microsoft Azure, developers can leverage cloud resources to manage and scale their SDG workflows efficiently. This capability is crucial for ensuring that the generated datasets are comprehensive and representative, ultimately leading to more accurate and reliable robotic models. As the field of robotics continues to evolve, these advancements in synthetic data generation and orchestration will play a pivotal role in the development of next-generation AI systems.

Read the original article here