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.
