From Simulation to Real-World Deployment: How to Build AI-Powered Robots

Next-generation robots are evolving into generalist-specialist models that can learn a wide range of skills while also specializing in specific tasks. These systems are built on VLA (Vision-Language-Action) models to intelligently perceive and act across diverse tasks.

The NVIDIA Isaac platform provides an integrated environment where robot developers can build and deploy robots at scale using models, data pipelines, simulation frameworks, and more. In particular, NVIDIA Isaac GR00T N, an open VLA model, serves as a powerful foundation for developers to build their own robot intelligence.

The way data is collected—a core element of robot learning—is also changing. In the past, manual data collection was relied upon, but now real-world signals are combined with simulation-generated data to convert cloud computing into large volumes of valid data. According to Gartner, by 2030, more than 90% of edge scenario data is expected to consist of Synthetic Data.

To support this, NVIDIA offers the following tools:

• NVIDIA Omniverse NuRec: Converts real-world sensor data into interactive simulations within NVIDIA Isaac Sim using 3D Gaussian splatting technology.
• NVIDIA Isaac Teleop: Supports easy import of real-world data through teleoperation.