The latest open models and frameworks from NVIDIA bring together simulation, robot learning and embedded compute to accelerate cloud-to-robot workflows.

The NVIDIA Developer Blog provides developers with a  knowledge on GPU computing, AI, and deep learning, offering tutorials, code samples, and real-world applications of NVIDIA technologies. From optimizing GPU-accelerated algorithms to implementing  AI models, developers can learn practical techniques and strategies for harnessing the power of NVIDIA GPUs in their projects. Moreover, the blog highlights advancements in GPU architectures, CUDA programming, and GPU-accelerated libraries, empowering developers to stay at the forefront of GPU computing innovation.

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NVIDIA's Isaac platform provides an end-to-end cloud-to-robot workflow for building generalist-specialist robots. The stack covers synthetic data generation via Omniverse NuRec and 3D Gaussian splatting, teleoperation data collection with Isaac Teleop, policy training in Isaac Lab 3.0 with parallel simulation environments, and deployment on Jetson edge hardware. Key components include the open GR00T VLA model for robot reasoning, the Newton physics engine for realistic simulation, the Physical AI Data Factory Blueprint for scalable data pipelines, and SOMA-X for standardizing skeleton and motion representations across platforms. Safety tooling (NVIDIA Halos), open datasets, and educational resources round out the offering announced at GTC.

From Simulation to Production: How to Build Robots With AI