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NVIDIA Launches Physical AI Data Factory Blueprint for Robotics Training Data

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NVIDIA released the Physical AI Data Factory Blueprint, an open reference architecture for generating synthetic training data for robotics, autonomous vehicles, and vision AI. The pipeline runs three Cosmos models in sequence: Cosmos Curator annotates real-world datasets, Cosmos Transfer expands them with synthetic edge-case variations, and Cosmos Evaluator scores output for physical accuracy.

Microsoft Azure and Nebius are integrating the blueprint into their cloud infrastructure, while FieldAI, Hexagon Robotics, Skild AI, Uber, and Teradyne Robotics are applying it across robotics, autonomous driving, and vision AI. OSMO, NVIDIA's open-source orchestration framework, manages these workflows and integrates with coding agents like Claude Code and Cursor.

The blueprint is built for teams where real-world data is too sparse or not diverse enough to train reliable physical AI models. With OSMO providing orchestration and Azure and Nebius integrations already shipping, you can connect it to your existing cloud training stack.

View the full update on nvidianews.nvidia.com
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#NVIDIAGTC news: NVIDIA announces the new NVIDIA Physical AI Data Factory Blueprint turns accelerated compute into high-quality training data for robotics, vision AI agents, and autonomous vehicles. ➡️ https://t.co/vyKQijztgU https://t.co/IUUiRWS15g

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