NVIDIA Announces Open Physical AI Data Factory Blueprint to Accelerate Robotics, Vision AI Agents and Autonomous Vehicle Development

News Summary:

• Blueprint enables massive-scale data processing and curation, synthetic data generation, reinforcement learning and evaluation of physical AI models for vision AI agents, robotics and autonomous vehicles.
• Cloud service providers including Microsoft Azure and Nebius provide the blueprint to transform world-scale compute into agent-driven turnkey data production engines.
• Leading physical AI developers FieldAI, Hexagon Robotics, Linker Vision, Milestone Systems, Skild AI, Uber and Teradyne Robotics are using the blueprint to accelerate robotics, vision AI agents and autonomous vehicle development.

GTC-NVIDIA today announced the NVIDIA Physical AI Data Factory Blueprint, an open reference architecture that unifies and automates how training data is generated, augmented and evaluated, reducing the costs, time and complexity of training physical AI systems at scale.

The blueprint enables developers to use NVIDIA Cosmos™ open world foundation models and leading coding agents to transform limited training data into large, diverse datasets - including rare edge cases and long-tail scenarios that are expensive, time-consuming and often impractical to capture in the real world.

NVIDIA is collaborating with Microsoft Azure and Nebius to integrate the open blueprint with their cloud infrastructure and services, enabling developers to turn accelerated computing power into high-volume training data. Leading physical AI developers FieldAI, Hexagon Robotics, Linker Vision, Milestone Systems, RoboForce, Skild AI, Teradyne Robotics and Uber are using the blueprint to accelerate robotics, vision AI agents and autonomous vehicle development.

"Physical AI is the next frontier of the AI revolution, where success depends on the ability to generate massive amounts of data," said Rev Lebaredian, vice president of Omniverse and simulation technologies at NVIDIA. "Together with cloud leaders, we're providing a new kind of agentic engine that transforms compute into the high-quality data required to bring the next generation of autonomous systems and robots to life. In this new era, compute is data."

A Unified Engine for Physical AI Development
Physical AI follows scaling laws: Performance improves as data, compute and model capacity grow. The Physical AI Data Factory Blueprint serves as a single reference architecture that moves teams from raw data to model-ready training sets through modular, automated workflows:

• Curate and Search: NVIDIA Cosmos Curator processes, refines and annotates large-scale real-world and synthetic datasets.
• Augment and Multiply: Cosmos Transfer exponentially expands and diversifies curated data, multiplying real and simulated inputs to better capture rare and long-tail scenarios across environments and lighting conditions.
• Evaluate and Validate: NVIDIA Cosmos Evaluator, powered by Cosmos Reason and now available on GitHub, automatically scores, verifies and filters generated data to ensure physical accuracy and training readiness.

NVIDIA is using the Physical AI Data Factory Blueprint to train and evaluate NVIDIA Alpamayo, the world's first open reasoning-based vision language action models for long-tail autonomous driving. Skild AI is applying the blueprint to advance general-purpose robot foundation models, while Uber is using it to accelerate autonomous vehicle development.

Agent Driven Orchestration at Scale
Many robotics developers are not equipped to stand up and manage the complex AI infrastructure required to generate data at scale.

NVIDIA OSMO, an open source orchestration framework, unifies and manages these workflows across compute environments, reducing manual tasks so developers can focus on building their models.

OSMO now integrates with leading coding agents such as Claude Code, OpenAI Codex and Cursor, enabling AI-native operations where agents proactively manage resources, resolve bottlenecks and accelerate model delivery at scale.

Powering the Global Physical AI Ecosystem
Cloud service providers play a critical role in providing the accelerated AI infrastructure, machine learning operations and orchestration services developers need to build and deploy physical AI at scale.

Microsoft Azure is integrating the Physical AI Data Factory Blueprint into an open physical AI toolchain, now available on GitHub. The blueprint offers integration with Azure services - including Azure IoT Operations, Microsoft Fabric, Real-Time Intelligence, Microsoft Foundry and GitHub Copilot - to provide enterprise-grade, agent-driven workflows for training and validating physical AI systems quickly and at scale.

FieldAI, Hexagon Robotics, Linker Vision and Teradyne Robotics are among the first to test the Azure physical AI toolchain for accelerating and scaling data generation, augmentation and evaluation across their perception, mobility and reinforcement learning pipelines.

Nebius has integrated OSMO into its AI Cloud, enabling developers to use the blueprint to deploy production-ready data pipelines tailored to their needs. Nebius's infrastructure powers the physical AI stack end to end, blending NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPUs with ultrafast object storage, native data management and labeling, serverless execution and built-in managed inference.

Early users Milestone Systems, Voxel51 and RoboForce are harnessing the blueprint on Nebius infrastructure to accelerate model development for video analytics AI agents, autonomous vehicles and industrial humanoid robots.

The NVIDIA Physical AI Data Factory Blueprint is expected to be available on GitHub in April.

Watch the GTC keynote from NVIDIA founder and CEO Jensen Huang and explore sessions.