Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in...

Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in OpenUSD, add physics properties, render preflight views, and validate the result against simulation-ready (SimReady) requirements. This workflow follows that process from a scene in Blender to a simulation-ready OpenUSD handoff for NVIDIA…

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Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run...

Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run locally on edge hardware. Developers building agents have had to route inference through a data center, adding network dependency, increasing costs, and exposing data that may need to stay on device. That constraint is lifting.

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A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline—for example, from an SUV to a sedan or another vehicle...

A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline—for example, from an SUV to a sedan or another vehicle variant in the portfolio—and its perception of the world changes. The sensor placement, calibration, fields of view, occlusions, body geometry, timing, and coverage all shift. A traffic light may appear in a different part of the frame.

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Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to...

Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to continuously localize the robot, interpret changing surroundings, select a route, and avoid obstacles to reach a goal safely. Moving this capability to a new robot or scene can require new data, simulation assets, robot interfaces…

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Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for...

Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for learning physical interactions, but their size can make on-device deployment difficult. This changes with the new NVIDIA Cosmos 3 Edge. Cosmos 3 Edge is a 4B omni-model (with a 2B NVIDIA Nemotron-based reasoner) in the Cosmos 3 family.

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A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene...

A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene often fails when object shapes, positions, or lighting change. Generalizing to these new conditions requires the policy to understand the tasks underlying physics, not just mimic the demonstrations. This ability comes from the backbone it’s…

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Unlike autonomous driving or industrial robotics, healthcare robotics can’t rely on internet-scale data collection or unlimited real-world experimentation....

Unlike autonomous driving or industrial robotics, healthcare robotics can’t rely on internet-scale data collection or unlimited real-world experimentation. Every demonstration requires specialized equipment, clinical expertise, and access to patients or laboratory environments. This creates three fundamental challenges for developers. First is the data gap. Training modern robotic policies…

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Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and...

Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and services they already use. Many of these workflows already depend on OpenUSD scenes, simulation-ready (SimReady) assets, Blender-based workflows, CAD pipelines, or domain-specific app stacks. The challenge is how to provide applications and…

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What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning...

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning models to production video tasks, developers often lose days to data formatting, container setup, training scripts, baseline evaluation, and hyperparameter sweeps before they even know whether post-training improves accuracy.

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NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such...

NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such as cameras and lidar. It is used to reconstruct dynamic scenes captured by autonomous vehicle (AV) and robotics platforms into simulation-ready digital environments that can be rendered, replayed, and analyzed inside NVIDIA Omniverse and…

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An increasingly common design pattern for autonomous vehicles (AVs), robotics, and spatial AI systems is bird's-eye-view (BEV) perception. BEV models project...

An increasingly common design pattern for autonomous vehicles (AVs), robotics, and spatial AI systems is bird’s-eye-view (BEV) perception. BEV models project multicamera image features into a shared top-down grid, providing downstream perception and planning modules with a common spatial layout for reasoning about lanes, vehicles, pedestrians, and free space. A key operation in this pipeline…

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Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional...

Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AI…

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Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can...

Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can reason over more complex driving scenes and produce richer intermediate reasoning are predominantly trained in open-loop, where model outputs are directly compared to ground-truth behaviors without considering their effect on the environment.

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Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what's...

Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s happening in their world, predict what’s likely to happen next, and generate actions for specific environments, embodiments, and tasks. NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning…

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