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Received β€” 20 July 2026 ⏭ NVIDIA Technical Blog

Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps

20 July 2026 at 15:00
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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Received β€” 30 June 2026 ⏭ NVIDIA Technical Blog

Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools

30 June 2026 at 16:00
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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Received β€” 24 June 2026 ⏭ NVIDIA Technical Blog

Accelerating BEV Pooling on NVIDIA GPUs for Physical AI Applications

24 June 2026 at 16:30
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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Received β€” 22 June 2026 ⏭ NVIDIA Technical Blog

Inside NVIDIA Halos for Robotics: A Full-Stack Functional Safety System for Physical AI

Physical AIβ€”robots working autonomously alongside people in factories, warehouses, hospitals, and homesβ€”is arriving faster than most expected. Traditional...

Physical AIβ€”robots working autonomously alongside people in factories, warehouses, hospitals, and homesβ€”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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Received β€” 1 June 2026 ⏭ NVIDIA Technical Blog

How to Post-Train Autonomous Vehicle Models in Closed-Loop with NVIDIA Alpamayo

1 June 2026 at 04:49
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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