# Omniverse Published articles for Omniverse. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt 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... ## Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video DevFeed: [Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video]() Original publisher: [Read original article]() Author: Sasa Docca Published: 2026-09-10T16:30:35Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [foundation-models](), [Cosmos](), [Simulation and Design](), [synthetic-data]() Tags: [ai](), [artificial-intelligence](), [cosmos](), [customer-stories](), [industrial-and-manufacturing](), [isaac](), [model](), [nvidia](), [nvidia-blackwell](), [omniverse](), [physical-ai](), [robotics](), [robots](), [simulation-and-design](), [synthetic-data-generation](), [tensorrt](), [video]() ### AI overview Skild AI's S1 robot foundation model learns new long-horizon physical tasks from a single video demonstration through in-context learning, without task-specific retraining. The article describes its development on NVIDIA AI infrastructure and use of NVIDIA Isaac Lab and Cosmos technologies. ### Source excerpt Manufacturing floors, warehouses and production lines rarely stay fixed -- tasks change, layouts shift and new products arrive, and most robots can't keep up without significant reprogramming. Skild AI's new S1 robot foundation model helps address this, designed to learn previously unseen, long-horizon tasks from a single video demonstration. The model, launched last week, uses [...] ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nvidia](), [synthetic-data](), [Simulation and Design](), [data](), [configuration]() Tags: [3d](), [autonomous](), [autonomous-vehicles](), [data](), [developer-tools-techniques](), [developers](), [development](), [model](), [models](), [nvidia](), [omniverse](), [performance](), [physical-ai](), [post](), [robotics](), [scale](), [simulation-modeling-design](), [synthetic-data](), [training](), [tutorial]() ### AI overview This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output. ### Source excerpt 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... ## How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents DevFeed: [How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-26T20:05:06Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Reinforcement learning](), [codex](), [coding](), [Simulation and Design](), [CUDA]() Tags: [agent](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-agents](), [architecture](), [codex](), [coding](), [cuda](), [development](), [evaluation](), [framework](), [nvidia](), [omniverse](), [physical-ai](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [skills](), [testing](), [training](), [tutorial](), [workflow]() ### AI overview This tutorial presents an agent-driven COMPASS workflow for training and evaluating cross-embodiment robot navigation policies. It covers asset preparation, smoke testing, residual reinforcement learning, checkpoint evaluation, runtime integration, and optional reconstructed environments using NVIDIA Omniverse NuRec. ### Source excerpt Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to... ## Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps DevFeed: [Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-20T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [SDKs]() Tags: [3d](), [agent-skill](), [apis](), [apps](), [autonomous-vehicles](), [c](), [featured](), [lidar](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [python](), [robotics](), [simulation-modeling-design]() ### AI overview The article explains how to integrate NVIDIA Omniverse's ovrtx RTX sensor-simulation library into existing applications. It covers using its C and Python SDK to generate camera, lidar, radar, and related sensor outputs from OpenUSD scenes. ### Source excerpt Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and... ## Develop Lightweight USD Runtimes Faster with AI Agents DevFeed: [Develop Lightweight USD Runtimes Faster with AI Agents]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-15T21:57:23Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation and Design](), [Code](), [Nvidia]() Tags: [agents](), [ai](), [ai-agents](), [code](), [developer-tools-techniques](), [featured](), [hackathon](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [robotics](), [simulation-modeling-design]() ### AI overview This article presents nanousd-labs, an experimental NVIDIA Omniverse Labs project that uses AI agents and the machine-readable USD Core Specification to generate lightweight, compliant OpenUSD runtimes. It explains how agents generate and validate code against specification-derived tests, enabling runtimes tailored to memory, performance, language, and deployment constraints. ### Source excerpt OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation... ## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-06-30T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Omniverse](), [Optimization](), [Tooling](), [GPU](), [Nvidia](), [CUDA](), [PyTorch](), [Physical AI](), [Robotics](), [Synthetic Data Generation](), [real-time](), [Reinforcement learning]() Tags: [3d](), [ai](), [autonomous](), [autonomous-vehicles](), [cuda](), [developer-tools](), [developer-tools-techniques](), [driving](), [engineering](), [featured](), [gpu](), [lidar](), [machine-learning](), [nvidia](), [omniverse](), [optimization](), [performance](), [physical-ai](), [real-time](), [reinforcement-learning](), [simulation-modeling-design](), [synthetic-data-generation]() ### AI overview This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance. ### Source excerpt NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such... ## NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets DevFeed: [NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets]() Original publisher: [Read original article]() Author: Ming-Yu Liu; Hanzi Mao; Jinwei Gu; Pranjali Joshi; Asawaree Published: 2025-03-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Physical AI](), [Artificial Intelligence](), [Robotics](), [Synthetic Data Generation](), [datasets](), [autonomous vehicles](), [foundation-models](), [Isaac](), [Omniverse](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [autonomous-vehicles](), [community](), [cosmos](), [datasets](), [foundation-models](), [isaac](), [nvidia](), [omniverse](), [open-source](), [physical-ai](), [research](), [robotics](), [synthetic-data-generation]() ### AI overview NVIDIA announces Cosmos Transfer, an open world foundation model that generates controllable, photorealistic video scenes from structured inputs such as depth maps, trajectories, LiDAR scans, and 3D bounding boxes. Together with NVIDIA Omniverse, it supports synthetic data generation for robotics and autonomous vehicle development. NVIDIA also introduces an open Physical AI Dataset on Hugging Face and the Isaac GR00T N1 foundation model for humanoid robot reasoning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.