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By Asma Adhimi
NVIDIA has unveiled a major open source collection of tools and skills aimed at speeding up development of physical AI systems for robotics, autonomous vehicles, industrial digital twins and vision AI applications.
Announced at NVIDIA GTC Taipei, the release expands the company’s push into agentic AI by allowing AI agents to execute complex physical AI workflows using NVIDIA software libraries, models and frameworks. The tools are designed to reduce the cost and complexity of building and scaling robotics and industrial AI systems.
For eeNews Europe readers working in embedded systems, automotive, robotics and industrial automation, the announcement highlights how AI agents are moving beyond software coding into real-world engineering workflows. The open source approach could also help accelerate adoption of simulation-driven design and AI-enabled manufacturing across Europe’s industrial sector.
The new collection includes agent-ready tools spanning NVIDIA Omniverse, Cosmos, Isaac, Metropolis, Alpamayo and Jetson technologies. NVIDIA says the tools transform tasks such as simulation, synthetic data generation, training, validation and deployment into repeatable workflows that AI agents can execute automatically.
“AI agents are revolutionizing software development, and that shift is now coming to physical AI, extending into the systems that will transform transportation, manufacturing, healthcare and robotics,” said Jensen Huang, founder and CEO of NVIDIA. “When agents can directly use NVIDIA libraries, models and frameworks, physical AI development will move faster, enabling developers to build the robots, autonomous vehicles and industrial systems of the future at an incredible pace.”
The company is also introducing new skills as part of the NVIDIA Agent Toolkit. These provide structured instructions that coding agents can follow, including which tools to use, expected outputs and validation steps.
NVIDIA said developers can deploy autonomous agents securely using the NemoClaw blueprint and OpenShell runtime, which add policy-based privacy and security controls for cloud or local deployments.
A number of industrial and automotive companies are already using the technology to accelerate AI development.
In electronics manufacturing, Pegatron used NVIDIA’s Defect Image Generation skill to reduce model training and deployment time by 67%, while Delta Electronics improved excess soldering defect detection rates by 17%. Foxconn reported a roughly 3% boost in first-pass manufacturing yield using the technology with partner DeepHow.
The automotive sector is also adopting the tools. Li Auto, Afari and DeepRoute.ai are using NVIDIA Omniverse NuRec models for neural scene reconstruction and rendering, generating more than 300,000 renders and simulations per day for autonomous vehicle development.
Industrial software companies including Cadence, Dassault Systèmes, Siemens and Synopsys are integrating NVIDIA Omniverse libraries and skills into digital twin and simulation workflows. Robotics developers such as Agile Robots, Agility, FieldAI and Universal Robots are also using the agent-ready stack to accelerate development from simulation through deployment.
Healthcare is another emerging application area. Foxconn and Compal are using NVIDIA Isaac for Healthcare to support hospital robotics and AI-powered automation systems.
The physical AI tools and skills are now available as open source through GitHub and skills.sh, with preconfigured launch environments also available through NVIDIA Brev.
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