NVIDIA GTC Taipei -- NVIDIA today announced a major collection of open source physical AI skills and tools that help developers turn complex robotics, autonomous vehicle (AV), vision AI and industrial digital twin workflows into agent-executable tasks — reducing the costs, time and complexity of building physical AI workflows at scale.

As AI agents move from writing code to orchestrating entire development tasks, physical AI is the next frontier. NVIDIA physical AI skills, available as part of NVIDIA Agent Toolkit, let agents use NVIDIA libraries, models and frameworks to speed the data generation, simulation, training, evaluation and deployment pipelines behind robots, AVs, factories and labs.

"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."

Agent-Ready Tools and Skills for Physical AI Development

NVIDIA is optimizing its entire physical AI stack for agents by turning libraries, models and frameworks into agent-callable tools. This includes NVIDIA Cosmos™ world foundation models for physical world reasoning and generation, NVIDIA Omniverse™ libraries for simulation and digital twins, NVIDIA Isaac™ for robotics simulation and robot learning, NVIDIA Metropolis for vision AI, NVIDIA Alpamayo for autonomous driving and the NVIDIA Jetson™ platform for edge AI development.

To help developers apply these tools, NVIDIA is launching new skills as part of NVIDIA Agent Toolkit to turn physical AI development processes into repeatable instructions that coding agents can follow. This includes which tools to call, what outputs to produce and how developers can validate results.

Developers can also safely build and deploy autonomous agents using these skills with the NVIDIA NemoClaw™ blueprint and the NVIDIA OpenShell™ runtime, which provides policy-based security and privacy governance on local or cloud hardware.

NVIDIA physical AI skills and tools are accelerating agentic development across:

  • Robotics and edge AI: Robot developers can use skills to accelerate the entire robotics development pipeline, from generating perception and mobility training data to simulation, automating navigation training, advancing robot learning and tuning Jetson-based edge systems for deployment.
  • Autonomous vehicles: For AV developers, skills can direct agents to reconstruct data captured by fleets into simulation environments, generate photorealistic driving scenarios at scale and run closed-loop reinforcement learning to expand training and evaluation coverage.
  • Real-time vision AI agents: For automated inspection and video intelligence, agent skills help teams generate synthetic training data, fine-tune models, automate labeling and build video AI agents that search, summarize and analyze live or recorded video.
  • Industrial AI: Industrial software developers can use these skills to convert engineering data into computer-aided design (CAD) assets for digital twin simulation, optimizing large OpenUSD scenes with less manual setup.
  • Healthcare: Before deploying automation in clinical environments, healthcare teams can guide agents through hospital-environment digital twin creation, sim-to-real data generation and software-in-the-loop policy testing.

     

The skills can be combined and integrated into larger agentic systems, enabling developers to orchestrate and automate complex workflows such as data generation, simulation, optimization, inference tuning, continuous evaluation and more.