At GTC Taipei and Computex today, NVIDIA Corp. revealed several open-source physical AI skills and tools to help developers of robotics, autonomous vehicles, visual AI, and industrial digital twins. The company claimed that they can help reduce the costs, time, and complexity of building physical AI workflows at scale. Available as part of the NVIDIA Agent Toolkit, the new skills will let AI agents speed the data generation, simulation, training, evaluation, and deployment pipelines behind robots, autonomous vehicles (AVs), factories, and laboratories, said the company. “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, at GTC Taipei. “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.” “Physical AI requires massive amounts of training data in diverse environments,” noted Rev Lebaredian, vice president for physical AI simulation at NVIDIA. “Teleoperation, simulation, and internet-scale data lead to world foundation models for an infinite diversity of use cases.” NVIDIA makes physical AI stack agent-ready NVIDIA said it is optimizing its entire physical AI stack for agents by turning libraries, models, and frameworks into agent-callable tools. This includes: - NVIDIA Cosmos 3 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 - NVIDIA Jetson platform for edge AI development “Cosmos 3 is the frontier foundation model for physical AI,” Lebaredian said. “It understands videos and text and can flag what matters. Cosmos is