Huang calls it the ‘ChatGPT moment for robotics’ - and now devs get open tools to build it.
Imagine training robots that learn in simulated worlds faster than real life ever could. Nvidia’s Jensen Huang just predicted a billion robots by 2027, and yesterday they made it real with a massive open-source drop for Physical AI.
Nvidia released a suite of open models, frameworks, and AI infrastructure specifically for physical AI - think customizable world models for synthetic data generation, robot policy evaluation in simulation, an open reasoning vision language model, and a vision language action model. This isn’t hype; it’s production-ready tools that slash training times using high-fidelity physics sims and photorealistic rendering[4]. Paired with exploding GPU compute, it’s solving the biggest bottlenecks in robot dev.
For developers, this is huge: no more siloed robot training. Reuse intelligence across tasks with multimodal foundation models that grok objects, space, and generalization. Build robots for warehouses, healthcare, or homes without starting from scratch every time[4].
Compared to closed systems like Boston Dynamics or Tesla Optimus, Nvidia’s stack is open and scalable. It democratizes Physical AI, letting indie devs and startups compete while Big Tech scales to billions of units. This shifts robotics from lab toy to deployable reality.
Grab the models from Nvidia’s repo today and spin up a sim. Train your first policy this week - what’s the first robot task you’re automating? Watch for integrations with ROS2 and real-world benchmarks.
Source: IBM Think