
FANUC is teaming with NVIDIA to bring Physical AI to industrial robots.
They combine simulation, learning, and edge AI to make robots adaptive.
A key demo shows two robots folding a T-shirt via imitation learning.
What happened
FANUC, a global leader in industrial robots, and NVIDIA have formed a strategic partnership to integrate what the article calls Physical AI — AI systems that understand and act on the physical world — into FANUC's entire robot lineup. The collaboration combines FANUC's robotics hardware with NVIDIA's Omniverse, Isaac Sim, Isaac Lab, and Jetson Thor edge computers.
Why it matters
Traditional industrial robots require manual programming and dedicated tooling, which makes switching between product variations slow and costly. The article says the goal is to let robots learn tasks in virtual simulations and then deploy those skills to real machines without extra tuning, which could cut commissioning time from weeks or months to virtually zero and make manufacturing lines far more flexible.
What to watch
The article highlights a demonstration where two FANUC CRX collaborative robots learned to fold a T-shirt by imitating a skilled worker, using NVIDIA's GR00T N foundation model. It also notes that FANUC's ROS 2 driver supports 1ms high-speed streaming control, and that the Jetson Thor module delivers 7.5× the AI performance of the previous Jetson AGX Orin.
Ask the AI about this article →
This partnership marks a shift in how industrial robots are developed. Previously, automation relied on deterministic, play-back programming where humans manually guided robots through every motion. The article frames this as moving from a 'human writes the motions' era to a 'design the conditions for AI to learn' era, enabled by training in virtual spaces before deployment on real machines.
A central technical breakthrough described is the use of FANUC's own control algorithms, via ROBOGUIDE, inside NVIDIA's simulation environment. Because the virtual robot behaves exactly like the real one, the gap between simulation and reality shrinks, so policies learned in software can transfer to hardware without fine-tuning. This combination addresses a long-standing challenge in robotics, where simulated results often fail in the real world.
The article also highlights how this could address structural pressures in manufacturing, such as skilled labor shortages and the need for high-mix, low-volume production. The implication is that robots could handle tasks like cable routing or bulk part picking that previously required human sensing and feedback. The ability to pre-validate entire production lines virtually, including PLCs and conveyors, could also reduce the risk and cost of factory automation projects.
For example, today's edition would include:
AI-summarized, only the topics you pick — one digest a day via Email, Slack, or Discord.
Free · takes 30 seconds · unsubscribe anytimeWhat is AIToday? →
Ask AI anything about this article. Q&As are published on this page for other readers too.
Panasonic Holdings announced on August 28, 2026, that it will reorganize its technology divisions effective Oc…

Applied Materials CEO Gary Dickerson said AI is driving the semiconductor industry's most consequential growth…

Researchers introduced Agent Seer, a pipeline that automatically creates realistic test scenarios for AI agent…

Google launched Expert Intelligence (EI), an AI feature for e-books bought on Google Play Books

Mastercard CEO Michael Miebach discussed the company's new Agent Pay protocol, which lets AI shopping agents h…

Caterpillar is applying AI across its operations, including the Cat AI Assistant for field technicians and AI…
