How physical AI, robotics will shape the region in 2026, NVIDIA execs share insights
We look at how how physical AI and robotics are intrinsically poised to transform the industrial sector not only in 2026, but in the decades to come
05 February, 2026
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The industrial landscape in the Middle East is undergoing immense transformation. By now, nearly everyone in the world has at least heard of AI, with its adoption rate faster than the early days of the world wide web. As AI enters a period of relative maturity, experts are unearthing new capabilities and identifying how they can accelerate almost every sector.
Recognisng this trajectory early on, Middle Eastern leaders have pivoted from traditional automation toward physical AI – a generation of autonomous models that perceive, understand, interact with and navigate the physical world.
Looking ahead, we examine how physical AI and robotics are intrinsically poised to transform the industrial sector not only in 2026, but in the decades to come.
Everything physical will be born in simulation
The most significant change expected in the coming year is the adoption of a “simulation first” philosophy, which implies that nothing physical is truly ‘new’ by the time it arrives on the factory floor.
“From breakthrough products to the factories they’re built in, everything manufactured will be born in a digital world. Simulation-first design breaks through the barriers of cost, risk, and speed, letting manufacturers iterate, test, and optimise long before breaking ground or cutting steel,” says Rev Lebaredian, NVIDIA’s VP of Omniverse and Simulation Technology.
This notion is already being adopted in the Middle East’s fast-paced development culture, where engineers are increasingly using high-fidelity digital twins to perfect every movement in a virtual environment, long before execution. By the time a robotic arm is installed in Abu Dhabi, its job has been practiced millions of times in a virtual replica, ensuring that the moment power is switched on, a facility operates with peak efficiency, saving billions in potential downtime and redesigning costs.
“This digital approach lays the foundation for intelligent automation, as robots and AI-powered industrial facilities can be trained, validated and continually improved through simulated environments before deployment,” Rev adds.
Robots with common sense
Thanks to new physical AI reasoning models, autonomous machines possess a foundation of core skills that adapt to the real world. Previously, a robot was only as good as its specific code.
However, present models enable a machine trained in a simulated warehouse to be deployed in a public facility, such as a hospital, and quickly learn how to navigate safely around people and obstacles.
It’s a versatility that enables robotics to scale into domains previously impractical. They’re now reasoning agents capable of identifying empty pallets, misplaced items, or hazardous spills, while autonomously deciding how to fix the problem without human intervention.
Vision language models operating as the control tower for outside-in-robotics
Deepu Talla, VP of Robotics and Edge AI at NVIDIA, says vision language models (VLMS) will manage fleets of robots from 2026. “VLMs, AI that can perceive and reason against physical objects and behaviours, will operate as the control tower for outside-in robotics, enabling robots to collaborate and communicate with their environments. Fixed overhead cameras will provide safety and operations co-pilots that help direct people and machines, while adapting in real-time to keep operations on schedule.”
Operators no longer need complex coding skills; they can simply type or sketch commands to instantly deploy an entire fleet, ensuring daily workflows run smoothly and solutions are quickly identified for any challenges or mishaps.
“This shift is already happening,” Deepu explains. “Ceiling-mounted cameras can now spot empty pallets, misplaced items, or spills, and send robots to fix them. Most teams run these systems onsite for privacy and speed, linking them to existing floor software and cameras.”
The payoff is clear: fewer incidents, faster changeovers and consistent performance across sites, making autonomy a dependable part of daily operations.


















