I've been spending time lately learning about the intersection of AI and robotics. There's this whole world where AI isn't generating text or summarising documents. It's navigating buildings, flying aircraft, and making split-second decisions in places where there's no internet connection and no human nearby to ask for help.
The more I read, the more I realise how different the problems are.
Intelligence under constraint
When AI runs on a drone or a ground vehicle, the margin for error is physical. A wrong decision doesn't produce a bad paragraph. It produces a crash. The system has to perceive its environment through cameras and sensors, figure out where it is, plan a path, and act on it. All in real time, all on hardware with limited power and limited compute.
One concept that caught my attention is GPS-denied navigation. We take GPS for granted, but it doesn't work indoors, underground, or in environments where the signal is jammed. So autonomous systems have to build their own understanding of where they are using whatever sensors they have. There's a technique called SLAM, simultaneous localisation and mapping, where the system constructs a map of an unknown space while tracking its own position within it.
Companies like Shield AI have built their entire approach around this problem. Their aircraft can fly and coordinate autonomously where GPS doesn't work. Not as a research demo. In actual operations.
Autonomy at scale
The thing that really pulled me in is multi-agent coordination though. Not one machine doing something smart, but dozens of them sharing what they see, dividing tasks, and adapting as a group without a central controller. The coordination has to be decentralised because you can't rely on a single point of communication. If one link goes down, the group keeps going. Each agent makes its own decisions while staying aligned with the others.
It's easy to think of this as purely military technology, and a lot of the current momentum is coming from defence. But the same capabilities apply to inspecting infrastructure that's difficult or dangerous to reach, monitoring agriculture, running search and rescue in disaster zones, or handling logistics in remote places with unreliable connectivity.
The common thread is simple. Move the intelligence closer to where the work actually happens instead of keeping it behind a screen.
Beyond the demo
The questions being worked on in this space feel foundational. How do you build something smart enough to act on its own and reliable enough to trust? Where do you draw the line between autonomy and human oversight? What does trust look like when the thing making decisions is a machine in the field?
I'm going to keep learning about this. If anything interesting comes out of it, I'll share it here.