AI and robotics are changing how machines behave in the real world. Not in theory or demos, but on factory floors, in warehouses, on roads, and increasingly in everyday spaces. This category focuses on how intelligence is being built into machines, what actually works, and where the gaps still are.

At one end are physical systems: robots that move, grip, balance, see, and sense their surroundings. At the other end is software: machine learning models, perception stacks, control systems, and decision-making logic that determine how those machines respond when the environment doesn't behave as expected. Most of the interesting work happens in the messy middle, where sensors misread, data is incomplete, and systems have to operate safely around people.

AI and robotics coverage at Interesting Engineering spans industrial robots, cobots, humanoids, drones, and service robots, along with the hardware and software that enable them to operate at scale. That includes sensors, actuators, batteries, AI chips, simulation tools, and the increasingly important role of edge computing. We also look at how these systems are trained, tested, deployed, and maintained after leaving the lab.

This category examines constraints such as power, cost, reliability, latency, safety, and regulation, and why many promising systems struggle outside controlled environments. Human–machine interaction, deployment failures, workforce impact, and real-world trade-offs are part of the story.

As robots move out of fenced-off spaces and into shared ones, the question isn't just what they can do, but whether they can be trusted to do it consistently. AI and Robotics tracks progress where it’s real, skepticism where it’s warranted, and the engineering work still needed to turn intelligent machines from impressive prototypes into dependable tools.

It also looks at who is building these systems and why the gap between research success and commercial reliability remains one of the field’s hardest problems.