AI and robotics are changing the way machines behave in the real world. Not in theory or at demos, but in factories, in warehouses, on streets and increasingly in everyday spaces. This category is about how intelligence is built into machines, what actually works and where there are still gaps.
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 logic that determine how these machines respond when the environment doesn’t behave as expected. Most of the interesting work happens in the chaotic middle, where sensors give erroneous results, data is incomplete, and systems must function safely in proximity to people.
Interesting Engineering’s AI and robotics coverage includes industrial robots, cobots, humanoids, drones, and service robots, as well as the hardware and software that enable them to operate at scale. These include sensors, actuators, batteries, AI chips, simulation tools and the increasingly important role of edge computing. We also examine how these systems are trained, tested, deployed, and maintained once they leave the lab.
This category examines limitations such as performance, cost, reliability, latency, security, and regulation, and why many promising systems struggle outside of controlled environments. Human-machine interaction, deployment failures, workforce impacts, and real-world trade-offs are part of the story.
As robots leave fenced-in areas and enter shared spaces, the question becomes not only what they can do, but also whether they can be trusted to do so consistently. AI and robotics pursue progress where it is real, skepticism where it is warranted, and the engineering work still needed to transform intelligent machines from impressive prototypes into reliable tools.
It also examines who builds these systems and why the gap between research success and commercial reliability remains one of the biggest problems in the field.
