For decades, robots excelled at repetition but struggled with adaptation. A robot trained to pick and sort apples in a well-lit warehouse might fail to succeed at the same task outdoors on an overcast day, for example. The problem wasn't with the hardware, but with the intelligence layer behind it. Traditional robotics required programmers to anticipate every possible scenario and code explicit instructions for how the bot should respond to each one, a process that couldn't scale.

Two AI-fueled advances are quickly changing this. The first is vision-language-action models, which teach robots to see, understand, and act in the moment. These systems process visual information, interpret natural language, and translate both into physical actions. When you tell a robot in plain language to pick up an apple, it can now understand the command, recognize an apple and its location in three-dimensional space, and figure out how to grasp it—all without having seen that exact apple before. Robots using these models can respond to commands, such as "Lift legs higher when climbing stairs," and immediately adjust their behavior. No code rewrites. No downtime.

The second breakthrough addresses the data problem in robotics. Until now, a robot transporting cargo at an airport would need rigid paths to follow and behaviors preprogrammed, requiring time-consuming field training that rendered their use costly and limited. Today, simulation technology can create hyper-realistic virtual environments where robotic systems learn from thousands of real-world scenarios in a compressed amount of time (such virtual environments are known as digital twins). With a wealth of knowledge gained from these simulations, AI robots can hit the airport tarmac running, so to speak, ready to deal with all types of contingencies, such as a variety of weather conditions and physical obstacles.

Consider how 1X trained humanoid robots for household chores. By simulating domestic environments, the company adapted robots faster than ever to tasks such as vacuuming. Stanford researchers extended this approach, training robots at super speeds on 1,000 household activities and then deploying them in real homes, where they performed reliably despite never having seen those spaces before.