Babies rapidly develop this ability by soaking up data from their external environments, forming a sort of “common sense” about the dynamics of the physical world. When things don’t move as expected—say, in magic tricks where objects disappear—they’ll show surprise.

For AI, it’s a completely different matter. While recent AI models have already trounced humans from game play to solving decades-old scientific conundrums, they still struggle at developing intuition about the physical world.

This month, researchers at Google-owned DeepMind took inspiration from developmental psychology and built an AI that naturally extracts simple rules about the world through watching videos. Netflix and chill didn’t work on its own; the AI model o nly learned the rules of our physical world when given a basic idea of objects, such as what their boundaries are, where they are, and how they move. Similar to babies, the AI expressed “surprise” when shown magical situations that didn’t make sense, like a ball rolling up a ramp.

A newborn giraffe or foal must learn to walk on its legs as fast as possible to avoid predators. Animals are born with muscle coordination networks located in their spinal cord. However, learning the precise coordination of leg muscles and tendons takes some time. Initially, baby animals rely heavily on hard-wired spinal cord reflexes. While somewhat more basic, motor control reflexes help the animal to avoid falling and hurting themselves during their first walking attempts. The following, more advanced and precise muscle control must be practiced, until eventually the nervous system is well adapted to the young animal’s leg muscles and tendons. No more uncontrolled stumbling—the young animal can now keep up with the adults.

Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart conducted a research study to find out how animals learn to walk and learn from stumbling. They built a four-legged, dog-sized robot, that helped them figure out the details.

“As engineers and roboticists, we sought the answer by building a robot that features reflexes just like an animal and learns from mistakes,” says Felix Ruppert, a former doctoral student in the Dynamic Locomotion research group at MPI-IS. “If an animal stumbles, is that a mistake? Not if it happens once. But if it stumbles frequently, it gives us a measure of how well the robot walks.”