At first glance, Facebook’s nascent robotic platform looks a bit … chaotic. In a new lab in its palatial Silicon Valley HQ, a red and black Sawyer robot arm (from the recently defunct company Rethink Robotics) is waving all over the place with a mechanical whine. It’s supposed to casually move its hand to a spot in space to its right, but it goes up, up, up and way off course, then resets to its starting position. Then the arm goes right and gets pretty close to its destination. But then, agh!, it resets again before—maddeningly for those of us rooting for it—veering wildly off course again.

But, like a hare zigzagging back and forth to avoid a falcon, this robot’s seeming madness is in fact a special brand of cleverness, one that Facebook thinks holds the key not only for better robots, but for developing better artificial intelligence. This robot, you see, is teaching itself to explore the world. And that, Facebook says, could one day lead to intelligent machines like telepresence robots.

At the moment robots are very dumb—generally you have to spell everything out in code for them: This is how you roll forward, this is how you move your arm. We humans are much smarter in how we learn. Even babies understand that an object that moves out of view hasn’t vanished from the physical universe. They learn they can roll a ball, but not a couch. It’s fine to fall off a couch, but not a cliff.

All of that experimentation builds a model of the world in your brain, which is why later on you can learn to drive a car without crashing it immediately. “We know in advance that if we're driving near a cliff and we turn the wheel to the right, the car is going to run off a cliff and nothing good is going to happen,” says Yann LeCun, chief AI scientist at Facebook. We have a self-learned model in our head that keeps us from doing dumb things. Facebook is trying to give that kind of model to the machines too. Systems that learn “models of the world are in my opinion the next challenge to really make significant progress in AI,” LeCun adds.

Matt Simon covers cannabis, robots, and climate science for WIRED.

Now, the group at Facebook isn’t the first to try to get a robot to teach itself to move. Over at UC Berkeley, a team of researchers used a technique called reinforcement learning to teach a two-armed robot named Brett to shove a square peg in a square hole. Simply put, the robot tries lots and lots of random movements. If one gets it closer to the goal, the system gives it a digital “reward.” If it screws up, it gets a digital “demerit,” which the robot keeps a tally of. Over many iterations, the reward-seeking robot gets its hand closer and closer to that square hole and eventually drops the peg in.

What Facebook is experimenting with is a bit different. “What we wanted to try out is to instill this notion of curiosity,” says Franziska Meier, an AI research scientist at Facebook. That’s how humans learn to manipulate objects: Children are driven by curiosity about their world. They don’t try something new, like yanking a cat’s tail, because they have to, but because they wonder what might happen if they do, much to the detriment of poor old Whiskers.

So whereas a robot like Brett refines its motions bit by bit—drawing closer to its target, resetting, and drawing closer still with the next try—Facebook’s robot arm might get closer and then veer way off course. That’s because the researchers aren’t rewarding it for incremental success, but instead giving it freedom to try non-optimal movements. It’s trying new things, like a baby, even if those things don’t seem particularly rational in the moment.