AI researchers have demonstrated a self-teaching algorithm that gives a robot hand remarkable new dexterity. Their creation taught itself to manipulate a cube with uncanny skill by practicing for the equivalent of a hundred years inside a computer simulation (though only a few days in real time).

The robotic hand is still nowhere near as agile as a human one, and far too clumsy to be deployed in a factory or a warehouse. Even so, the research shows the potential for machine learning to unlock new robotic capabilities. It also suggests that someday robots might teach themselves new skills inside virtual worlds, which could greatly speed up the process of programming or training them.

The robotic system, dubbed Dactyl, was developed by researchers at OpenAI, a nonprofit based in Silicon Valley. It uses an off-the-shelf robotic hand from a UK company called Shadow, an ordinary camera, and an algorithm that’s already mastered a sprawling multiplayer video game, DotA, using the same self-teaching approach (see “A team of AI algorithms just crushed humans in a complex computer game”).

The algorithm uses a machine-learning technique known as reinforcement learning. Dactyl was given the task of maneuvering a cube so that a different face was upturned. It was left to figure out, through trial and error, which movements would produce the desired results.

Videos of Dactyl show it rotating the cube with impressive agility. It automatically figured out several grips that humans commonly use. But the research also showed how far AI still has to go: the robot was able to manipulate the cube successfully just 13 out of 50 times after its hundred years of virtual training time—far more than a human child needs.

“It is not going to fit into an industrial workflow any time soon,” says Rodney Brooks, a professor emeritus at MIT and the founder of Rethink Robotics, a startup that makes more intelligent industrial robots. “But that is fine—research is a good thing to do.”