Over a year ago, OpenAI, the San Francisco–based for-profit AI research lab, announced that it had trained a robotic hand to manipulate a cube with remarkable dexterity. That might not sound earth-shattering. But in the AI world, it was impressive for two reasons. First, the hand had taught itself how to fidget with the cube using a reinforcement-learning algorithm, a technique modeled on the way animals learn. Second, all the training had been done in simulation, but it managed to successfully translate to the real world.

To avoid this, roboticists use simulation: they build a virtual model of their robot and train it virtually to do the task at hand. The algorithm learns in the safety of the digital space and can be ported into a physical robot afterwards. But that process comes with its own challenges. It's nearly impossible to build a virtual model that exactly replicates all the same laws of physics, material properties, and manipulation behaviors seen in the real world--let alone unexpected circumstances. Thus, the more complex the robot and task, the more difficult it is to apply a virtually trained algorithm in physical reality.