Virtual limitations: Reinforcement learning has been used to train many bots to walk inside simulations, but transferring that ability to the real world is hard. “Many of the videos that you see of virtual agents are not at all realistic,” says Chelsea Finn, an AI and robotics researcher at Stanford University, who was not involved in the work. Small differences between the simulated physical laws inside a virtual environment and the real physical laws outside it—such as how friction works between a robot’s feet and the ground—can lead to big failures when a robot tries to apply what it has learned. A heavy two-legged robot can lose balance and fall if its movements are even a tiny bit off.

Double simulation: But training a large robot through trial and error in the real world would be dangerous. To get around these problems, the Berkeley team used two levels of virtual environment. In the first, a simulated version of…

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