Learning Agile and Dynamic Motor Skills for Legged Robots was published in Science Robotics. The paper introduces a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system. The approach is applied to the ANYmal robot, a sophisticated medium-dog–sized quadrupedal machine. Using policies trained in simulation, the robot achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.