  Agile maneuvers such as sprinting and high-speed turning in the wild are
challenging for legged robots. We present an end-to-end learned controller that
achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9
m/s. This system runs and turns fast on natural terrains like grass, ice, and
gravel and responds robustly to disturbances. Our controller is a neural
network trained in simulation via reinforcement learning and transferred to the
real world. The two key components are (i) an adaptive curriculum on velocity
commands and (ii) an online system identification strategy for sim-to-real
transfer leveraged from prior work. Videos of the robot's behaviors are
available at: https://agility.csail.mit.edu/
