  In this paper, we propose a locomotion training framework where a control
policy and a state estimator are trained concurrently. The framework consists
of a policy network which outputs the desired joint positions and a state
estimation network which outputs estimates of the robot's states such as the
base linear velocity, foot height, and contact probability. We exploit a fast
simulation environment to train the networks and the trained networks are
transferred to the real robot. The trained policy and state estimator are
capable of traversing diverse terrains such as a hill, slippery plate, and
bumpy road. We also demonstrate that the learned policy can run at up to 3.75
m/s on normal flat ground and 3.54 m/s on a slippery plate with the coefficient
of friction of 0.22.
