In this paper, we propose a robust controller that achieves natural and
stably fast locomotion on a real blind quadruped robot. With only
proprioceptive information, the quadruped robot can move at a maximum speed of
10 times its body length, and has the ability to pass through various complex
terrains. The controller is trained in the simulation environment by model-free
reinforcement learning. In this paper, the proposed loose neighborhood control
architecture not only guarantees the learning rate, but also obtains an action
network that is easy to transfer to a real quadruped robot. Our research finds
that there is a problem of data symmetry loss during training, which leads to
unbalanced performance of the learned controller on the left-right symmetric
quadruped robot structure, and proposes a mirror-world neural network to solve
the performance problem. The learned controller composed of the mirror-world
network can make the robot achieve excellent anti-disturbance ability. No
specific human knowledge such as a foot trajectory generator are used in the
training architecture. The learned controller can coordinate the robot's gait
frequency and locomotion speed, and the locomotion pattern is more natural and
reasonable than the artificially designed controller. Our controller has
excellent anti-disturbance performance, and has good generalization ability to
reach locomotion speeds it has never learned and traverse terrains it has never
seen before.

使用无模型强化学习在仿真环境下训练的，基于镜像世界神经网络的四足机器人运动控制器具有极强的抗扰动性能和泛化能力，能够协调机器人的动作频率和运动速度，实现更加自然和合理的运动模式。