  This paper presents a method for achieving high-speed running of a quadruped
robot by considering the actuator torque-speed operating region in
reinforcement learning. The physical properties and constraints of the actuator
are included in the training process to reduce state transitions that are
infeasible in the real world due to motor torque-speed limitations. The gait
reward is designed to distribute motor torque evenly across all legs,
contributing to more balanced power usage and mitigating performance
bottlenecks due to single-motor saturation. Additionally, we designed a
lightweight foot to enhance the robot's agility. We observed that applying the
motor operating region as a constraint helps the policy network avoid
infeasible areas during sampling. With the trained policy, KAIST Hound, a 45 kg
quadruped robot, can run up to 6.5 m/s, which is the fastest speed among
electric motor-based quadruped robots.
