Reinforcement Learning for High-Speed Quadrupedal Locomotion With Motor Operating Region Constraints: Mitigating Motor Model Discrepancies through Torque Clipping in Realistic Motor Operating Region

This article 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. 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.