Gait-Parameterized Reinforcement Learning for a Hydraulic Quadruped Robot

Designing reward functions for reinforcement learning (RL)-based quadruped locomotion often requires extensive trial-and-error, limiting efficiency and interpretability. Lack of interpretability is particularly critical for large-scale hydraulic quadrupeds, where undetected unstable behaviors during deployment can cause significant mechanical damage. This paper presents a training framework that integrates biologically inspired gait parameters into RL policies, allowing robots to learn locomotio