Gait-Parameterized Reinforcement Learning for a Hydraulic Quadruped Robot

Abstract

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 locomotion that is both system-aware and human-interpretable. The actor outputs gait parameters—such as gait period, phase offset, stride length, foot clearance, duty factor, and base height—which are coupled with gait-shaping rewards to encourage intent–execution alignment and physically plausible gait patterns. The framework improves training efficiency and provides interpretable signals for monitoring policy behavior. In simulation, we show that locomotion follows the self-generated gait intents as soft constraints, and ablation results demonstrate faster convergence than a pure-RL baseline. We further present reward-term ablation and coefficient sensitivity analyses, indicating that performance is not driven by a single shaping term and is robust to moderate coefficient changes. We validate the approach on BeTheX-Q (over 1.8 m, 350 kg), demonstrating robust real-world locomotion across 2 and 4 km/h walking, stepping-stones, and external disturbances. Finally, gait-parameter analysis reveals interpretable trends that reflect locomotion intent and adaptation across varying conditions.