Intelligent Gait Synthesis for Autonomous Ground Robots: A Reinforcement Learning Approach

We propose Reinforcement Learning Contrastive Optimization (RLCO), a novel quadruped robot locomotion control framework that synergistically integrates contrastive learning with reinforcement learning. This framework addresses two critical limitations of existing reinforcement learning methods in quadruped motion control: low sample efficiency and insufficient stability in action sequences. To meet the temporal coherence requirements of motion policies in complex environments, we develop a history–prediction action alignment mechanism through contrastive learning. This approach ensures that an action sequence is consistent over time. It does this by reducing the difference between past actions and predicted actions. This approach greatly enhances the stability and reliability of motion control. The proposed co-optimization mechanism preserves reinforcement learning’s exploration capability for complex tasks while improving the physical plausibility and predictability of action sequences. Experimental results demonstrate that our method achieves notable improvements in motion control precision and environmental adaptability in unstructured terrains. Through comparative analysis of different training strategies, we systematically validate the effectiveness of the RLCO framework. Field tests in outdoor environments with stairs, slopes, and grassy terrain confirm the robot’s capabilities. The quadruped robot rapidly adapts to diverse ground conditions.