The recent advancements in quadruped robotics have significantly enhanced the adaptability, efficiency, and collaborative capabilities of these systems. Researchers are increasingly leveraging deep reinforcement learning (DRL) to develop control frameworks that mimic biological locomotion strategies, enabling robots to transition seamlessly between gaits and adapt to complex terrains without prior training. Notably, the integration of bio-inspired gait strategies and adaptive motion adjustments has led to unprecedented adaptability, allowing for zero-shot deployment in challenging environments. Additionally, the field is witnessing innovative approaches to collaborative transportation, where bilevel learning frameworks are being employed to manage kinematic and anisotropic velocity constraints, enhancing the reliability of multi-robot systems. Energy efficiency has also become a focal point, with adaptive gain control mechanisms being introduced to dynamically tune joint PD gains, thereby improving performance across various terrains. These developments collectively push the boundaries of what quadruped robots can achieve, making them more versatile and capable of handling real-world scenarios with greater robustness and efficiency.