Advancements in humanoid robot dynamics and learning-based locomotion control methods

Humanoid robots are attracting increasing global attention owing to their potential applications and advances in embodied intelligence. Enhancing their practical usability remains a major challenge that requires robust frameworks that can reliably execute tasks. This review systematically categorizes and summarizes existing methods for motion control and planning in humanoid robots, dividing the control approaches into traditional dynamics-based and modern learning-based methods. It also examines the navigation and obstacle-avoidance capabilities of humanoid robots. By providing a detailed comparison of the advantages and limitations of various control methods, this review offers a comprehensive understanding of current technological progress, real-world application challenges, and future development directions in humanoid robotics. Key topics include the principles and applications of simplified dynamic models, widely used control algorithms, reinforcement learning, imitation learning, and the integration of large language models. This review highlights the importance of both traditional and innovative approaches in advancing the adaptability, efficiency, and overall performance of humanoid robots.