Motion Control Technology for Quadruped Robots Based on Reinforcement Learning

Abstract

Quadruped robots possess outstanding terrain adaptability and boast extensive application prospects in scenarios such as search and rescue, field exploration, and more. Nevertheless, conventional model-based motion control methods suffer from cumbersome modeling processes and poor generalization performance, making them ill-suited for unstructured complex environments. To address these limitations, this paper presents a comprehensive review of deep reinforcement learning-based motion control technologies for quadruped robots. It first organizes the fundamental theories concerning robot kinematics and reinforcement learning, then categorizes and summarizes research advances across three core research branches: gait generation, autonomous navigation, and adaptive gait transition. Furthermore, this paper analyzes prevailing challenges and corresponding countermeasures regarding hardware deployment, sample efficiency, and model generalization capacity. It points out that further integration of multi-algorithms, optimization of sim-to-real transformation and overall strategy design will be the main trends in this field. By identifying current technical bottlenecks and forecasting future development trends, this work offers valuable references for practical technical implementation and subsequent research within this field.