Biped Robot Terrain Adaptability Based on Improved SAC Algorithm

This study introduces an improved Soft Actor-Critic (SAC) algorithm designed to improve the gait stability of biped robots in complex terrain conditions. The core innovations lie in the redesign of the network model and the creation of a tailored reward function. The network model revision enhances the learning process and responsiveness of the robot to varied terrain conditions, while the customized reward function ensures effective adaptation and stability maintenance. Experimental results show that biped robots using our advanced learning model exhibit substantial improvements in stability while navigating complex terrains, compared to those employing traditional methods. These improvements significantly increase the robustness and adaptability of the algorithm, enabling it to effectively meet diverse environmental challenges. This research marks a significant step forward in the development of advanced and reliable biped robot systems, emphasizing the power of deep reinforcement learning to transcend the limitations of conventional robotic control approaches, especially in complex environmental interactions.