| Summary: | 碩士 === 國立中山大學 === 電機工程學系研究所 === 101 === In the research of the humanoid biped robot, for building a robot model with 18 dimensions and applying this model to achieve the balance of robot behavior, it needs for large amount of calculation of mathematical derivations. The study on biped walking control using reinforcement learning is presented in this paper.
When the robot keeps balance to walk, the zero moment point (ZMP) position of a biped robot has to be considered. If the ZMP of a biped robot could be controlled in an ideal state, the robot would walk steadily on the plain, even when the robot walks on a slope. In the robot walking process, a robot is easy to fall down when standing with one leg. Therefore, the research topic is mainly focused on how the robot keeps balance with one leg. The balance control way that utilized the motion of robot arm and leg to transfer the ZMP of the robot would maintain the ZMP in a stable state. In addition, the balance control way also can simplify the complexity of control of many servo motors. In this paper, the agent learns to control the ZMP by some balance control experience of human walking. It not only enhances learning efficiency, but also enables the robot walking gait more like human behavior.
Furthermore, the proposed method integrates the balanced algorithm with the balance control way and is applied on biped walking on the plain or seesaw make the biped walk more stable. Finally, there are several simulations that demonstrate the feasibility and effectiveness of the proposed learning scheme. The Research results are presented by the video at YouTube: http://youtu.be/05a0hamjt9Q
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