Design and Implementation of Fuzzy Policy Gradient Gait Learning Method for Humanoid Robot

• Main Authors: Kiah-YangChong, 張家揚
• Other Authors: Tzuu-Hseng S. Li
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/90100127378597192142

碩士 === 國立成功大學 === 電機工程學系碩博士班 === 98 === The design and implementation of Fuzzy Policy Gradient Learning (FPGL) method for small-sized humanoid robot is proposed in this thesis. This thesis not only introduces the mechanism structure of the humanoid robot and the hardware system adapted on the robot, which is named as aiRobots-V, but also improves and parameterizes the gait pattern of the robot. The movement of arms is added to the gait pattern to reduce the tilt of trunk while walking. FPGL method is an integrated machine learning method based on Policy Gradient Reinforcement Learning (PGRL) method and fuzzy logic concept in order to improve the efficiency and speed of gait learning computation. The humanoid robot is trained with FPGL method which is using the walking distance in constant walking cycles as the reward to learn faster and stable gait automatically. The tilt degree of trunk is chosen as the reward to learn the movement of arms in the walking cycle. The result of the experiment shows that FPGL method could train the gait pattern from 9.26 mm/s walking speed to 162.27 mm/s in about an hour. The training data of experiments also shows that this method could improve the efficiency of basic PGRL method up to 13%. The effect of arm movement to reduce the tilt degree of trunk is also proved by the experimental results. This robot is also applied to participate in the throw-in technical challenge of RoboCup 2010.