关键词: 演示学习, 轨迹模仿学习, 任务策略学习, 动态运动基元, 运动基元库

Abstract: In order to solve the problems of demonstration data optimization, action and task strategy storage and call in the process of robot demonstration learning, a demonstration learning method based on primitive library is proposed. Action learning uses experts to drag the manipulator to perform actions to obtain demonstration data. Gaussian mixture model and Gaussian mixture regression are used to improve the data quality, and the final demonstration data is converted into the weight value of the basis function by the dynamic motion primitive algorithm. Strategy learning creates task steps as action primitives, adds the obtained weight value to the primitives, builds the primitive business card containing task execution strategy, and forms the primitive library to complete storage. When executing tasks, the primitives are sequentially called from the primitive library. YOLOv5 target detection network and AlexNet image classification network are used to detect target information to match actions and generalize new actions with original action characteristics. This method realizes learning actions and strategy storage from the demonstration, and combining appropriate actions to complete tasks according to actual goals. According to the experiment of steel bar binding scene, 5 action primitives are created, 10 basic actions are learned through expert teaching, the robot successfully completes the lashing task at the intersection of horizontal and vertical reinforcement by using the action primitive library.

Key words: demonstration learning, trajectory imitation learning, task strategy learning, dynamic motion primitives, motion primitive library