Robot Manipulation Skill Learning Based on Dynamic Movement Primitive^*

This paper proposes a robot automatic valve turning control strategy based on teaching learning, which consists of teaching, model learning and task repetition. The first stage is the teaching and learning stage. The robot learns motor skills by observing the human performing tasks. In order to accurately learn motor skills from demonstrations, data alignment is performed on the teaching data through Dynamic Time Warping (DTW). The second stage is the model construction and learning stage. The high-level learning strategy aims to learn motor skills from demonstrations through Dynamic Movement Primitives (DMP), using the statistical approach Gaussian Mixture Model and Gaussian Mixture Regression (GMM-GMR) to analyze the data from demonstrations. And the valve turning is repetition. To verify the effectiveness of the proposed control strategy, the experiment of the butterfly valve closing is performed. The results show that the robot is able to learn and reproduce the valve reaching and turning tasks. It completes the valve closing action by turning the valve for 7 turns.