High Precision Trajectory Learning Method Based Improved Dynamic Movement Primitives for Robot Skill Learning

Abstract

Abstract In robot skill learning, the higher accuracy of trajectory reproduction, the smaller difference between the trajectory reproduced by the robot and the expected trajectory, and the higher accuracy and reliability of the robot’s task execution. In robot skill learning methods based on dynamic movement primitives, the problems are found that the tail exponential decay of Gaussian kernel function and the number of shape parameters have a great influence on the accuracy of trajectory reproduction, a high precision trajectory learning method is proposed. A novel truncation processing method is proposed to eliminate the impact of tail exponential decay, and the optimization on the number of shape parameters is used to improve the approximation of the local gradient of the target forcing term, which are combined to improve trajectory reproduction accuracy. The principle of the proposed method is described in detail. The simulation and comparison experiments are performed to verify the effectiveness of the proposed method in improving trajectory reproduction accuracy. This paper makes contributions to the field of robot skill trajectory learning and provides a promising method for improving trajectory reproduction accuracy.