Robot Skill Learning Based on Dynamic Motion Primitives and Adaptive Control

Abstract

A novel robot skill learning method using dynamic movement primitive (DMP) and adaptive control is proposed. The existing DMP method learns actions from a single teaching trajectory, and its Gaussian basis function distribution mode is fixed, which is not suitable for multiple movement trajectories with different characteristics. Therefore, the Gaussian mixture model (GMM) and Gaussian mixture regression are introduced into DMP to enable the robot to learn skills from multi-teaching trajectory. Moreover, radial basis function neural network (RBFNN) is introduced into DMP to establish the RBF-DMP method, which is able to learn the central position and weight of Gaussian basis through gradient descent and improves the accuracy of skill modeling. Furthermore, an adaptive neural network controller is designed to control the learned actions of the manipulator in redemonstration. Finally, experiments on Franka Emika Panda manipulator prove the effectiveness of the proposed method.

Keywords

• dynamic movement primitive (dmp)
• gaussian mixture model (gmm)
• radial basis function neural network (rbfnn)
• robot learning