Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 3; pp. 777 - 787
• Main Authors: Yang, Chenguang, Chen, Chuize, He, Wei, Cui, Rongxin, Li, Zhijun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Adaptive control; Basis functions; Control systems design; Dynamic movement primitives (DMPs); Dynamic stability; Dynamics; Environmental effects; Feature extraction; Gaussian mixture model (GMM); Learning; Motion stability; neural network (NN); Neural networks; Probabilistic models; Radial basis function; Regression models; Robot dynamics; Robot learning; Robots; Stability analysis; Tracking; Trajectories; Trajectory
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2018.2852711

This paper proposes an enhanced robot skill learning system considering both motion generation and trajectory tracking. During robot learning demonstrations, dynamic movement primitives (DMPs) are used to model robotic motion. Each DMP consists of a set of dynamic systems that enhances the stability of the generated motion toward the goal. A Gaussian mixture model and Gaussian mixture regression are integrated to improve the learning performance of the DMP, such that more features of the skill can be extracted from multiple demonstrations. The motion generated from the learned model can be scaled in space and time. Besides, a neural-network-based controller is designed for the robot to track the trajectories generated from the motion model. In this controller, a radial basis function neural network is used to compensate for the effect caused by the dynamic environments. The experiments have been performed using a Baxter robot and the results have confirmed the validity of the proposed methods.