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

Chenguang Yang, Chuize Chen, Wei He, Rongxin Cui, Zhijun Li

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312 引用 (Scopus)

摘要

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.

源语言英语
文章编号8421037
页(从-至)777-787
页数11
期刊IEEE Transactions on Neural Networks and Learning Systems
30
3
DOI
出版状态已出版 - 3月 2019

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