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

IEEE transaction on neural networks and learning systems, 2019-03, Vol.30 (3), p.777-787

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2018.2852711 ;CODEN: ITNNAL

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  • Title:
    Robot Learning System Based on Adaptive Neural Control and Dynamic Movement Primitives
  • Author: Chenguang Yang ; Chuize Chen ; Wei He ; Rongxin Cui ; Zhijun Li
  • Subjects: Acceleration ; Dynamic movement primitives (DMPs) ; Dynamics ; Feature extraction ; Gaussian mixture model (GMM) ; neural network (NN) ; Robot learning ; Stability analysis ; Trajectory
  • Is Part Of: IEEE transaction on neural networks and learning systems, 2019-03, Vol.30 (3), p.777-787
  • Description: 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.
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 2162-237X
    EISSN: 2162-2388
    DOI: 10.1109/TNNLS.2018.2852711
    CODEN: ITNNAL
  • Source: IEEE Open Access Journals
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