[1]YU Jianjun,YAO Hongke,ZUO Guoyu,et al.Research on robot imitation learning method based on dynamical system[J].CAAI Transactions on Intelligent Systems,2019,14(5):1026-1034.[doi:10.11992/tis.201807018]
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Research on robot imitation learning method based on dynamical system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 1026-1034 Column: 学术论文—智能系统 Public date: 2019-09-05
Title:
Research on robot imitation learning method based on dynamical system
Author(s):
YU Jianjun1; 2;  YAO Hongke1; 2;  ZUO Guoyu1; 2;  RUAN Xiaogang1; 2;  AN Shuo1; 2
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Compu-tational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
Keywords:
robot; imitation learning; trajectory level; Gaussian mixture model; dynamical system; parameter learning; 7bot manipulator; generalization performance
CLC:
TP242.6
DOI:
10.11992/tis.201807018
Abstract:
In the current robot imitation learning process, the motion imitation cannot converge to the target point, and the generalization ability is poor. To solve this problem, an imitation learning method based on dynamical system (DS) is introduced. First, the demonstration motion data is modeled as a nonlinear DS by Gaussian mixture model (GMM). Second, the sufficient condition of DS global stability is used as a constraint to ensure that all the DS-generated trajectories converge to the target. Finally, the parameter learning problem of the DS model is transformed into seeking for a solution to a constrained optimization problem to obtain the model parameters. Simulation experiments and robot experiments were carried out using the 7bot manipulator. The experimental results show that all the trajectories generated by the DS model from different starting points converged to the target point, and the trajectory was smooth and the generalization performance was improved.
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