中国机械工程

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基于耦合算法的类人机器人模仿学习控制方法

李文华1;杨子凝1;王来贵2   

  1. 1.辽宁工程技术大学机械工程学院,阜新,123000
    2.辽宁工程技术大学力学与工程学院,阜新,123000
  • 出版日期:2017-07-25 发布日期:2017-07-26
  • 基金资助:
    国家自然科学基金资助项目(51474121);
    辽宁省教育厅资助项目(L2015214)
    National Natural Science Foundation of China (No. 51474121)

Imitation Learning Control Method of Humanoid Robots Based on Coupling Algorithm

LI Wenhua1;YANG Zining1;WANG Laigui2   

  1. 1.School of Mechanical Engineering,Liaoning Technical University,Fuxin,Liaoning,123000
    2.School of Mechanics and Engineering,Liaoning Technical University,Fuxin,Liaoning,123000
  • Online:2017-07-25 Published:2017-07-26
  • Supported by:
    National Natural Science Foundation of China (No. 51474121)

摘要: 为提高类人机器人模仿学习的准确性及效率,建立了一种改进的粒子群算法优化超限学习机的模仿学习模型。采用非线性动态系统对示教时的相关数据进行建模;以动态自适应策略改进粒子群算法的惯性权重,并利用改进后的粒子群算法对超限学习机的网络参数进行寻优;利用该耦合学习模型对模仿学习动态系统的参数进行学习,并重现了模仿学习动作。实验结果表明,该耦合算法应用在类人机器人模仿学习方面具有很好的拟合精度、自适应性及泛化能力,重现模仿学习动作时的平均误差为0.0172。

关键词: 模仿学习, 超限学习机, 改进粒子群优化, 非线性动态系统, 耦合模型

Abstract: To improve accuracy and efficiency in learning from demonstrations by humanoid robots, an imitation learning model was established based on improved PSO to optimize extreme learning machine, to learn human motions on the robots herein. A set of motions which was performed by a human demonstrator were collected to model as a nonlinear autonomous dynamical system. PSO was improved with the dynamic adaptive inertia weight. Then the improved PSO was merged with ELM to optimize network parameters. Using a mathematical model of improved PSO-ELM to learn the parameters of the dynamic system and reproduce human motions. The experimental results prove the method has a better fitting precision, adaptability and generalization ability on imitation learning of humanoid robots. The average relative errors are as 0.0172 of human motion reproductions.

Key words: imitation learning, extreme learning machine(ELM), improved particle swarm optimization(MPSO), nonlinear dynamic system, coupling model

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