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基于改进动态系统稳定估计器的机器人技能学习方法

金聪聪 刘安东 StevenLiu 张文安

金聪聪, 刘安东, StevenLiu, 张文安. 基于改进动态系统稳定估计器的机器人技能学习方法. 自动化学报, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200341
引用本文: 金聪聪, 刘安东, StevenLiu, 张文安. 基于改进动态系统稳定估计器的机器人技能学习方法. 自动化学报, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200341
Jin Cong-Cong, Liu An-Dong, Steven Liu, Zhang Wen-An. A robot skill learning method based on improved stable estimator of dynamical systems. Acta Automatica Sinica, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200341
Citation: Jin Cong-Cong, Liu An-Dong, Steven Liu, Zhang Wen-An. A robot skill learning method based on improved stable estimator of dynamical systems. Acta Automatica Sinica, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c200341

基于改进动态系统稳定估计器的机器人技能学习方法

doi: 10.16383/j.aas.c200341
基金项目: 国家自然科学基金(61822311, 61973275)资助
详细信息
    作者简介:

    金聪聪:浙江工业大学信息工程学院硕士生. 主要研究方向为人体动作识别和机器人技能学习. E-mail: jcc19960602@gmail.com

    刘安东:浙江工业大学信息工程学院副教授. 主要研究方向为模型预测控制, 网络化控制系统和移动机器人. E-mail: lad@zjut.edu.cn

    StevenLiu:凯泽斯劳滕工业大学电气与计算机工程系教授. 主要研究方向为机电和电力系统控制, 机器人技术, 网络控制和基于模型的故障诊断. E-mail: sliu@eit.uni-kl.de

    张文安:浙江工业大学信息工程学院教授. 主要研究方向为多源信息融合, 机器人技能学习, 本文通信作者. E-mail: wazhang@zjut.edu.cn

A Robot Skill Learning Method Based on Improved Stable Estimator of Dynamical Systems

Funds: Supported by National Natural Science Foundation of P. R. China (61822311, 61973275)
  • 摘要: 本文提出了一种基于改进动态系统稳定估计器(Stable Estimator of Dynamical Systems, SEDS)的机器人技能学习方法. 现有的SEDS方法可以通过非线性优化来确保学习系统的全局稳定性, 但是存在确定高斯混合分量个数困难以及稳定性和精度无法兼顾的问题. 因此, 本文根据贝叶斯非参数模型可以自动确定合适分量个数的特性, 采用狄利克雷过程高斯混合模型对演示进行初始拟合. 随后利用参数化二次李雅普诺夫函数重新推导新的稳定性约束, 有效的解决了SEDS方法中稳定性和精度难以兼顾的问题. 最后, 在LASA数据集和Franka机器人上的实验验证了新方法的有效性和优越性.
  • 图  1  基于SEDS的机器人示教学习流程图

    Fig.  1  Flow chart of LfD based on SEDS

    图  2  DPGMM概率结构图

    Fig.  2  DPGMM probability structure diagram

    图  3  DPGMM对数似然值关于超参数的变化趋势

    Fig.  3  The change trend of DPGMM log-likelihood value on hyperparameters

    图  4  $i$ -SEDS方法在LASA数据集上的复现效果

    Fig.  4  The reproductions of $i$ -SEDS method on LASA dataset

    图  5  SEDS和 $i$ -SEDS在不收缩轨迹上的复现结果

    Fig.  5  $i$ -SEDS and SEDS reproductions on the non-contractive demonstrations

    图  6  扫描误差区域(SEA)示意图

    Fig.  6  Schematic diagram of swept error area

    图  7  四种方法在收缩和不收缩轨迹上的SEA分析

    Fig.  7  Performance analysis of four methods on contraction and non-contraction demonstrations with SEA

    图  8  物品搬运任务示教流程图

    Fig.  8  The process of object transport task teaching

    图  9  预处理后的物品搬运任务的演示轨迹

    Fig.  9  Preprocessed demonstrations of object transport task

    图  10  物品搬运任务复现位置和速度轨迹

    Fig.  10  Position and velocity trajectories of reproduction in object transport task

    图  11  机械臂运行时改变目标位置后的轨迹变化

    Fig.  11  Trajectory changes after moving the target container when the robot arm is running

    图  12  对机械臂施加外界扰动时的轨迹变化

    Fig.  12  Trajectory changes when external disturbance is applied to the robot arm

    表  1  四种GMR算法在数据库LASA上的性能比较

    Table  1  Performance comparison of four GMR algorithmson database LASA

    方法 总RMSE 总训练时间( $s$ )
    BIC-GMM(EM) 269.43 75.26
    DdGMM(VI) 206.15 49.39
    DPGMM(Gibbs) 118.58 157.48
    DPGMM(VI) 130.79 39.79
    下载: 导出CSV
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  • 收稿日期:  2020-05-22
  • 录用日期:  2020-09-30

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