于建均, 韩春晓, 阮晓钢, 刘涛. 基于高斯过程的机器人模仿学习研究与实现[J]. 北京工业大学学报, 2015, 41(7): 1000-1004. DOI: 10.11936/bjutxb2014070031
    引用本文: 于建均, 韩春晓, 阮晓钢, 刘涛. 基于高斯过程的机器人模仿学习研究与实现[J]. 北京工业大学学报, 2015, 41(7): 1000-1004. DOI: 10.11936/bjutxb2014070031
    YU Jian-jun, HAN Chun-xiao, RUAN Xiao-gang, LIU Tao. Robot Imitation Learning Based on Gaussian Processes[J]. Journal of Beijing University of Technology, 2015, 41(7): 1000-1004. DOI: 10.11936/bjutxb2014070031
    Citation: YU Jian-jun, HAN Chun-xiao, RUAN Xiao-gang, LIU Tao. Robot Imitation Learning Based on Gaussian Processes[J]. Journal of Beijing University of Technology, 2015, 41(7): 1000-1004. DOI: 10.11936/bjutxb2014070031

    基于高斯过程的机器人模仿学习研究与实现

    Robot Imitation Learning Based on Gaussian Processes

    • 摘要: 针对机器人模仿学习控制策略获取的问题,基于高斯过程的方法,建立示教机器人示教行为的样本数据的高斯过程回归模型并加以训练,以求解示教机器人的感知和行为之间的映射关系,并将此映射关系作为模仿机器人的控制策略来实现对示教行为的模仿.以Braitenberg车为仿真对象,研究趋光模仿学习行为.仿真实验表明:基于高斯过程的机器人模仿学习算法具有有效性,模仿机器人在不同任务环境下具有很好的适应性.

       

      Abstract: To acquire the control strategy in robot imitation learning, a Gaussian processes ( GP ) regression model based on GP is used to construct the mapping relationship between actions of teaching robot and perception by the sample data of the teaching behavior. Then, the mapping relationship is used as control the strategy to imitate the teaching action. The Braitenberg vehicles are used as simulation subject to study phototaxis imitation learning action. Simulation results show that robot imitation learning based on Gaussian processes is effective. Furthermore, the simulation results in various task environments indicate that the method is adaptive.

       

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