Learning whole-body robot motionLin, Hsien-I is a challenging task because balance control should be taken into consideration. An intuitiveChen, Yung-Yao way to teach motion to a humanoid robot is to apply human demonstration data to the robot. Since balance control was usually done by presetting the zero-moment-point (ZMP) trajectory of a robot, the challenge became the conversionHuang, Yu-Cheproblem from human motion to robot motion, making the ZMP trajectory satisfy the stability. In this paper, we use kinesthetic teaching to teach whole-body robot motion by directly pulling the limbs of a robot without any conversion from human to robot motion. To keep the robot balanced, we propose a trade-off function by considering motion similarity and balance simultaneously and adopt the genetic algorithm (GA) to find the solution for adapting the taught motion. We validated the proposed method on an Aldebaran NAO robot and the results showed that the robot was taught to perform side and back kicks via kinesthetic teaching.


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    Title :

    Whole-Body Robot Motion Learning by Kinesthetic Teaching


    Additional title:

    Advs in Intelligent Syst., Computing


    Contributors:


    Publication date :

    2015-09-03


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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