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Whole-Body Robot Motion Learning by Kinesthetic Teaching

  • Hsien-I Lin
  • Yung-Yao Chen
  • Yu-Che Huang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

Learning whole-body robot motion is a challenging task because balance control should be taken into consideration. An intuitive 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 conversion problem 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.

Keywords

Whole-body motion Zero-moment-point (ZMP) Kinesthetic teaching Trade-off function Genetic algorithm (GA) 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Graduate Institute of Automation TechnologyNational Taipei University of TechnologyTaipeiTaiwan

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