The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Imitation Learning of Bimanual Manipulation Skills Considering Both Position and Force Trajectory

힘과 위치를 동시에 고려한 양팔 물체 조작 솜씨의 모방학습

  • Kwon, Woo Young (Department of Electronics and Computer Engineering, Hanyang University) ;
  • Ha, Daegeun (SimLab) ;
  • Suh, Il Hong (Department of Electronics and Computer Engineering, Hanyang University)
  • Received : 2012.09.11
  • Accepted : 2012.10.26
  • Published : 2013.02.28
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Abstract

Large workspace and strong grasping force are required when a robot manipulates big and/or heavy objects. In that situation, bimanual manipulation is more useful than unimanual manipulation. However, the control of both hands to manipulate an object requires a more complex model compared to unimanual manipulation. Learning by human demonstration is a useful technique for a robot to learn a model. In this paper, we propose an imitation learning method of bimanual object manipulation by human demonstrations. For robust imitation of bimanual object manipulation, movement trajectories of two hands are encoded as a movement trajectory of the object and a force trajectory to grasp the object. The movement trajectory of the object is modeled by using the framework of dynamic movement primitives, which represent demonstrated movements with a set of goal-directed dynamic equations. The force trajectory to grasp an object is also modeled as a dynamic equation with an adjustable force term. These equations have an adjustable force term, where locally weighted regression and multiple linear regression methods are employed, to imitate complex non-linear movements of human demonstrations. In order to show the effectiveness our proposed method, a movement skill of pick-and-place in simulation environment is shown.

Keywords

References

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