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힘과 위치를 동시에 고려한 양팔 물체 조작 솜씨의 모방학습 KCI 등재

Imitation Learning of Bimanual Manipulation Skills Considering Both Position and Force Trajectory

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  • URLhttps://db.koreascholar.com/Article/Detail/240914
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로봇학회논문지 (The Journal of Korea Robotics Society)
한국로봇학회 (Korea Robotics Society)
초록

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.

저자
  • 권우영(Department of Electronics and Computer Engineering, Hanyang University) | Woo Young Kwon
  • 하대근(SimLab, Korea) | Daegeun Ha
  • 서일홍(Department of Electronics and Computer Engineering, Hanyang University) | Il Hong Suh Corresponding author