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http://dx.doi.org/10.7746/jkros.2013.8.1.020

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

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)
Publication Information
The Journal of Korea Robotics Society / v.8, no.1, 2013 , pp. 20-28 More about this Journal
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
Bimanual Manipulation; Imitation Learning; Movement Primitives;
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