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Acquiring Visual_Motor Models for Robot Hand Fine-Manipulation
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@InProceedings{Jagersand:96,
  author = 	 {Martin Jagersand, Olac Fuentes, and Randal C. Nelson},
  title = 	 {Acquiring Visual_Motor Models for Robot Hand Fine-Manipulation},  
  booktitle = {Proc. European Conference on Computer Vision (ECCV96)},
  year = 	 {1996}, 
  address = 	 {Cambridge England},
  month = 	 {April},
  abstract =     {  Dextrous high degree of freedom (DOF) robotic hands provide versatile
motions for fine manipulation of potentially very different objects. However, fine
manipulation of an object grasped by a multifinger hand is much more complex
than if the object is rigidly attached to a robot arm. Creating an accurate model is
difficult if not impossible. We instead propose a combination of two techniques: the
use of an approximate estimated motor model, based on the grasp tetrahedron acquired
when grasping an object, and the use of visual feedback to achieve accurate fine
manipulation. We present a novel active vision based algorithm for visual servoing,
capable of learning the manipulator kinematics and camera calibration online while
executing a manipulation task. The approach differs from previous work in that a full,
coupled image Jacobian is estimated online without prior models, and that a trust
region control method is used, improving stability and convergence. We present an
extensive experimental evaluation of visual model acquisition and visual serving in
3, 4 and 6 DOF.
}