Acquiring Visual-Motor Models for
Robot Hand Fine-Manipulation

Martin Jagersand, Olac Fuentes, and Randal C. Nelson
Department of Computer Science
University of Rochester
Rochester, NY 14627

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 servoing in 3, 4 and 6 DOF.