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An SVM learning approach to robotic grasping

Raphael Pelossof; Andrew T. Miller; Peter K. Allen; Tony Jebara

Title:
An SVM learning approach to robotic grasping
Author(s):
Pelossof, Raphael; Miller, Andrew T.; Allen, Peter K.; Jebara, Tony
Date:
Type:
Articles
Department:
Computer Science
Permanent URL:
Book/Journal Title:
2004 IEEE International Conference on Robotics and Automation : proceedings : April 26-May 1, 2004, Hilton New Orleans Riverside, New Orleans, LA, USA
Publisher:
IEEE
Abstract:
Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non- smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp.
Subject(s):
Robotics
Publisher DOI:
http://dx.doi.org/10.1109/ROBOT.2004.1308797
Item views:
70
Metadata:
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