NDLI: Efficient and effective grasping of novel objects through learning and adapting a knowledge base

Content Provider	IEEE Xplore Digital Library
Author	Curtis, N. Jing Xiao
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of North Carolina-Charlotte, Charlotte, NC (Curtis, N.; Jing Xiao)
Abstract	This paper introduces a new approach to establish a good grasp for a novel object quickly. A comprehensive knowledge base for grasping is learned that takes into account the geometrical and physical knowledge of grasping. To automate the learning process as much as possible, learning happens in a virtual environment. We used the GraspIt! simulation environment with the Barrett hand for this work. As only approximate features of objects are used for training the grasping knowledge base (GKB), the knowledge gained is rather robust to object uncertainty. Based on the guidance of the GKB, a suitable grasp for a novel object can be found quickly. The newly gained grasping information of the new object can also be feedback to the GKB so that the knowledge base continues to improve as it is exposed to more grasping cases. The GKB serves as the ldquoexperiencerdquo of the robotic gripper to make grasping more and more skillful. We implemented the approach and tested it on This paper introduces a new approach to establish a good grasp for a novel object quickly. A comprehensive knowledge base for grasping is learned that takes into account the geometrical and physical knowledge of grasping. To automate the learning process as much as possible, learning happens in a virtual environment. We used the GraspIt! [T. Miller and P.K. Allen, 2000] simulation environment with the Barrett hand for this work. As only approximate features of objects are used for training the grasping knowledge base (GKB), the knowledge gained is rather robust to object uncertainty. Based on the guidance of the GKB, a suitable grasp for a novel object can be found quickly. The newly gained grasping information of the new object can also be feedback to the GKB so that the knowledge base continues to improve as it is exposed to more grasping cases. The GKB serves as the ldquoexperiencerdquo of the robotic gripper to make grasping more and more skillful. We implemented the approach and tested it on a wide variety of objects. The results show the effectiveness of this approach to achieve quick and good grasps of novel objects.a wide variety of objects. The results show the effectiveness of this approach to achieve quick and good grasps of novel objects.
Starting Page	2252
Ending Page	2257
File Size	299022
Page Count	6
File Format	PDF
ISBN	9781424420575
DOI	10.1109/IROS.2008.4651062
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Grasping Training Shape Materials Knowledge based systems Fingers Glass
Content Type	Text
Resource Type	Article

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