Example-based grasp adaptation

Finding a way to provide intelligent humanoid robots with the ability to grasp objects has been a question of great interest. Most approaches, however, assume that objects are composed of primitive shapes such as box, sphere, and cylinder. In the thesis, we explore an efficient and robust method to decide grasps given new objects that are irregularly-shaped (3D polygon meshes). To solve the problem, we use an example-based approach. We first find grasps for objects geometrically similar to those the system has seen before. For example, if the system has been shown a cup being grasped by the handle, it should now be able to grasp any new cup. There are two problems to be solved in order to adapt example grasps to the new object. First, the system should be able to retrieve objects that are geometrically similar to the given object from the database storing previously seen objects. After collecting objects the system knows how to grasp, it needs to adapt example grasps to new object.Already, there are some working algorithms for the first problem (shape retrieval). Therefore, our main contribution is to present an algorithm that performs grasp adaptation. Before we adapt a grasp, we first find the geometric correspondence between a demo object and new object using probabilistic graphical model. Based on correlation information together with the demo grasp, we generate a grasp for the new object. To ensure that a robot can effectively grasp the object, we adjust the position of grasp contacts until the quality of the grasp is reasonably high. In test cases, the system successfully uses this method to find the correspondence between objects and adapt demo grasps.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Kim, Jiwon, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Tomás Lozano-Pérez.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/45978

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/45978