Show simple item record

dc.contributor.advisorTomás Lozano-Pérez and Leslie Kaelbling.en_US
dc.contributor.authorHsiao, Kaijenen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2010-05-25T20:44:10Z
dc.date.available2010-05-25T20:44:10Z
dc.date.copyright2009en_US
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/55115
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 135-138).en_US
dc.description.abstractThis thesis presents an approach for grasping objects robustly under significant positional uncertainty. In the field of robot manipulation there has been a great deal of work on how to grasp objects stably, and in the field of robot motion planning there has been a great deal of work on how to find collision-free paths to those grasp positions. However, most of this work assumes exact knowledge of the shapes and positions of both the object and the robot; little work has been done on how to grasp objects robustly in the presence of position uncertainty. To reason explicitly about uncertainty while grasping, we model the problem as a partially observable Markov decision process (POMDP). We derive a closed-loop strategy that maintains a belief state (a probability distribution over world states), and select actions with a receding horizon using forward search through the belief space. Our actions are world-relative trajectories (WRT): fixed trajectories expressed relative to the most-likely state of the world. We localize the object, ensure its reachability, and robustly grasp it at a specified position by using information-gathering, reorientation, and goal-seeking WRT actions. This framework is used to grasp objects (including a power drill and a Brita pitcher) despite significant uncertainty, using a 7-DOF Barrett Arm and attached 4-DOF Barrett Hand equipped with force and contact sensors. Our approach is generalizable to almost any sensor type, as well as wide ranges of sensor error and pose uncertainty.en_US
dc.description.statementofresponsibilityby Kaijen Hsiao.en_US
dc.format.extent138 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleRelatively robust graspingen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc591541776en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record