Integrated perception, planning and feedback control for generalizable robotic manipulation
Name
1192476051-MIT.pdf
Size
13.52 MB
Format
Adobe PDF
Checksum (MD5)
9f3ea56fbf70054ebd642a9d57b5faae
Author(s)
Gao, Wei,(Scientist in electrical engineering and computer science)Massachusetts Institute of Technology.
Advisor(s)
Russ Tedrake.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Humans can easily adapt their manipulation skills to unseen objects, new environment and different tasks. However, existing robot manipulators are typically limited to known object instance and skill transferring is challenging. In this thesis, we take a step further by formulating a manipulation framework that can achieve precise, reliable and dexterous manipulation while being generalizable to potentially unknown object instances. To achieve it, we propose key-point affordances, an object representation consists of 3D semantic key-points. This object representation captures task-related geometric information while ignoring irrelevant details, which enables our method to handle unknown objects with potentially large shape variations. We implement perception, planning and feedback control modules on top of key-point affordances and integrate them into a fully functionally perception-to-action manipulation pipeline. Extensive experiments demonstrate our method can reliably accomplish a variety of challenging tasks with never-before seen objects.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 69-75).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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