2026-09-22T00:08:38Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1273452022-08-18T13:42:21Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Russ Tedrake.
Gao, Wei,(Scientist in electrical engineering and computer science)Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2020-09-15T21:53:17Z
2020-09-15T21:53:17Z
2020
2020
https://hdl.handle.net/1721.1/127345
1192476051
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).
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.
by Wei Gao.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
75 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Integrated perception, planning and feedback control for generalizable robotic manipulation
Thesis
application/pdf
2020-09-15T21:53:16Z
Publication
Master
EECS
unknown
unknown
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<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.</Abstract>
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