Integrated perception, planning and feedback control for generalizable robotic manipulation

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.

Name thesis:degree_name

Master

Department dc:contributor.department

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

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Gao, Wei,(Scientist in electrical engineering and computer science)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Russ Tedrake.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127345

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127345