Visual Transfer Learning for Robotic Manipulation
Humans are remarkable at manipulating unfamiliar objects. For the past decades of robotics, tremendous efforts have been dedicated to endow robot manipulation systems with such capabilities. As classic solutions typically require prior knowledge of the objects (e.g., 3D CAD models) which are not ava...
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Format: | Thesis |
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Massachusetts Institute of Technology
2022
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Online Access: | https://hdl.handle.net/1721.1/139048 |