Robot learning of everyday object manipulations via human demonstration
Dang
Hao
author
Columbia University. Computer Science
Allen
Peter K.
author
Columbia University. Computer Science
Columbia University. Computer Science
originator
text
Articles
2010
English
We deal with the problem of teaching a robot to manipulate everyday objects through human demonstration. We first design a task descriptor which encapsulates important elements of a task. The design originates from observations that manipulations involved in many everyday object tasks can be considered as a series of sequential rotations and translations, which we call manipulation primitives. We then propose a method that enables a robot to decompose a demonstrated task into sequential manipulation primitives and construct a task descriptor. We also show how to transfer a task descriptor learned from one object to similar objects. In the end, we argue that this framework is highly generic. Particularly, it can be used to construct a robot task database that serves as a manipulation knowledge base for a robot to succeed in manipulating everyday objects.
Robotics
The IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2010: Taipei International Convention Center, Taipei, Taiwan, October 18-22, 2010: conference proceedings
Piscataway, N.J.
IEEE
2010
1284
1289
http://dx.doi.org/10.1109/IROS.2010.5651244
http://hdl.handle.net/10022/AC:P:15083
NNC
NNC
2012-10-25 14:55:32 -0400
2012-10-25 15:02:34 -0400
9081
eng