Robot learning of everyday object manipulations via human demonstration

Dang, Hao; Allen, Peter K.

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.



Also Published In

The IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2010: Taipei International Convention Center, Taipei, Taiwan, October 18-22, 2010: conference proceedings

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Academic Units
Computer Science
Published Here
October 25, 2012