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- W2017279084 abstract "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." @default.
- W2017279084 created "2016-06-24" @default.
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- W2017279084 date "2010-10-01" @default.
- W2017279084 modified "2023-09-23" @default.
- W2017279084 title "Robot learning of everyday object manipulations via human demonstration" @default.
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- W2017279084 doi "https://doi.org/10.1109/iros.2010.5651244" @default.
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