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- W21935167 abstract "We present an approach for building high-level task representations from a robot’s own experiences of interacting with a teacher. A learner robot follows a human teacher and maps its own observations of the environment to the known effects of its available skills, building at run-time a representation of the experienced task in the form of a behavior network. The learner can act next as a teacher, and transfer the acquired knowledge to other robots. To enable this we introduce an architecture that extends the capabilities of behavior-based systems by allowing the representation and execution of complex and flexible sequences of behaviors. We demonstrate our approach in a set of experiments in which robots learn representations for multiple tasks from both human and robot teachers." @default.
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- W21935167 modified "2023-09-27" @default.
- W21935167 title "Learning task representations from experienced demonstrations" @default.
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