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Learning skills while interacting with human users allows robots to work in human environments and become efficient when performing everyday tasks. This paper presents a method of analyzing data obtained from human demonstrations of a task in order to: (a) extract specific constraints for each part of the task (important variables, the frame of reference to be used, and the most suitable controller for reproducing the task); (b) use these task features as continuously embeddable constraints in the learned robot motion (c) properly segment into subtask; The proposed method has been tested on a common kitchen task and its performance was compared against standard control modes.

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Last update: 25/08/06