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Motivated by the current state-of-the-art in Robot Learning from Demonstration (LfD), in this paper, we tackle two central issues in the learning pipeline: namely, dealing with (1) heterogeneity and (2) unstructuredness in demonstrations of complex manipulation tasks. We build upon our previous work on transform-invariant segmentation and action discovery [1], to learn the underlying action sequence of tasks demonstrated in different reference frames or contexts. We then construct and parametrize a multi-phase task-space control architecture, boot-strapped by the segmented data and model parameters learned from the action discovery approach. Successful case studies of the proposed methodology are presented for uni/bi-manual cooking tasks demonstrated through kinesthetic teaching.

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Last update: 25/08/06