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Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to addressdoi:10.1109/iros40897.2019.8968496 dblp:conf/iros/DuanCFHCRP19 fatcat:ouz2ktcexfecbo2q75pdiyvh4y