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In this work, we address the problem of learning arm gestures from imitation by humanoid robots when the training set contains missing data. We assume that multiple gesture demonstrations are available. The problem is challenging because of the fact that there is no temporal alignment between the demonstrations. In this work, we propose two approaches to handle the missing data problem. One approach...