Billard, A.Calinon, S.Guenter, F.2007-04-152007-04-152007-04-15200610.1016/j.robot.2006.01.007https://infoscience.epfl.ch/handle/20.500.14299/46568674This paper addresses the problems of what to imitate and how to imitate in simple uni- and bi-manual manipulatory tasks. To solve the what to imitate issue, we use a probabilistic method, based on Hidden Markov Models, for extracting the relative importance of reproducing either the gesture or the specific hand path in a given task. This allows us to determine a metric of imitation performance. To solve the how to imitate issue, we compute the trajectory that optimizes the metric, given a set of robot's body constraints. We validate the methods in a series of experiments, where a human demonstrator teaches through kinesthetic a humanoid robot how to manipulate simple objects.Robot Programming by Demonstration (RbD)Learning by ImitationHuman-Robot Interaction (HRI)Hidden Markov Model (HMM)text::journal::journal article::research article