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Learning from demonstrations (LfD) enables humans to easily teachcollaborative robots (cobots) new motions that can be generalized to new taskconfigurations without retraining. However, state-of-the-art LfD methodsrequire manually tuning intrinsic parameters and have rarely been used inindustrial contexts without experts. We propose a parameter-free LfD methodbased on probabilistic movement primitives, where parameters are determinedusing Jensen-Shannon divergence and Bayesian optimization, and users do nothave to perform manual parameter tuning. The cobot's precision in reproducinglearned motions, and its ease of teaching and use by non-expert users areevaluated in two field tests. In the first field test, the cobot works onelevator door maintenance. In the second test, three factory workers teach thecobot tasks useful for their daily workflow. Errors between the cobot andtarget joint angles are insignificant -- at worst 0.28 deg -- and the motion isaccurately reproduced -- GMCC score of 1. Questionnaires completed by theworkers highlighted the method's ease of use and the accuracy of the reproducedmotion. Public implementation of our method and datasets are made availableonline.

TEAM: a parameter-free algorithm to teach collaborative robots motions from user demonstrations