  Learning from demonstrations (LfD) enables humans to easily teach
collaborative robots (cobots) new motions that can be generalized to new task
configurations without retraining. However, state-of-the-art LfD methods
require manually tuning intrinsic parameters and have rarely been used in
industrial contexts without experts. We propose a parameter-free LfD method
based on probabilistic movement primitives, where parameters are determined
using Jensen-Shannon divergence and Bayesian optimization, and users do not
have to perform manual parameter tuning. The cobot's precision in reproducing
learned motions, and its ease of teaching and use by non-expert users are
evaluated in two field tests. In the first field test, the cobot works on
elevator door maintenance. In the second test, three factory workers teach the
cobot tasks useful for their daily workflow. Errors between the cobot and
target joint angles are insignificant -- at worst 0.28 deg -- and the motion is
accurately reproduced -- GMCC score of 1. Questionnaires completed by the
workers highlighted the method's ease of use and the accuracy of the reproduced
motion. Public implementation of our method and datasets are made available
online.
