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

Collaborative robots (cobots) built to work alongside humans must be able to quickly learn new skills and adapt to new task configurations. Learning from demonstration (LfD) enables cobots to learn and adapt motions to different use conditions. However, state-of-the-art LfD methods require manually tuning intrinsic parameters and have rarely been used in industrial contexts without experts. In this paper, the development and implementation of a LfD framework for industrial applications with naive users is presented. We propose a parameter-free method based on probabilistic movement primitives, where all the parameters are pre-determined using Jensen-Shannon divergence and bayesian optimization; thus, users do not have to perform manual parameter tuning. This method learns motions from a small dataset of user demonstrations, and generalizes the motion to various scenarios and conditions. We evaluate the method extensively in two field tests: one where the cobot works on elevator door maintenance, and one where three Schindler workers teach the cobot tasks useful for their workflow. Errors between the cobot end-effector and target positions range from $0$ to $1.48\pm0.35$mm. For all tests, no task failures were reported. Questionnaires completed by the Schindler workers highlighted the method's ease of use, feeling of safety, and the accuracy of the reproduced motion. Our code and recorded trajectories are made available online for reproduction.