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.

翻译：从演示中学习（LfD）使人类能够轻松教导协作机器人（cobot）新动作，这些动作可泛化到新任务配置而无需重新训练。然而，最先进的LfD方法需要手动调整内在参数，且在没有专家参与的工业环境中鲜少使用。我们提出一种基于概率运动基元的无参数LfD方法，其中参数通过詹森-香农散度和贝叶斯优化确定，用户无需执行手动参数调整。通过两项现场测试评估了协作机器人复现所学动作的精度，以及非专家用户教导和使用的便捷性。第一项现场测试中，协作机器人执行电梯门维护任务；第二项测试中，三名工厂工人教导协作机器人完成其日常工作中实用的任务。协作机器人与目标关节角度之间的误差极小（最差为0.28度），且运动被精确复现（GMCC评分1分）。工人填写的问卷凸显了该方法的易用性和复现动作的准确性。我们的方法及数据集的公开实现已在线提供。