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. Our code and recorded trajectories are made available online.

翻译：摘要：基于示范的学习（LfD）使人类能够轻松地向协作机器人教授新动作，且无需重新训练即可泛化至新任务配置。然而，现有最先进的LfD方法需要手动调整内部参数，并且在没有专家参与的工业环境中鲜少应用。我们提出了一种基于概率运动基元的无参数LfD方法，该方法利用詹森-香农散度和贝叶斯优化确定参数，用户无需手动调参。通过两项现场测试评估了协作机器人在再现所学动作时的精度，以及非专家用户的教学与使用便捷性。第一项现场测试中，机器人执行电梯门维护任务；第二项测试中，三名工厂工人教授机器人完成其日常工作中实用的任务。机器人与目标关节角度之间的误差不显著（最差为0.28度），且动作被精确再现（GMCC评分为1）。工人完成的问卷强调了该方法的使用便捷性与动作再现的准确性。我们的代码与记录的轨迹已在线公开。