Traditional robot programming is often not feasible in small-batch production, as it is time-consuming, inefficient, and expensive. To shorten the time necessary to deploy robot tasks, we need appropriate tools to enable efficient reuse of existing robot control policies. Incremental Learning from Demonstration (iLfD) and reversible Dynamic Movement Primitives (DMP) provide a framework for efficient policy demonstration and adaptation. In this paper, we extend our previously proposed framework with improvements that provide better performance and lower the algorithm’s computational burden. Further, we analyse the learning stability and evaluate the proposed framework with a comprehensive user study. The proposed methods have been evaluated on two popular collaborative robots, Franka Emika Panda and Universal Robot UR10.

传统的机器人编程在小批量生产中通常是不可行的，因为它既费时，效率低下又昂贵。为了缩短部署机器人任务所需的时间，我们需要适当的工具以有效地重用现有的机器人控制策略。示范增量学习（iLfD）和可逆动态运动基元（DMP）为有效的政策演示和适应提供了框架。在本文中，我们通过改进提供了更好的性能并降低了算法的计算负担，从而扩展了我们先前提出的框架。此外，我们分析了学习稳定性，并通过全面的用户研究评估了提出的框架。在两种流行的协作机器人Franka Emika Panda和Universal Robot UR10上对提出的方法进行了评估。