Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill learning paradigm by using intuitive teleoperation devices to generate high-quality human demonstrations efficiently for robotic skill learning in a data-driven manner. By using a reliable teleoperation interface, the da Vinci Research Kit (dVRK) master, a system called dVRK-Simulator-for-Demonstration (dS4D) is proposed in this paper. Various manipulation tasks show the system's effectiveness and advantages in efficiency compared to other interfaces. Using the collected data for policy learning has been investigated, which verifies the initial feasibility. We believe the proposed paradigm can facilitate robot learning driven by high-quality demonstrations and efficiency while generating them.

翻译：机器人技能学习日益受到关注，但与计算机视觉中的图像/视频收集以及自然语言处理中的文本收集相比，演示数据的采集更具挑战性。本文提出了一种技能学习范式，利用直观的遥操作设备以数据驱动的方式高效生成高质量的人类演示数据，用于机器人技能学习。通过采用可靠的遥操作接口——达芬奇研究套件（dVRK）主手，本文构建了名为dS4D（dVRK-Simulator-for-Demonstration）的系统。多种操作任务验证了该系统相比其他接口在效率上的有效性与优势。研究进一步探索了利用采集的数据进行策略学习，初步验证了其可行性。我们认为，该范式能够通过高质量演示数据的高效生成，推动机器人学习的进步。