Acquiring high-quality demonstration data is essential for the success of data-driven methods, such as imitation learning. Existing platforms for providing demonstrations for manipulation tasks often impose significant physical and mental demands on the demonstrator, require additional hardware systems, or necessitate specialized domain knowledge. In this work, we present a novel augmented reality (AR) interface for teleoperating robotic manipulators, emphasizing the demonstrator's experience, particularly in the context of performing complex tasks that require precision and accuracy. This interface, designed for the Microsoft HoloLens 2, leverages the adaptable nature of mixed reality (MR), enabling users to control a physical robot through digital twin surrogates. We assess the effectiveness of our approach across three complex manipulation tasks and compare its performance against OPEN TEACH, a recent virtual reality (VR) teleoperation system, as well as two traditional control methods: kinesthetic teaching and a 3D SpaceMouse for end-effector control. Our findings show that our method performs comparably to the VR approach and demonstrates the potential for AR in data collection. Additionally, we conduct a pilot study to evaluate the usability and task load associated with each method. Results indicate that our AR-based system achieves higher usability scores than the VR benchmark and significantly reduces mental demand, physical effort, and frustration experienced by users. An accompanying video can be found at https://youtu.be/w-M58ohPgrA.

翻译：获取高质量演示数据对于模仿学习等数据驱动方法的成功至关重要。现有的操作任务演示平台通常对演示者施加显著的生理与心理负担，需要额外的硬件系统，或要求具备专业领域知识。本研究提出了一种用于遥操作机器人操作器的新型增强现实（AR）界面，重点关注演示者在执行需要精度与准确度的复杂任务时的操作体验。该基于微软HoloLens 2开发的界面充分利用混合现实（MR）技术的适应性优势，使用户能够通过数字孪生代理控制物理机器人。我们在三项复杂操作任务中评估本方法的有效性，并将其性能与近期开发的虚拟现实（VR）遥操作系统OPEN TEACH，以及两种传统控制方法（动觉示教与末端执行器3D SpaceMouse控制）进行对比。实验结果表明，本方法性能与VR方案相当，并展现了AR在数据采集领域的应用潜力。此外，我们通过试点研究评估了各方法的可用性与任务负荷。数据显示，基于AR的系统在可用性评分上优于VR基准方案，并显著降低了用户的心理负担、生理消耗与操作挫败感。演示视频详见：https://youtu.be/w-M58ohPgrA。