Imitation learning relies on high-quality demonstrations, and teleoperation is a primary way to collect them, making teleoperation interface choice crucial for the data. Prior work mainly focused on static tasks, i.e., discrete, segmented motions, yet demonstrations also include dynamic tasks requiring reactive control. As dynamic tasks impose fundamentally different interface demands, insights from static-task evaluations cannot generalize. To address this gap, we conduct a within-subjects study comparing a VR controller and a SpaceMouse across two static and two dynamic tasks ($N=25$). We assess success rate, task duration, cumulative success, alongside NASA-TLX, SUS, and open-ended feedback. Results show statistically significant advantages for VR: higher success rates, particularly on dynamic tasks, shorter successful execution times across tasks, and earlier successes across attempts, with significantly lower workload and higher usability. As existing VR teleoperation systems are rarely open-source or suited for dynamic tasks, we release our VR interface to fill this gap.

翻译：模仿学习依赖于高质量的演示数据，而遥操作是采集此类数据的主要方式，因此遥操作界面的选择对数据质量至关重要。先前的研究主要集中于静态任务，即离散、分段式的动作，然而演示数据同样包含需要反应式控制的动态任务。由于动态任务对界面提出了根本不同的需求，从静态任务评估中获得的见解无法直接推广。为填补这一空白，我们开展了一项被试内研究，比较VR控制器与SpaceMouse在两项静态任务和两项动态任务中的表现（$N=25$）。我们评估了成功率、任务完成时间、累积成功率，同时结合NASA-TLX量表、SUS量表以及开放式反馈。结果显示VR具有统计学上的显著优势：更高的成功率（尤其在动态任务中）、更短的成功执行时间（跨任务）以及更早的成功尝试，同时其工作负荷显著更低、可用性更高。鉴于现有的VR遥操作系统很少开源或适用于动态任务，我们开源了我们的VR界面以填补这一空白。