Virtual Reality (VR) is increasingly used for training and demonstration purposes including a variety of applications ranging from robot learning to rehabilitation. However, the choice of input device and its visualization might influence workload and thus user performance leading to suboptimal demonstrations or reduced training effects. This study investigates how different VR input configurations - motion capture gloves, controllers with hand visualization, and controllers with controller visualization - affect user experience and task execution, with the goal of identifying which configuration is best suited for which type of task. Participants performed various kitchen-related activities of daily living (ADLs), including object placement, cutting, cleaning, and pouring in a simulated environment. To address two research questions, we evaluated user experience using the System Usability Scale and NASA Task Load Index (RQ1), and task-specific interaction behavior (RQ2). The latter was assessed using trajectory segmentation, analyzing movement efficiency, unnecessary actions, and execution precision. While no significant differences in overall usability and workload were found, trajectory analysis revealed configuration-specific execution behaviors with different movement strategies. Controllers enabled significantly faster task completion with less movement variability in pick-and-place style tasks such as table setting. In contrast, motion capture gloves produced more natural movements with fewer unnecessary actions, but also showed greater variance in movement patterns for manner-oriented tasks such as cutting bread. These findings highlight trade-offs between efficiency and naturalism, and have implications for optimizing VR-based training, improving the quality of user-generated demonstrations, and tailoring interaction design to specific application goals.

翻译：虚拟现实（VR）正日益广泛地应用于培训与演示领域，涵盖从机器人学习到康复治疗等多种应用场景。然而，输入设备的选择及其可视化方式可能影响用户的工作负荷，进而导致演示效果欠佳或训练效果降低。本研究探讨了不同VR输入配置——动作捕捉手套、带手部可视化的控制器以及带控制器可视化的控制器——如何影响用户体验与任务执行，旨在确定各类配置分别最适合何种任务类型。参与者在模拟环境中执行了多种厨房相关的日常生活活动（ADLs），包括物品摆放、切割、清洁和倾倒等操作。针对两个研究问题，我们分别采用系统可用性量表和NASA任务负荷指数评估用户体验（RQ1），并分析任务特定的交互行为（RQ2）。后者通过轨迹分割方法进行评估，重点分析运动效率、冗余动作和执行精度。虽然整体可用性与工作负荷未呈现显著差异，但轨迹分析揭示了不同配置下具有差异运动策略的执行行为。在摆盘等抓放式任务中，控制器能显著加快任务完成速度并降低运动变异性；相比之下，动作捕捉手套在切面包等注重操作方式的任務中能产生更自然的运动轨迹并减少冗余动作，但同时也表现出更大的运动模式方差。这些发现揭示了效率与自然性之间的权衡关系，对优化基于VR的培训系统、提升用户生成演示的质量，以及根据特定应用目标定制交互设计具有重要启示。