Robotic solutions, in particular robotic arms, are becoming more frequently deployed for close collaboration with humans, for example in manufacturing or domestic care environments. These robotic arms require the user to control several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving grasping and manipulating objects. Standard input devices predominantly have two DoFs, requiring time-consuming and cognitively demanding mode switches to select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have shown to decrease the necessary number of mode switches but were up to now not able to significantly reduce the perceived workload. Users still bear the mental workload of incorporating abstract mode switching into their workflow. We address this by providing feed-forward multimodal feedback using updated recommendations of ADMC, allowing users to visually compare the current and the suggested mapping in real-time. We contrast the effectiveness of two new approaches that a) continuously recommend updated DoF combinations or b) use discrete thresholds between current robot movements and new recommendations. Both are compared in a Virtual Reality (VR) in-person study against a classic control method. Significant results for lowered task completion time, fewer mode switches, and reduced perceived workload conclusively establish that in combination with feedforward, ADMC methods can indeed outperform classic mode switching. A lack of apparent quantitative differences between Continuous and Threshold reveals the importance of user-centered customization options. Including these implications in the development process will improve usability, which is essential for successfully implementing robotic technologies with high user acceptance.

翻译：摘要：机器人解决方案，特别是机械臂，正越来越多地部署于与人类紧密协作的场景，例如制造业或居家照护环境。这些机械臂要求用户控制多个自由度以执行任务，主要涉及抓取与操作物体。标准输入设备通常仅具备两个自由度，导致用户需通过耗时且认知负荷高的模式切换来选择各个自由度。当前的自适应自由度映射控制方法虽已证明能减少必要模式切换次数，但至今未能显著降低感知工作负荷——用户仍需承受将抽象模式切换融入工作流程的心理负担。我们通过提供基于更新推荐的自适应自由度映射的多模态前馈反馈来解决这一问题，使用户能实时直观对比当前映射与建议映射。我们对比了两种新方法的有效性：（a）持续推荐更新的自由度组合，或（b）在当前机器人运动与新推荐之间使用离散阈值。两者均通过虚拟现实在场实验与经典控制方法进行了比较。显著的结果表明，结合前馈反馈的自适应自由度映射方法在缩短任务完成时间、减少模式切换次数及降低感知工作负荷方面确实优于经典模式切换。连续模式与阈值模式之间缺乏显著量化差异，揭示了以用户为中心的定制选项的重要性。将这些启示融入开发流程将提升可用性，而这对于成功实现具有高用户接受度的机器人技术至关重要。