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：机器人解决方案，尤其是机械臂，正越来越多地被部署于与人密切协作的场景，例如制造业或家庭护理环境。这些机械臂需要用户控制多个自由度（DoFs）来执行任务，主要涉及抓取和操作物体。标准输入设备通常只有两个自由度，需要用户进行耗时且认知负荷高的模式切换以选择单个自由度。当代自适应自由度映射控制（ADMCs）已显示出能减少必要的模式切换次数，但迄今为止未能显著降低感知工作量。用户仍需承受将抽象模式切换融入工作流程的心理负担。我们通过利用ADMC的更新建议提供前馈多模态反馈来解决这一问题，使用户能够实时直观地比较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建议之间使用离散阈值。两种方法均在虚拟现实（VR）现场研究中进行比较，并与经典控制方法对照。显著的结果表明，在任务完成时间缩短、模式切换次数减少及感知工作量降低方面，结合前馈的ADMC方法确实能优于经典模式切换。连续方法与阈值方法之间缺乏明显的量化差异，揭示了以用户为中心的定制化选项的重要性。将这些启示纳入开发过程将提升可用性，这对于成功实现高用户接受度的机器人技术至关重要。