对比了机器人操作中身体感整合与分离的优劣,发现耦合控制更利于数据质量。
The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation
- 将机械臂与底盘控制整合为整体,提升操作连贯性
- 虚拟现实反馈使任务时间与认知负荷显著增加
- 耦合控制虽不降低工作量,但采集数据更适合模仿学习
直观的远程操作界面对于移动操作机器人高效收集高质量数据、降低操作员负担至关重要。强身体感结合低物理与认知需求不仅能改善大规模数据采集时的用户体验,也有助于维持长时间操作的数据质量。这在需要全身协调的长时程移动操作任务中尤为关键。本文比较了两种控制范式:耦合式(将机械臂操作与底盘导航一体化)与解耦式(分别控制)。同时评估了沉浸式虚拟现实与传统屏幕视图两种视觉反馈方式。在复杂多阶段任务序列中系统测试发现,使用虚拟现实会增加任务完成时间、认知负荷和操作者感知努力。耦合控制与解耦控制在用户负荷上无显著差异,但初步实验表明,耦合式操作获取的数据在模仿学习中表现更优。本研究为以人为核心、规模化采集高维移动操作数据提供了综合视角。
原文摘要 · Abstract (English)
Intuitive Teleoperation interfaces are essential for mobile manipulation robots to ensure high quality data collection while reducing operator workload. A strong sense of embodiment combined with minimal physical and cognitive demands not only enhances the user experience during large-scale data collection, but also helps maintain data quality over extended periods. This becomes especially crucial for challenging long-horizon mobile manipulation tasks that require whole-body coordination. We compare two distinct robot control paradigms: a coupled embodiment integrating arm manipulation and base navigation functions, and a decoupled embodiment treating these systems as separate control entities. Additionally, we evaluate two visual feedback mechanisms: immersive virtual reality and conventional screen-based visualization of the robot's field of view. These configurations were systematically assessed across a complex, multi-stage task sequence requiring integrated planning and execution. Our results show that the use of VR as a feedback modality increases task completion time, cognitive workload, and perceived effort of the teleoperator. Coupling manipulation and navigation leads to a comparable workload on the user as decoupling the embodiments, while preliminary experiments suggest that data acquired by coupled teleoperation leads to better imitation learning performance. Our holistic view on intuitive teleoperation interfaces provides valuable insight into collecting high-quality, high-dimensional mobile manipulation data at scale with the human operator in mind. Project website:https://sophiamoyen.github.io/role-embodiment-wbc-moma-teleop/
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