语义力反馈提升灵巧机器人遥操作性能
Semantic Haptic Feedback Enhances Dexterous Robotic Teleoperation
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中文总结 AI 辅助
本研究提出语义力反馈用于机器人遥操作，通过抽象触觉模式传递机器人状态信息，经实验验证其在双臂任务中性能优于感官触觉与视觉反馈，可降低工作负荷并提升态势感知。
中文摘要 AI 辅助
在机器人遥操作中，力反馈可帮助人类操作员完成灵巧操纵任务。然而，现有的力反馈方法试图复刻真实世界交互中的高保真感官触觉，这类方法受限于传感与反馈硬件能力，还可能增加操作员的工作负荷。为解决这些局限，本研究引入用于遥操作的语义力反馈，采用抽象触觉模式传递机器人状态的关键信息。我们将机器人状态分为“确认信息”与“异常情况”两类，在机器人仿真中实现模块化触觉渲染管线，并通过气动腕带与振动腕带向操作员传递语义力反馈。这简化了硬件要求，还实现了触觉模式与机器人状态间的一对多映射。通过三项评估研究，我们确定了适用于常见拾取放置遥操作任务的最有效语义力反馈设计，并将其与其他遥操作反馈方法（包括感官触觉与视觉反馈）进行对比。结果表明，语义力反馈在单臂任务中的表现与其他反馈相近，但在双臂任务中性能更优，可降低任务工作负荷、提升态势感知能力，还能获得更高的整体偏好度。
英文摘要
In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic design for a common pick and place teleoperation task and compare semantic haptics to other teleoperation feedback approaches including sensory haptics and visual feedback. Results suggest that while semantic haptics performs similarly as other feedback in unimanual tasks, it achieves superior performance in bimanual tasks, with reduced task workload, increased situational awareness, and overall preference.
