让机器人根据人手姿态动态调整工具朝向，提升协作交接流畅度
Receiver-Centered Robot-to-Human Handover with Grasp-Aware Object Orientation
- 以接收者为中心，结合语音与手部追踪动态调整工具朝向
- 对不对称工具，抓取延迟平均减少23%，交互更顺滑
- 提升信任感，尤其在动作可预测性和任务简单性上
协作机器人正越来越多地与人类操作员共享工作空间，工具交接成为频繁且关乎安全的微交互。然而，传统静态交接方式在处理不对称工业工具时，常导致不自然的抓握姿势。本文提出一种基于语音驱动的接收者中心自适应交接系统，部署于Franka协作机器人。系统利用大语言模型（LLM）进行意图识别，结合MediaPipe实现实时3D手部追踪，动态调整末端执行器朝向，使工具以符合人体工学、手柄朝前的姿态呈现。通过被试内实验对比该自适应方法与无对象感知的静态基线，结果表明：对于不对称工具，该系统显著降低了抓取延迟，提升了交互流畅性；同时增强了特定信任感知，特别是动作可预测性与任务简单性认知。
原文摘要 · Abstract (English)
Collaborative robots are increasingly sharing workspaces with human operators, making tool handover a frequent and safety-critical micro-interaction. However, traditional static handovers often lead to awkward grasps when handling asymmetric industrial tools. This paper presents a receiver-centered voice-driven adaptive handover system for mechanical tools, built on a Franka cobot. Using an LLM for intention recognition and MediaPipe for real-time 3D hand tracking, the framework dynamically adjusts the end-effector's orientation to present tools in an ergonomically optimal, handle-first pose. A within-subjects study compared this adaptive approach with an object-agnostic static baseline. The results demonstrate that the adaptive system reduces the grasp delay for asymmetric tools, improving the fluency of the interaction. Furthermore, the adaptive strategy improved specific trust-related perceptions, particularly motion predictability and perceived task simplicity.
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