发表机构
University of Modena and Reggio Emilia（摩德纳大学和雷焦艾米利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对协作机器人与人类操作员工具交接问题，提出以接收者为中心、语音驱动的自适应交接系统，基于Franka cobot，用LLM识别意图、MediaPipe跟踪手部，动态调整末端执行器方向，经实验验证该系统能减少抓握延迟，提升交互流畅性与信任感知。
AI 中文摘要
协作机器人越来越多地与人类操作员共享工作空间，使得工具交接成为频繁且对安全至关重要的微交互。然而，传统的静态交接在处理不对称工业工具时常常导致抓握笨拙。本文提出了一种以接收者为中心的、基于Franka协作机器人的语音驱动机械工具自适应交接系统。该框架使用语言模型进行意图识别，利用MediaPipe进行实时3D手部跟踪，动态调整末端执行器的方向，以符合人体工程学的最佳、手柄优先姿势呈现工具。一项受试者内研究将这种自适应方法与与物体无关的静态基线进行了比较。结果表明，自适应系统减少了不对称工具的抓握延迟，提高了交互的流畅性。此外，自适应策略改善了与特定信任相关的感知，特别是运动可预测性和感知任务简单性。
英文摘要
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.
CommentsAccepted for presentation at the 19th International Workshop on Human-Friendly Robotics (HFR 2026), Trento, Italy. The paper will appear in Springer's Proceedings in Advanced Robotics
