RAMPA：通过演示进行机器人增强现实机器编程
RAMPA: Robotic Augmented Reality for Machine Programming by DemonstrAtion
AI总结：
本文提出RAMPA，一种集成ML和XR的端到端机器人系统，通过AR头显实现现场数据录制与技能微调，解决PbD的安全、编程障碍及演示收集低效问题，实验显示其在教学效率与用户体验上优于传统方法。
摘要：
本文介绍了通过演示进行机器人增强现实机器编程（RAMPA），这是首个集成机器学习、由XR驱动的端到端机器人系统，允许即时训练和部署如ProMPs等机器学习模型，并利用最先进且商业可用的AR头显（如Meta Quest 3）的能力，促进在工业机器人手臂（如Universal Robots UR10）上应用编程通过演示（PbD）方法。我们的方法使用户能够在物理环境中直接进行现场数据记录、可视化和技能演示的微调。RAMPA解决了PbD的关键挑战，如安全问题、编程障碍以及在真实硬件上收集演示的低效性。我们的系统性能通过与传统的动力学控制方法在教授三种不同机器人操作任务时进行比较评估，并使用标准化调查通过定量指标分析，测量任务性能和完成时间、轨迹平滑度、系统可用性、用户体验和任务负荷。我们的发现表明，在机器人任务的教学和细化方式上取得了实质性进展，有望在机器人编程的操作安全性、效率和用户参与度方面带来改进。
英文摘要：
This paper introduces Robotic Augmented Reality for Machine Programming by Demonstration (RAMPA), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as ProMPs on the fly, and utilizing the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate the application of Programming by Demonstration (PbD) approaches on industrial robotic arms, e.g., Universal Robots UR10. Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. RAMPA addresses critical challenges of PbD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
