用AR实现实时机器人编程，让工人轻松教机械臂干活
RAMPA: Robotic Augmented Reality for Machine Programming by DemonstrAtion
- 结合AR头显与机器学习，在真实环境中边看边教机械臂动作
- 相比传统手动示教，任务完成时间更短，轨迹更平滑，用户满意度高
- 适合制造业人员快速上手，降低机器人编程门槛
本文提出首个融合机器学习与扩展现实（XR）的端到端机器人系统——RAMPA，支持在Meta Quest 3等商用AR头显上实时训练与部署如ProMPs等机器学习模型，并应用于Universal Robots UR10等工业机械臂。该系统可在用户实际工作环境中直接记录、可视化和微调技能演示，解决传统示教中安全风险高、编程门槛高、数据采集效率低等问题。通过对比传统力控示教方式，在三项不同机器人操作任务中进行量化评估，涵盖任务完成时间、轨迹平滑度、系统易用性、用户体验及任务负荷等指标。结果表明，该系统显著提升机器人教学与优化效率，有望改善操作安全性、编程效率与用户参与度。
原文摘要 · Abstract (English)
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.
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