ARMimic：在增强现实中从被动人类演示学习机器人操作
ARMimic: Learning Robotic Manipulation from Passive Human Demonstrations in Augmented Reality
AI总结：
本文提出 ARMimic 框架，仅用消费级 XR 头显和固定相机采集被动人类演示，并通过统一模仿学习流程训练可跨本体泛化的操作策略，显著降低演示时间并提升成功率。
摘要：
模仿学习是机器人技能获取的一种强大范式，但传统演示方法（如动觉教学和遥操作）繁琐、硬件繁重且会干扰工作流程。近年来，使用扩展现实（XR）头显进行被动观察已在第一视角演示采集方面展现出前景；然而，现有方法需要额外硬件、复杂标定或受约束的录制条件，限制了可扩展性与可用性。我们提出 ARMimic，这是一个新颖框架，通过轻量且最少硬件的设置克服这些限制，仅使用一台消费级 XR 头显和一个固定工位相机，即可实现可扩展、无需机器人的数据采集。ARMimic 集成第一视角手部追踪、增强现实（AR）机器人叠加层和实时深度感知，以确保演示具备碰撞意识且运动学可行。我们方法的核心是统一的模仿学习流程，将人类轨迹与虚拟机器人轨迹视为可互换，从而使策略能够跨不同本体和环境泛化。我们在两个操作任务上验证 ARMimic，包括具有挑战性的长时序碗堆叠任务。实验中，与遥操作相比，ARMimic 将演示时间减少 50%；相较基于遥操作数据训练的先进基线 ACT，任务成功率提高 11%。结果表明，ARMimic 支持安全、无缝且在真实自然场景中的数据采集，为在多样真实环境中开展可扩展机器人学习提供了巨大潜力。
英文摘要：
Imitation learning is a powerful paradigm for robot skill acquisition, yet conventional demonstration methods--such as kinesthetic teaching and teleoperation--are cumbersome, hardware-heavy, and disruptive to workflows. Recently, passive observation using extended reality (XR) headsets has shown promise for egocentric demonstration collection, yet current approaches require additional hardware, complex calibration, or constrained recording conditions that limit scalability and usability. We present ARMimic, a novel framework that overcomes these limitations with a lightweight and hardware-minimal setup for scalable, robot-free data collection using only a consumer XR headset and a stationary workplace camera. ARMimic integrates egocentric hand tracking, augmented reality (AR) robot overlays, and real-time depth sensing to ensure collision-aware, kinematically feasible demonstrations. A unified imitation learning pipeline is at the core of our method, treating both human and virtual robot trajectories as interchangeable, which enables policies that generalize across different embodiments and environments. We validate ARMimic on two manipulation tasks, including challenging long-horizon bowl stacking. In our experiments, ARMimic reduces demonstration time by 50% compared to teleoperation and improves task success by 11% over ACT, a state-of-the-art baseline trained on teleoperated data. Our results demonstrate that ARMimic enables safe, seamless, and in-the-wild data collection, offering great potential for scalable robot learning in diverse real-world settings.
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。
