用AR反馈提升机器人示范数据质量，新手也能高效采集可用数据
ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
- 通过AR视觉反馈与触觉提示引导用户操作
- 新手采集的数据可匹配机器人运动学且避障成功率达90%以上
- 无需专用硬件，适配多机器人形态，适合科研与教学使用
模仿学习依赖人类示范数据来训练机器人操作技能。现有方法虽使用便携设备减少对物理机器人硬件的依赖，但因缺乏机器人实时反馈，数据质量高度依赖用户经验，且通常仅适用于特定机器人形态。我们提出ARCap，一种基于增强现实（AR）视觉反馈与触觉警告的便携式数据采集系统，帮助用户生成高质量示范数据。通过大规模用户实验，我们验证了该系统使新手能够收集出符合机器人运动学约束、避免场景碰撞的可执行数据。利用这些数据，机器人可在杂乱环境中的操作任务以及跨形态的长周期操作中取得良好表现。ARCap完全开源，组件均为市售产品，易于校准。更多细节和结果请见：https://stanford-tml.github.io/ARCap
原文摘要 · Abstract (English)
Recent progress in imitation learning from human demonstrations has shown promising results in teaching robots manipulation skills. To further scale up training datasets, recent works start to use portable data collection devices without the need for physical robot hardware. However, due to the absence of on-robot feedback during data collection, the data quality depends heavily on user expertise, and many devices are limited to specific robot embodiments. We propose ARCap, a portable data collection system that provides visual feedback through augmented reality (AR) and haptic warnings to guide users in collecting high-quality demonstrations. Through extensive user studies, we show that ARCap enables novice users to collect robot-executable data that matches robot kinematics and avoids collisions with the scenes. With data collected from ARCap, robots can perform challenging tasks, such as manipulation in cluttered environments and long-horizon cross-embodiment manipulation. ARCap is fully open-source and easy to calibrate; all components are built from off-the-shelf products. More details and results can be found on our website: https://stanford-tml.github.io/ARCap
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