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

翻译：近年来，基于人类演示的模仿学习在教授机器人操作技能方面展现出令人瞩目的成果。为进一步扩大训练数据集规模，近期研究开始采用无需实体机器人硬件的便携式数据采集设备。然而，由于数据采集过程中缺乏机器人本体反馈，数据质量高度依赖用户专业水平，且许多设备仅限于特定机器人构型。我们提出ARCap——一种通过增强现实（AR）提供视觉反馈并辅以触觉警示的便携式数据采集系统，可引导用户采集高质量演示数据。通过大量用户研究表明，ARCap能使新手用户采集到符合机器人运动学约束且能规避场景碰撞的可执行数据。利用ARCap采集的数据，机器人能够完成具有挑战性的任务，例如杂乱环境中的操作任务和长时程跨构型操作任务。ARCap完全开源且易于校准，所有组件均采用市售产品构建。更多细节与实验结果请访问项目网站：https://stanford-tml.github.io/ARCap