ARCap：利用增强现实反馈为机器人学习收集高质量人类演示
ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
AI总结：
提出ARCap便携式数据收集系统，通过增强现实视觉反馈和触觉警告指导新手收集符合机器人运动学且无碰撞的高质量演示数据，使机器人能完成杂乱环境及长周期跨本体操作任务。
摘要：
近期从人类演示中进行模仿学习的进展在教授机器人操作技能方面展现出可喜的成果。为了进一步扩大训练数据集规模，近期的研究开始使用便携式数据收集设备，而无需物理机器人硬件。然而，由于数据收集期间缺乏机器人上的反馈，数据质量严重依赖用户专业知识，且许多设备仅限于特定的机器人本体。我们提出ARCap，一种通过增强现实（AR）提供视觉反馈和触觉警告来指导用户收集高质量演示的便携式数据收集系统。通过广泛的用户研究，我们表明ARCap使新手用户能够收集符合机器人运动学并避免与场景碰撞的机器人可执行数据。利用从ARCap收集的数据，机器人能够执行具有挑战性的任务，例如在杂乱环境中的操作和长周期的跨本体操作。ARCap是完全开源且易于校准的；所有组件均由现成产品构建。更多细节和结果可在我们的网站找到：https://stanford-tml.github.io/ARCap
英文摘要：
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
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。
