The increasing affordability of robot hardware is accelerating the integration of robots into everyday activities. However, training robots to automate tasks typically requires physical robots and expensive demonstration data from trained human annotators. Consequently, only those with access to physical robots produce demonstrations to train robots. To mitigate this issue, we introduce EVE, an iOS app that enables everyday users to train robots using intuitive augmented reality visualizations without needing a physical robot. With EVE, users can collect demonstrations by specifying waypoints with their hands, visually inspecting the environment for obstacles, modifying existing waypoints, and verifying collected trajectories. In a user study ($N=14$, $D=30$) consisting of three common tabletop tasks, EVE outperformed three state-of-the-art interfaces in success rate and was comparable to kinesthetic teaching-physically moving a real robot-in completion time, usability, motion intent communication, enjoyment, and preference ($mean_{p}=0.30$). We conclude by enumerating limitations and design considerations for future AR-based demonstration collection systems for robotics.

翻译：机器人硬件的日益普及正在加速机器人融入日常活动的进程。然而，训练机器人自动化完成任务通常需要实物机器人以及经过训练的人类标注员提供的昂贵演示数据。因此，只有能够接触实物机器人的用户才能生成演示数据以训练机器人。为解决这一问题，我们提出EVE——一款iOS应用，使普通用户无需实物机器人即可利用直观的增强现实可视化效果训练机器人。通过EVE，用户可以手动指定路径点、视觉检查环境障碍物、修改现有路径点并验证采集的轨迹，从而收集演示数据。一项包含三种常见桌面任务的用户研究（N=14, D=30）表明，EVE在成功率上优于三种前沿交互界面，并在完成时间、可用性、运动意图沟通、愉悦度和偏好方面与动觉教学（即手动移动实物机器人）相当（平均p=0.30）。最后，我们总结了未来基于增强现实的机器人演示采集系统的局限性及设计考量。