  The increasing affordability of robot hardware is accelerating the
integration of robots into everyday activities. However, training a robot to
automate a task requires expensive trajectory data where a trained human
annotator moves a physical robot to train it. Consequently, only those with
access to robots produce demonstrations to train robots. In this work, we
remove this restriction with 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 physical robot-in completion time,
usability, motion intent communication, enjoyment, and preference (mean of
p=0.30). EVE allows users to train robots for personalized tasks, such as
sorting desk supplies, organizing ingredients, or setting up board games. We
conclude by enumerating limitations and design considerations for future
AR-based demonstration collection systems for robotics.
