EVE: Enabling Anyone to Train Robot using Augmented Reality
arxiv(2024)
摘要
The increasing affordability of robot hardware is accelerating the
integration of robots into everyday activities. However, training a robot to
automate a task 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.
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