  Large-scale semantic image annotation is a significant challenge for
learning-based perception systems in robotics. Current approaches often rely on
human labelers, which can be expensive, or simulation data, which can visually
or physically differ from real data. This paper proposes Labels from
UltraViolet (LUV), a novel framework that enables rapid, labeled data
collection in real manipulation environments without human labeling. LUV uses
transparent, ultraviolet-fluorescent paint with programmable ultraviolet LEDs
to collect paired images of a scene in standard lighting and UV lighting to
autonomously extract segmentation masks and keypoints via color segmentation.
We apply LUV to a suite of diverse robot perception tasks to evaluate its
labeling quality, flexibility, and data collection rate. Results suggest that
LUV is 180-2500 times faster than a human labeler across the tasks. We show
that LUV provides labels consistent with human annotations on unpainted test
images. The networks trained on these labels are used to smooth and fold
crumpled towels with 83% success rate and achieve 1.7mm position error with
respect to human labels on a surgical needle pose estimation task. The low cost
of LUV makes it ideal as a lightweight replacement for human labeling systems,
with the one-time setup costs at $300 equivalent to the cost of collecting
around 200 semantic segmentation labels on Amazon Mechanical Turk. Code,
datasets, visualizations, and supplementary material can be found at
https://sites.google.com/berkeley.edu/luv
