  We present GTGraffiti, a graffiti painting system from Georgia Tech that
tackles challenges in art, hardware, and human-robot collaboration. The problem
of painting graffiti in a human style is particularly challenging and requires
a system-level approach because the robotics and art must be designed around
each other. The robot must be highly dynamic over a large workspace while the
artist must work within the robot's limitations. Our approach consists of three
stages: artwork capture, robot hardware, and planning & control. We use motion
capture to capture collaborator painting motions which are then composed and
processed into a time-varying linear feedback controller for a cable-driven
parallel robot (CDPR) to execute. In this work, we will describe the capturing
process, the design and construction of a purpose-built CDPR, and the software
for turning an artist's vision into control commands. Our work represents an
important step towards faithfully recreating human graffiti artwork by
demonstrating that we can reproduce artist motions up to 2m/s and 20m/s$^2$
within 9.3mm RMSE to paint artworks. Changes to the submitted manuscript are
colored in blue.
