We present PaintRL, a framework that
enables research on optimizing industrial spray painting for arbitrary objects with reinforcement learning.
PaintRL implements a toolkit to simulate and visualize
spray painting based on the physics engine PyBullet.
By means of this toolkit, we train neural networks to
predict coverage paths and evaluate the results on two
objects: a quadratic sheet and a real car door. Our
initial results show that the generated coverage path
of the car door performs on a par with a manually
implemented zigzag baseline. To allow sim2real transfer
for non-resettable tasks like spray painting, we replace
paint by light using projection mapping. This approach
opens up new possibilities to visualize the results from
simulation, collect human demonstrations and capture
real-world images. PaintRL is part of our endeavor
to utilize the recent advances in deep reinforcement
learning for economically important industrial tasks.