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PaintRL: Coverage Path Planning for Industrial Spray Painting with Reinforcement Learning

Kiemel, Jonas C.; Yang, Peiren; Meißner, Pascal; Kröger, Torsten

Abstract (englisch):
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.

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000096523
Erschienen in Workshop on Closing the Reality Gap in Sim2real Transfer for Robotic Manipulation, Freiburg, June 23, 2019
Vorab online veröffentlicht am 25.06.2019
Externe Relationen Abstract/Volltext
Schlagwörter spray painting, reinforcement learning, sim2real, projection mapping, industrial robot learning
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