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Popular industrial robotic problems such as spray painting and weldingrequire (i) conditioning on free-shape 3D objects and (ii) planning of multipletrajectories to solve the task. Yet, existing solutions make strong assumptionson the form of input surfaces and the nature of output paths, resulting inlimited approaches unable to cope with real-data variability. By leveraging onrecent advances in 3D deep learning, we introduce a novel framework capable ofdealing with arbitrary 3D surfaces, and handling a variable number of unorderedoutput paths (i.e. unstructured). Our approach predicts local path segments,which can be later concatenated to reconstruct long-horizon paths. Weextensively validate the proposed method in the context of robotic spraypainting by releasing PaintNet, the first public dataset of expertdemonstrations on free-shape 3D objects collected in a real industrialscenario. A thorough experimental analysis demonstrates the capabilities of ourmodel to promptly predict smooth output paths that cover up to 95% ofpreviously unseen object surfaces, even without explicitly optimizing for paintcoverage.

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting