Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task. Yet, existing solutions make strong assumptions on the form of input surfaces and the nature of output paths, resulting in limited approaches unable to cope with real-data variability. By leveraging on recent advances in 3D deep learning, we introduce a novel framework capable of dealing with arbitrary 3D surfaces, and handling a variable number of unordered output paths (i.e. unstructured). Our approach predicts local path segments, which can be later concatenated to reconstruct long-horizon paths. We extensively validate the proposed method in the context of robotic spray painting by releasing PaintNet, the first public dataset of expert demonstrations on free-shape 3D objects collected in a real industrial scenario. A thorough experimental analysis demonstrates the capabilities of our model to promptly predict smooth output paths that cover up to 95% of previously unseen object surfaces, even without explicitly optimizing for paint coverage.

翻译：流行的工业机器人任务如喷涂和焊接需要（i）以自由形态三维物体为条件，以及（ii）规划多条轨迹以完成任务。然而，现有方法对输入表面形式和输出路径性质施加了强假设，导致方法受限，难以应对真实数据的多样性。通过利用三维深度学习的最新进展，我们提出了一种新颖框架，能够处理任意三维表面，并应对数量可变的无序输出路径（即非结构化）。该方法预测局部路径片段，可后续拼接以重建长时域路径。我们在机器人喷涂场景中广泛验证了所提方法，并发布了PaintNet——首个在真实工业环境下收集的自由形态三维物体专家示范公开数据集。深入实验分析表明，即使未显式优化喷涂覆盖率，我们的模型也能快速预测出覆盖高达95%未见物体表面的平滑输出路径。