PaintNet：从3D点云进行非结构化多路径学习以实现机器人喷涂
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
AI总结：
针对机器人喷涂中自由形状3D对象与可变多轨迹规划问题，提出基于3D深度学习的PaintNet框架，预测并拼接局部路径片段，同时发布真实工业专家演示数据集，实现对未见对象最高95%的表面覆盖。
摘要：
喷涂和焊接等常见工业机器人问题需要：（i）以自由形状3D对象为条件，以及（ii）规划多条轨迹以完成任务。然而，现有解决方案对输入表面的形式和输出路径的性质作出了强假设，导致方法受限，无法应对真实数据的多变性。借助3D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3D表面，并处理数量可变的无序输出路径（即非结构化路径）。我们的方法预测局部路径片段，随后可将其拼接以重建长时域路径。我们通过发布PaintNet，在机器人喷涂场景中广泛验证了所提出的方法；PaintNet是首个在真实工业场景中采集的、关于自由形状3D对象专家演示的公开数据集。详尽的实验分析表明，即使没有明确针对喷涂覆盖率进行优化，我们的模型也能够迅速预测平滑的输出路径，覆盖高达95%的此前未见过的对象表面。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
