论文标题
油漆网：从3D点云中学习的非结构化多路径学习机器人喷漆
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
论文作者
论文摘要
流行的工业机器人问题，例如喷漆和焊接需要（i）自由形状3D对象进行调节，以及（ii）多个轨迹来解决任务。然而，现有的解决方案对输入表面的形式和输出路径的性质做出了强有力的假设，从而导致无法应对实际数据可变性的有限方法。通过利用3D深度学习的最新进展，我们介绍了一个能够处理任意3D表面的新型框架，并处理可变数量的无序输出路径（即非结构化）。我们的方法预测了局部路径段，以后可以将其连接到重建长马路径。我们通过释放PaintNet，在机器人喷漆的背景下广泛验证了所提出的方法，这是第一个在实际工业场景中收集的自由形状3D对象的专家演示的公共数据集。彻底的实验分析表明，即使没有明确优化涂料覆盖率，我们模型的功能迅速预测了覆盖以前看不见的对象表面95％的光滑输出路径。
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.
