Dense packing in pick-and-place systems is an important feature in many warehouse and logistics applications. Prior work in this space has largely focused on planning algorithms in simulation, but real-world packing performance is often bottlenecked by the difficulty of perceiving 3D object geometry in highly occluded, partially observed scenes. In this work, we present a fully-convolutional shape completion model, F-CON, which can be easily combined with off-the-shelf planning methods for dense packing in the real world. We also release a simulated dataset, COB-3D-v2, that can be used to train shape completion models for real-word robotics applications, and use it to demonstrate that F-CON outperforms other state-of-the-art shape completion methods. Finally, we equip a real-world pick-and-place system with F-CON, and demonstrate dense packing of complex, unseen objects in cluttered scenes. Across multiple planning methods, F-CON enables substantially better dense packing than other shape completion methods.

翻译：在拣选-放置系统中实现密集装箱是许多仓储和物流应用的重要特征。以往研究主要集中于仿真环境中的规划算法，但实际装箱性能常因高度遮挡、部分观测场景下三维物体几何感知困难而受到制约。本文提出了一种全卷积形状补全模型F-CON，该模型可轻松与现成的规划方法结合，用于真实世界中的密集装箱。我们还发布了模拟数据集COB-3D-v2，可用于训练面向真实机器人应用的形状补全模型，并利用该数据集证明F-CON优于其他最先进的形状补全方法。最后，我们将F-CON集成至真实拣选-放置系统，展示了在杂乱场景中对复杂、未见物体的密集装箱能力。在多种规划方法下，F-CON实现的密集装箱效果显著优于其他形状补全方法。