Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the requirement for a complete 3D model of the object or the inability to handle complex shapes and novel objects that restrict the applicability of robots in the real world. Herein, we focus on addressing the Unseen Object Placement (UOP}=) problem. We tackled the UOP problem using two methods: (1) UOP-Sim, a large-scale dataset to accommodate various shapes and novel objects, and (2) UOP-Net, a point cloud segmentation-based approach that directly detects the most stable plane from partial point clouds. Our UOP approach enables robots to place objects stably, even when the object's shape and properties are not fully known, thus providing a promising solution for object placement in various environments. We verify our approach through simulation and real-world robot experiments, demonstrating state-of-the-art performance for placing single-view and partial objects. Robot demos, codes, and dataset are available at https://gistailab.github.io/uop/

翻译：物体放置是机器人的一项基本任务，但对于部分观测物体而言仍具有挑战性。现有的物体放置方法存在局限性，例如需要物体的完整三维模型，或无法处理复杂形状和新奇物体，这限制了机器人在现实世界中的适用性。本文聚焦于解决未见物体放置问题。我们采用两种方法应对该问题：（1）UOP-Sim，一个包含多样形状和新奇物体的大规模数据集；（2）UOP-Net，一种基于点云分割的方法，可直接从部分点云中检测最稳定平面。我们的方法使机器人能够在物体形状和属性不完全已知的情况下稳定放置物体，从而为不同环境中的物体放置提供有前景的解决方案。通过仿真和真实机器人实验验证，该方法在放置单视角和部分观测物体方面达到最优性能。机器人演示、代码及数据集均可在 https://gistailab.github.io/uop/ 获取。