Object placement is a crucial task for robots in unstructured environments as it enables them to manipulate and arrange objects safely and efficiently. However, 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 object shapes, which restrict the applicability of robots in unstructured scenarios. In this paper, we propose an Unseen Object Placement (UOP) method that directly detects stable planes of unseen objects from a single-view and partial point cloud. We trained our model on large-scale simulation data to generalize over relationships between the shape and properties of stable planes with a 3D point cloud. We verify our approach through simulation and real-world robot experiments, demonstrating state-of-the-art performance for placing single-view and partial objects. Our UOP approach enables robots to place objects stably, even when the object's shape and properties are not fully known, providing a promising solution for object placement in unstructured environments. Our research has potential applications in various domains such as manufacturing, logistics, and home automation. Additional results can be viewed on https://sites.google.com/view/uop-net-anonymous/, and we will release our code, dataset upon publication.

翻译：物体放置是机器人在非结构化环境中的关键任务，因为它能使机器人安全高效地操作和排列物体。然而，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物体形状，这限制了机器人在非结构化场景中的适用性。本文提出了一种未见物体放置方法（Unseen Object Placement, UOP），该方法直接从单视图局部点云中检测未见物体的稳定平面。我们在大规模仿真数据上训练模型，以泛化具有三维点云的稳定平面形状与属性之间的关系。通过仿真和真实机器人实验验证，我们的方法在放置单视图局部物体方面达到了当前最优性能。即使物体的形状和属性不完全已知，UOP方法也能使机器人稳定地放置物体，为非结构化环境中的物体放置提供了一种有前景的解决方案。该研究在制造、物流和家庭自动化等多个领域具有潜在应用价值。更多结果详见https://sites.google.com/view/uop-net-anonymous/，我们将在论文发表后公开代码和数据集。