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/uop-net, 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，使其能够安全高效地操控和整理物体。然而，现有物体放置方法存在局限性，例如需要物体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。本文提出一种未见物体放置（UOP）方法，可直接通过单视角部分点云检测未见物体的稳定平面。我们利用大规模仿真数据训练模型，使其泛化理解三维点云中物体形状与稳定平面属性之间的关系。通过仿真和真实机器人实验验证，该方法在单视角部分物体放置任务中达到最优性能。即使物体形状和属性不完全已知，我们的UOP方法仍能使机器人稳定放置物体，为非结构化环境中的物体放置提供了有前景的解决方案。该研究在制造、物流和家庭自动化等领域具有潜在应用价值。更多结果可访问https://sites.google.com/uop-net，代码与数据集将在论文发表后公开。