Reorienting objects by using supports is a practical yet challenging manipulation task. Owing to the intricate geometry of objects and the constrained feasible motions of the robot, multiple manipulation steps are required for object reorientation. In this work, we propose a pipeline for predicting various object placements from point clouds. This pipeline comprises three stages: a pose generation stage, followed by a pose refinement stage, and culminating in a placement classification stage. We also propose an algorithm to construct manipulation graphs based on point clouds. Feasible manipulation sequences are determined for the robot to transfer object placements. Both simulated and real-world experiments demonstrate that our approach is effective. The simulation results underscore our pipeline's capacity to generalize to novel objects in random start poses. Our predicted placements exhibit a 20% enhancement in accuracy compared to the state-of-the-art baseline. Furthermore, the robot finds feasible sequential steps in the manipulation graphs constructed by our algorithm to accomplish object reorientation manipulation.

翻译：利用支撑物进行物体重定向是一项实用但具有挑战性的操作任务。由于物体几何形状复杂且机器人可行运动受限，实现物体重定向需要多步操作。本文提出一种基于点云预测物体多种放置方式的流水线。该流水线包含三个阶段：位姿生成阶段、位姿优化阶段以及放置分类阶段。同时提出一种基于点云构建操作图的算法，通过该算法可确定机器人实现物体放置转移的可行操作序列。仿真与实物实验均验证了本方法的有效性。仿真结果凸显了本流水线对随机初始位姿的新颖物体具备泛化能力。与最先进基线方法相比，本方法预测的放置精度提升20%。此外，机器人通过本算法构建的操作图成功找到实现物体重定向操作的可行序列步骤。