学习利用外部支撑提供的稳定放置来重新定向物体
Learning to Reorient Objects with Stable Placements Afforded by Extrinsic Supports
AI总结：
提出从点云预测物体放置的三阶段流程并构建操作图，使机器人能利用外部支撑稳定放置来重新定向物体，仿真与真实实验验证有效，精度较基线提升20%。
摘要：
利用支撑物来重新定向物体是一项实用但具有挑战性的操作任务。由于物体几何形状的复杂性和机器人可行运动的受限，物体重新定向需要多个操作步骤。在这项工作中，我们提出了一种从点云预测各种物体放置的流程。该流程包含三个阶段：姿态生成阶段，随后是姿态细化阶段，最后是放置分类阶段。我们还提出了一种基于点云构建操作图的算法。为机器人确定了可行的操作序列，以转移物体放置。仿真和真实世界实验都证明了我们方法的有效性。仿真结果强调了我们的流程能够泛化到随机起始姿态的新物体。与最先进基线相比，我们预测的放置精度提高了20%。此外，机器人在我们算法构建的操作图中找到了可行的顺序步骤，以完成物体重新定向操作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
