搁置、堆叠、悬挂：面向多模态重排的关系位姿扩散
Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
AI总结：
本文提出基于关系位姿扩散的多模态物体重排系统，通过迭代位姿去噪和局部几何条件化，在仿真与真实环境中实现对新物体形状、位姿及场景布局的精确泛化。
摘要：
我们提出一个系统，用于重排场景中的物体以实现期望的物体-场景放置关系，例如将一本书插入书架的空槽中。该流程能够泛化到场景和物体的新几何形状、位姿与布局，并通过演示训练直接在三维点云上运行。我们的系统克服了给定场景存在许多几何相似重排解所带来的挑战。通过利用迭代位姿去噪训练过程，我们能够拟合多模态演示数据并产生多模态输出，同时保持精确和准确。我们还展示了以相关局部几何特征为条件而忽略无关全局结构的优势，后者会损害泛化能力和精度。我们在三个不同的重排任务上演示了我们的方法，这些任务要求在仿真和现实世界中处理多模态以及对物体形状和位姿的泛化。项目网站、代码和视频：https://anthonysimeonov.github.io/rpdiff-multi-modal/
英文摘要：
We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline generalizes to novel geometries, poses, and layouts of both scenes and objects, and is trained from demonstrations to operate directly on 3D point clouds. Our system overcomes challenges associated with the existence of many geometrically-similar rearrangement solutions for a given scene. By leveraging an iterative pose de-noising training procedure, we can fit multi-modal demonstration data and produce multi-modal outputs while remaining precise and accurate. We also show the advantages of conditioning on relevant local geometric features while ignoring irrelevant global structure that harms both generalization and precision. We demonstrate our approach on three distinct rearrangement tasks that require handling multi-modality and generalization over object shape and pose in both simulation and the real world. Project website, code, and videos: https://anthonysimeonov.github.io/rpdiff-multi-modal/
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。
