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We propose a system for rearranging objects in a scene to achieve a desiredobject-scene placing relationship, such as a book inserted in an open slot of abookshelf. The pipeline generalizes to novel geometries, poses, and layouts ofboth scenes and objects, and is trained from demonstrations to operate directlyon 3D point clouds. Our system overcomes challenges associated with theexistence of many geometrically-similar rearrangement solutions for a givenscene. By leveraging an iterative pose de-noising training procedure, we canfit multi-modal demonstration data and produce multi-modal outputs whileremaining precise and accurate. We also show the advantages of conditioning onrelevant local geometric features while ignoring irrelevant global structurethat harms both generalization and precision. We demonstrate our approach onthree distinct rearrangement tasks that require handling multi-modality andgeneralization over object shape and pose in both simulation and the realworld. Project website, code, and videos:https://anthonysimeonov.github.io/rpdiff-multi-modal/

Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement