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

Published: 30 Aug 2023, Last Modified: 03 Jul 2024CoRL 2023 PosterReaders: Everyone
Keywords: Object Rearrangement, Multi-modality, Manipulation, Point Clouds, Relations, Diffusion
TL;DR: A method for rearranging objects in scenes that present multi-modal placement locations via iterative pose de-noising on object-scene point clouds.
Abstract: 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:
Student First Author: yes
Supplementary Material: zip
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Publication Agreement: pdf
Poster Spotlight Video: mp4
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