Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement
Anthony Simeonov, Ankit Goyal, Lucas Manuelli, Yen-Chen Lin, Alina Sarmiento, Alberto Rodriguez Garcia, Pulkit Agrawal, Dieter Fox
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: https://anthonysimeonov.github.io/rpdiff-multi-modal
BibTeX
@inproceedings{
simeonov2023shelving,
title={Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement},
author={Anthony Simeonov and Ankit Goyal and Lucas Manuelli and Yen-Chen Lin and Alina Sarmiento and Alberto Rodriguez Garcia and Pulkit Agrawal and Dieter Fox},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=_xFJuqBId8c}
}