Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects
Arpit Bahety, Shreeya Jain, Huy Ha, Nathalie Hager, Benjamin Burchfiel, Eric Cousineau, Siyuan Feng, Shuran Song
Abstract
We introduce a practical robotics solution for the task of heterogeneous bagging, requiring the placement of multiple rigid and deformable objects into a deformable bag. This is a difficult task as it features complex interactions between multiple highly deformable objects under limited observability. To tackle these challenges, we propose a robotic system consisting of two learned policies: a rearrangement policy that learns to place multiple rigid objects and fold deformable objects in order to achieve desirable pre-bagging conditions, and a lifting policy to infer suitable grasp points for bi-manual bag lifting. We evaluate these learned policies on a real-world three-arm robot platform that achieves a 70% heterogeneous bagging success rate with novel objects. To facilitate future research and comparison, we also develop a novel heterogeneous bagging simulation benchmark that will be made publicly available.
BibTeX
@inproceedings{iros2023_bagallyouneedlea,
title = {Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects},
author = {Arpit Bahety and Shreeya Jain and Huy Ha and Nathalie Hager and Benjamin Burchfiel and Eric Cousineau and Siyuan Feng and Shuran Song},
booktitle = {IROS 2023},
year = {2023}
}