# Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects

arXiv: https://arxiv.org/abs/2210.09997 · HTML: https://mlsanity.com/paper/2210.09997/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2022.

## Built on by (most cited first, 12 of 12)

- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation](https://mlsanity.com/paper/2411.01200/) (2024)
- [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024)
- [General-purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2408.08160/) (2024)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse](https://mlsanity.com/paper/2603.03836/) (2026)
- [Deliberate Planning of 3D Bin Packing on Packing Configuration Trees](https://mlsanity.com/paper/2504.04421/) (2025)
- [Non-rigid Relative Placement through 3D Dense Diffusion](https://mlsanity.com/paper/2410.19247/) (2024)
- [Learning to bag with a simulation-free reinforcement learning framework for robots](https://mlsanity.com/paper/2310.14398/) (2023)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints](https://mlsanity.com/paper/2403.12685/) (2024)
- [DexKnot: Generalizable Visuomotor Policy Learning for Dexterous Bag-Knotting Manipulation](https://mlsanity.com/paper/2603.07136/) (2026)

## Abstract (arXiv)

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.