# Learning to bag with a simulation-free reinforcement learning framework for robots

arXiv: https://arxiv.org/abs/2310.14398 · HTML: https://mlsanity.com/paper/2310.14398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation](https://mlsanity.com/paper/1703.02018/) (2017)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data](https://mlsanity.com/paper/2003.01835/) (2020)
- [Challenges and Outlook in Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2105.01767/) (2021)
- [Dynamic Cloth Manipulation with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.14475/) (2019)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects](https://mlsanity.com/paper/2104.12149/) (2021)
- [A Grasping-centered Analysis for Cloth Manipulation](https://mlsanity.com/paper/1906.08202/) (2019)
- [AutoBag: Learning to Open Plastic Bags and Insert Objects](https://mlsanity.com/paper/2210.17217/) (2022)
- [Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects](https://mlsanity.com/paper/2210.09997/) (2022)
- [Deep Reinforcement Learning with Explicit Context Representation](https://mlsanity.com/paper/2310.09924/) (2023)
- [Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies](https://mlsanity.com/paper/2207.00911/) (2022)

## Built on by (most cited first, 1 of 1)

- [Chemist Eye: A Visual Language Model-Powered System for Safety Monitoring and Robot Decision-Making in Self-Driving Laboratories](https://mlsanity.com/paper/2508.05148/) (2025)

## Abstract (arXiv)

Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. This paper presents an efficient learning-based framework that enables robots to learn bagging. The novelty of this framework is its ability to perform bagging without relying on simulations. The learning process is accomplished through a reinforcement learning algorithm introduced in this work, designed to find the best grasping points of the bag based on a set of compact state representations. The framework utilizes a set of primitive actions and represents the task in five states. In our experiments, the framework reaches a 60 % and 80 % of success rate after around three hours of training in the real world when starting the bagging task from folded and unfolded, respectively. Finally, we test the trained model with two more bags of different sizes to evaluate its generalizability.