Learning to bag with a simulation-free reinforcement learning framework for robots
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
17 of 40 references.
References · 17
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
- 2017Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulationcited by 90
- 2020Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networkscited by 78
- 2020Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Datacited by 51
- 2021Challenges and Outlook in Robotic Manipulation of Deformable Objectscited by 43
- 2019Dynamic Cloth Manipulation with Deep Reinforcement Learningcited by 29
- 2021VisuoSpatial Foresight for Physical Sequential Fabric Manipulationcited by 18
- 2021Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objectscited by 17
5 more
- 2019A Grasping-centered Analysis for Cloth Manipulationcited by 15
- 2022AutoBag: Learning to Open Plastic Bags and Insert Objectscited by 14
- 2022Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objectscited by 12
- 2023Deep Reinforcement Learning with Explicit Context Representationcited by 3
- 2022Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policiescited by 2
Built on by
Citing papers per year: 2025: 1.
1 paper.