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

Munguia‐Galeano, Francisco, Zhu, Jihong, Hernández, Juan David ORCID: and Ji, Ze ORCID: 2024. Learning to bag with a simulation‐free reinforcement learning framework for robots. IET Cyber-Systems and Robotics 6 (2) , e12113. 10.1049/csy2.12113

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Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. A learning‐based framework that enables robots to learn bagging is presented. The novelty of this framework is its ability to learn and perform bagging without relying on simulations. The learning process is accomplished through a reinforcement learning (RL) algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state representations. The framework utilises a set of primitive actions and represents the task in five states. In our experiments, the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states, respectively. Finally, the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Computer Science & Informatics
Additional Information: License information from Publisher: LICENSE 1: URL:
Publisher: Wiley Open Access
ISSN: 2631-6315
Date of First Compliant Deposit: 15 April 2024
Date of Acceptance: 6 March 2024
Last Modified: 15 Apr 2024 09:30

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