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http://hdl.handle.net/10119/19966
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| Title: | Stability Ensured Deep Reinforcement Learning for Online Bin Packing |
| Authors: | Gao, Ziyan
Chong, Nak Young |
| Issue Date: | 2025-07-18 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Magazine name: | 2025 22nd International Conference on Ubiquitous Robots (UR) |
| Start page: | 193 |
| End page: | 198 |
| DOI: | 10.1109/UR65550.2025.11078105 |
| Abstract: | The Online Bin Packing Problem (OBPP) aims to determine the optimal loading position for each incoming item to maximize bin utilization, a critical challenge in various industrial applications. While many studies have focused on learningbased policies and heuristic approaches to enhance packing efficiency, stability constraints have largely been overlooked. In this work, we propose a computationally efficient method to validate stable loading positions for incoming items without requiring exact knowledge of their physical properties, such as mass. Our approach leverages the concept of Load-Bearable Convex Polygons (LBCPs), which provide substantial support forces to ensure structural stability. We further integrate our static stability validation framework into a state-of-the-art deep reinforcement learning (DRL) model, guiding it to learn physicsfeasible packing strategies. Experimental results demonstrate that our stability-aware DRL model achieves comparable packing efficiency while ensuring robust bin stability, offering a significant advancement in practical OBPP applications. |
| Rights: | This is the author's version of the work. Copyright (C) 2025 IEEE. 2025 22nd International Conference on Ubiquitous Robots (UR), College Station, TX, USA, pp. 193-198. DOI: https://doi.org/10.1109/UR65550.2025.11078105. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/19966 |
| Material Type: | author |
| Appears in Collections: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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| N-CHONG-I-0723.pdf | | 1242Kb | Adobe PDF | View/Open |
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