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Learning Practically Feasible Policies for Online 3D Bin Packing [article]

Hang Zhao, Chenyang Zhu, Xin Xu, Hui Huang, Kai Xu
2021 arXiv   pre-print

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2021 pre-print arXiv:2108.13680v1 fatcat:dt3ifyfwhjerzg7hjk5clns7jq
We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem.  ...  To learn a practically feasible packing policy, we propose three critical designs. First, we propose an online analysis of packing stability based on a novel stacking tree.  ...  To learn a feasible packing policy, we propose three new designs. First, we propose an online analysis of packing stability with a novel stacking tree.  ... 
arXiv:2108.13680v2 fatcat:futvzrptc5f7zbeajoph64yzxy
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Hang Zhao, Chenyang Zhu, Xin Xu, Hui Huang, Kai Xu. "Learning Practically Feasible Policies for Online 3D Bin Packing." arXiv (2021)

Online 3D Bin Packing with Constrained Deep Reinforcement Learning

Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  

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We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP).  ...  Such supervision and projection facilitate the agent to learn feasible policies very efficiently.  ...  We are also grateful to the colleagues of SpeedBot Robotics for their help on real robot test. Thanks also go to Chi Trung Ha for providing the source code of their work (Ha et al. 2017) .  ... 
doi:10.1609/aaai.v35i1.16155 fatcat:tqipsvxadzdhvi6vtzemnp6f3y
Citation

Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu. "Online 3D Bin Packing with Constrained Deep Reinforcement Learning." PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE 35.1 (2021) 741-749

Online 3D Bin Packing with Constrained Deep Reinforcement Learning [article]

Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu
2022 arXiv   pre-print

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2021 pre-print arXiv:2006.14978v4 fatcat:5c3ogbi3evervkfrg2ehx36z3i

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2021 pre-print
arXiv:2006.14978v3 fatcat:aiezhf7erzerpanqs733os3uwe
2021 pre-print
arXiv:2006.14978v2 fatcat:75tm7evetbdpzojzqbjjwwqkme
2020 pre-print
arXiv:2006.14978v1 fatcat:o4guh7hz5re4fa37jmava5cnmq
We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP).  ...  Such supervisions and transformations to DRL facilitate the agent to learn feasible policies efficiently.  ...  We are also grateful to the colleagues of Speed-Bot Robotics for their help on real robot test. Thanks also go to Chi Trung Ha for providing the source code of their work (Ha et al. 2017) .  ... 
arXiv:2006.14978v5 fatcat:p6y24xw5erberfyi6ik7s4i56i
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Citation

Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, Kai Xu. "Online 3D Bin Packing with Constrained Deep Reinforcement Learning." arXiv (2022)

A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing [article]

Richa Verma, Aniruddha Singhal, Harshad Khadilkar, Ansuma Basumatary, Siddharth Nayak, Harsh Vardhan Singh, Swagat Kumar, Rajesh Sinha
2020 arXiv   pre-print

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We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size.  ...  First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori.  ...  This version of the problem is thus online or real-time 3D bin-packing, which we abbreviate to RT-3D-BPP.  ... 
arXiv:2007.00463v1 fatcat:fnvpgbpfifappodedsuatlgqum
Citation

Richa Verma, Aniruddha Singhal, Harshad Khadilkar, Ansuma Basumatary, Siddharth Nayak, Harsh Vardhan Singh, Swagat Kumar, Rajesh Sinha. "A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing." arXiv (2020)

Online 3D Bin Packing Reinforcement Learning Solution with Buffer [article]

Aaron Valero Puche, Sukhan Lee
2022 arXiv   pre-print

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The 3D Bin Packing Problem (3D-BPP) is one of the most demanded yet challenging problems in industry, where an agent must pack variable size items delivered in sequence into a finite bin with the aim to  ...  In this paper, we present a new reinforcement learning (RL) framework for a 3D-BPP solution for improving performance. First, a buffer is introduced to allow multi-item action selection.  ...  In our work, we opt for a more *This work is supported, in part, by "3D Bin Packing with Deep Reinforcement Learning" project funded by Hyundai Robotics Co.  ... 
arXiv:2208.07123v1 fatcat:vhvruxc7hvbk5kxgxpv6nionqq
Citation

Aaron Valero Puche, Sukhan Lee. "Online 3D Bin Packing Reinforcement Learning Solution with Buffer." arXiv (2022)

Adjustable Robust Reinforcement Learning for Online 3D Bin Packing [article]

Yuxin Pan, Yize Chen, Fangzhen Lin
2023 arXiv   pre-print

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Designing effective policies for the online 3D bin packing problem (3D-BPP) has been a long-standing challenge, primarily due to the unpredictable nature of incoming box sequences and stringent physical  ...  To address these issues, we first introduce a permutation-based attacker to investigate the practical robustness of both DRL-based and heuristic methods proposed for solving online 3D-BPP.  ...  We would like to thank the Turing AI Computing Cloud (TACC) (Xu et al., 2021) and HKUST iSING Lab for providing us computation resources on their platform.  ... 
arXiv:2310.04323v1 fatcat:gp3jl24z2vbkhgnj7rwna3cbei
Citation

Yuxin Pan, Yize Chen, Fangzhen Lin. "Adjustable Robust Reinforcement Learning for Online 3D Bin Packing." arXiv (2023)

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Ricardo Lopes, Marcello Trovati, Ella Pereira
2024 Future Internet  

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Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community.  ...  This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to  ...  Pattern-Based Algorithm The Pattern-Based Algorithm for the Online Bin Packing Problem, proposed by Lin et al.  ... 
doi:10.3390/fi16020039 fatcat:bi6iuxqjwzaptcgbcj6escv33u
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Ricardo Lopes, Marcello Trovati, Ella Pereira. "Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review." Future Internet 16.2 (2024) 39

A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem [article]

Lu Duan, Haoyuan Hu, Yu Qian, Yu Gong, Xiaodong Zhang, Yinghui Xu, Jiangwen Wei
2019 arXiv   pre-print

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2018 pre-print
arXiv:1804.06896v1 fatcat:rszvi4u4m5fyblqawqyxibo44m
2018 pre-print
arXiv:1804.06896v2 fatcat:x3kxx52eu5e5xhap4fwzdoxcsu
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce.  ...  The existing heuristic methods for classic 3D bin packing don't work well for this particular NP-hard problem and designing a good problem-specific heuristic is non-trivial.  ...  In this paper, we formalize this real-world scenario into a specific variant of the classical three-dimensional bin packing problem (3D-BPP) named 3D flexible bin packing problem (3D-FBPP).  ... 
arXiv:1804.06896v3 fatcat:ldg7aohvwnhnzbjxcshxkst4u4
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Lu Duan, Haoyuan Hu, Yu Qian, Yu Gong, Xiaodong Zhang, Yinghui Xu, Jiangwen Wei. "A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem." arXiv (2019)

Learning Physically Realizable Skills for Online Packing of General 3D Shapes [article]

Hang Zhao, Zherong Pan, Yang Yu, Kai Xu
2022 arXiv   pre-print

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We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems.  ...  The packing policy should understand the 3D geometry of the object to be packed and make effective decisions to accommodate it in the container in a physically realizable way.  ...  We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems.  ... 
arXiv:2212.02094v1 fatcat:6hsdhetfs5gelor3w3ykzxjdeq
Citation

Hang Zhao, Zherong Pan, Yang Yu, Kai Xu. "Learning Physically Realizable Skills for Online Packing of General 3D Shapes." arXiv (2022)

Efficient Algorithm based on Adaptive Window – Model Predictive Control for Automatic Stacking in Warehouse Center

Qing Chang, Ashkan Sebghati
2021 IEEE Access  

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A deep reinforcement learning approach has been proposed in [10] to solve the online 3D BPP by formulating it as a Markov decision process.  ...  In [7] , a multi-task selected learning approach has been proposed which aims to pack a given number of rectangular packages by generating a heuristic policy.  ...  CONCLUSION We mathematically described the stacking problem taking into account several important practical and intrinsic constraints.  ... 
doi:10.1109/access.2021.3094417 fatcat:dg63o2oagfcfjecgmfi2nqo5re
DOAJ
Citation

Qing Chang, Ashkan Sebghati. "Efficient Algorithm based on Adaptive Window – Model Predictive Control for Automatic Stacking in Warehouse Center." IEEE Access (2021) 1-1

Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm

Bingchen Lin, Jiawei Li, Ruibin Bai, Rong Qu, Tianxiang Cui, Huan Jin
2022 Symmetry  

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Inspired by duality in optimization, we proposed pattern-based adaptive heuristics for the online bin packing problem.  ...  In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into a bin immediately after its arrival.  ...  A Deep Reinforcement Learning (DRL) method was adopted learn the policies to solve a 3D online BPP [16] .  ... 
doi:10.3390/sym14071301 fatcat:fpfri2bblfdw3ez64vulzy3ssu
DOAJ Szczepanski
Citation

Bingchen Lin, Jiawei Li, Ruibin Bai, Rong Qu, Tianxiang Cui, Huan Jin. "Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm." Symmetry 14.7 (2022) 1301

Object Rearrangement with Nested Nonprehensile Manipulation Actions [article]

Changkyu Song, Abdeslam Boularias
2019 arXiv   pre-print

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Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern.  ...  Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter.  ...  While the algorithm described here is general, we found from our real bin packing experiments that it is practical only for m ≤ 2.  ... 
arXiv:1905.07505v1 fatcat:7ap46z5odrcevbkwm6w2nbkoqq
Citation

Changkyu Song, Abdeslam Boularias. "Object Rearrangement with Nested Nonprehensile Manipulation Actions." arXiv (2019)

Object Detection Using Deep CNNs Trained on Synthetic Images [article]

Param S. Rajpura, Hristo Bojinov, Ravi S. Hegde
2017 arXiv   pre-print

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2017 pre-print
arXiv:1706.06782v1 fatcat:3lcicllxpfecdlvvmtiad66wou
We apply this strategy for detecting pack- aged food products clustered in refrigerator scenes.  ...  We analyze factors like training data set size and 3D model dictionary size for their influence on detection performance.  ...  We thank Aalok Gangopadhyay for the insightful discussions.  ... 
arXiv:1706.06782v2 fatcat:iscp6h74rbbffflordzc6jm6aq
Open Access Multiple Versions
Citation

Param S. Rajpura, Hristo Bojinov, Ravi S. Hegde. "Object Detection Using Deep CNNs Trained on Synthetic Images." arXiv (2017)

Reinforcement Learning for Combinatorial Optimization: A Survey [article]

Nina Mazyavkina and Sergey Sviridov and Sergei Ivanov and Evgeny Burnaev
2020 arXiv   pre-print

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2020 pre-print arXiv:2003.03600v2 fatcat:mevn5qxa5zhurnvnvbfram745q

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2020 pre-print
arXiv:2003.03600v1 fatcat:ieuslapqorhuzbfcbcrepqawq4
Our survey provides the necessary background for operations research and machine learning communities and showcases the works that are moving the field forward.  ...  a promising direction for solving combinatorial problems.  ...  Another important CO problem -3D Bin Packing -has been tackled in [14] in a manner similar to [12] .  ... 
arXiv:2003.03600v3 fatcat:ofc6gzf2fzhchjawbxfiin3354
Multiple Versions
Citation

Nina Mazyavkina and Sergey Sviridov and Sergei Ivanov and Evgeny Burnaev. "Reinforcement Learning for Combinatorial Optimization: A Survey." arXiv (2020)

Practice Makes Perfect: Planning to Learn Skill Parameter Policies [article]

Nishanth Kumar, Tom Silver, Willie McClinton, Linfeng Zhao, Stephen Proulx, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Jennifer Barry
2024 arXiv   pre-print

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2024 pre-print arXiv:2402.15025v1 fatcat:xhg2i74f5fbahexr36a5sengle
Through experiments in simulation, we find that our approach learns effective parameter policies more sample-efficiently than several baselines.  ...  for selecting skill parameters.  ...  Finally, we wish to thank our three Spot robots, Moana, Donner, and Kepler, for being so reliable throughout the extensive prototyping and experimentation required for this paper.  ... 
arXiv:2402.15025v2 fatcat:5kcvcx454jh6djkci26eofukoy
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Nishanth Kumar, Tom Silver, Willie McClinton, Linfeng Zhao, Stephen Proulx, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Jennifer Barry. "Practice Makes Perfect: Planning to Learn Skill Parameter Policies." arXiv (2024)

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