research-article

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

Authors:

Kai XuAuthors Info & Claims

ACM Transactions on Graphics, Volume 42, Issue 5

Article No.: 165, Pages 1 - 21

https://doi.org/10.1145/3603544

Published: 28 July 2023 Publication History

Abstract

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 goal is to consecutively move a sequence of 3D objects with arbitrary shapes into a designated container with only partial observations of the object sequence. We take physical realizability into account, involving physics dynamics and constraints of a placement. 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 propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex irregular geometry and imperfect object placement together lead to huge solution space. Direct training in such space is prohibitively data intensive. We instead propose a theoretically provable method for candidate action generation to reduce the action space of RL and the learning burden. A parameterized policy is then learned to select the best placement from the candidates. Equipped with an efficient method of asynchronous RL acceleration and a data preparation process of simulation-ready training sequences, a mature packing policy can be trained in a physics-based environment within 48 hours. Through extensive evaluation on a variety of real-life shape datasets and comparisons with state-of-the-art baselines, we demonstrate that our method outperforms the best-performing baseline on all datasets by at least 12.8% in terms of packing utility. We also release our datasets and source code to support further research in this direction.

Supplementary Material

tog-22-0121-File003 (tog-22-0121-file003.zip)

Supplementary material

Download
96.53 MB

References

[1]

Sara Ali, António Galrão Ramos, Maria Antónia Carravilla, and José Fernando Oliveira. 2022. On-line three-dimensional packing problems: A review of off-line and on-line solution approaches. Computers & Industrial Engineering (2022), 108122. DOI:

Digital Library

[2]

Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, and Timothy P. Lillicrap. 2018. Distributed distributional deterministic policy gradients. In International Conference on Learning Representations. OpenReview.net, Vancouver, BC, Canada. https://openreview.net/forum?id=SyZipzbCb.

[3]

Marc G. Bellemare, Will Dabney, and Rémi Munos. 2017. A distributional perspective on reinforcement learning. In International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 70. PMLR, Sydney, NSW, Australia, 449–458. http://proceedings.mlr.press/v70/bellemare17a.html.

[4]

Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez, and Bruno Lévy. 2010. Polygon Mesh Processing. AK Peters. http://www.crcpress.com/product/isbn/9781568814261.

[5]

Stephen Boyd, Stephen P. Boyd, and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press.

[6]

Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. OpenAI gym. arXiv preprint arXiv:1606.01540 (2016). http://arxiv.org/abs/1606.01540.

[7]

Berk Çalli, Arjun Singh, James Bruce, Aaron Walsman, Kurt Konolige, Siddhartha S. Srinivasa, Pieter Abbeel, and Aaron M. Dollar. 2017. Yale-CMU-Berkeley dataset for robotic manipulation research. The International Journal of Robotics Research 36, 3 (2017), 261–268. DOI:

Digital Library

[8]

Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015). http://arxiv.org/abs/1512.03012.

[9]

Rulin Chen, Ziqi Wang, Peng Song, and Bernd Bickel. 2022. Computational design of high-level interlocking puzzles. Transactions on Graphics 41, 4 (2022), 150:1–150:15. DOI:

Digital Library

[10]

Xuelin Chen, Hao Zhang, Jinjie Lin, Ruizhen Hu, Lin Lu, Qi-Xing Huang, Bedrich Benes, Daniel Cohen-Or, and Baoquan Chen. 2015. Dapper: Decompose-and-pack for 3D printing. Transactions on Graphics 34, 6 (2015), 213:1–213:12. DOI:

Digital Library

[11]

John H. Conway and Salvatore Torquato. 2006. Packing, tiling, and covering with tetrahedra. National Academy of Sciences 103, 28 (2006), 10612–10617.

[12]

Erwin Coumans and Yunfei Bai. 2016. PyBullet, a Python module for physics simulation for games, robotics and machine learning. PyBullet (2016).

[13]

Lu Duan, Haoyuan Hu, Yu Qian, Yu Gong, Xiaodong Zhang, Jiangwen Wei, and Yinghui Xu. 2019. A multi-task selected learning approach for solving 3D flexible bin packing problem. In International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, Montreal, QC, Canada, 1386–1394. http://dl.acm.org/citation.cfm?id=3331847.

[14]

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel. 2016. Benchmarking deep reinforcement learning for continuous control. In International Conference on Machine Learning. PMLR, 1329–1338. http://proceedings.mlr.press/v48/duan16.html.

Digital Library

[15]

Emanuel Falkenauer. 1996. A hybrid grouping genetic algorithm for bin packing. Journal of Heuristics 2, 1 (1996), 5–30. DOI:

[16]

Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, and Shane Legg. 2018. Noisy networks for exploration. In International Conference on Learning Representations. OpenReview.net, Vancouver, BC, Canada. https://openreview.net/forum?id=rywHCPkAW.

[17]

Niklas Funk, Georgia Chalvatzaki, Boris Belousov, and Jan Peters. 2021. Learn2Assemble with structured representations and search for robotic architectural construction. In Conference on Robot Learning (Proceedings of Machine Learning Research), Vol. 164. PMLR, London, UK, 1401–1411. https://proceedings.mlr.press/v164/funk22a.html.

[18]

Niklas Funk, Svenja Menzenbach, Georgia Chalvatzaki, and Jan Peters. 2022. Graph-based reinforcement learning meets mixed integer programs: An application to 3D robot assembly discovery. In International Conference on Intelligent Robots and Systems. IEEE, Kyoto, Japan, 10215–10222. DOI:

[19]

Ken Goldberg, Brian Mirtich, Yan Zhuang, John Craig, Brian Carlisle, and John F. Canny. 1999. Part pose statistics: Estimators and experiments. Transactions on Robotics and Automation 15, 5 (1999), 849–857. DOI:

[20]

Ankit Goyal and Jia Deng. 2020. PackIt: A virtual environment for geometric planning. In International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 119. PMLR, 3700–3710. http://proceedings.mlr.press/v119/goyal20b.html.

[21]

Chi Trung Ha, Trung Thanh Nguyen, Lam Thu Bui, and Ran Wang. 2017. An online packing heuristic for the three-dimensional container loading problem in dynamic environments and the physical internet. In Applications of Evolutionary Computation (Lecture Notes in Computer Science), Vol. 10200. Amsterdam, The Netherlands, 140–155. DOI:

[22]

Thomas Hales, Mark Adams, Gertrud Bauer, Tat Dat Dang, John Harrison, Hoang Le Truong, Cezary Kaliszyk, Victor Magron, Sean McLaughlin, Tat Thang Nguyen, et al. 2017. A formal proof of the Kepler conjecture. In Forum of Mathematics, Vol. 5. Cambridge University Press.

[23]

Shuai D. Han, Si Wei Feng, and Jingjin Yu. 2019. Toward fast and optimal robotic pick-and-place on a moving conveyor. Robotics and Automation Letters 5, 2 (2019), 446–453.

[24]

Juris Hartmanis. 1982. Computers and intractability: A guide to the theory of NP-completeness (Michael R. Garey and David S. Johnson). SIAM Review 24, 1 (1982), 90.

Digital Library

[25]

Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, and David Silver. 2018. Rainbow: Combining improvements in deep reinforcement learning. In AAAI Conference on Artificial Intelligence. AAAI Press, New Orleans, Louisiana, USA, 3215–3222. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17204.

[26]

Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, and David Silver. 2018. Distributed prioritized experience replay. In International Conference on Learning Representations. OpenReview.net, Vancouver, BC, Canada. https://openreview.net/forum?id=H1Dy---0Z.

[27]

Haoyuan Hu, Xiaodong Zhang, Xiaowei Yan, Longfei Wang, and Yinghui Xu. 2017. Solving a new 3D bin packing problem with deep reinforcement learning method. arXiv preprint arXiv:1708.05930 (2017). http://arxiv.org/abs/1708.05930.

[28]

Ruizhen Hu, Juzhan Xu, Bin Chen, Minglun Gong, Hao Zhang, and Hui Huang. 2020. TAP-Net: Transport-and-pack using reinforcement learning. Transactions on Graphics 39, 6 (2020), 232:1–232:15. DOI:

Digital Library

[29]

Haojie Huang, Dian Wang, Robin Walters, and Robert Platt. 2022. Equivariant transporter network. Proceedings of Robotics: Science and Systems (2022).

[30]

Sichao Huang, Ziwei Wang, Jie Zhou, and Jiwen Lu. 2023. Planning irregular object packing via hierarchical reinforcement learning. Robotics and Automation Letters 8, 1 (2023), 81–88. DOI:

[31]

Josef Kallrath. 2017. Packing ellipsoids into volume-minimizing rectangular boxes. Journal of Global Optimization 67, 1-2 (2017), 151–185. DOI:

Digital Library

[32]

Daniel Kappler, Jeannette Bohg, and Stefan Schaal. 2015. Leveraging big data for grasp planning. In International Conference on Robotics and Automation. IEEE, Seattle, WA, USA, 4304–4311. DOI:

[33]

Korhan Karabulut and Mustafa Murat Inceoglu. 2004. A hybrid genetic algorithm for packing in 3D with deepest bottom left with fill method. In Advances in Information Systems (Lecture Notes in Computer Science), Vol. 3261. Springer, Izmir, Turkey, 441–450. DOI:

Digital Library

[34]

Alexander Kasper, Zhixing Xue, and Rüdiger Dillmann. 2012. The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics. The International Journal of Robotics Research 31, 8 (2012), 927–934. DOI:

[35]

Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo. 2019. ABC: A big CAD model dataset for geometric deep learning. In Conference on Computer Vision and Pattern Recognition. Computer Vision Foundation/IEEE, Long Beach, CA, USA, 9601–9611. DOI:

[36]

Bruno Lévy, Sylvain Petitjean, Nicolas Ray, and Jérôme Maillot. 2002. Least squares conformal maps for automatic texture atlas generation. Transactions on Graphics 21, 3 (2002), 362–371. DOI:

Digital Library

[37]

Max Limper, Nicholas Vining, and Alla Sheffer. 2018. Box cutter: Atlas refinement for efficient packing via void elimination. Transactions on Graphics 37, 4 (2018), 153. DOI:

Digital Library

[38]

Hao-Yu Liu, Xiao-Ming Fu, Chunyang Ye, Shuangming Chai, and Ligang Liu. 2019. Atlas refinement with bounded packing efficiency. Transactions on Graphics 38, 4 (2019), 33:1–33:13. DOI:

Digital Library

[39]

Xiao Liu, Jia-min Liu, An-xi Cao, and Zhuang-le Yao. 2015. HAPE3D - a new constructive algorithm for the 3D irregular packing problem. Frontiers of Information Technology & Electronic Engineering 16, 5 (2015), 380–390. DOI:

[40]

Kui-Yip Lo, Chi-Wing Fu, and Hongwei Li. 2009. 3D polyomino puzzle. Transactions on Graphics 28, 5 (2009), 157. DOI:

Digital Library

[41]

Andrea Lodi, Silvano Martello, and Michele Monaci. 2002. Two-dimensional packing problems: A survey. European Journal of Operational Research 141, 2 (2002), 241–252.

[42]

Linjie Luo, Ilya Baran, Szymon Rusinkiewicz, and Wojciech Matusik. 2012. Chopper: Partitioning models into 3D-printable parts. Transactions on Graphics 31, 6 (2012), 1–9.

Digital Library

[43]

Y. Ma, Zhonggui Chen, W. Hu, and W. Wang. 2018. Packing irregular objects in 3D space via hybrid optimization. Computer Graphics Forum 37, 5 (2018), 49–59. DOI:

[44]

Jeffrey Mahler and Ken Goldberg. 2017. Learning deep policies for robot bin picking by simulating robust grasping sequences. In Conference on Robot Learning (Proceedings of Machine Learning Research), Vol. 78. PMLR, Mountain View, California, USA, 515–524. http://proceedings.mlr.press/v78/mahler17a.html.

[45]

Jeffrey Mahler, Florian T. Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai Kohlhoff, Torsten Kröger, James J. Kuffner, and Ken Goldberg. 2016. Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards. In International Conference on Robotics and Automation. IEEE, Stockholm, Sweden, 1957–1964. DOI:

Digital Library

[46]

Khaled Mamou, E. Lengyel, and A. Peters. 2016. Volumetric hierarchical approximate convex decomposition. In Game Engine Gems 3. AK Peters, 141–158.

[47]

M. Berg, Otfried Cheong, Marc van Kreveld, and Mark Overmars. 2008. Computational Geometry Algorithms and Applications. Springer.

[48]

Silvano Martello, David Pisinger, and Daniele Vigo. 2000. The three-dimensional bin packing problem. Operations Research 48, 2 (2000), 256–267. DOI:

Digital Library

[49]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529–533. DOI:

[50]

Tobias Nöll and D. Strieker. 2011. Efficient packing of arbitrary shaped charts for automatic texture atlas generation. In Computer Graphics Forum, Vol. 30. Wiley Online Library, 1309–1317.

Digital Library

[51]

Zherong Pan, Xifeng Gao, and Dinesh Manocha. 2020. Grasping fragile objects using a stress-minimization metric. In International Conference on Robotics and Automation. 517–523. DOI:

[52]

Zherong Pan and Kris Hauser. 2021. Decision making in joint push-grasp action space for large-scale object sorting. In International Conference on Robotics and Automation. 6199–6205. DOI:

Digital Library

[53]

Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017. PointNet: Deep learning on point sets for 3D classification and segmentation. In Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, Honolulu, HI, USA, 77–85. DOI:

[54]

Urs Ramer. 1972. An iterative procedure for the polygonal approximation of plane curves. Computer Graphics and Image Processing 1, 3 (1972), 244–256. DOI:

[55]

A. Galrão Ramos, José F. Oliveira, José F. Gonçalves, and Manuel P. Lopes. 2016. A container loading algorithm with static mechanical equilibrium stability constraints. Transportation Research Part B: Methodological 91 (2016), 565–581.

[56]

Nicolas Ray, Jean-Christophe Ulysse, Xavier Cavin, and Bruno Levy. 2003. Generation of radiosity texture atlas for realistic real-time rendering. In Eurographics 2003 — Short Presentations. Eurographics Association. DOI:

[57]

Colin Rennie, Rahul Shome, Kostas E. Bekris, and Alberto F. De Souza. 2016. A dataset for improved RGBD-based object detection and pose estimation for warehouse pick-and-place. Robotics and Automation Letters 1, 2 (2016), 1179–1185. DOI:

[58]

Daniel Saakes, Thomas Cambazard, Jun Mitani, and Takeo Igarashi. 2013. PacCAM: Material capture and interactive 2D packing for efficient material usage on CNC cutting machines. In Symposium on User Interface Software and Technology. ACM, St. Andrews, United Kingdom, 441–446. DOI:

Digital Library

[59]

Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2016. Prioritized experience replay. In International Conference on Learning Representations. San Juan, Puerto Rico. http://arxiv.org/abs/1511.05952.

[60]

Nico Schertler, Daniele Panozzo, Stefan Gumhold, and Marco Tarini. 2018. Generalized motorcycle graphs for imperfect quad-dominant meshes. Transactions on Graphics 37, 4 (2018).

Digital Library

[61]

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017). http://arxiv.org/abs/1707.06347.

[62]

Steven S. Seiden. 2002. On the online bin packing problem. J. ACM 49, 5 (2002), 640–671. DOI:

Digital Library

[63]

Rahul Shome, Wei N. Tang, Changkyu Song, Chaitanya Mitash, Hristiyan Kourtev, Jingjin Yu, Abdeslam Boularias, and Kostas E. Bekris. 2019. Towards robust product packing with a minimalistic end-effector. In International Conference on Robotics and Automation. 9007–9013. DOI:

Digital Library

[64]

Arjun Singh, James Sha, Karthik S. Narayan, Tudor Achim, and Pieter Abbeel. 2014. BigBIRD: A large-scale 3D database of object instances. In International Conference on Robotics and Automation. IEEE, Hong Kong, China, 509–516. DOI:

[65]

David Stutz and Andreas Geiger. 2020. Learning 3D shape completion under weak supervision. International Journal of Computer Vision 128, 5 (2020), 1162–1181. DOI:

Digital Library

[66]

Satoshi Suzuki and Keiichi Abe. 1985. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing 30, 1 (1985), 32–46. DOI:

[67]

Santosh Tiwari, Georges Fadel, and Peter Fenyes. 2010. A fast and efficient compact packing algorithm for SAE and ISO luggage packing problems. Journal of Computing and Information Science in Engineering 10, 2 (2010), 021010.

[68]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations. OpenReview.net, Vancouver, BC, Canada. https://openreview.net/forum?id=rJXMpikCZ.

[69]

Fan Wang and Kris Hauser. 2019. Stable bin packing of non-convex 3D objects with a robot manipulator. In International Conference on Robotics and Automation. IEEE, Montreal, QC, Canada, 8698–8704. DOI:

Digital Library

[70]

Fan Wang and Kris Hauser. 2021. Robot packing with known items and nondeterministic arrival order. Transactions on Automation Science and Engineering 18, 4 (2021), 1901–1915. DOI:

[71]

Fan Wang and Kris Hauser. 2022. Dense robotic packing of irregular and novel 3D objects. Transactions on Robotics 38, 2 (2022), 1160–1173. DOI:

[72]

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, and Nando de Freitas. 2016. Dueling network architectures for deep reinforcement learning. In International Conference on Machine Learning (JMLR Workshop and Conference Proceedings), Vol. 48. JMLR.org, New York, NY, USA, 1995–2003. http://proceedings.mlr.press/v48/wangf16.html.

[73]

Ziqi Wang, Peng Song, and Mark Pauly. 2021. MOCCA: Modeling and optimizing cone-joints for complex assemblies. Transactions on Graphics 40, 4 (2021), 1–14.

Digital Library

[74]

Yuhuai Wu, Elman Mansimov, Roger B. Grosse, Shun Liao, and Jimmy Ba. 2017. Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 5279–5288. https://proceedings.neurips.cc/paper/2017/hash/361440528766bbaaaa1901845cf4152b-Abstract.html.

[75]

Zifei Yang, Shuo Yang, Shuai Song, Wei Zhang, Ran Song, Jiyu Cheng, and Yibin Li. 2021. PackerBot: Variable-sized product packing with heuristic deep reinforcement learning. In International Conference on Intelligent Robots and Systems. IEEE, Prague, Czech Republic, 5002–5008. DOI:

Digital Library

[76]

Miaojun Yao, Zhili Chen, Linjie Luo, Rui Wang, and Huamin Wang. 2015. Level-set-based partitioning and packing optimization of a printable model. Transactions on Graphics 34, 6 (2015), 1–11.

Digital Library

[77]

Hang Yin, Anastasia Varava, and Danica Kragic. 2021. Modeling, learning, perception, and control methods for deformable object manipulation. Science Robotics 6, 54 (2021), 8803. DOI:

[78]

Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, and Johnny Lee. 2020. Transporter networks: Rearranging the visual world for robotic manipulation. In Conference on Robot Learning (Proceedings of Machine Learning Research), Vol. 155. PMLR, Cambridge, MA, USA, 726–747. https://proceedings.mlr.press/v155/zeng21a.html.

[79]

Chi Zhang, Mao-Feng Xu, Shuangming Chai, and Xiao-Ming Fu. 2020. Robust atlas generation via angle-based segmentation. Computer Aided Geometric Design 79 (2020), 101854. DOI:

[80]

Hang Zhao, Qijin She, Chenyang Zhu, Yin Yang, and Kai Xu. 2021. Online 3D bin packing with constrained deep reinforcement learning. In AAAI Conference on Artificial Intelligence. AAAI Press, 741–749. https://ojs.aaai.org/index.php/AAAI/article/view/16155.

[81]

Hang Zhao, Yang Yu, and Kai Xu. 2022a. Learning efficient online 3D bin packing on packing configuration trees. In International Conference on Learning Representations. https://openreview.net/forum?id=bfuGjlCwAq.

[82]

Hang Zhao, Chenyang Zhu, Xin Xu, Hui Huang, and Kai Xu. 2022b. Learning practically feasible policies for online 3D bin packing. Science China Information Sciences 65, 1 (2022). DOI:

Cited By

Huertas Arango JPantoja-Benavides GValero SÁlvarez-Martínez D(2024)Approaches for the On-Line Three-Dimensional Knapsack Problem with Buffering and RepackingMathematics10.3390/math1220322312:20(3223)Online publication date: 15-Oct-2024
https://doi.org/10.3390/math12203223
Pantoja-Benavides GGiraldo DMontes AGarcía ARodríguez CMarín CÁlvarez-Martínez D(2024)Comprehensive Review of Robotized Freight PackingLogistics10.3390/logistics80300698:3(69)Online publication date: 8-Jul-2024
https://doi.org/10.3390/logistics8030069
Pan JGao XHui KZhu SLiu YHeng PFu C(2024)PPN-Pack: Placement Proposal Network for Efficient Robotic Bin PackingIEEE Robotics and Automation Letters10.1109/LRA.2024.33856129:6(5086-5093)Online publication date: Jun-2024
https://doi.org/10.1109/LRA.2024.3385612
Show More Cited By

Index Terms

Learning Physically Realizable Skills for Online Packing of General 3D Shapes
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Shape analysis

Recommendations

Solving 3D packing problem using Transformer network and reinforcement learning
Abstract
The three-dimensional packing problem (3D-PP) is a classic NP-hard problem in operations research and computer science. One of the most popular ways to solve the problem is heuristic methods with a search strategy. However, approaches ...
Highlights
- A deep reinforcement learning method for 3D packing problem is proposed.
- The ...
Improved approximation algorithm for two-dimensional bin packing
SODA '14: Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms

We study the two-dimensional bin packing problem with and without rotations. Here we are given a set of two-dimensional rectangular items I and the goal is to pack these into a minimum number of unit square bins. We consider the orthogonal packing case ...
A Q-learning-based algorithm for the 2D-rectangular packing problem
Abstract
This paper presents a Q-learning-based algorithm for sequence and orientation optimization toward the 2D rectangular strip packing problem. The width-filled skyline is used to represent the interior packing state, and a constructive rectangular ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 5

October 2023

195 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3607124

Editor:
Carol O'Sullivan
Trinity College Dublin, Ireland

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2023

Online AM: 06 June 2023

Accepted: 29 May 2023

Revised: 12 April 2023

Received: 05 December 2022

Published in TOG Volume 42, Issue 5

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key Research and Development Program of China
National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
852
Total Downloads

Downloads (Last 12 months)433
Downloads (Last 6 weeks)31

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Huertas Arango JPantoja-Benavides GValero SÁlvarez-Martínez D(2024)Approaches for the On-Line Three-Dimensional Knapsack Problem with Buffering and RepackingMathematics10.3390/math1220322312:20(3223)Online publication date: 15-Oct-2024
https://doi.org/10.3390/math12203223
Pantoja-Benavides GGiraldo DMontes AGarcía ARodríguez CMarín CÁlvarez-Martínez D(2024)Comprehensive Review of Robotized Freight PackingLogistics10.3390/logistics80300698:3(69)Online publication date: 8-Jul-2024
https://doi.org/10.3390/logistics8030069
Pan JGao XHui KZhu SLiu YHeng PFu C(2024)PPN-Pack: Placement Proposal Network for Efficient Robotic Bin PackingIEEE Robotics and Automation Letters10.1109/LRA.2024.33856129:6(5086-5093)Online publication date: Jun-2024
https://doi.org/10.1109/LRA.2024.3385612
Santos ADuarte NDehban ASantos-Victor J(2024)Learning the Sequence of Packing Irregular Objects from Human Demonstrations: Towards Autonomous Packing Robots2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)10.1109/BioRob60516.2024.10719974(951-957)Online publication date: 1-Sep-2024
https://doi.org/10.1109/BioRob60516.2024.10719974
Zhuang QChen ZHe KCao JWang W(2024)Dynamics simulation-based packing of irregular 3D objectsComputers & Graphics10.1016/j.cag.2024.103996123(103996)Online publication date: Oct-2024
https://doi.org/10.1016/j.cag.2024.103996
Pan JHui KGao XZhu SLiu YHeng PFu C(2023)SDF-Pack: Towards Compact Bin Packing with Signed-Distance-Field Minimization2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS55552.2023.10341940(10612-10619)Online publication date: 1-Oct-2023
https://doi.org/10.1109/IROS55552.2023.10341940

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents