Towards Online 3D Bin Packing: Learning Synergies between Packing and Unpacking via DRL

Shuai Song, Shuo Yang, Ran Song, Shilei Chu, yibin Li, Wei Zhang
Proceedings of The 6th Conference on Robot Learning, PMLR 205:1136-1145, 2023.

Abstract

There is an emerging research interest in addressing the online 3D bin packing problem (3D-BPP), which has a wide range of applications in logistics industry. However, neither heuristic methods nor those based on deep reinforcement learning (DRL) outperform human packers in real logistics scenarios. One important reason is that humans can make corrections after performing inappropriate packing actions by unpacking incorrectly packed items. Inspired by such an unpacking mechanism, we present a DRL-based packing-and-unpacking network (PUN) to learn the synergies between the two actions for the online 3D-BPP. Experimental results demonstrate that PUN achieves the state-of-the-art performance and the supplementary video shows that the system based on PUN can reliably complete the online 3D bin packing task in the real world.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-song23a, title = {Towards Online 3D Bin Packing: Learning Synergies between Packing and Unpacking via DRL}, author = {Song, Shuai and Yang, Shuo and Song, Ran and Chu, Shilei and Li, yibin and Zhang, Wei}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {1136--1145}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/song23a/song23a.pdf}, url = {https://proceedings.mlr.press/v205/song23a.html}, abstract = {There is an emerging research interest in addressing the online 3D bin packing problem (3D-BPP), which has a wide range of applications in logistics industry. However, neither heuristic methods nor those based on deep reinforcement learning (DRL) outperform human packers in real logistics scenarios. One important reason is that humans can make corrections after performing inappropriate packing actions by unpacking incorrectly packed items. Inspired by such an unpacking mechanism, we present a DRL-based packing-and-unpacking network (PUN) to learn the synergies between the two actions for the online 3D-BPP. Experimental results demonstrate that PUN achieves the state-of-the-art performance and the supplementary video shows that the system based on PUN can reliably complete the online 3D bin packing task in the real world.} }
Endnote
%0 Conference Paper %T Towards Online 3D Bin Packing: Learning Synergies between Packing and Unpacking via DRL %A Shuai Song %A Shuo Yang %A Ran Song %A Shilei Chu %A yibin Li %A Wei Zhang %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-song23a %I PMLR %P 1136--1145 %U https://proceedings.mlr.press/v205/song23a.html %V 205 %X There is an emerging research interest in addressing the online 3D bin packing problem (3D-BPP), which has a wide range of applications in logistics industry. However, neither heuristic methods nor those based on deep reinforcement learning (DRL) outperform human packers in real logistics scenarios. One important reason is that humans can make corrections after performing inappropriate packing actions by unpacking incorrectly packed items. Inspired by such an unpacking mechanism, we present a DRL-based packing-and-unpacking network (PUN) to learn the synergies between the two actions for the online 3D-BPP. Experimental results demonstrate that PUN achieves the state-of-the-art performance and the supplementary video shows that the system based on PUN can reliably complete the online 3D bin packing task in the real world.
APA
Song, S., Yang, S., Song, R., Chu, S., Li, y. & Zhang, W.. (2023). Towards Online 3D Bin Packing: Learning Synergies between Packing and Unpacking via DRL. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:1136-1145 Available from https://proceedings.mlr.press/v205/song23a.html.

Related Material