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