Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing
This work is significant for warehouse automation, providing a method to improve the efficiency of robotic bin packing systems by explicitly balancing space utilization and execution time.
This paper addresses the trade-off between packing density and operational time in robotic 3D bin packing by proposing a selection-based formulation. The method, STEP, uses a preference-conditioned Transformer-based reinforcement learning policy, achieving a 44% reduction in operational time without compromising packing density.
Robotic bin packing is widely deployed in warehouse automation, with current systems achieving robust performance through heuristic and learning-based strategies. These systems must balance compact placement with rapid execution, where selecting alternative items or reorienting them can improve space utilization but introduce additional time. We propose a selection-based formulation that explicitly reasons over this trade-off: at each step, the robot evaluates multiple candidate actions, weighing expected packing benefit against estimated operational time. This enables time-aware strategies that selectively accept increased operational time when it yields meaningful spatial improvements. Our method, STEP (Space-Time Efficient Packing), uses a preference-conditioned, Transformer-based reinforcement learning policy, and allows generalization across candidate set sizes and integration with standard placement modules. It achieves a 44% reduction in operational time without compromising packing density. Additional material is available at https://step-packing.github.io.