Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

• URL: http://arxiv.org/abs/2603.07800v1
• Date: Sun, 08 Mar 2026 20:55:15 GMT
• Title: Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing
• Abstract summary: Robotic bin packing is widely deployed in warehouse automation.<n>Current systems balance compact placement with rapid execution, where selecting alternative items or reorienting them can improve space utilization but introduce additional time.<n>We propose a selection-based formulation that explicitly reasons over this trade-off.<n>It achieves a 44% reduction in operational time without compromising packing density.
• Score: 21.573486686003903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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