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arXiv · 2603.07800

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

Nikita Sarawgi·Omey M. Manyar·Fan Wang·Thinh H. Nguyen·Daniel Seita·Satyandra K. Gupta
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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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Nikita Sarawgi, Omey M. Manyar, Fan Wang, Thinh H. Nguyen, Daniel Seita, Satyandra K. Gupta. 2026-03-08. Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing. https://arxiv.org/abs/2603.07800

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