让机器人智能权衡装箱速度与空间利用率，提升仓库自动分拣效率。
Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing
- 基于偏好条件的强化学习，动态评估动作的装箱收益与耗时成本。
- 操作时间减少44%，同时保持装箱密度不下降。
- 适用于多种物品组合，可无缝接入现有装箱系统。
机器人装箱广泛应用于仓储自动化，当前系统通过启发式和学习策略实现稳定性能。这些系统需在紧凑摆放与快速执行间取得平衡：选择不同物品或调整方向虽能提升空间利用率，但会增加时间成本。本文提出一种基于选择的动作建模方法，显式权衡这一矛盾——每一步评估多个候选动作，权衡预期装箱收益与估算操作时间。这使得系统能在带来显著空间优化时，合理接受额外耗时。所提方法STEP（Space-Time Efficient Packing）采用基于Transformer的偏好条件强化学习策略，支持候选集规模的泛化，并可与标准放置模块集成。实验表明，该方法在不降低装箱密度的前提下，将操作时间减少44%。更多内容见https://step-packing.github.io。
原文摘要 · Abstract (English)
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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