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

arXiv: https://arxiv.org/abs/2603.07800 · HTML: https://mlsanity.com/paper/2603.07800/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Tianshou: a Highly Modularized Deep Reinforcement Learning Library](https://mlsanity.com/paper/2107.14171/) (2021)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention](https://mlsanity.com/paper/2107.04333/) (2021)
- [Critically fast pick-and-place with suction cups](https://mlsanity.com/paper/1809.03151/) (2018)
- [Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation](https://mlsanity.com/paper/2312.08103/) (2023)
- [GOPT: Generalizable Online 3D Bin Packing via Transformer-based Deep Reinforcement Learning](https://mlsanity.com/paper/2409.05344/) (2024)
- [GOMP-ST: Grasp Optimized Motion Planning for Suction Transport](https://mlsanity.com/paper/2203.08359/) (2022)
- [Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction](https://mlsanity.com/paper/2506.09765/) (2025)
- [Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management](https://mlsanity.com/paper/2506.13153/) (2025)

## Abstract (arXiv)

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.