mlsanityData to 14 Oct 2026

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

arXiv 2603.07800 ↗First posted 8 Mar 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

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.

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.

Builds on

12 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024GOPT: Generalizable Online 3D Bin Packing via Transformer-based Deep Reinforcement Learning9 Sep

Other references · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  4. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  5. 2020Online 3D Bin Packing with Constrained Deep Reinforcement Learningcited by 41
  6. 2021Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attentioncited by 13
  7. 2018Critically fast pick-and-place with suction cupscited by 4
  8. 2023Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluationcited by 4
  9. 2022GOMP-ST: Grasp Optimized Motion Planning for Suction Transportcited by 1
  10. 2025Learning to Optimize Package Picking for Large-Scale, Real-World Robot Inductioncited by 1
  11. 2025Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Managementcited by 1

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  1. 2025A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
  2. 2026PackLab: A Comprehensive Framework for Developing, Training, and Evaluating MLLMs in Robotic Bin Packing ↗
  3. 2026Effective Online 3D Bin Packing with Lookahead Parcels Using Monte Carlo Tree Search
  4. 2025One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem
  5. 2024An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement
  6. 2025Collaborate sim and real: Robot Bin Packing Learning in Real-world and Physical Engine ↗
  7. 2025HERB: Human-augmented Efficient Reinforcement learning for Bin-packing
  8. 2026Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization