# A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.01628 · HTML: https://mlsanity.com/paper/2506.01628/

## Key facts

- Cited by 1 papers in the map (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 11 of its 37 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## 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, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation](https://mlsanity.com/paper/2310.17274/) (2023)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method](https://mlsanity.com/paper/1708.05930/) (2017)
- [Learning Practically Feasible Policies for Online 3D Bin Packing](https://mlsanity.com/paper/2108.13680/) (2021)
- [A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing](https://mlsanity.com/paper/2007.00463/) (2020)
- [Coordination of two robotic manipulators for object retrieval in clutter](https://mlsanity.com/paper/2109.15220/) (2021)
- [Synchronized Dual-arm Rearrangement via Cooperative mTSP](https://mlsanity.com/paper/2403.08191/) (2024)
- [An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement](https://mlsanity.com/paper/2408.09694/) (2024)
- [Stop-N-Go: Search-based Conflict Resolution for Motion Planning of Multiple Robotic Manipulators](https://mlsanity.com/paper/2410.07606/) (2024)

## Built on by (most cited first, 1 of 1)

- [Evaluating LLMs for Combinatorial Optimization: One-Phase and Two-Phase Heuristics for 2D Bin-Packing](https://mlsanity.com/paper/2509.22255/) (2025)

## Abstract (arXiv)

We address the bin packing problem (BPP), which aims to maximize bin utilization when packing a variety of items. The offline problem, where the complete information about the item set and their sizes is known in advance, is proven to be NP-hard. The semi-online and online variants are even more challenging, as full information about incoming items is unavailable. While existing methods have tackled both 2D and 3D BPPs, the 2D BPP remains underexplored in terms of fully maximizing utilization. We propose a hierarchical approach for solving the 2D online and semi-online BPP by combining deep reinforcement learning (RL) with heuristic search. The heuristic search selects which item to pack or unpack, determines the packing order, and chooses the orientation of each item, while the RL agent decides the precise position within the bin. Our method is capable of handling diverse scenarios, including repacking, varying levels of item information, differing numbers of accessible items, and coordination of dual manipulators. Experimental results demonstrate that our approach achieves near-optimal utilization across various practical scenarios, largely due to its repacking capability. In addition, the algorithm is evaluated in a physics-based simulation environment, where execution time is measured to assess its real-world performance.