RoboBPP: Benchmarking Robotic Online Bin Packing with Physics-based Simulation
RoboBPP is a comprehensive benchmark for Robotic Online 3D Bin Packing, designed to bridge the gap between algorithmic packing performance and physical feasibility in real robotic scenarios. The benchmark integrates real-world industrial datasets, standardized evaluation metrics, and a physics-based simulation environment to support fair, reproducible, and realistic comparisons.
📄 Paper
RoboBPP: Benchmarking Robotic Online Bin Packing with Physics-based Simulation 📌 Under review at International Journal of Robotics Research (IJRR)
The repository is organized into three main directories: code, test, and experiment, reflecting the full pipeline from algorithm implementation to evaluation and analysis.
code/
This directory contains the training and implementation code for all packing algorithms reproduced in this benchmark.
heuristics/ Implements classical heuristic-based online bin packing methods, reproduced by us according to their original papers.
learning_based/ Contains learning-based approaches, including model definitions and training pipelines, implemented following the corresponding publications.
All algorithms in this folder are our own re-implementations to ensure consistency and fair comparison across methods.
test/
This directory provides the testing and evaluation code for running all algorithms in the physics-based simulation environment.
Unified testing interfaces for both heuristic and learning-based methods
Integration with the PyBullet-based simulator
Scripts for executing online packing under different benchmark settings
Includes pre-trained models for selected learning-based methods to facilitate reproducibility
experiment/
Experiments validating the effectiveness and rationality of the proposed benchmark metrics
🚀 Getting Started
git clone https://gitlink.org.cn/wangzhoufeng/RoboBPP.git
cd RoboBPP
RoboBPP: Benchmarking Robotic Online Bin Packing with Physics-based Simulation
RoboBPP is a comprehensive benchmark for Robotic Online 3D Bin Packing, designed to bridge the gap between algorithmic packing performance and physical feasibility in real robotic scenarios.
The benchmark integrates real-world industrial datasets, standardized evaluation metrics, and a physics-based simulation environment to support fair, reproducible, and realistic comparisons.
📄 Paper
RoboBPP: Benchmarking Robotic Online Bin Packing with Physics-based Simulation
📌 Under review at International Journal of Robotics Research (IJRR)
If you find this project useful, please consider citing our paper (see Citation).
🌐 Project Website
🔗 Project Homepage:
https://robot-bin-packing-benchmark.github.io/
The website provides:
📦 Datasets
RoboBPP includes three real-world industrial datasets, covering diverse packing characteristics and challenges:
📥 All datasets can be downloaded from the project homepage:
https://robot-bin-packing-benchmark.github.io/download.html
🧩 Code Structure
The repository is organized into three main directories:
code,test, andexperiment, reflecting the full pipeline from algorithm implementation to evaluation and analysis.code/This directory contains the training and implementation code for all packing algorithms reproduced in this benchmark.
heuristics/Implements classical heuristic-based online bin packing methods, reproduced by us according to their original papers.
learning_based/Contains learning-based approaches, including model definitions and training pipelines, implemented following the corresponding publications.
All algorithms in this folder are our own re-implementations to ensure consistency and fair comparison across methods.
test/This directory provides the testing and evaluation code for running all algorithms in the physics-based simulation environment.
experiment/Experiments validating the effectiveness and rationality of the proposed benchmark metrics
🚀 Getting Started