RoboBPP: Benchmarking Robotic Online Bin Packing with Physics-based Simulation

2025-12-03

AI Summary

To address key challenges in industrial robot online bin-packing—including difficulty in physical feasibility verification, lack of realistic benchmarks, and data distortion—this paper introduces the first physics-aware simulation benchmark tailored to real-world scenarios. Methodologically, it integrates a high-fidelity physics engine to model full-scale robotic arms and containers, covering three representative industrial workflows: assembly lines, logistics packaging, and furniture manufacturing. It proposes two novel evaluation metrics—structural stability and operational safety—and releases three industrial datasets sourced directly from production lines. Contributions include: (1) the first open-source online bin-packing evaluation platform supporting physics-based feasibility validation; (2) standardized test scenarios, data distributions, and evaluation protocols; and (3) integrated visualization tools and an online leaderboard to enable reproducible, comparable, and deployable algorithm research.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Physical feasibility in 3D bin packing is a key requirement in modern industrial logistics and robotic automation. With the growing adoption of industrial automation, online bin packing has gained increasing attention. However, inconsistencies in problem settings, test datasets, and evaluation metrics have hindered progress in the field, and there is a lack of a comprehensive benchmarking system. Direct testing on real hardware is costly, and building a realistic simulation environment is also challenging. To address these limitations, we introduce RoboBPP, a benchmarking system designed for robotic online bin packing. RoboBPP integrates a physics-based simulator to assess physical feasibility. In our simulation environment, we introduce a robotic arm and boxes at real-world scales to replicate real industrial packing workflows. By simulating conditions that arise in real industrial applications, we ensure that evaluated algorithms are practically deployable. In addition, prior studies often rely on synthetic datasets whose distributions differ from real-world industrial data. To address this issue, we collect three datasets from real industrial workflows, including assembly-line production, logistics packing, and furniture manufacturing. The benchmark comprises three carefully designed test settings and extends existing evaluation metrics with new metrics for structural stability and operational safety. We design a scoring system and derive a range of insights from the evaluation results. RoboBPP is fully open-source and is equipped with visualization tools and an online leaderboard, providing a reproducible and extensible foundation for future research and industrial applications (https://robot-bin-packing-benchmark.github.io).

Problem

• Benchmarks robotic online bin packing with physics simulation
• Addresses lack of standardized datasets and evaluation metrics
• Ensures algorithm feasibility for real industrial deployment

Innovation

• Physics-based simulation for robotic bin packing
• Real industrial datasets from three workflows
• Comprehensive benchmark with stability and safety metrics