基于真实数据和物理仿真，国防科大开源具身在线装箱基准RoboBPP

Core Viewpoint - The article discusses the introduction of RoboBPP, a comprehensive benchmarking system for robotic online bin packing, which integrates real industrial data, physics-based simulation, and embodied execution evaluation, addressing the limitations of existing research in the field [4][28]. Group 1: RoboBPP Overview - RoboBPP is developed by a collaboration between National University of Defense Technology, Institute of Industrial Artificial Intelligence, Wuhan University, and Shenzhen University [2][4]. - It features a highly realistic physics-based simulation environment to assess the physical feasibility and embodied executability of online bin packing algorithms [4][10]. - The system includes three large-scale diverse datasets derived from real industrial processes, which are essential for systematic benchmarking [4][13]. Group 2: Testing and Evaluation Framework - The project employs a scientifically designed multi-level testing setup, progressing from pure mathematical evaluations to physical constraint simulations and finally to robotic execution [15][16]. - Three distinct testing settings are established: Math Pack (pure geometric placement), Physics Pack (introducing physical effects), and Execution Pack (full robotic execution) [16][18]. - A multi-dimensional evaluation metric and normalization scoring system are implemented to provide a comprehensive analysis of algorithm performance across different scenarios [19][20]. Group 3: Experimental Results - The team conducted extensive experiments across three testing settings and three datasets, ranking algorithms based on their overall performance scores [22][23]. - Specific algorithms such as PCT and TAP-Net++ excel in highly repetitive production environments, while transformer-based reinforcement learning strategies are effective in diverse logistics scenarios [24][29]. - The analysis of individual metrics like Occupancy, Trajectory Length, and Collapsed Placement reveals performance characteristics that are not captured in overall scores, guiding algorithm selection for practical packing tasks [24][30]. Group 4: Practical Implications - The findings suggest that algorithms prioritizing compact and efficient space utilization tend to achieve higher occupancy rates [26]. - Stability-related metrics are evaluated for their effectiveness in guiding learning-based methods towards more robust and physically feasible strategies [27][30]. - RoboBPP provides a reproducible and scalable foundation for future research and industrial applications in robotic online bin packing [28].