Multi-Heuristic Robotic Bin Packing of Regular and Irregular Objects

Abstract

The increasing demand in e-commerce, combined with labor shortages and rising wages, is driving the rapid automation of warehouse operations. A critical aspect of this shift is bin packing, where diverse unknown items of varying sizes and shapes must be optimally arranged within a bin or container. Robot bin packing is receiving growing attention and presents unique challenges due to the broad range of objects, packing rules, and task-specific requirements. In response, we propose So-Pack, a generalist packing heuristic for irregularly shaped objects integrated into a flexible, weighted multi-heuristic planning system. The system demonstrates robust performance across general packing scenarios and flexibility to adapt to changing packing rules and specific end-user requirements. Experimental results show that the system outperforms state-of-the-art approaches in key metrics on a new challenging dataset of retail objects in real-world applications.