MultiPack: Hierarchical Planning with Physics Feedback for 3D Multi-Bin Packing

Packing household objects into storage bins requires deciding which objects to pack together, in what order, and in which poses. These decisions are tightly coupled: locally compact placements may increase overall bin usage, while physical settling can change the space available for subsequent objects. In this paper, we introduce , a hierarchical multi-bin packing framework that integrates cross-bin composition planning with physically validated in-bin pose search. Our Object–Group–Bin planning strategy groups objects by LLM-derived support, protection, and access priorities, connecting cross-bin allocation with in-bin scheduling. A capacity-based mixed-integer linear program assigns groups to bins by minimizing volume overflow. Within each bin, physics-feedback geometric search combines heightmap-guided candidate generation with height-envelope and weighted surface-clearance scoring. Physical validation, rollback, and settled-state synchronization close the loop between placements. Experiments on two datasets show reduced bin usage and competitive or improved layout quality under a shared evaluation protocol, while a real-world demonstration with 54 objects shows how the generated plans can guide human packing.