Snuggle-Pack: Speeding Up Multi-Heuristic Packing Planning of Complex Objects

Abstract

Efficient object packing is a fundamental challenge in logistics and industrial automation. This work introduces Snuggle-Pack, a novel 3D packing algorithm that integrates Fast Fourier Transform (FFT)-based spatial analysis with a multi-heuristic optimization framework to achieve real-time, high-density packing. Unlike traditional heuristic-based approaches that rely on 2D simplifications, our method operates in a fully 3D volumetric space, ensuring collision-free, stable, and physically feasible placements. At its core, our approach employs a proximity-aware and support-sensitive placement strategy, which encourages objects to fit snugly within their surroundings —hence the name—, optimizing space utilization through ne-grained collision metrics. We evaluate our method on the YCB and IPA-3D1K datasets in both previewed and ad-hoc packing scenarios. Our experiments show that Snuggle-Pack significantly outperforms the state of the art, achieving up to 25% higher packing densities or, alternatively, accelerating computation by up to 10×. Moreover, our framework allows for dynamic adaptation to custom constraints, such as balanced center of mass, weight limitations on fragile items, and safety proximity constraints. These results highlight Snuggle-Pack as an efficient, flexible, and scalable solution for industrial robotic packing tasks.