用大模型自动规划超市商品打包顺序，避免损坏。
iPack: Intuitive Bin Packing with Large Language Models
- 结合视觉与语言模型识别商品并模仿人类打包策略
- 无需额外训练即可处理新商品，打包顺序有效防损
- 模块化设计便于升级模型，适合零售自动化场景
机器人与自动化在物流领域日益重要，但主要局限于传统仓库。在生鲜零售中，虽然无收银员超市已出现，顾客仍需手动拣选和打包商品。尽管机器人在物品抓取方面已有大量研究，商品打包任务却长期未受关注。正确排序打包对防止商品损坏至关重要，例如重物不应置于易碎品之上。然而，在商店中商品种类繁多，难以定义通用的打包标准。本文提出 LLM-Pack，一种基于语言与视觉基础模型的新颖商品打包方法，可识别商品并生成类似人类的打包序列。该方法无需为新商品进行专门训练，其模块化设计支持底层模型的便捷更新。我们进行了广泛评估以验证其性能，并将在论文发表后公开 LLM-Pack 的源代码。
原文摘要 · Abstract (English)
Robotics and automation are increasingly influential in logistics but remain largely confined to traditional warehouses. In grocery retail, advancements such as cashier-less supermarkets exist, yet customers still manually pick and pack groceries. While there has been a substantial focus in robotics on the bin picking problem, the task of packing objects and groceries has remained largely untouched. However, packing grocery items in the right order is crucial for preventing product damage, e.g., heavy objects should not be placed on top of fragile ones. However, the exact criteria for the right packing order are hard to define, in particular given the huge variety of objects typically found in stores. In this paper, we introduce LLM-Pack, a novel approach for grocery packing. LLM-Pack leverages language and vision foundation models for identifying groceries and generating a packing sequence that mimics human packing strategy. LLM-Pack does not require dedicated training to handle new grocery items and its modularity allows easy upgrades of the underlying foundation models. We extensively evaluate our approach to demonstrate its performance. We will make the source code of LLMPack publicly available upon the publication of this manuscript.
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