# PackLab：面向机器人装箱的 MLLM 开发、训练与评估框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-20 08:00
- AIHOT 分数：39
- AIHOT 链接：https://aihot.news/items/cmuf32nqo08gzroodmfmyizj7
- 原文链接：https://arxiv.org/abs/2609.23784

## AI 摘要

PackLab 是面向闭环机器人装箱的 MLLM 开发、训练与评估框架，包含物理仿真平台 PackLab-Suite、装箱专用模型 PackLab-VLM 和多难度标准化评测 PackLab-Bench。

## 正文

Robotic bin packing requires long-horizon sequential decision-making, as each object placement affects the available space for subsequent packing. Existing methods primarily rely on hand-crafted geometric heuristics that optimize predefined objectives or reinforcement learning policies learned through trial and error over predefined training configurations. Despite recent advances in multimodal large language models (MLLMs) for this task, their potential for closed-loop sequential decisions across heterogeneous packing configurations remains underexplored. To address this gap, we introduce PackLab, a comprehensive framework for developing, training, and evaluating MLLMs for closed-loop robotic bin packing. PackLab-Suite provides a physics-based simulation platform for scalable generation of diverse training packing trajectories and evaluation of their physical outcomes. PackLab-VLM is a packing-specialized MLLM that understands the evolving object and container states to jointly select objects and predict placements in a closed-loop manner. PackLab-Bench provides standardized packing scenarios at multiple difficulty levels for systematic evaluation. Extensive experiments demonstrate that, on average, PackLab-VLM outperforms conventional packing heuristics, traditional reinforcement learning methods, and general-purpose MLLMs across object sets and container configurations, highlighting the potential of MLLMs for long-horizon robotic packing. The code, model, dataset, and benchmark are available at https://github.com/Correr-Zhou/PackLab .