发表机构
The Chinese University of Hong Kong; M-A-P（香港中文大学; M-A-P）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PackLab提出面向机器人装箱的MLLM综合框架，含仿真平台、专用模型和基准，实验证明其优于传统方法和通用MLLMs，实现长时域闭环决策。
AI 中文摘要
机器人装箱需要长时域序贯决策，因为每次物体放置都会影响后续装箱的可用空间。现有方法主要依赖手工设计的几何启发式算法，这些算法优化预定义目标，或依赖通过试错在预定义训练配置上学习的强化学习策略。尽管多模态大语言模型（MLLMs）在该任务上近期取得进展，但其在异构装箱配置中实现闭环序贯决策的潜力仍未得到充分探索。为填补这一空白，我们提出PackLab，一个用于开发、训练和评估面向闭环机器人装箱的MLLMs的综合框架。PackLab-Suite提供基于物理的仿真平台，用于可扩展地生成多样化的训练装箱轨迹并评估其物理结果。PackLab-VLM是一种装箱专用的MLLM，能够理解不断变化的物体和容器状态，以闭环方式联合选择物体并预测放置位置。PackLab-Bench提供多个难度级别的标准化装箱场景，用于系统评估。大量实验表明，平均而言，PackLab-VLM在物体集合和容器配置上优于传统装箱启发式算法、传统强化学习方法和通用MLLMs，凸显了MLLMs在长时域机器人装箱中的潜力。代码、模型、数据集和基准可在以下网址获取：https URL。
英文摘要
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 .
CommentsThe code, model, dataset, and benchmark are available at https://github.com/Correr-Zhou/PackLab
