Multimodal large language model improves robot packing decisions
PackLab: A Comprehensive Framework for Developing, Training, and Evaluating MLLMs in Robotic Bin Packing
Robotics
Summary
Packing objects into a container is tricky because each item’s placement changes the remaining space. The authors present PackLab, a system that uses a special language model designed to understand visual and spatial information to decide which object to place next and where. They built a simulator to train and test this approach on many packing scenarios. Their model consistently did better than traditional rules and other AI methods at fitting objects efficiently. This demonstrates that language models can help robots make smart, step-by-step packing choices.
What this means in practice
- •For robotics engineers: Develop robotic systems that place objects efficiently by integrating a language model aware of changing bin states for improved packing strategies.
- •For logistics software developers: Incorporate a simulation-based benchmark and trained model to optimize automated packing software handling diverse container and item sets.
Authors
Donghao Zhou, Jia-Hui Pan, Fan Zhang, Xingyuan Bu, Shilong Li, Xiaojie Gao, Yun-Hui Liu, Chi-Wing Fu, Pheng-Ann Heng
Abstract
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 .