PackLab: A Comprehensive Framework for Developing, Training, and Evaluating MLLMs in Robotic Bin Packing

arXiv:2609.23784 · cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.RO

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: The code, model, dataset, and benchmark are available at https://github.com/Correr-Zhou/PackLab

Code: https://github.com/Correr-Zhou/PackLab

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Ablation Studies — 3× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Maximum Touching Area — 1× in this paper
• Point Clouds — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes

Sources

• OPA-Pack: Object-Property-Aware Robotic Bin Packing
• iPack: Intuitive Bin Packing with Large Language Models
• A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
• A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
• PackingGPT: 3D Packing Agent for Real Furniture in Last-Mile Delivery

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