NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing
Abstract
This paper presents NeSyPack, a neuro-symbolic framework for bimanual logistics packing. NeSyPack combines data-driven models and symbolic reasoning to build an explainable hierarchical system that is generalizable, data-efficient, and reliable. It decomposes a task into subtasks via hierarchical reasoning, and further into atomic skills managed by a symbolic skill graph. The graph selects skill parameters, robot configurations, and task-specific control strategies for execution. This modular design enables robustness, adaptability, and efficient reuse - outperforming end-to-end models that require large-scale retraining. Using NeSyPack, our team won the First Prize in the What Bimanuals Can Do (WBCD) competition at the 2025 IEEE International Conference on Robotics and Automation.
Keywords
Cite
@article{arxiv.2506.06567,
title = {NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing},
author = {Bowei Li and Peiqi Yu and Zhenran Tang and Han Zhou and Yifan Sun and Ruixuan Liu and Changliu Liu},
journal= {arXiv preprint arXiv:2506.06567},
year = {2025}
}
Comments
10 pages, 5 figures. Accepted to the RSS 2025 Workshop on Benchmarking Robot Manipulation: Improving Interoperability and Modularity. First Prize in the WBCD competition at ICRA 2025. Equal contribution by Bowei Li and Peiqi Yu