NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing

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.

@article{li2025_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 }
}