# NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing

arXiv: https://arxiv.org/abs/2506.06567 · HTML: https://mlsanity.com/paper/2506.06567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [ManiSkillFormer: Demonstration-Free Compositional Manipulation via Geometric Contracts and Agentic Skill Graph](https://mlsanity.com/paper/2609.16331/) (2026)
- [ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation](https://mlsanity.com/paper/2609.24563/) (2026)

## Abstract (arXiv)

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.