NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916782806138880 |
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| author | Li, Bowei Yu, Peiqi Tang, Zhenran Zhou, Han Sun, Yifan Liu, Ruixuan Liu, Changliu |
| author_facet | Li, Bowei Yu, Peiqi Tang, Zhenran Zhou, Han Sun, Yifan Liu, Ruixuan Liu, Changliu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing Li, Bowei Yu, Peiqi Tang, Zhenran Zhou, Han Sun, Yifan Liu, Ruixuan Liu, Changliu Robotics 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. |
| title | NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.06567 |