NeSyPack用符号推理+数据模型实现双臂物流包装，可解释且省数据。
NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing
- 分层符号图管理原子技能，自动选参数和策略。
- 在机器人竞赛中夺冠，比端到端模型更省数据、更稳定。
- 适合需要可靠性和可解释性的工业自动化场景。
本文提出NeSyPack，一种用于双臂物流包装的神经符号框架。该框架结合数据驱动模型与符号推理，构建可解释、可泛化、数据高效且可靠的分层系统。任务通过分层推理分解为子任务，进一步拆解为由符号技能图管理的原子技能。该图负责选择技能参数、机器人配置及特定任务控制策略。模块化设计提升了鲁棒性、适应性与复用效率，显著优于需大规模重训练的端到端模型。使用NeSyPack，团队在2025年IEEE国际机器人与自动化会议（ICRA）举办的What Bimanuals Can Do（WBCD）竞赛中获得一等奖。
原文摘要 · Abstract (English)
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.
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