Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human-swarm collaboration, translating the operator's intent into coordinated swarm actions and helping determine when validation or intervention is required during execution. In long-horizon missions under dynamic scenarios, however, reliable task planning becomes difficult to maintain: emerging events and changing conditions demand continual adaptation, and sustained operator oversight imposes substantial cognitive burden. Existing LLM-based planning tools can support plan generation, yet they remain susceptible to invalid task orderings and infeasible robot actions, resulting in frequent manual adjustment. Here we introduce a neuro-symbolic framework for long-horizon human-swarm collaboration that tightly melds verifiable task planning with context-grounded LLM reasoning. We formalize mission goals and operational rules as temporal logic formulas and admissible task orderings as task automata. Conditioned on these formal constraints and live perceptual context, LLMs generate executable subtask sequences that satisfy mission rules and remain grounded in the current scene. An uncertainty-aware scheduler then assigns subtasks across the heterogeneous swarm to maximize parallelisms while remaining resilient to disruptions. An event-triggered interaction protocol further limits operator involvement to sparse, high-level confirmation and guidance. Deployment on a heterogeneous robotic fleet yields similar results while remaining robust to hardware-specific actuation and communication uncertainties. Together, these results support a formal and scalable paradigm for reliable and low-overhead human-swarm collaboration in dynamic environments

翻译：机器人集群有望在复杂和危险环境中提供可扩展的辅助。任务规划是人机集群协作的核心环节，它将操作者的意图转化为协调的集群行动，并帮助确定在执行过程中何时需要验证或干预。然而，在动态场景下的长期任务中，可靠的任务规划难以维持：新出现的事件和不断变化的条件要求持续适应，而持续的操作者监督会带来沉重的认知负担。现有的基于大语言模型（LLM）的规划工具可以支持计划生成，但它们仍然容易产生无效的任务排序和不可行的机器人动作，导致频繁的人工调整。本文介绍了一种用于长期人机集群协作的神经符号框架，该框架将可验证的任务规划与基于具体情境的LLM推理紧密结合。我们将任务目标和操作规则形式化为时间逻辑公式，将可接受的任务排序形式化为任务自动机。在此基础上，LLM基于这些形式化约束和实时感知上下文，生成满足任务规则且与当前场景保持一致的可执行子任务序列。随后，一个不确定性感知调度器将子任务分配给异构集群，以最大化并行性，同时保持对干扰的鲁棒性。事件触发的交互协议进一步将操作者的参与限制在稀疏的高层确认和指导上。在异构机器人编队上的部署结果表明，该方法在保持对特定硬件驱动和通信不确定性的鲁棒性的同时，取得了相似的结果。这些结果共同支持了一种形式化且可扩展的范式，用于在动态环境中实现可靠且低开销的人机集群协作。