将大语言模型与时序逻辑融合以实现复杂场景中可靠的人-群智协作
Melding LLM and temporal logic for reliable human-swarm collaboration in complex scenarios
AI总结：
本文提出一种神经符号框架，结合时序逻辑与大语言模型，实现长周期人-群智协作，通过形式化任务规划与上下文感知推理，提升动态环境中任务规划的可靠性与效率。
摘要：
机器人群落有望在复杂和危险环境中提供可扩展的帮助。任务规划是人-群智协作的核心，将操作员的意图转化为协调的群落行动，并帮助确定执行过程中何时需要验证或干预。然而，在长周期任务中，动态场景下的可靠任务规划变得困难：新兴事件和变化的条件要求持续适应，持续的操作员监督带来显著的认知负担。现有的基于大语言模型的规划工具可以支持计划生成，但仍然容易产生无效的任务顺序和不可行的机器人动作，导致频繁的手动调整。本文介绍了一种神经符号框架，用于长周期的人-群智协作，紧密融合可验证的任务规划与上下文感知的大语言模型推理。我们将任务目标和操作规则形式化为时序逻辑公式，并将可接受的任务顺序形式化为任务自动机。在这些形式化约束和实时感知上下文中，大语言模型生成可执行的子任务序列，以满足任务规则并保持在当前场景中。一个考虑不确定性的调度器则在异构群中分配子任务，以最大化并行性，同时保持对中断的韧性。一个事件触发的交互协议进一步将操作员的参与限制在稀疏的、高层次的确认和指导上。在异构机器人舰队上的部署产生了相似的结果，同时保持对硬件特定的执行和通信不确定性的鲁棒性。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-群智协作。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
