Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi-robot systems in long-horizon tasks. Despite this potential, several challenges remain underexplored: (1) Centralized LLM schedulers scale poorly as team size and environmental complexity increase. A single model must process excessive contextual information, and long-context approximation may degrade reasoning quality; (2) Existing task formulations insufficiently consider dynamic settings, while robust adaptation to evolving task conditions is essential for real-world deployment; (3) Domain-specific data scarcity limits specialized robotic reasoning, making proprietary general-purpose models inefficient for expert tasks. To address these limitations, we propose DynaHMRC, a decentralized framework in which each robot acts as a role-aware LLM agent. This design mitigates the single-model context bottleneck and supports flexible collaboration across heterogeneous team configurations. DynaHMRC organizes collaboration as a four-stage closed-loop process: self-description, task allocation with leadership bidding, leader election, and reflective execution, supported by executable robot interfaces. We further develop a benchmark covering three task families, four dynamic variations, and six team configurations to systematically study dynamic task modeling. In addition, we conduct an empirical analysis to guide the construction of domain-specific expert datasets and fine-tune pretrained LLMs to improve specialized competence. Experiments show that DynaHMRC achieves higher success rates than strong baselines with fewer action and communication steps, while demonstrating promising scalability trends as team size grows within the evaluated settings.

翻译：大语言模型（LLMs）使机器人具备更丰富的任务理解与适应性，为协调异构多机器人系统完成长周期任务提供了可行方案。然而，现有研究仍存在以下待解决挑战：（1）集中式LLM调度器在团队规模与系统复杂性增加时扩展性不足——单一模型需处理过量的上下文信息，长上下文近似机制可能降低推理质量；（2）现有任务建模未能充分考虑动态环境，而现实部署中系统需具备对动态任务条件的鲁棒自适应能力；（3）领域数据稀缺限制了机器人专业推理能力，这使得通用大规模预训练模型在专家任务中效率低下。针对上述问题，本文提出DynaHMRC去中心化框架，其中每个机器人作为角色感知的LLM智能体独立运行。该设计缓解了单模型上下文瓶颈，支持异构团队配置下的灵活协作。DynaHMRC将协作组织为四阶段闭环流程：自描述、基于领导权投标的任务分配、领导者选举与反射式执行，并通过可执行的机器人接口提供支撑。我们进一步构建覆盖三类任务族、四种动态变体及六种团队配置的基准测试，系统研究动态任务建模方法。此外，通过实证分析指导领域专家数据集的构建，并对预训练LLM进行微调以提升专业能力。实验表明，DynaHMRC在更少的行动与通信步骤下取得高于强基线的任务成功率，且在评估设定中展现出随团队规模增长的良好可扩展性趋势。