DynaHMRC: 基于大语言模型的动态任务去中心化异构多机器人协作
DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
AI总结：
提出DynaHMRC去中心化框架，每个机器人作为角色感知的LLM智能体，通过四阶段闭环流程（自我描述、任务分配与领导竞标、领导者选举、反思执行）实现动态异构多机器人协作，并构建基准测试验证其高效性和可扩展性。
摘要：
大型语言模型（LLMs）为机器人提供了更丰富的任务理解和适应性，使其在协调长期任务中的异构多机器人系统方面具有前景。尽管有这种潜力，但仍存在几个挑战尚未充分探索：（1）集中式LLM调度器随着团队规模和环境复杂性的增加而扩展性差。单个模型必须处理过多的上下文信息，长上下文近似可能降低推理质量；（2）现有任务公式未能充分考虑动态设置，而对不断变化的任务条件的鲁棒适应对于实际部署至关重要；（3）领域特定数据稀缺限制了专门的机器人推理，使得专有通用模型在专家任务上效率低下。为了解决这些限制，我们提出了DynaHMRC，一个去中心化框架，其中每个机器人充当角色感知的LLM智能体。这种设计减轻了单模型上下文瓶颈，并支持跨异构团队配置的灵活协作。DynaHMRC将协作组织为四阶段闭环过程：自我描述、带有领导竞标的任务分配、领导者选举和反思执行，由可执行的机器人接口支持。我们进一步开发了一个基准测试，涵盖三个任务族、四种动态变化和六种团队配置，以系统研究动态任务建模。此外，我们进行了实证分析，以指导领域特定专家数据集的构建，并微调预训练LLM以提高专业能力。实验表明，DynaHMRC在更少的动作和通信步骤下实现了比强基线更高的成功率，同时在评估的设置中随着团队规模的增长显示出有希望的可扩展性趋势。
英文摘要：
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.
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。
