Increased robot deployment, such as in warehousing, has revealed a need for collaboration among heterogeneous robot teams to resolve unforeseen conflicts. To this end, we propose a peer-to-peer coordination protocol that enables robots to request and provide help without a central task allocator. The process begins when a robot detects a conflict and uses a Large Language Model (LLM) to decide whether external assistance is required. If so, it crafts and broadcasts a natural language (NL) help request. Potential helper robots reason over the request and respond with offers of assistance, including information about the effect on their ongoing tasks. Helper reasoning is implemented via an LLM grounded in Signal Temporal Logic (STL) using a Backus-Naur Form (BNF) grammar, ensuring syntactically valid NL-to-STL translations, which are then solved as a Mixed Integer Linear Program (MILP). Finally, the requester robot selects a helper by reasoning over the expected increase in system-level total task completion time. We evaluated our framework through experiments comparing different helper-selection strategies and found that considering multiple offers allows the requester to minimize added makespan. Our approach significantly outperforms heuristics such as selecting the nearest available candidate helper robot, and achieves performance comparable to a centralized "Oracle" baseline but without heavy information demands.

翻译：随着机器人部署的增加（例如在仓储领域），异构机器人团队之间的协作需求日益凸显，以解决不可预见的冲突。为此，我们提出了一种点对点协调协议，使机器人能够在没有中央任务分配器的情况下请求和提供帮助。该过程始于机器人检测到冲突，并使用大型语言模型（LLM）判断是否需要外部协助。如果需要，它将构建并广播一个自然语言（NL）帮助请求。潜在的协助机器人对请求进行推理，并回复协助提议，包括对其当前任务影响的说明。协助推理通过基于信号时序逻辑（STL）的LLM实现，使用巴科斯-诺尔范式（BNF）语法确保自然语言到STL的翻译在句法上有效，随后将翻译结果作为混合整数线性规划（MILP）求解。最后，请求机器人通过推理系统级总任务完成时间的预期增长来选择协助者。我们通过实验评估了该框架，比较了不同的协助者选择策略，发现考虑多个提议能使请求者最小化增加的完工时间。我们的方法显著优于启发式策略（如选择最近可用候选协助机器人），且性能与集中式“Oracle”基线相当，同时无需大量信息需求。