Multi-robot task planning requires decomposing natural-language instructions into executable actions for heterogeneous robot teams. Conventional Planning Domain Definition Language (PDDL) planners provide rigorous guarantees but struggle to handle ambiguous or long-horizon missions, while large language models (LLMs) can interpret instructions and propose plans but may hallucinate or produce infeasible actions. We present a hierarchical multi-agent LLM-based planner with prompt optimization: an upper layer decomposes tasks and assigns them to lower-layer agents, which generate PDDL problems solved by a classical planner. When plans fail, the system applies TextGrad-inspired textual-gradient updates to optimize each agent's prompt and thereby improve planning accuracy. In addition, meta-prompts are learned and shared across agents within the same layer, enabling efficient prompt optimization in multi-agent settings. On the MAT-THOR benchmark, our planner achieves success rates of 0.95 on compound tasks, 0.84 on complex tasks, and 0.60 on vague tasks, improving over the previous state-of-the-art LaMMA-P by 2, 7, and 15 percentage points respectively. An ablation study shows that the hierarchical structure, prompt optimization, and meta-prompt sharing contribute roughly +59, +37, and +4 percentage points to the overall success rate.

翻译：多机器人任务规划需要将自然语言指令分解为异构机器人团队可执行的动作。传统的规划域定义语言（PDL）规划器能提供严格的保证，但难以处理模糊或长周期的任务；而大语言模型（LLM）能够解释指令并提出规划，但可能产生幻觉或不可行的动作。我们提出了一种基于提示优化的分层多智能体LLM规划器：上层分解任务并将其分配给下层智能体，下层智能体生成可由经典规划器求解的PDDL问题。当规划失败时，系统应用受TextGrad启发的文本梯度更新来优化每个智能体的提示，从而提高规划准确性。此外，元提示在同一层内的智能体之间学习和共享，实现了多智能体设置下的高效提示优化。在MAT-THOR基准测试中，我们的规划器在复合任务、复杂任务和模糊任务上分别实现了0.95、0.84和0.60的成功率，较先前最先进的LaMMA-P分别提高了2、7和15个百分点。消融研究表明，分层结构、提示优化和元提示共享对总体成功率的贡献分别约为+59、+37和+4个百分点。