In embodied artificial intelligence, enabling heterogeneous robot teams to execute long-horizon tasks from high-level instructions remains a critical challenge. While large language models (LLMs) show promise in instruction parsing and preliminary planning, they exhibit limitations in long-term reasoning and dynamic multi-robot coordination. We propose Hierarchical Autonomous Intelligent Multi-Robot Planning(H-AIM), a novel embodied multi-robot task planning framework that addresses these issues through a three-stage cascaded architecture: 1) It leverages an LLM to parse instructions and generate Planning Domain Definition Language (PDDL) problem descriptions, thereby transforming commands into formal planning problems; 2) It combines the semantic reasoning of LLMs with the search capabilities of a classical planner to produce optimized action sequences; 3) It compiles the resulting plan into behavior trees for reactive control. The framework supports dynamically sized heterogeneous robot teams via a shared blackboard mechanism for communication and state synchronization. To validate our approach, we introduce the MACE-THOR benchmark dataset, comprising 42 complex tasks across 8 distinct household layouts. Experimental results demonstrate that H-AIM achieves a remarkable performance improvement, elevating the task success rate from 12% to 55% and boosting the goal condition recall from 32% to 72% against the strongest baseline, LaMMA-P.

翻译：在具身人工智能领域，如何使异构机器人团队根据高层指令执行长时程任务仍是一个关键挑战。尽管大语言模型（LLM）在指令解析和初步规划方面展现出潜力，但其在长期推理和动态多机器人协调方面存在局限。我们提出分层自主智能多机器人规划框架（H-AIM），这是一种新型的具身多机器人任务规划框架，通过三级级联架构解决上述问题：1）利用LLM解析指令并生成规划领域定义语言（PDDL）问题描述，从而将指令转化为形式化规划问题；2）结合LLM的语义推理能力与经典规划器的搜索能力，生成优化的动作序列；3）将生成的规划编译为行为树以实现反应式控制。该框架通过共享黑板机制支持通信与状态同步，从而适应动态规模的异构机器人团队。为验证所提方法，我们构建了MACE-THOR基准数据集，包含8种不同家庭布局下的42项复杂任务。实验结果表明，相较于最强基线LaMMA-P，H-AIM实现了显著性能提升：任务成功率从12%提高至55%，目标条件召回率从32%提升至72%。