HEART: 异构专家智能体协调实现物理接地机器人任务规划
HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planning
AI总结：
提出异构多LLM框架HEART，将指令分解为原子推理任务分配给角色专家，通过约束驱动规划合成，提升机器人任务规划的物理可行性和效率。
摘要：
大型语言模型（LLM）能够对复杂指令进行推理，但通常无法满足机器人任务规划所需的物理和空间约束。最近的基于LLM的规划器直接将文本转化为动作序列，但缺乏关于可行性、可达性和逻辑顺序的结构化推理，导致无效或不完整的规划。我们提出一个异构多LLM框架，将指令分解为原子推理任务，并在令牌预算下分配给角色专业化的专家智能体，以应对现实世界的计算和通信约束。通过结合来自异构智能体的角色导向推理，随后进行约束驱动的规划合成，HEART在规划前验证能力、可达性和约束条件，并帮助生成物理可执行的规划，同时保持效率。在不同家庭基准测试中的实验表明，与单LLM和基于规则的规划器相比，HEART持续提高了规划成功率，证明了异构LLM协作能够在资源约束下实现鲁棒且可扩展的机器人任务规划。
英文摘要：
Large Language Models (LLMs) can reason over complex instructions but often fail to satisfy the physical and spatial constraints required for robotic task planning. Recent LLM-based planners directly translate text into action sequences, yet they lack structured reasoning about feasibility, reachability, and logical order, resulting in invalid or incomplete plans. We present a heterogeneous multi-LLM framework that decomposes instructions into atomic reasoning tasks and allocates them to role-specialized expert agents under a token budget for real-world computational and communicational constraints. By combining role-oriented reasoning from heterogeneous agents followed by constraint-driven plan synthesis, HEART validates capability, reachability, and constraint conditions before planning and helps produce physically executable plans while maintaining efficiency. Experiments across different household benchmarks show that HEART consistently improves plan success compared to single-LLM and rule-based planners, demonstrating that heterogeneous LLM collaboration enables robust and scalable robotic task planning under resource constraints.
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