H-AIM: Orchestrating LLMs, PDDL, and Behavior Trees for Hierarchical Multi-Robot Planning

• URL: http://arxiv.org/abs/2601.11063v1
• Date: Fri, 16 Jan 2026 07:59:50 GMT
• Title: H-AIM: Orchestrating LLMs, PDDL, and Behavior Trees for Hierarchical Multi-Robot Planning
• Authors: Haishan Zeng, Peng Li,
• Abstract summary: H-AIM is a novel embodied multi-robot task planning framework.<n>It exploits large language models (LLMs) to parse instructions and generate Planning Domain Definition Language (PDDL) problem descriptions.<n>It compiles the resulting plan into behavior trees for reactive control.
• Score: 3.2800662172795114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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