![[2602.21670] Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.21670] Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
Summary
This article presents a novel hierarchical framework for multi-robot task planning using large language models (LLMs) with prompt optimization, enhancing task execution accuracy in complex scenarios.
Why It Matters
As robotics and AI continue to advance, effective multi-robot coordination is crucial for applications ranging from industrial automation to autonomous vehicles. This framework addresses limitations in traditional planning methods by leveraging LLMs, potentially transforming how robots interpret and execute tasks.
Key Takeaways
- Introduces a hierarchical multi-agent framework for task planning.
- Utilizes prompt optimization to enhance LLM performance in robotics.
- Achieves significant improvements in task success rates over previous models.
- Incorporates meta-prompt sharing for efficient multi-agent coordination.
- Demonstrates effectiveness on the MAT-THOR benchmark with high success rates.
Computer Science > Robotics arXiv:2602.21670 (cs) [Submitted on 25 Feb 2026] Title:Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning Authors:Tomoya Kawabe (1), Rin Takano (1) ((1) NEC Corporation) View a PDF of the paper titled Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning, by Tomoya Kawabe (1) and Rin Takano (1) ((1) NEC Corporation) View PDF HTML (experimental) Abstract: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, ...
