🤖 AI Summary
This work addresses the challenge of reliably decomposing ambiguous or temporally extended natural language instructions into executable actions for heterogeneous robots in multi-robot task planning, where existing approaches often lack robustness and feasibility. The authors propose a hierarchical multi-agent LLM-based planning framework: a high-level module performs task decomposition and assignment, while low-level agents generate PDDL problem instances solved by classical planners. Upon planning failure, the system employs TextGrad-inspired textual gradient optimization to refine prompts and shares meta-prompts among peer agents to enhance efficiency. Evaluated on the MAT-THOR benchmark, the method achieves success rates of 0.95, 0.84, and 0.60 on composite, complex, and ambiguous tasks, respectively—outperforming state-of-the-art baselines by 2–15 percentage points. Ablation studies confirm the contribution of each component to overall performance.
📝 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, 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.