LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner
<|reference_start|>LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner: Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks. Nevertheless, it remains a significant challenge to handle long-horizon tasks, especially in subtask identification and allocation for cooperative heterogeneous robot teams. To address this issue, we propose a Language Model-Driven Multi-Agent PDDL Planner (LaMMA-P), a novel multi-agent task planning framework that achieves state-of-the-art performance on long-horizon tasks. LaMMA-P integrates the strengths of the LMs' reasoning capability and the traditional heuristic search planner to achieve a high success rate and efficiency while demonstrating strong generalization across tasks. Additionally, we create MAT-THOR, a comprehensive benchmark that features household tasks with two different levels of complexity based on the AI2-THOR environment. The experimental results demonstrate that LaMMA-P achieves a 105% higher success rate and 36% higher efficiency than existing LM-based multi-agent planners. The experimental videos, code, and datasets of this work as well as the detailed prompts used in each module are available at https://lamma-p.github.io.<|reference_end|>
@article{zhang2024lamma-p:,
    title={LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and
  Planning with LM-Driven PDDL Planner},
    author={Xiaopan Zhang and Hao Qin and Fuquan Wang and Yue Dong and Jiachen Li},
    journal={arXiv preprint arXiv:2409.20560},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20560},
    primaryClass={cs.RO cs.AI cs.CV cs.LG cs.MA}
}