# LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner

arXiv: https://arxiv.org/abs/2409.20560 · HTML: https://mlsanity.com/paper/2409.20560/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 21 of 21)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [RoCo: Dialectic Multi-Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2307.04738/) (2023)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models](https://mlsanity.com/paper/2402.14848/) (2024)
- [Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://mlsanity.com/paper/2305.11014/) (2023)
- [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023)
- [ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning](https://mlsanity.com/paper/2308.13724/) (2023)
- [Dynamic Planning with a LLM](https://mlsanity.com/paper/2308.06391/) (2023)
- [Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems](https://mlsanity.com/paper/1804.08229/) (2018)
- [LLaMAR: Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments](https://mlsanity.com/paper/2407.10031/) (2024)
- [TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models](https://mlsanity.com/paper/2403.17246/) (2024)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [An Overview of Hierarchical Task Network Planning](https://mlsanity.com/paper/1403.7426/) (2014)
- [Cooperative Multi-Agent Planning: A Survey](https://mlsanity.com/paper/1711.09057/) (2017)

## Built on by (most cited first, 30 of 32)

- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025)
- [Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction](https://mlsanity.com/paper/2508.05294/) (2025)
- [Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning](https://mlsanity.com/paper/2509.13351/) (2025)
- [Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review](https://mlsanity.com/paper/2502.11518/) (2025)
- [Benchmarking LLMs' Swarm intelligence](https://mlsanity.com/paper/2505.04364/) (2025)
- [Generalizability of Large Language Model-Based Agents: A Comprehensive Survey](https://mlsanity.com/paper/2509.16330/) (2025)
- [REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation](https://mlsanity.com/paper/2503.22122/) (2025)
- [PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization](https://mlsanity.com/paper/2506.01475/) (2025)
- [HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models](https://mlsanity.com/paper/2506.15065/) (2025)
- [Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning](https://mlsanity.com/paper/2506.19592/) (2025)
- [Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving](https://mlsanity.com/paper/2603.25740/) (2026)
- [Compositional Coordination for Multi-Robot Teams with Large Language Models](https://mlsanity.com/paper/2507.16068/) (2025)
- [CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction](https://mlsanity.com/paper/2602.06038/) (2026)
- [SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents](https://mlsanity.com/paper/2510.12985/) (2025)
- [PIP-LLM: Integrating PDDL-Integer Programming with LLMs for Coordinating Multi-Robot Teams Using Natural Language](https://mlsanity.com/paper/2510.22784/) (2025)
- [Priority-Aware Multi-Robot Coverage Path Planning](https://mlsanity.com/paper/2601.00580/) (2026)
- [KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning](https://mlsanity.com/paper/2602.04129/) (2026)
- [Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning](https://mlsanity.com/paper/2602.21670/) (2026)
- [SafeGen-LLM: Enhancing Safety Generalization in Task Planning for Robotic Systems](https://mlsanity.com/paper/2602.24235/) (2026)
- [BrainMem: Brain-Inspired Evolving Memory for Embodied Agent Task Planning](https://mlsanity.com/paper/2604.16331/) (2026)
- [Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments](https://mlsanity.com/paper/2504.07283/) (2025)
- [Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2603.08814/) (2026)
- [A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation](https://mlsanity.com/paper/2607.06990/) (2026)
- [Mosaic: Runtime-Efficient Multi-Agent Embodied Planning](https://mlsanity.com/paper/2607.09603/) (2026)
- [Transforming Monolithic Foundation Models into Embodied Multi-Agent Architectures for Human-Robot Collaboration](https://mlsanity.com/paper/2512.00797/) (2025)
- [LLM-Grounded Dynamic Task Planning with Hierarchical Temporal Logic for Human-Aware Multi-Robot Handover](https://mlsanity.com/paper/2602.09472/) (2026)
- [Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection](https://mlsanity.com/paper/2603.23800/) (2026)
- [NEXUS: Continual Learning of Symbolic Constraints for Safe and Robust Embodied Planning](https://mlsanity.com/paper/2605.09387/) (2026)

## Abstract (arXiv)

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 multiagent planners. The experimental videos, code, datasets, and detailed prompts used in each module can be found on the project website: https://lamma-p.github.io.