# NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions

arXiv: https://arxiv.org/abs/2405.04215 · HTML: https://mlsanity.com/paper/2405.04215/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 7 May 2024.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://mlsanity.com/paper/2206.10498/) (2022)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (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)
- [Self-Evaluation Guided Beam Search for Reasoning](https://mlsanity.com/paper/2305.00633/) (2023)
- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning](https://mlsanity.com/paper/2308.13724/) (2023)
- [Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks](https://mlsanity.com/paper/2205.05718/) (2022)
- [Plansformer: Generating Symbolic Plans using Transformers](https://mlsanity.com/paper/2212.08681/) (2022)

## Built on by (most cited first, 22 of 22)

- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [Cocoa: Co-Planning and Co-Execution with AI Agents](https://mlsanity.com/paper/2412.10999/) (2024)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code](https://mlsanity.com/paper/2503.18809/) (2025)
- [Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach](https://mlsanity.com/paper/2411.14484/) (2024)
- [Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study](https://mlsanity.com/paper/2507.23589/) (2025)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [Automating Thought of Search: A Journey Towards Soundness and Completeness](https://mlsanity.com/paper/2408.11326/) (2024)
- [A Roadmap to Guide the Integration of LLMs in Hierarchical Planning](https://mlsanity.com/paper/2501.08068/) (2025)
- [Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning](https://mlsanity.com/paper/2506.19592/) (2025)
- [Symmetry-Aware Transformer Training for Automated Planning](https://mlsanity.com/paper/2508.07743/) (2025)
- [Using large language models for embodied planning introduces systematic safety risks](https://mlsanity.com/paper/2604.18463/) (2026)
- [Model Space Reasoning as Search in Feedback Space for Planning Domain Generation](https://mlsanity.com/paper/2604.08712/) (2026)
- [Make Planning Research Rigorous Again!](https://mlsanity.com/paper/2505.21674/) (2025)
- [Towards a General Framework for HTN Modeling with LLMs](https://mlsanity.com/paper/2511.18165/) (2025)
- [Fuzzy Categorical Planning: Autonomous Goal Satisfaction with Graded Semantic Constraints](https://mlsanity.com/paper/2601.20021/) (2026)
- [SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems](https://mlsanity.com/paper/2603.21523/) (2026)
- [Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents](https://mlsanity.com/paper/2606.21740/) (2026)
- [Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback](https://mlsanity.com/paper/2606.29700/) (2026)
- [PDDLCoder: Agentic PDDL Generation for LLM-Assisted Symbolic Planning](https://mlsanity.com/paper/2608.16637/) (2026)
- [PDDL-ART: Autonomous Symbolic Abstraction From Demonstration For Long-Horizon Robotic Manipulation Using Vision-Language Models](https://mlsanity.com/paper/2608.17146/) (2026)
- [VLMs Can Describe, But Not Measure: Object-Centric Scene Understanding for Robotic Manipulation](https://mlsanity.com/paper/2609.28184/) (2026)

## Abstract (arXiv)

Classical planners are powerful systems, but modeling tasks in input formats such as PDDL is tedious and error-prone. In contrast, planning with Large Language Models (LLMs) allows for almost any input text, but offers no guarantees on plan quality or even soundness. In an attempt to merge the best of these two approaches, some work has begun to use LLMs to automate parts of the PDDL creation process. However, these methods still require various degrees of expert input or domain-specific adaptations. We present NL2Plan, the first fully automatic system for generating complete PDDL tasks from minimal natural language descriptions. NL2Plan uses an LLM to incrementally extract the necessary information from the short text input before creating a complete PDDL description of both the domain and the problem which is finally solved by a classical planner. We evaluate NL2Plan on seven planning domains, five of which are novel and thus not in the LLM training data, and find that NL2Plan outperforms directly generating the files with an LLM+validator combination. As such, NL2Plan is a powerful tool for assistive PDDL modeling and a step towards solving natural language planning task with interpretability and guarantees.