# Planning with Vision-Language Models and a Use Case in Robot-Assisted Teaching

arXiv: https://arxiv.org/abs/2501.17665 · HTML: https://mlsanity.com/paper/2501.17665/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 7 of 7)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (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)
- [Vision-Language Interpreter for Robot Task Planning](https://mlsanity.com/paper/2311.00967/) (2023)

## Built on by (most cited first, 5 of 5)

- [ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models](https://mlsanity.com/paper/2505.13180/) (2025)
- [Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning](https://mlsanity.com/paper/2510.03182/) (2025)
- [Vision Language Models Cannot Plan, but Can They Formalize?](https://mlsanity.com/paper/2509.21576/) (2025)
- [Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation](https://mlsanity.com/paper/2606.06076/) (2026)
- [Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene](https://mlsanity.com/paper/2610.19234/) (2026)

## Abstract (arXiv)

Automating the generation of Planning Domain Definition Language (PDDL) with Large Language Model (LLM) opens new research topic in AI planning, particularly for complex real-world tasks. This paper introduces Image2PDDL, a novel framework that leverages Vision-Language Models (VLMs) to automatically convert images of initial states and descriptions of goal states into PDDL problems. By providing a PDDL domain alongside visual inputs, Imasge2PDDL addresses key challenges in bridging perceptual understanding with symbolic planning, reducing the expertise required to create structured problem instances, and improving scalability across tasks of varying complexity. We evaluate the framework on various domains, including standard planning domains like blocksworld and sliding tile puzzles, using datasets with multiple difficulty levels. Performance is assessed on syntax correctness, ensuring grammar and executability, and content correctness, verifying accurate state representation in generated PDDL problems. The proposed approach demonstrates promising results across diverse task complexities, suggesting its potential for broader applications in AI planning. We will discuss a potential use case in robot-assisted teaching of students with Autism Spectrum Disorder.