# OOWM: Structuring Embodied Reasoning and Planning via Object-Oriented Programmatic World Modeling

arXiv: https://arxiv.org/abs/2604.09580 · HTML: https://mlsanity.com/paper/2604.09580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)
- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 13 of 13)

- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization](https://mlsanity.com/paper/2411.10442/) (2024)
- [From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding](https://mlsanity.com/paper/2409.18938/) (2024)
- [Stable Language Guidance for Vision-Language-Action Models](https://mlsanity.com/paper/2601.04052/) (2026)
- [Physical Autoregressive Model for Robotic Manipulation without Action Pretraining](https://mlsanity.com/paper/2508.09822/) (2025)
- [VLA Models Are More Generalizable Than You Think: Revisiting Physical and Spatial Modeling](https://mlsanity.com/paper/2512.02902/) (2025)
- [E0: Enhancing Generalization and Fine-Grained Control in VLA Models via Tweedie Discrete Diffusion](https://mlsanity.com/paper/2511.21542/) (2025)
- [In-Situ Tweedie Discrete Diffusion Models](https://mlsanity.com/paper/2510.01047/) (2025)
- [Distilled-3DGS:Distilled 3D Gaussian Splatting](https://mlsanity.com/paper/2508.14037/) (2025)
- [Style4D-Bench: A Benchmark Suite for 4D Stylization](https://mlsanity.com/paper/2508.19243/) (2025)
- [Bridging the Discrete-Continuous Gap: Unified Multimodal Generation via Coupled Manifold Discrete Absorbing Diffusion](https://mlsanity.com/paper/2601.04056/) (2026)

## Abstract (arXiv)

Standard Chain-of-Thought (CoT) prompting empowers Large Language Models (LLMs) with reasoning capabilities, yet its reliance on linear natural language is inherently insufficient for effective world modeling in embodied tasks. While text offers flexibility, it fails to explicitly represent the state-space, object hierarchies, and causal dependencies required for robust robotic planning. To address these limitations, we propose Object-Oriented World Modeling (OOWM), a novel framework that structures embodied reasoning through the lens of software engineering formalisms. We redefine the world model not as a latent vector space, but as an explicit symbolic tuple W = langle S, T rangle: a State Abstraction (Gstate) instantiating the environmental state S, coupled with a Control Policy (Gcontrol) representing the transition logic T: S × A rightarrow S'. OOWM leverages the Unified Modeling Language (UML) to materialize this definition: it employs Class Diagrams to ground visual perception into rigorous object hierarchies, and Activity Diagrams to operationalize planning into executable control flows. Furthermore, we introduce a three-stage training pipeline combining Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). Crucially, this method utilizes outcome-based rewards from the final plan to implicitly optimize the underlying object-oriented reasoning structure, enabling effective learning even with sparse annotations. Extensive evaluations on the MRoom-30k benchmark demonstrate that OOWM significantly outperforms unstructured textual baselines in planning coherence, execution success, and structural fidelity, establishing a new paradigm for structured embodied reasoning.