# Path Planning in Physically Viable World Models

arXiv: https://arxiv.org/abs/2607.00673 · HTML: https://mlsanity.com/paper/2607.00673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for navigation, one of 11 directions in World models.
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Placed in

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

## Builds on (most cited first, 12 of 12)

- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Genie: Generative Interactive Environments](https://mlsanity.com/paper/2402.15391/) (2024)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [Learning-on-the-Drive: Self-supervised Adaptation of Visual Offroad Traversability Models](https://mlsanity.com/paper/2306.15226/) (2023)
- [Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields (NeRF)](https://mlsanity.com/paper/2205.01389/) (2022)
- [Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments](https://mlsanity.com/paper/2506.04484/) (2025)
- [Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments](https://mlsanity.com/paper/2509.12516/) (2025)
- [Physically Viable World Models: A Case for Query-Conditioned Embodied AI](https://mlsanity.com/paper/2605.30542/) (2026)

## Abstract (arXiv)

Robots deployed in unstructured outdoor environments often plan from scene reconstructions collected before deployment because operators cannot remap large or remote sites before every mission. As a result, robots must make long-horizon planning decisions using stale maps that assume the terrain remains unchanged, even though physical changes to the environment may render previously feasible routes unsafe or unreachable at execution time. We present a physically viable world model for evaluating what-if queries for robot navigation under future terrain change. The system augments reconstructed 3D Gaussian splat scenes with physics-based simulation to generate physically modified versions of the same environment without recollecting sensor data or rebuilding the map. We then implement a terrain-aware planner that accounts for physical events, obstacles, and deformations that are simulated by the world model. This allows robots and human operators to evaluate whether planned routes remain feasible before committing to a planned route, particularly in constrained environments where retreat or recovery may become impossible once conditions change. We evaluate the system on a real outdoor field site in Central Texas using simulated flooding across multiple severity levels. We measure route and mission feasibility as terrain conditions deteriorate under physically simulated interventions. Our results show that physically viable world models expose long-horizon route failures and rerouting behavior that are not apparent when planning only on the original reconstructed environment, allowing robots to evaluate how future terrain changes may affect route feasibility before deployment.