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[2602.18812] GenPlanner: From Noise to Plans -- Emergent Reasoning in Flow Matching and Diffusion Models
Summary
The paper presents GenPlanner, a novel approach to path planning in complex environments using generative models, specifically diffusion models and flow matching, to generate effective trajectories from random noise.
Why It Matters
Path planning is critical in AI applications, especially in robotics and navigation. GenPlanner offers a new methodology that enhances the efficiency and effectiveness of trajectory generation, potentially improving performance in real-world applications where traditional methods fall short.
Key Takeaways
- GenPlanner utilizes generative models for effective path planning.
- The approach outperforms traditional CNN models in trajectory generation.
- FlowPlanner variant shows high performance with fewer generation steps.
- The method starts with random noise, iteratively refining it into a valid path.
- Multi-channel conditions enhance the model's ability to navigate complex environments.
Computer Science > Artificial Intelligence arXiv:2602.18812 (cs) [Submitted on 21 Feb 2026] Title:GenPlanner: From Noise to Plans -- Emergent Reasoning in Flow Matching and Diffusion Models Authors:Agnieszka Polowczyk, Alicja Polowczyk, Michał Wieczorek View a PDF of the paper titled GenPlanner: From Noise to Plans -- Emergent Reasoning in Flow Matching and Diffusion Models, by Agnieszka Polowczyk and 1 other authors View PDF Abstract:Path planning in complex environments is one of the key problems of artificial intelligence because it requires simultaneous understanding of the geometry of space and the global structure of the problem. In this paper, we explore the potential of using generative models as planning and reasoning mechanisms. We propose GenPlanner, an approach based on diffusion models and flow matching, along with two variants: DiffPlanner and FlowPlanner. We demonstrate the application of generative models to find and generate correct paths in mazes. A multi-channel condition describing the structure of the environment, including an obstacle map and information about the starting and destination points, is used to condition trajectory generation. Unlike standard methods, our models generate trajectories iteratively, starting with random noise and gradually transforming it into a correct solution. Experiments conducted show that the proposed approach significantly outperforms the baseline CNN model. In particular, FlowPlanner demonstrates high performance eve...
