StepNav: Efficient Planning with Structured Trajectory Priors

Project Page | Paper | Code

Official PyTorch implementation of StepNav, an efficient visual navigation framework accepted at ICRA 2026.

💡 Abstract

We present StepNav, an efficient planning framework for visual navigation that generates reliable trajectories using structured trajectory priors. Unlike existing methods that rely on unstructured noise, StepNav leverages multi-modal trajectory initialization combined with conditional flow matching for efficient and safe path generation.

🪛 Installation

git clone https://github.com/LuoXubo/StepNav.git
cd StepNav
conda create -n stepnav python=3.8
conda activate stepnav
pip install -r requirements.txt
pip install -e .

🤖 Deployment

The deployment code will be released soon. Stay tuned!

Citation

If you find this work useful, please consider citing:

@misc{luo2026stepnavstructuredtrajectorypriors,
      title={StepNav: Structured Trajectory Priors for Efficient and Multimodal Visual Navigation}, 
      author={Xubo Luo and Aodi Wu and Haodong Han and Xue Wan and Wei Zhang and Leizheng Shu and Ruisuo Wang},
      year={2026},
      eprint={2602.02590},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2602.02590}, 
}

Acknowledgments

This work builds upon NoMaD, Flownav, and NaviBridger.