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license: cc-by-4.0
pipeline_tag: robotics
tags:
- visual-navigation
- sim-to-real
- topological-navigation
---
# FAINT
Fast, Appearance-Invariant Navigation Transformer (FAINT) is a learned policy for vision-based topological navigation.
This model is presented in the paper [Synthetic vs. Real Training Data for Visual Navigation](https://huggingface.co/papers/2509.11791).
[**Project Page**](https://lasuomela.github.io/faint/) | [**Code**](https://github.com/lasuomela/faint)
## Model Details
The `FAINT-Sim` model uses [`Theia-Tiny-CDDSV`](https://theia.theaiinstitute.com/) as backbone, and was trained for 10 rounds of DAgger with ~12M samples from the Habitat simulator.
It is capable of zero-shot transfer for navigation with real robots.
This repo contains two versions of the trained model weights.
- `model_pytorch.pt`: Weights-only state dict of the Pytorch model.
- `model_torchscript.pt`: A 'standalone' Torchscript model for deployment.
## Usage
See the main Github [repo](https://github.com/lasuomela/FAINT) for details, input preprocessing etc.
### Torchscript
Only dependency is Pytorch.
```python
import torch
ckpt_path = 'FAINT-Sim/model_torchscript.pt'
model = torch.jit.load(ckpt_path)
```
### Pytorch
Need to have the Faint library installed.
```python
import torch
from faint.common.models.faint import FAINT
ckpt_path = 'FAINT-Sim/model_pytorch.pt'
state_dict = torch.load(ckpt_path)
model = FAINT() # The weights in this repo correspond to FAINT initialized with the default arguments
model.load_state_dict(state_dict)
```
## Citation
If you use FAINT in your research, please use the following BibTeX entry:
```bibtex
@article{suomela2025synthetic,
title={Synthetic vs. Real Training Data for Visual Navigation},
author={Suomela, Lauri and Kuruppu Arachchige, Sasanka and Torres, German F. and Edelman, Harry and Kämäräinen, Joni-Kristian},
journal={arXiv:2509.11791},
year={2025}
}
``` |