README.md

---
pipeline_tag: robotics
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---

# PointGoal Navigation Policy (FrodoBots8K)

PointGoal navigation policy trained with the FrodoBots8K dataset, as presented in the paper [Data Scaling for Navigation in Unknown Environments](https://arxiv.org/abs/2601.09444).

[**Project Page**](https://lasuomela.github.io/navigation_scaling/) | [**Code**](https://github.com/lasuomela/NavigationScaling)

## Authors
Lauri Suomela, Naoki Takahata, Sasanka Kuruppu Arachchige, Harry Edelman, Joni-Kristian Kämäräinen

## Model Description
This model is an end-to-end, map-free visual navigation policy designed for sidewalk robots. It was trained using imitation learning on crowd-sourced data to achieve zero-shot navigation in unknown environments. The research demonstrates that data diversity (geographical variety) is significantly more important than data quantity for real-world generalization.

- **Task:** Zero-shot PointGoal navigation.
- **Generalization:** Evaluated across 125 km of autonomous driving in four different countries.
- **Key Finding:** Doubling geographical locations in training decreases navigation errors by ~15%, while adding more data from existing locations leads to rapid performance saturation.

## Details

### Architecture
- **Type:** MLP-BC
- **Visual Encoder:** [Theia encoder](https://huggingface.co/theaiinstitute/theia-base-patch16-224-cddsv)
- **Action Head:** MLP action head

### Training Dataset
- **Dataset:** FrodoBots8K (subset)
- **Scale:** 1024 total trajectories (~32 hours of data) from 32 distinct geographical locations.

## Usage
The model is built for deployment on EarthRover Zero robots using ROS2. For detailed instructions on environment setup, training, and deployment, please refer to the [official GitHub repository](https://github.com/lasuomela/NavigationScaling).

## Citation
If you use this model or code in your research, please cite:
```bibtex
@misc{suomela2026data_scaling,
  title={Data Scaling for Navigation in Unknown Environments},
  author={Suomela, Lauri and Takahata, Naoki and Kuruppu Arachchige, Sasanka and Edelman, Harry and Kämäräinen, Joni-Kristian},
  journal={arXiv:2601.09444},
  year={2026},
}
```