Update model card with paper details and authors

Hi! I'm Niels, part of the community science team at Hugging Face. I've updated the model card to include more details from the associated paper, "Data Scaling for Navigation in Unknown Environments." This includes adding the author list, a summary of the model's architecture, dataset details, and the BibTeX citation to make the artifact more useful and discoverable for the community.

README.md

@@ -1,26 +1,47 @@
 ---
+pipeline_tag: robotics
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
-pipeline_tag: robotics
 ---
 
-PointGoal navigation policy trained with the FrodoBots8K dataset.
+# PointGoal Navigation Policy (FrodoBots8K)
 
-Presented in the paper [Data scaling for navigation in unknown environments](https://arxiv.org/abs/2601.09444)
+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)
+[**Project Page**](https://lasuomela.github.io/navigation_scaling/) | [**Code**](https://github.com/lasuomela/NavigationScaling)
 
-## Details
+## Authors
+Lauri Suomela, Naoki Takahata, Sasanka Kuruppu Arachchige, Harry Edelman, Joni-Kristian Kämäräinen
 
-Architecture:
+## 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.
 
-MLP-BC with Theia encoder + MLP action head
+- **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.
 
-Dataset:
+## Details
 
-1024 total, 32H from 32 distinct geographical locations
+### Architecture
+- **Type:** MLP-BC
+- **Visual Encoder:** [Theia encoder](https://huggingface.co/theaiinstitute/theia-base-patch16-224-cddsv)
+- **Action Head:** MLP action head
 
-## Usage
+### Training Dataset
+- **Dataset:** FrodoBots8K (subset)
+- **Scale:** 1024 total trajectories (~32 hours of data) from 32 distinct geographical locations.
 
-See the Github repo
+## 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},
+}
+```