---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- iros 2025
- navigation challenge
- visual-language navigation (vln)
- robotics dataset
- matterport3d
- interiornav
- r2r dataset
---
<div id="top" align="center">
    <img src="https://github.com/InternRobotics/InternNav/raw/main/challenge/demo.gif" width=60% >
</div>

# IROS-2025-Challenge-Nav Dataset

## Dataset Summary 📖

This dataset includes the R2R dataset and the InteriorNav dataset, constructed from Matterport3D scanned environments and InteriorNav(kujiale) high-quality modeled environments, respectively, with corresponding navigation trajectories and language instructions. 

### Trajectory Statistics by Subset

| Dataset          | Train | Val Seen        | Val Unseen | Test Unseen |
|------------------|------------------------|-------------------|---------------------|------------------------|
| VLN-PE-R2R       | 8,679 (stair-filtered) | 778               | 1,839               | 3,408                  |
| InteriorNav      | 649                    | 44                | 99                  | 165                    |
| **Total**        | **9,328**              | **822**           | **1,938**           | **3,573**              |


# Get started 🔥    
## Download the Dataset 
```
# Make sure git-lfs is installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/datasets/InternRobotics/IROS-2025-Challenge-Nav

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/InternRobotics/IROS-2025-Challenge-Nav
```


## Dataset Structure 📁

```
vln_pe
├── raw_data/                       # JSON files defining tasks, navigation goals, and dataset splits
│   └── r2r/
│       ├── mini/
│       │   └── mini.json.gz        # For quick Model and Environments validation
│       ├── train/
│       ├── val_seen/
│       │   └── val_seen.json.gz
│       ├── val_unseen/
│       │   └── val_unseen.json.gz
│       └── embeddings.json.gz
└── traj_data                       # training sample data for two types of scenes
    ├── interiornav/
    │   ├── kujiale_xxxx.tar.gz  
    │   └── ...
    └── r2r/
        ├── traj_index/
        │   ├── data/
        │   ├── meta/
        │   └── videos/
        └── ...
```
        

# License and Citation
All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Please consider citing our project if it helps your research.

```BibTeX
@misc{contributors2025internroboticsrepo,
  title={IROS-2025-Challenge-Nav Colosseum},
  author={IROS-2025-Challenge-Nav Colosseum contributors},
  howpublished={\url{https://github.com/InternRobotics/InternNav/tree/main/challenge}},
  year={2025}
}
```