Datasets:

InternRobotics
/

IROS-2025-Challenge-Nav

license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - iros 2025
  - navigation challenge
  - visual-language navigation (vln)
  - robotics dataset
  - matterport3d
  - interiornav
  - r2r dataset

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. Please consider citing our project if it helps your research.

@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}
}