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---
license: cc-by-nc-sa-4.0
---
# IROS-2025-Challenge-Manip
# Dataset Summary 📖
This dataset contains the **IROS Challenge - Manipulation Track** benchmark, organized into **pretrain**, **train**, and **validation** splits.
* **Pretrain split**: \~20,000 single pick-and-place trajectories, packaged into tar files (each containing \~1,000 trajectories).
* **Train split**: task-specific demonstrations, with \~100 trajectories provided per task.
* **Validation split**: includes the test-time scenes and object assets in **USD format**.
Each trajectory in the pretrain and train splits contains:
* **Multi-view video** recordings (three perspectives: head-mounted camera and two wrist cameras)
* **Robot states** (joint positions, gripper states, etc.)
* **Actions** corresponding to the task execution
This dataset is designed to support **pretraining, task-specific fine-tuning, and evaluation** for robotic manipulation in the IROS Challenge setting.
# Get started 🔥
## Download the Dataset
To download the full dataset, you can use the following code. If you encounter any issues, please refer to the official Hugging Face documentation.
```python
from huggingface_hub import snapshot_download
dataset_path = snapshot_download("InternRobotics/IROS-2025-Challenge-Manip", repo_type="dataset")
```
Please execute this Python file to post-process the validation set.
```bash
cd IROS-2025-Challenge-Manip
python dataset_post_processing.py validation
````
## Unzip the pretrain dataset
```bash
cd pretrain
for i in {1..20}; do
echo "Extracting $i.tar.gz ..."
tar -xzf "$i.tar.gz"
done
```
## Dataset Structure
### pretrain Folder hierarchy
```
pretrain
├── 1.tar.gz
│ └── 1/
│ ├── data/
│ ├── meta/
│ └── videos/
├── 2.tar.gz
│ └── 2/
│ ├── data/
│ ├── meta/
│ └── videos/
...
├── 20.tar.gz
└── 20/
├── data/
├── meta/
└── videos/
```
### train Folder hierarchy
```
train
├── collect_three_glues
│ ├── data/
│ ├── meta/
│ └── videos/
├── collect_two_alarm_clocks/
├── collect_two_shoes/
├── gather_three_teaboxes/
├── make_sandwich/
├── oil_painting_recognition/
├── organize_colorful_cups/
├── purchase_gift_box/
├── put_drink_on_basket/
└── sort_waste/
```
### validation Folder hierarchy
```
validation
├── IROS_C_V3_Aloha_seen
│ ├── collect_three_glues
│ │ ├── 000
│ │ │ ├── meta_info.pkl
│ │ │ ├── scene.usd
│ │ │ └── SubUSDs -> ../SubUSDs
│ │ ├── 001/
│ │ ├── 002/
│ │ ├── 003/
│ │ ├── 004/
│ │ ├── 005/
│ │ ├── 006/
│ │ ├── 007/
│ │ ├── 008/
│ │ ├── 009/
│ │ └── SubUSDs
│ │ ├── materials/
│ │ └── textures/
│ ├── collect_two_alarm_clocks/
│ ├── collect_two_shoes/
│ ├── gather_three_teaboxes/
│ ├── make_sandwich/
│ ├── oil_painting_recognition/
│ ├── organize_colorful_cups/
│ ├── purchase_gift_box/
│ ├── put_drink_on_basket/
│ └── sort_waste/
└── IROS_C_V3_Aloha_unseen
├── collect_three_glues/
├── collect_two_alarm_clocks/
├── collect_two_shoes/
├── gather_three_teaboxes/
├── make_sandwich/
├── oil_painting_recognition/
├── organize_colorful_cups/
├── purchase_gift_box/
├── put_drink_on_basket/
└── sort_waste/
```
# 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-Manip Colosseum},
author={IROS-2025-Challenge-Manip Colosseum contributors},
howpublished={\url{https://github.com/internrobotics/IROS-2025-Challenge-Manip}},
year={2025}
}
``` |