Datasetpublic

InternRobotics/IROS-2025-Challenge-Manip

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… See the full description on the dataset page: https://huggingface.co/datasets/InternRobotics/IROS-2025-Challenge-Manip.

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Dataset Card

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. 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}
}