| --- |
| license: h-research |
| pretty_name: WAM-0.1 |
| tags: |
| - robotics |
| - panda |
| - robosuite |
| - robocasa |
| - h-jepa |
| - world-model |
| --- |
| |
| # WAM-0.1 Dataset |
|
|
| WAM-0.1 is an image-and-action dataset for learning Panda robot dynamics and world representations. |
|
|
| Created by **Atom Ctrl** and **Aditya Panigrahi**. |
|
|
| Website: https://atomctrl.com |
|
|
| GitHub: https://github.com/adityapp1213/Wam-0.1 |
|
|
| Hugging Face: https://huggingface.co/datasets/atomctrl/wam-0.1 |
|
|
| ## Dataset overview |
|
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| The dataset contains two aligned learning levels. |
|
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|  |
|
|
| ### Level 0 |
|
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| One action predicts the next observation: |
|
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| `input image + 7D action -> next image` |
|
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| ### Level 1 |
|
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| An action chunk predicts a future subgoal: |
|
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| `start image + action chunk -> end image` |
|
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| Level 1 samples correspond directly to sequences of Level 0 transitions. |
|
|
| ## Data sources |
|
|
| - Official RoboSuite Panda tasks |
| - Randomized Panda trajectories |
| - Official RoboCasa demonstrations |
| - RGB and depth images |
| - Robot state and Panda actions |
| - Level 0 transitions and Level 1 action chunks |
|
|
| ## Visual examples |
|
|
| ### Normal trajectories |
|
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|  |
|
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| ### Randomized trajectories |
|
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|  |
|
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| ### RoboCasa trajectories |
|
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|  |
|
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| ### Additional examples |
|
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|  |
|
|
| ## Core tasks |
|
|
| - Lift |
| - Stack |
| - Door |
| - Wipe |
| - PickPlaceBread |
| - PickPlaceMilk |
| - PickPlaceCereal |
| - PickPlaceCan |
| - PickPlaceSingle |
| - NutAssemblySquare |
|
|
| RoboCasa contributes additional official kitchen and household manipulation tasks. |
|
|
| ## Dataset layout |
|
|
| data/ |
| └── final/ |
| ├── level_0/ |
| │ ├── normal/ |
| │ ├── random/ |
| │ └── robocasa/ |
| └── level_1/ |
| ├── normal/ |
| ├── random/ |
| └── robocasa/ |
| |
| ## Google Colab example |
|
|
| This example downloads only one shard instead of the complete dataset. |
|
|
| !pip -q install huggingface_hub |
| |
| from huggingface_hub import hf_hub_download |
| |
| shard = hf_hub_download( |
| repo_id="atomctrl/wam-0.1", |
| repo_type="dataset", |
| filename="shards/lift-normal.tar", |
| local_dir="/content/wam-test" |
| ) |
| |
| print(shard) |
| |
| Extract the shard: |
|
|
| import tarfile |
| from pathlib import Path |
| |
| extract_dir = Path("/content/wam-test/extracted") |
| extract_dir.mkdir(parents=True, exist_ok=True) |
| |
| with tarfile.open(shard, "r") as tar: |
| tar.extractall(extract_dir) |
| |
| print("Extracted to:", extract_dir) |
| |
| ## Intended use |
|
|
| WAM-0.1 is intended for H-JEPA training, image-based world models, action-conditioned prediction, robot dynamics learning, subgoal prediction, and robotics planning research. |
|
|
| ## Download |
|
|
| pip install -U huggingface_hub |
| |
| hf download atomctrl/wam-0.1 \ |
| --repo-type dataset \ |
| --local-dir wam-0.1 |
| |
| ## Links |
|
|
| - GitHub code: https://github.com/adityapp1213/Wam-0.1 |
| - Dataset: https://huggingface.co/datasets/atomctrl/wam-0.1 |
| - Website: https://atomctrl.com |
|
|
| ## Citation |
|
|
| @dataset{panigrahi_wam_0_1, |
| author = {Aditya Panigrahi}, |
| title = {WAM-0.1: World Action Model Dataset}, |
| year = {2026}, |
| publisher = {Atom Ctrl}, |
| url = {https://huggingface.co/datasets/atomctrl/wam-0.1} |
| } |
