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

The dataset contains two aligned learning levels.

Level 0

One action predicts the next observation:

input image + 7D action -> next image

Level 1

An action chunk predicts a future subgoal:

start image + action chunk -> end image

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

Randomized trajectories

RoboCasa trajectories

Additional examples

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}
}