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
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}
}