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# ConfigWM Dataset

Data for [**World Models with Configurable Perception**](https://arxiv.org/abs/2610.27914), covering **Push-T**, **Two-Room**, and **OGBench-Cube** with distraction variants `d0` through `d8`.

The dataset supports query-conditioned visual representation learning, action-conditioned world modeling, and online control evaluation.

## Environments

`d0` has no distractors; `d1` through `d8` add one through eight. Click the figure to view the original image.

[![Two-Room, Push-T, and OGBench-Cube with zero through eight distractors](assets/environments/noisy_world.png)](assets/environments/noisy_world.png)

## Data

Each environment uses the same three collections:

| Collection | Contents | Use |
| --- | --- | --- |
| `trajectories/` | RGB observations, actions, episode metadata, and queries | Training ConfigWM |
| `perception/` | Source images, text queries, and target images | Training Configurable Encoder |
| `control/` | Reset trajectories and references to shared trajectory data | Online evaluation |

```text
dataset.json
assets/environments/       # README previews
pusht/                     # also tworoom/ and ogbcube/
  trajectories/d0...d8/
    manifest.json
    queries.jsonl
    shard_*/frames.h5
    shard_*/episodes.jsonl
  perception/
    manifest.json
    shard_*/pairs.h5
  control/
    d0...d8/manifest.json
    base.h5                # Push-T and Two-Room
```

### Format

| Data | Format |
| --- | --- |
| RGB observations | HDF5, `uint8`, `[frame, height, width, channel]` |
| Actions | 2 dimensions for Push-T/Two-Room; 5 for OGBench-Cube |
| Perception pairs | HDF5 `source_pixels`, `target_pixels`, and `query` |
| Descriptions | JSONL episode metadata and training queries |

- `dataset.json`: entry manifests for each environment and task.
- `field_map`: HDF5 file role and array key for each field.
- `ep_offset`: starting frame index of each episode.
- `ep_len`: number of frames in each episode.

## Download

The full dataset is approximately **1.64 TB**. To download one environment and variant, run this from the ConfigWM code repository:

```bash
python tools/download_data.py \
  --repo YijunShen/ConfigWM \
  --local-dir ./configwm-data \
  --env pusht --task stage_b --variant d3
```

- `--env`: `pusht`, `tworoom`, or `ogbcube`.
- `--task stage_b --variant d3`: data for training ConfigWM on one variant.
- `--task stage_a`: data for training Configurable Encoder in one environment.
- `--task control --variant d3`: evaluation resources and their dependencies.
- `--list-only`: fetch small manifests and list the selected files.

Reuse the same local directory across downloads to share common files.

To download everything:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="YijunShen/ConfigWM",
    repo_type="dataset",
    local_dir="configwm-data",
)
```

## Real-world perception

The [real-world collection](https://huggingface.co/datasets/YijunShen/ConfigWM/tree/main/realworld) contains image pairs with task descriptions.

```bash
hf download YijunShen/ConfigWM --repo-type dataset \
  --include "realworld/**" --local-dir ./configwm-data
```

Use `./configwm-data/realworld` as `--data-root` with `--env realworld`. See the [format and usage](https://huggingface.co/datasets/YijunShen/ConfigWM/blob/main/realworld/README.md).

## Citation

```bibtex
@misc{shen2026configwm,
      title={World Models with Configurable Perception},
      author={Yijun Shen and Delong Chen and Xianming Hu and Kai Zhang and Pascale Fung},
      year={2026},
      eprint={2610.27914},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.27914},
}
```

## Acknowledgments

We would like to thank Willy Chung, Quentin Garrido, Basile Terver for providing valuable feedback to this work.