---
license: mit
tags:
- robotics
- simulation
- domain-randomization
- textures
---

# CLab scene textures: RoboTwin seen library

This repository hosts a byte-preserving mirror of **10,000 seen textures** from
the local RoboTwin `assets/background_texture/seen` library, for reproducible
background and tabletop appearance randomization in robotic simulation.
The images were not created by CLab. Original PNG filenames and bytes are
preserved; there is no resizing, cropping, recompression of image pixels, or
content-based filtering. All source images are 768 x 768 RGB PNGs.

## Contents

- `seen.zip`: one ZIP64 archive containing all 10,000 original seen PNGs.
- `LICENSE`: the upstream MIT copyright and license notice.
- `README.md`: this dataset card.

The original PNGs total **10,010,671,214 bytes** (about 10.01 GB / 9.32 GiB).
The archive also contains `seen/LICENSE` and `seen/selection.json`, for exactly
10,002 members. The metadata lists all filenames, the 10,000-image source pool,
count, seed and packaging method. Its seed is retained for the generic packaging
utility; because count equals pool size, **all seen images are included**, not a
random subset. Members are ordered lexicographically and compressed with ZIP
DEFLATE level 6; source PNG bytes are unchanged. Every image was read back from
the completed local archive and checked byte-for-byte against its source before
upload.

The separate 1,000-image `unseen` directory is **not included**. The upstream split
name is preserved; it is not evidence that these images were independently
deduplicated or that train/test content independence was established.

## Source and attribution

- Upstream project: [RoboTwin](https://github.com/RoboTwin-Platform/RoboTwin).
- Original asset distribution: `background_texture.zip`, named by RoboTwin's
  `assets/_download.py`, from
  [TianxingChen/RoboTwin2.0](https://huggingface.co/datasets/TianxingChen/RoboTwin2.0).
- The [upstream dataset card](https://huggingface.co/datasets/TianxingChen/RoboTwin2.0/blob/main/README.md)
  declares MIT. This mirror retains the repository's copyright notice in
  [LICENSE](LICENSE) and inside the archive. It does not transfer image authorship
  to CLab or claim that CLab collected new images.
- Usage context: [RoboTwin domain randomization](https://robotwin-platform.github.io/doc/usage/domain-randomization.html).

## Download and extract

Use a new directory; do not merge into or overwrite another texture library.
The following requires curl and Python 3, but no Hugging Face token for this
public dataset:

```bash
mkdir clab-textures-download
cd clab-textures-download
curl --fail --location --retry 3 --continue-at - \
  --output seen.zip \
  https://huggingface.co/datasets/emopointer/clab-textures/resolve/main/seen.zip
python -m zipfile -t seen.zip
python -m zipfile -e seen.zip .
```

Extraction produces `seen/*.png`, `seen/LICENSE` and `seen/selection.json`.
Allow space for both the roughly 10 GB archive and roughly 10 GB extracted
library. If a download is interrupted, rerun only the curl command in the same
download directory to resume, then repeat the archive integrity check. An
interrupted extraction is incomplete; keep it separate and extract into a new
directory rather than silently mixing files.

For CLab, point `--texture-dir` or `CLAB_TEXTURE_DIR` to the directory containing
`seen`, not to `seen` itself. Select `--texture-split seen`. Runtime code reads
the library and does not download, extract or modify its contents. Runtime
resizing to 512 x 512 is separate from the original assets stored here.

## Intended use and limitations

This is a texture asset library, not an action-demonstration dataset. It contains
no robot trajectories, actions, checkpoints, camera calibration, labels or
evaluation results. Use it for scene appearance variation; the mirror alone is
not evidence of successful sim-to-real transfer or learned-policy robustness.
No unseen benchmark, content-quality filtering, deduplication or license audit
beyond preserving the upstream declaration is claimed. Respect upstream rights
and attribution when redistributing the assets.