| --- |
| license: odc-by |
| language: |
| - en |
| pretty_name: EmbodiedRestore |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - image-to-image |
| - robotics |
| - other |
| tags: |
| - image-restoration |
| - image-quality-assessment |
| - robotics |
| - robosuite |
| - mujoco |
| - distortion |
| - tid2013 |
| - kadid-10k |
| - pi0 |
| - pi0.5 |
| - openvla |
| - benchmark |
| configs: |
| - config_name: frames |
| data_files: |
| - split: train |
| path: manifest.csv |
| - config_name: rollouts |
| data_files: |
| - split: train |
| path: results.csv |
| - config_name: taxonomy |
| data_files: |
| - split: train |
| path: distort_taxonomy.csv |
| --- |
| |
| # EmbodiedRestore |
|
|
| Paired robotic first-frame observations (low-quality / ground-truth) under 25 distortions from the TID2013 / KADID-10k taxonomy, evaluated by three policies (π0.5, π0, OpenVLA). Built for benchmarking image restoration / IQA on robot-observation distributions, with downstream policy success rates(SR) and steps to successas(StS) as secondary signals. |
|
|
| To promote the development of image restoration model for robot vision systems, we will continue to maintain this dataset and release more first-frame observations generated by our benchmarked image restoration models, along with their corresponding SR&StS data. |
|
|
| A full Croissant 1.0 + RAI metadata document is shipped at the repo root: [`croissant.json`](./croissant.json). It is richer than the auto-generated Croissant served by Hugging Face — please prefer it when ingesting the dataset programmatically. |
|
|
| ## At a glance |
|
|
| | | | |
| | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| | Frames (PNG) | 3 policies × 25 distortions × 100 episodes × 4 views = **30,000** | |
| | Rollouts | 3 policies × 25 distortions × 100 episodes = **7,500** | |
| | Distortions | 25, taxonomized in `distort_taxonomy.csv` (noise / blur / color / compression / brightness / spatial / sharpness) | |
| | Policies | π0.5, π0, OpenVLA | |
| | Cameras | base, wrist | |
| | Roles | LQ (distorted) ↔ GT (clean) | |
| | License | ODC-BY | |
| | Source simulator | Robosuite (MIT) on top of MuJoCo (Apache-2.0) | |
|
|
| ## Repository layout |
|
|
| ``` |
| EmbodiedRestore/ |
| ├── README.md |
| ├── croissant.json Croissant 1.0 + RAI metadata |
| ├── manifest.csv 30,000 rows — one per PNG frame |
| ├── results.csv 7,500 rows — one per rollout |
| ├── distort_taxonomy.csv 25 rows — id → name / family / reference |
| ├── build_metadata.py regenerates the four files above |
| ├── Pi05_DistortOnly/ |
| │ ├── distort_0/ |
| │ │ ├── firstframe_distort_0_eps1_0.png ... eps100_3.png (400 PNGs) |
| │ │ └── result.json success_rate + 100 env_metadata |
| │ └── distort_1/ ... distort_24/ |
| ├── Pi0_DistortOnly/ same shape as above |
| └── openvla_DistortOnly/ same shape as above |
| ``` |
|
|
| ## File naming |
|
|
| `<policy>_DistortOnly/distort_<id>/firstframe_distort_<id>_eps<e>_<v>.png` |
|
|
| - `<id>`: 0–24, see `distort_taxonomy.csv` |
| - `<e>`: 1–100, episode index (1-indexed). The corresponding entry in the matching `result.json` is `env_metadata[e-1]` (0-indexed). |
| - `<v>`: view × role suffix |
| - `0` = base camera, LQ (distorted) |
| - `1` = wrist camera, LQ |
| - `2` = base camera, GT (clean) |
| - `3` = wrist camera, GT |
|
|
| `results.csv` already normalises eps to the 1-indexed filename convention, so you can join it with `manifest.csv` directly: |
|
|
| ```python |
| import pandas as pd |
| m = pd.read_csv("manifest.csv") |
| r = pd.read_csv("results.csv") |
| joined = m.merge(r, on=["policy", "distort_id", "eps", "distort_name"]) |
| ``` |
|
|
| ## Tabular schemas |
|
|
| ### `manifest.csv` (30,000 rows) |
|
|
| | column | type | notes | |
| | -------------- | ---- | ------------------------------ | |
| | `path` | str | relative path from repo root | |
| | `policy` | str | `pi0.5` / `pi0` / `openvla` | |
| | `distort_id` | int | 0..24 | |
| | `distort_name` | str | matches `distort_taxonomy.csv` | |
| | `eps` | int | 1..100 | |
| | `camera` | str | `base` / `wrist` | |
| | `role` | str | `lq` / `gt` | |
|
|
| ### `results.csv` (7,500 rows) |
|
|
| | column | type | notes | |
| | ---------------- | ----- | ------------------------------------------------------------------------------------------- | |
| | `policy` | str | `pi0.5` / `pi0` / `openvla` | |
| | `distort_id` | int | 0..24 | |
| | `distort_name` | str | | |
| | `eps` | int | 1..100, joins manifest | |
| | `wall_material` | str | from simulator | |
| | `table_material` | str | from simulator | |
| | `object` | str | manipulation target (cube / lemon / hammer / ...) | |
| | `success` | str | literal `success` / `failure` | |
| | `steps` | int | episode length; capped at 250 on failure | |
| | `success_rate` | float | distort-level rate; same value repeats over the 100 rows of that (policy, distort_id) group | |
| |
| ### `distort_taxonomy.csv` (25 rows) |
|
|
| `id`, `name`, `family`, `reference` — distortion families follow the TID2013 / KADID-10k benchmark taxonomy. |
|
|
| ## Quick start |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| local_dir = snapshot_download( |
| repo_id="qruisjtu/EmbodiedRestore", |
| repo_type="dataset", |
| ) |
| |
| import pandas as pd |
| m = pd.read_csv(f"{local_dir}/manifest.csv") |
| |
| # all LQ base-camera frames under JPEG compression for OpenVLA |
| sel = m.query("policy == 'openvla' and distort_name == 'jpeg_compression' " |
| "and camera == 'base' and role == 'lq'") |
| print(sel.head()) |
| ``` |
|
|
| Pair LQ ↔ GT for an image-restoration training loop: |
|
|
| ```python |
| lq = m.query("role == 'lq'") |
| gt = m.query("role == 'gt'").rename(columns={"path": "gt_path"}) |
| pairs = lq.merge(gt, on=["policy", "distort_id", "distort_name", "eps", "camera"]) \ |
| .rename(columns={"path": "lq_path"}) |
| ``` |
|
|
| ## Croissant |
|
|
| ```python |
| import mlcroissant as mlc |
| ds = mlc.Dataset(jsonld="croissant.json") |
| for r in ds.records("rollouts"): |
| print(r); break |
| ``` |
|
|
| The `frames` and `rollouts` RecordSets are the recommended entry points. For raw per-distortion JSON outputs, see the `policy_results` FileSet (one `result.json` per `<policy>_DistortOnly/distort_<id>/`). |
|
|
| ## Intended use |
|
|
| - **Primary**: benchmarking image restoration and image-quality-assessment algorithms on the distribution of robot first-frame observations. |
| - **Secondary**: studying how restoration / denoising affects downstream manipulation success across modern VLA policies. |
|
|
| ## Limitations |
|
|
| - Only first-frame observations are kept. |
| - Single simulator (Robosuite/MuJoCo). |
| - Each distortion is applied at a fixed strength. |
|
|
| ## Ethics |
|
|
| Fully synthetic; contains no humans, faces, voices, or personally identifiable information. |
|
|
| ## Citation |
|
|
| ``` |
| TODO: arXiv / NeurIPS bibtex |
| ``` |
|
|
| ## Acknowledgements |
|
|
| Built on top of [Robosuite](https://github.com/ARISE-Initiative/robosuite) and [MuJoCo](https://github.com/google-deepmind/mujoco). Distortion taxonomy follows [TID2013](https://www.ponomarenko.info/tid2013.htm) and [KADID-10k](http://database.mmsp-kn.de/kadid-10k-database.html). Policies evaluated: π0.5, π0, OpenVLA. |
