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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. |