Update README.md

README.md

@@ -9,8 +9,6 @@ task_categories:
   - image-to-image
   - image-classification
   - other
-task_ids:
-  - image-quality-estimation
 tags:
   - image-restoration
   - image-quality-assessment
@@ -24,7 +22,6 @@ tags:
   - pi0.5
   - openvla
   - benchmark
-  - paired-data
 configs:
   - config_name: frames
     data_files:
@@ -40,9 +37,11 @@ configs:
         path: distort_taxonomy.csv
 ---
 
-# EIR-DistortOnly
+# 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 as a secondary signal.
+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.
 
@@ -52,7 +51,7 @@ A full Croissant 1.0 + RAI metadata document is shipped at the repo root: [`croi
 | ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | 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 / denoising / compression / pattern / structural / intensity_color / color / optical / sampling) |
+| 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)                                                                                                                              |
@@ -177,10 +176,9 @@ The `frames` and `rollouts` RecordSets are the recommended entry points. For raw
 
 ## Limitations
 
-- Only first-frame observations are kept; no temporal information.
-- Single simulator (Robosuite/MuJoCo); the gap to real robot cameras is not characterised.
-- Three policies share a single task suite, so cross-task generalisation is not measured.
-- Each distortion is applied at a fixed strength; severity sweeps are not provided.
+- Only first-frame observations are kept.
+- Single simulator (Robosuite/MuJoCo).
+- Each distortion is applied at a fixed strength.
 
 ## Ethics