Update README.md

README.md

@@ -53,8 +53,8 @@ extra_gated_button_content: Submit
 
 
 # Key Features 🔑
-- **1 million+** episodes of atomic tasks automatically generated by agents.
-- **1 million+** episodes of enhanced digital twins of long-horizon real-world tasks from Agibot World.
+- **1 million+** steps of enhanced digital twins of long-horizon real-world tasks from Agibot World.
+- **500,000+** steps of atomic tasks automatically generated by agents.
 - **180+ classes of objects**.
 - **5 classes of scenes**.
 
@@ -64,7 +64,7 @@ extra_gated_button_content: Submit
 
 
 # TODO List 📅
-- [ ] **AgiBot Digital World Beta**: More high-quality simulation data, including atomic skill task data and digital twin-enhanced data aligned with tasks in Agibot World. (expected release date: Q2 2025)
+- [ ] **AgiBot Digital World**: More high-quality simulation data, including atomic skill task data and digital twin-enhanced data aligned with tasks in Agibot World. (ongoing open source)
 <!-- - [ ] **AgiBot Digital World FEEDBACK**: Data quality feedback and improvement. -->
 
 
@@ -88,15 +88,15 @@ git lfs install
 
 # When prompted for a password, use an access token with write permissions.
 # Generate one from your settings: https://huggingface.co/settings/tokens
-git clone https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld/tree/main
+git clone https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld
 
 # If you want to clone without large files - just their pointers
-GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld/tree/main
+GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld
 ```
 
-If you only want to download a specific task, such as `digitaltwin_5`, you can use the following code.
+If you only want to download a specific task from the AgiBotDigitalWorld dataset, such as `digitaltwin_5`,follow these steps:.
 ```
-# Make sure you have git-lfs installed (https://git-lfs.com)
+# Ensure Git LFS is installed (https://git-lfs.com)
 git lfs install
 
 # Initialize an empty Git repository
@@ -104,41 +104,43 @@ git init AgiBotDigitalWorld
 cd AgiBotDigitalWorld
 
 # Set the remote repository
-git remote add origin https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld/tree/main
+git remote add origin https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld
 
 # Enable sparse-checkout
 git sparse-checkout init
 
-# Specify the folders and files
+# Specify the folders and files you want to download
 git sparse-checkout set observations/digitaltwin_5 task_info/digitaltwin_5.json scripts proprio_stats parameters
 
-# Pull the data
+# Pull the data from the main branch
 git pull origin main
+
 ```
 
-To facilitate the inspection of the dataset's internal structure and examples, we also provide a sample dataset. Please refer to `sample_dataset.tar`.
+To facilitate the inspection of the dataset's internal structure and examples, we also provide a sample dataset. Please refer to `sample_dataset.zip`.
 
 ## Dataset Preprocessing
 Our project relies solely on the [lerobot library](https://github.com/huggingface/lerobot) (dataset `v2.0`), please follow their [installation instructions](https://github.com/huggingface/lerobot?tab=readme-ov-file#installation).
 Here, we provide scripts for converting it to the lerobot format. 
 
+### Requirements
 Requeir ffmpeg>=7.1(You can install with **conda install -y -c conda -forge ffmpeg**)
 ```
 export SVT_LOG=0
-python convert_to_lerobot.py —data_dir DATASET_FOLDER —save_dir SAVE_FOLDER --repo_id=agibot/agibotdigital --preprocess_video
+python scripts/convert_to_lerobot.py --data_dir DATASET_FOLDER --save_dir SAVE_FOLDER --repo_id=agibot/agibotdigital --preprocess_video
 
 ## Example
-# python convert_to_lerobot.py --data_dir final_format_data --save_dir ./output --repo_id=agibot/agibotdigital --preprocess_video
+# python scripts/convert_to_lerobot.py --data_dir final_format_data --save_dir ./output --repo_id=agibot/agibotdigital --preprocess_video
 
 ```
-Visulization
+### Visualization
 ```
-python visualize_dataset.py --repo-id='agibot/agibotdigital' --episode-index=0 --dataset-path=SAVE_FOLDER
+python scripts/visualize_dataset.py --repo-id='agibot/agibotdigital' --episode-index=0 --dataset-path=SAVE_FOLDER
 
 ## Example
-# python visualize_dataset.py --repo-id='agibot/agibotdigital'--episode-index=0 --dataset-path=output
+# python scripts/visualize_dataset.py --repo-id='agibot/agibotdigital'--episode-index=0 --dataset-path=output
 ```
-We would like to express our gratitude to the developers of lerobot for their outstanding contributions to the open-source community.
+We sincerely thank the developers of lerobot for their exceptional contributions to the open-source community..
 
 ## Dataset Structure
 ### Folder hierarchy
@@ -159,30 +161,30 @@ data
 │   │   ├── edb0774b-13bb-4a8b-8bb0-71e82fe3ff6a
 │   │   │   └── ...
 │   └── ...
-├── states
+├── meta_info
 │   ├── digitaltwin_0 # This represents the task id.
 │   │   ├── 9b21cf2e-829f-4aad-9b61-9edc5b947163  # This represents the episode uuid.
 │   │   │   ├── task_info.json # This represents the task information.
-│   │   │   ├── proprio_states.h5 # This file contains all the robot's proprioceptive information.
+│   │   │   ├── proprio_meta_info.h5 # This file contains all the robot's proprioceptive information.
 │   │   │   ├── camera_parameter.json # This contains all the cameras' intrinsic and extrinsic parameters.
 │   │   ├── 131e407a-b828-4937-a554-e6706cbc5e2f
 │   │   │   ├── task_info.json 
-│   │   │   ├── proprio_states.h5
+│   │   │   ├── proprio_meta_info.h5
 │   │   │   ├── camera_parameter.json
 │   │   └── ...
 │   └── digitaltwin_1
 │       ├── 95808182-501f-4dca-984b-7404df844d31
 │   │   │   ├── task_info.json 
-│   │   │   ├── proprio_states.h5
+│   │   │   ├── proprio_meta_info.h5
 │   │   │   ├── camera_parameter.json
 │       └── edb0774b-13bb-4a8b-8bb0-71e82fe3ff6a
 │   │   │   ├── task_info.json 
-│   │   │   ├── proprio_states.h5
+│   │   │   ├── proprio_meta_info.h5
 │   │   │   ├── camera_parameter.json
 |       └── ...
 ```
 ### json file format
-In the `task_[id].json` file, we store the basic information of every episode along with the language instructions. Here, we will further explain several specific keywords.
+In the `task_info.json` file, we store the basic information of every episode along with the language instructions. Here, we will further explain several specific keywords.
 - **action_config**: The content corresponding to this key is a list composed of all **action slices** from the episode. Each action slice includes a start and end time, the corresponding atomic skill, and the language instruction.
 - **key_frame**: The content corresponding to this key consists of annotations for keyframes, including the start and end times of the keyframes and detailed descriptions.
 
@@ -352,7 +354,7 @@ All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creat
 
 ```BibTeX
 @misc{contributors2025agibotworldrepo,
-  title={AgiBot World Colosseum},
+  title={AgiBot DigitalWorld},
   author={AgiBot World Colosseum contributors},
   howpublished={\url{https://agibot-digitalworld.com/}},
   year={2025}