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README.md

@@ -1,8 +1,107 @@
-# convert_to_lerobot
-This script generates a ready-to-use [LeRobot](https://github.com/huggingface/lerobot) dataset repository from RoboChallenge dataset.
 
-## Prerequisites
+# RoboChallenge Dataset
 
+## Dataset Structure
+### Available Tasks
+
+The dataset includes 30 diverse manipulation tasks (Table30):
+
+- `arrange_flowers`
+- `arrange_fruits_in_basket` 
+- `arrange_paper_cups` 
+- `clean_dining_table` 
+- `fold_dishcloth` 
+- `hang_toothbrush_cup` 
+- `make_vegetarian_sandwich` 
+- `move_objects_into_box` 
+- `open_the_drawer` 
+- `place_shoes_on_rack` 
+- `plug_in_network_cable` 
+- `pour_fries_into_plate` 
+- `press_three_buttons` 
+- `put_cup_on_coaster` 
+- `put_opener_in_drawer` 
+- `put_pen_into_pencil_case` 
+- `scan_QR_code` 
+- `search_green_boxes` 
+- `set_the_plates` 
+- `shred_scrap_paper` 
+- `sort_books` 
+- `sort_electronic_products` 
+- `stack_bowls` 
+- `stack_color_blocks` 
+- `stick_tape_to_box`
+- `sweep_the_rubbish` 
+- `turn_on_faucet` 
+- `turn_on_light_switch` 
+- `water_potted_plant` 
+- `wipe_the_table` 
+
+### Hierarchy
+The dataset is organized by tasks, with each task containing multiple demonstration episodes:
+```
+.
+├── <task_name>/                    # e.g., arrange_flowers, fold_dishcloth
+│   ├── task_desc.json              # Task description
+│   ├── meta/                       # Task-level metadata
+│   │   ├── task_info.json         
+│   └── data/                       # Episode data
+│       ├── episode_000000/         # Individual episode
+│       │   ├── meta/
+│       │   │   └── episode_meta.json    # Episode metadata
+│       │   ├── states/
+│       │   │   └── states.jsonl         # Robot states
+│       │   └── videos/
+│       │       ├── arm_realsense_rgb.mp4      # Arm-mounted camera
+│       │       ├── global_realsense_rgb.mp4   # Global view camera
+│       │       └── right_realsense_rgb.mp4    # Right-side camera (BEV)
+│       ├── episode_000001/
+│       └── ...
+├── convert_to_lerobot.py           # Conversion script
+└── README.md
+```
+### JSON File Format
+`task_info.json`
+```json
+{
+    "robot_id": "arx5_1",                    // Robot model identifier
+    "task_desc": {
+        "task_name": "arrange_flowers",      // Task identifier
+        "prompt": "insert the three flowers on the table into the vase one by one",
+        "scoring": "...",                    // Scoring criteria
+        "task_tag": [                        // Task characteristics
+            "repeated",
+            "single-arm", 
+            "ARX5",
+            "precise3d"
+        ]
+    },
+    "video_info": {
+        "fps": 30,                           // Video frame rate
+        "ext": "mp4",                        // Video format
+        "encoding": {
+            "vcodec": "libx264",             // Video codec
+            "pix_fmt": "yuv420p"             // Pixel format
+        }
+    }
+}
+```
+`episode_meta.json`
+```json
+{
+    "episode_index": 0,                      // Episode number
+    "start_time": 1750405586.3430033,       // Unix timestamp (start)
+    "end_time": 1750405642.5247612,         // Unix timestamp (end)
+    "frames": 1672                          // Total video frames
+}
+```
+
+
+## Convert to Lerobot
+While you can implement a custom Dataset class to read RoboChallenge data directly, **we strongly recommend converting to LeRobot format** to take advantage of [LeRobot](https://github.com/huggingface/lerobot)'s comprehensive data processing and loading utilities.
+The `convert_to_lerobot.py` script we provided generates a ready-to-use [LeRobot](https://github.com/huggingface/lerobot) dataset repository from RoboChallenge dataset.
+
+### Prerequisites
 - Python 3.9+ with the following packages:
   - `lerobot`
   - `opencv-python`
@@ -14,7 +113,7 @@ pip install lerobot opencv-python numpy
 export LEROBOT_HOME="/path/to/lerobot_home"
 ```
 
-## Usage
+### Usage
 
 Run the converter from the repository root (or provide an absolute path):
 
@@ -25,6 +124,6 @@ python convert_to_lerobot.py \
   --frame-interval 1 
 ```
 
-## Output
+### Output
 - Frames and metadata are saved to $LEROBOT_HOME/<repo-name>.
 - At the end, the script calls dataset.consolidate(run_compute_stats=False). If you require aggregated statistics, run it with run_compute_stats=True or execute a separate stats job.