# 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`
  - `numpy`
- Configure `$LEROBOT_HOME`  (defaults to `~/.lerobot` if unset).

```bash
pip install lerobot opencv-python numpy
export LEROBOT_HOME="/path/to/lerobot_home"
```

### Usage

Run the converter from the repository root (or provide an absolute path):

```bash
python convert_to_lerobot.py \
  --repo-name example_repo \
  --raw-dataset /path/to/example_dataset \
  --frame-interval 1 
```

### 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.