OXE Standard Transformations 🔄: We have integrated OXE's standard transformations to ensure uniformity across data.
Alignment of State and Action Information 🤖: State and action information are now perfectly aligned, enhancing the clarity and coherence of the dataset.
Robot Type and Control Frequency 📊: Annotations have been added for robot type and control frequency to improve dataset comprehensibility.
Joint Information 🦾: Joint-specific details have been included to assist with fine-grained understanding.

Dataset Structure of meta/info.json:

{
  "codebase_version": "v2.1", // lastest lerobot format
  "robot_type": "franka", // specific robot type, unknown if not provided
  "fps": 3, // control frequency, 10 if not provided
  // will add an additional key "control_frequency"
  "features": {
    "observation.images.image_key": {
      "dtype": "video",
      "shape": [128, 128, 3],
      "names": ["height", "width", "rgb"], // bgr to rgb if needed
      "info": {
        "video.fps": 3.0,
        "video.height": 128,
        "video.width": 128,
        "video.channels": 3,
        "video.codec": "av1",
        "video.pix_fmt": "yuv420p",
        "video.is_depth_map": false,
        "has_audio": false
      }
    },
    "observation.state": {
      "dtype": "float32",
      "shape": [8],
      "names": {
        "motors": ["x", "y", "z", "roll", "pitch", "yaw", "pad", "gripper"] 
        // unified 8-dim vector: [xyz, state type, gripper], motor_x if not provided
      }
    },
    "action": {
      "dtype": "float32",
      "shape": [7],
      "names": {
        "motors": ["x", "y", "z", "roll", "pitch", "yaw", "gripper"] 
        // unified 7-dim vector: [xyz, action type, gripper], motor_x if not provided
      }
    }
  }
}

Installation

Install LeRobot: Follow instructions in official repo.
Install others: For reading tfds/rlds, we need to install tensorflow-datasets:
```
pip install tensorflow
pip install tensorflow-datasets
```

Get started

Important

1.Before running the following code, modify save_episode() function in lerobot.

def save_episode(self, episode_data: dict | None = None, keep_images: bool | None = False) -> None:
    ...
    # delete images
    if not keep_images:
        img_dir = self.root / "images"
        if img_dir.is_dir():
            shutil.rmtree(self.root / "images")
    ...

2.for bc_z dataset, modify encode_video_frames() in lerobot/common/datasets/video_utils.py.

# add the following content to line 141:
vf: str = "pad=ceil(iw/2)*2:ceil(ih/2)*2",
# Add the following content to line 171:
ffmpeg_args["-vf"] = vf

Tip

We recommend using libsvtav1 as the vcodec for ffmpeg when encoding videos during dataset conversion. If you can't use libsvtav1 after installing lerobot, you need to compile it yourself. Follow this link for detailed compilation instructions.

Download source code:

git clone https://github.com/Tavish9/any4lerobot.git

Modify path in openx2lerobot/convert.sh:

python openx_rlds.py \
    --raw-dir /path/to/droid/1.0.0 \
    --local-dir /path/to/LEROBOT_DATASET \
    --repo-id your_hf_id \
    --use-videos \
    --push-to-hub

Execute the script:
```
cd openx2lerobot && bash convert.sh
```

Available OpenX_LeRobot Dataset

We have upload most of the OpenX datasets in huggingface🤗.

You can visualize the dataset in this space.