README.md

grain-oxe

A dataloader framework for loading robotics data.

Warning

This is a work in progress and the API is subject to change.

grain-oxe is designed with the following goals:

• Flexibility: Support loading data for behavior cloning, RL, etc.
• Mixed-modal data: Support loading data where different datasets use different formats (e.g. action chunk lengths, etc.)
• Masked data: The dataloader is implemented using np.ma.MaskedArray, so masks (e.g. when passing the end of an episode) are preserved.
• Performance: Optimize for performance when loading data from disk.

Installation

grain-oxe can be installed using pip:

pip install git+https://github.com/kylestach/grain-oxe.git

For development, setup is managed using uv.

Usage

First, convert your data into a format that grain-oxe can load. We use array-record as the storage backend, with messages encoded using msgpack-numpy:

python scripts/convert_from_rlds.py --data_dir /path/to/tensorflow/datasets --output_dir /path/to/output --dataset_name dataset_name

You can then load the dataset using grain-oxe:

from grain_oxe.core import create_dataset, BCDatasetStructure

dataset = create_dataset(
    dataset_name,
    data_dir,
    dataset_structure=BCDatasetStructure(
        num_obs_steps=1,
        num_action_steps=10,
    ),
    split="train",
    seed=42,
)

dataset can then be used as a regular grain.MapDataset. You can apply additional transformations using .map().

We recommend using grain.DataLoader to iterate over the dataset.

Roadmap

• Support image augmentations
• Support padding for datasets with different lengths
• Support loading from RLDS (https://github.com/google-research/rlds)