fog-x 0.1.0b3

An Efficient and Scalable Data Collection and Management Framework For Robotics Learning

🦊 Fog-RT-X

🦊 Fog-RT-X: An Efficient and Scalable Data Collection and Management Framework For Robotics Learning. Support Open-X-Embodiment, 🤗HuggingFace.

🦊 Fog-RT-X considers both speed 🚀 and memory efficiency 📈 with active metadata and lazily-loaded trajectory data. It supports flexible and distributed dataset partitioning. It provides native support to cloud storage.

Design Doc | Dataset Visualization

Install

pip install fog_x

Usage

import fog_x

# 🦊 Dataset Creation 
# from distributed dataset storage 
dataset = fog_x.Dataset(
    name="demo_ds",
    path="~/test_dataset", # can be AWS S3, Google Bucket! 
)  

# 🦊 Data collection: 
# create a new trajectory
episode = dataset.new_episode()
# collect step data for the episode
episode.add(feature = "arm_view", value = "image1.jpg")
# Automatically time-aligns and saves the trajectory
episode.close()

# 🦊 Data Loading:
# load from existing RT-X/Open-X datasets
dataset.load_rtx_episodes(
    name="berkeley_autolab_ur5",
    additional_metadata={"collector": "User 2"}
)

# 🦊 Data Management and Analytics: 
# Compute and memory efficient filter, map, aggregate, groupby
episode_info = dataset.get_episode_info()
desired_episodes = episode_info.filter(episode_info["collector"] == "User 2")

# 🦊 Data Sharing and Usage:
# Export and share the dataset as standard Open-X-Embodiment format
# it also supports hugging face, and more!
dataset.export(desired_episodes, format="rtx")
# Load with pytorch dataloader
torch.utils.data.DataLoader(dataset.as_pytorch_dataset(desired_episodes))

Design

🦊 Fog-RT-X recognizes most post-processing, analytics and management involves the trajectory-level data, such as tags, while actual trajectory steps are rarely read, written and transformed. Acessing and modifying trajectory data is very expensive and hard.

As a result, 🦊 Fog-RT-X proposes

• a user-friendly metadata table via Pandas Datframe for speed and freedom
• a LazyFrame from Polars for the trajectory dataset that only loads and transform the data if needed
• parquet as storage format for distributed storage and columnar support compared to tensorflow records
• Easy and automatic RT-X/Open-X dataset export and pytorch dataloading

More Coming Soon!

Currently we see a more than 60% space saving on some existing RT-X datasets. This can be even more by re-paritioning the dataset. Our next steps can be found in the planning doc. Feedback welcome through issues or PR to planning doc!

We also note we are at a beta-testing phase. We make our best effort to be backward-compatible but interfaces may be unstable.

Development

Read the CONTRIBUTING.md file.