Community Dataset v2

A large-scale community-contributed robotics dataset for vision-language-action learning, featuring 340 datasets from 117 contributors worldwide.

This dataset represents the second major release of community-contributed robotics data, expanding upon the success of Community Dataset v1. The data was collected and processed using the LeRobot framework with automated processing and validation pipelines to ensure high quality and compatibility.

🌟 Overview

This dataset represents a collaborative effort from the robotics and AI community to build comprehensive training data for embodied AI systems. Each contribution contains demonstrations of robotic manipulation tasks with the SO100 arm, recorded using LeRobot tools, primarily focused on tabletop scenarios and everyday object interactions.

📊 Dataset Statistics

Metric	Value
Total Datasets	340
Total Episodes	6,325
Total Frames	5,033,418
Total Videos	12,650
Contributors	117
Weighted Average FPS	30.0
Average Episodes per Dataset	38.1
Total Duration	46.6 hours
Average Hours per Dataset	0.28
Primary Tasks	Manipulation, Pick & Place, Sorting
Robot Types	SO-100 (various colors)
Data Format	LeRobot v2.0 and v2.1 dataset format
Total Size	59 GB

🗂️ Structure

The dataset maintains a clear hierarchical structure:

community_dataset_v2/
├── contributor1/
│   ├── dataset_name_1/
│   │   ├── data/           # Parquet files with observations
│   │   ├── videos/         # MP4 recordings
│   │   └── meta/           # Metadata and info
│   └── dataset_name_2/
├── contributor2/
│   └── dataset_name_3/
└── ...

Each dataset follows the LeRobot format standard, ensuring compatibility with existing frameworks and easy integration.

🏆 Top Contributors

Contributor	Datasets Quantity
kantine	27
duthvik	17
aractingi	13
liyitenga	11
yuz1wan	10
sihyun77	9
abokinala	9
1g0rrr	9
pranavsaroha	8
danaaubakirova	8

🚀 Usage

Prerequisites

1. Install LeRobot

Follow the official LeRobot installation guide:

# Create conda environment with Python 3.10
conda create -y -n lerobot python=3.10
conda activate lerobot

# Install ffmpeg (required for video processing)
conda install ffmpeg -c conda-forge

git clone https://github.com/huggingface/lerobot.git
cd lerobot

# Install LeRobot from Source
pip install -e .

2. Authenticate with Hugging Face

You need to be logged in to access the dataset:

# Login to Hugging Face
huggingface-cli login

# Or alternatively, set your token as an environment variable
# export HF_TOKEN=your_token_here

Get your token from https://huggingface.co/settings/tokens

Download the Dataset

from huggingface_hub import snapshot_download

# Download entire collection (requires authentication)
dataset_path = snapshot_download(
    repo_id="HuggingFaceVLA/community_dataset_v2",
    repo_type="dataset",
    local_dir="./community_dataset_v2"
)

Load Individual Datasets

from lerobot.datasets.lerobot_dataset import LeRobotDataset
import os

# Browse available datasets
for contributor in os.listdir("./community_dataset_v2"):
    contributor_path = f"./community_dataset_v2/{contributor}"
    if os.path.isdir(contributor_path):
        for dataset in os.listdir(contributor_path):
            print(f"📁 {contributor}/{dataset}")

# Load a specific dataset (requires authentication)
dataset = LeRobotDataset(
    repo_id="local",
    root="./community_dataset_v2/contributor_name/dataset_name"
)

# Access episodes and observations
print(f"Episodes: {len(dataset.episode_indices)}")
print(f"Total frames: {len(dataset)}")

Integration with SmolVLA


COMING SOON
# This dataset is designed for training VLA models
# Compatible with SmolVLA training pipelines
# needs testing

🔧 Dataset Format

Each dataset contains:

data/: Parquet files with timestamped observations
- Robot states (joint positions, velocities)
- Action sequences
- Camera observations (multiple views)
- Language instructions
videos/: Synchronized video recordings
- Multiple camera angles
- High-resolution capture
- Timestamp alignment
meta/: Metadata and configuration
- Dataset info (fps, episode count)
- Robot configuration
- Task descriptions

🎯 Intended Use

This dataset is designed for:

Vision-Language-Action (VLA) model training
Robotic manipulation research
Imitation learning experiments
Multi-task policy development
Embodied AI research

📈 Revisions

v2.0: Expanded community collection
- 340 datasets from 117 contributors
- Standardized LeRobot v2.0/v2.1 format
- Advanced quality filtering and validation
- Comprehensive automated processing pipeline

🤝 Community Contributions

This dataset exists thanks to the generous contributions from researchers, hobbyists, and institutions worldwide. Each dataset represents hours of careful data collection and curation.

Contributing Guidelines

Future contributions should follow:

LeRobot dataset format
Consistent naming conventions for the features, camera views etc.
Quality validation checks
Proper task descriptions, describing the actions precisely.

Check the blogpost for more information

📄 License

Released under Apache 2.0 license. Individual datasets may have additional attribution requirements - please check contributor documentation.

🔗 Related Work

Built with ❤️ by the SmolVLA team and LeRobot Community