Community Dataset v1

A large-scale community-contributed robotics dataset for vision-language-action learning, featuring 128 datasets from 55 contributors worldwide.

We used this dataset to pretrain SmolVLA. However, this is not a complete set, but the dataset that we selected using specific filters, like fps, min num of episodes, and some qualitative assessment of video qualities, using the https://huggingface.co/spaces/Beegbrain/FilterLeRobotData tool. We also manually curated the task descriptions for this subset of the dataset.

🌟 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

🗂️ Structure

The dataset maintains a clear hierarchical structure:

community_dataset_v1/
├── 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

🚀 Usage

1. 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

hf download HuggingFaceVLA/community_dataset_v1 \
       --repo-type=dataset \
       --local-dir /path/local_dir/community_dataset_v1

Load Individual Datasets

from lerobot.datasets.lerobot_dataset import LeRobotDataset
import os

# Browse available datasets
for contributor in os.listdir("./community_dataset_v1"):
    contributor_path = f"./community_dataset_v1/{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_v1/contributor_name/dataset_name"
)

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

Integration with SmolVLA pretraining framework

This dataset is designed for training VLA models You can download this dataset and use it for Vision Language Action Models training framework, VLAb:

1. Visit the VLAb repository.
2. Follow the training instructions in the repo
3. Point the training script to this dataset

accelerate launch --config_file accelerate_configs/multi_gpu.yaml \
    src/lerobot/scripts/train.py \
    --policy.type=smolvla2 \
    --policy.repo_id=HuggingFaceTB/SmolVLM2-500M-Video-Instruct \
    --dataset.repo_id="community_dataset_v1/AndrejOrsula/lerobot_double_ball_stacking_random,community_dataset_v1/aimihat/so100_tape" \
    --dataset.root="local/path/to/datasets" \
    --dataset.video_backend=pyav \
    --dataset.features_version=2 \
    --output_dir="./outputs/training" \
    --batch_size=8 \
    --steps=200000 \
    --wandb.enable=true \
    --wandb.project="smolvla2-training"

🔧 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

🤝 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

🔗 Related Work

• VLAb Framework
• SmolVLA model
• SmolVLA Blogpost
• SmolVLA Paper
• Docs
• How to Build a successful Robotics dataset with Lerobot?

---

Built with ❤️ by the SmolVLA team and LeRobot Community