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README.md

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+---
+license: apache-2.0
+tags:
+- robotics
+- community
+- so100
+- so101
+- manipulation
+- smolvla
+- lerobot community
+- vision-language-action
+- embodied-ai
+- cross-embodiment
+task_categories:
+- robotics
+language:
+- en
+size_categories:
+- 10M<n<100M
+pretty_name: Community Dataset v3
+---
+
+# Community Dataset v3 - A Cross-Embodiment Pretraining Dataset
+
+A large-scale robotics dataset for vision-language-action learning, featuring **791 datasets** across **46 robot types**, enabling cross-embodiment pretraining for generalist robot policies.
+
+![Community Dataset v3](./comm_v3.png)
+
+## Overview
+
+This is a **crowdsourced, open-source dataset** compiled from **235 community contributors** worldwide. Building upon the pretraining datasets used for [SmolVLA](https://huggingface.co/blog/smolvla), [Community Datasets v1](https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v1) and [v2](https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v2), this cleaned and organized version opens the door for **cross-embodiment training** on another completely new batch of community-contributed data.
+
+The dataset spans **46+ robot embodiments** including single-arm, bimanual, mobile manipulation, and a few humanoid robots. All data was collected using the [LeRobot](https://github.com/huggingface/lerobot) framework and is compatible with the [VLAb](https://github.com/huggingface/VLAb) pretraining framework.
+
+
+## 📊 Dataset Statistics
+
+| Metric | Value |
+|--------|-------|
+| **Total Datasets** | 791 |
+| **Total Episodes** | 50,622 |
+| **Total Frames** | 25,971,082 |
+| **Total Duration** | 240.47 hours (10.02 days) |
+| **Contributors** | 235 |
+| **Robot Types** | 46 different embodiments |
+| **Action Dimensions** | 12 different configurations |
+| **Average Hours/Dataset** | 0.30 |
+
+## 🤖 Robot Distribution
+
+### By Category
+- **Single-arm manipulators**: 72% (571 datasets)
+- **Bimanual systems**: 12% (95 datasets)
+- **Mobile manipulation**: 8% (63 datasets)
+- **Humanoid platforms**: 1% (8 datasets)
+- **Other configurations**: 7% (54 datasets)
+
+### Top 10 Robot Types
+
+| Robot Type | Datasets | % | Category |
+|------------|----------|---|----------|
+| **so100** | 248 | 31.4% | Single-arm |
+| **so101_follower** | 124 | 15.7% | Single-arm |
+| **so100_follower** | 121 | 15.3% | Single-arm |
+| **so101** | 82 | 10.4% | Single-arm |
+| **arx5** | 43 | 5.4% | Single-arm |
+| **koch** | 38 | 4.8% | Single-arm |
+| **trossen_ai_mobile** | 25 | 3.2% | Mobile |
+| **bi_xarm6_follower** | 16 | 2.0% | Bimanual |
+| **so100_bimanual** | 12 | 1.5% | Bimanual |
+| **koch_follower** | 8 | 1.0% | Single-arm |
+
+## 🎮 Action Space Distribution
+
+| Action Shape | Datasets | % | Description |
+|--------------|----------|---|-------------|
+| **(6,)** | 607 | 76.7% | Single-arm + gripper (standard) |
+| **(14,)** | 60 | 7.6% | Bimanual robots |
+| **(7,)** | 58 | 7.3% | Single-arm extended DoF |
+| **(16,)** | 26 | 3.3% | Complex bimanual |
+| **(12,)** | 19 | 2.4% | Bimanual + grippers |
+| Others (7 shapes) | 21 | 2.7% | Specialized configs |
+
+## 🗂️ Dataset Structure
+
+```
+community_dataset_v3_clean/
+├── contributor1/
+│   ├── dataset_name_1/
+│   │   ├── data/           # Parquet files with observations
+│   │   │   ├── episode_000000.parquet
+│   │   │   ├── episode_000001.parquet
+│   │   │   └── ...
+│   │   ├── videos/         # MP4 recordings (multi-view)
+│   │   │   ├── episode_000000_image.mp4
+│   │   │   └── ...
+│   │   └── meta/           # Metadata
+│   │       └── info.json
+│   └── dataset_name_2/
+├── contributor2/
+└── ...
+```
+
+## 🚀 Usage
+
+### Prerequisites
+
+Install LeRobot and authenticate:
+
+```bash
+# Create conda environment
+conda create -y -n lerobot python=3.10
+conda activate lerobot
+
+# Install ffmpeg and LeRobot
+conda install ffmpeg -c conda-forge
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+pip install -e .
+
+# Authenticate with Hugging Face
+huggingface-cli login
+# Get your token from https://huggingface.co/settings/tokens
+```
+
+### Download the Dataset
+
+```bash
+huggingface-cli download HuggingFaceVLA/community_dataset_v3_clean \
+    --repo-type=dataset \
+    --local-dir ./community_dataset_v3_clean
+```
+
+### Load Individual Datasets
+
+```python
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+import os
+
+# Browse available datasets
+for contributor in os.listdir("./community_dataset_v3_clean"):
+    contributor_path = f"./community_dataset_v3_clean/{contributor}"
+    if os.path.isdir(contributor_path):
+        for dataset in os.listdir(contributor_path):
+            print(f"📁 {contributor}/{dataset}")
+
+# Load a specific dataset
+dataset = LeRobotDataset(
+    repo_id="local",
+    root="./community_dataset_v3/contributor_name/dataset_name"
+)
+
+# Access data
+print(f"Episodes: {len(dataset.episode_data_index)}")
+print(f"Total frames: {len(dataset)}")
+```
+
+### Train with VLAb
+
+This dataset is designed for cross-embodiment VLA training using [VLAb](https://github.com/huggingface/VLAb):
+
+```bash
+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_v3/contributor1/dataset1,community_dataset_v3/contributor2/dataset2" \
+    --dataset.root="./community_dataset_v3" \
+    --dataset.video_backend=pyav \
+    --dataset.features_version=2 \
+    --output_dir="./outputs/training" \
+    --batch_size=8 \
+    --steps=200000 \
+    --wandb.enable=true \
+    --wandb.project="smolvla2-cross-embodiment"
+```
+
+## 🔧 Training Challenges with Cross-Embodiment Data
+
+### The Reality of Community-Contributed Data
+
+This dataset includes 791 datasets recorded by community members under different conditions worldwide, creating an authentic in-the-wild setup. While this diversity is valuable for cross-embodiment learning, it comes with real challenges: varying data quality, inconsistent recording setups, and heterogeneous robot configurations. Using these datasets out-of-the-box will likely result in random collate errors and warnings during training.
+
+### What We Encountered During Data Cleaning
+
+Starting with 851 datasets, we systematically debugged and cleaned the collection. Here's what we found:
+
+#### Missing Video Files (Primary Removal Reason)
+Some datasets had incomplete episode recordings where video files were missing:
+```
+ERROR Failed to load video for key 'observation.images.image' at episode X: 
+[Errno 2] No such file or directory: '/path/to/episode_XXXXXX.mp4'
+```
+**Impact:** Training crashes when these episodes were sampled  
+**Action:** Removed ~15-20 datasets with missing files
+
+#### Data Type Incompatibilities
+Certain datasets returned inconsistent data types during batch formation:
+```
+RuntimeError: Could not infer dtype of dict
+AttributeError: 'list' object has no attribute 'device'
+```
+**Impact:** Random crashes during forward pass  
+**Action:** Removed ~10-15 problematic datasets, implemented resilient batch collation
+
+#### Multi-Camera Configuration Issues
+Different datasets had varying numbers of camera views, causing tensor shape mismatches:
+
+**Root cause:** The `max_num_images` parameter wasn't properly propagated in the codebase, leading to inconsistent image tensor shapes when datasets had different numbers of cameras (some had 2, others had 4+ views).
+
+**Impact:** Thousands of dimension/channel erros for the datasets with more than 3 images. 
+**Action:** Set `config.max_num_images = 3` to standardize input. This number balances multi-view information (essential for spatial reasoning) while being compatible with most datasets in the collection - the majority of community datasets use 2-3 camera views for manipulation tasks.
+
+#### Video Timing Misalignments
+Frame timestamps occasionally violated tolerance thresholds:
+```
+Some query timestamps violate tolerance (tensor([2.0667]) > tolerance_s=0.0001)
+```
+**Impact:** Minor temporal inconsistency, but training continued  
+**Action:** Automatic fallback to closest frames
+
+#### Final Dataset Cleaning Results
+- **Original datasets:** 851
+- **Datasets with missing files:** ~15-20 (removed)
+- **Datasets with data type issues:** ~10-15 (removed)
+- **Datasets with conversion failures:** 16 (fixed and reprocessed)
+- **Datasets with different FPS values:** Many datasets remain valid but have varying frame rates (some recorded at different fps than the standard 30fps)
+- **Final clean dataset:** 791 datasets
+
+## 🎯 Intended Use
+
+This dataset enables:
+
+- **Cross-embodiment VLA training** - Learn policies that generalize across robot types
+- **Multi-task manipulation** - Pick & place, sorting, assembly, bimanual tasks
+- **Transfer learning** - Leverage diverse demonstrations for new robots
+- **Imitation learning research** - Large-scale behavior cloning
+- **Generalist robot policies** - Train models that work on multiple platforms
+- **Mobile manipulation** - Navigation + manipulation tasks
+- **Embodied AI research** - Vision-motor coordination
+
+
+## 🏆 Top Contributors
+
+| Contributor | Datasets | % |
+|-------------|----------|---|
+| **shuohsuan** | 57 | 7.2% |
+| **villekuosmanen** | 47 | 5.9% |
+| **LeRobot-worldwide-hackathon** | 31 | 3.9% |
+| **lt-s** | 27 | 3.4% |
+| **Qipei** | 23 | 2.9% |
+| **bjb7** | 18 | 2.3% |
+| **kumarhans** | 18 | 2.3% |
+| **Ryosei2** | 17 | 2.1% |
+| **kyomangold** | 16 | 2.0% |
+| **psg777** | 16 | 2.0% |
+| Others (225) | 521 | 65.9% |
+
+## 🤝 Contributing
+
+Future contributions should follow:
+- LeRobot dataset format (v2.1+)
+- Consistent naming for features and camera views
+- Quality validation checks
+- Precise task descriptions
+- Robot type and action space metadata
+
+See the [LeRobot dataset guide](https://huggingface.co/blog/lerobot-datasets) for best practices.
+
+Please acknowledge all individual contributors who created the original datasets.
+
+## 📄 License
+
+Released under **Apache 2.0 license**. Individual datasets may have additional attribution requirements.
+
+When using this dataset:
+- ✅ Cite the dataset and VLAb framework
+- ✅ Acknowledge community contributors
+- ✅ Follow Apache 2.0 license terms
+- ✅ Consider contributing your own data
+
+## 🔗 Related Resources
+
+- [VLAb Framework](https://github.com/huggingface/VLAb) - Large-scale pre-training
+- [SmolVLA Model](https://huggingface.co/lerobot/smolvla_base) - Pre-trained VLA
+- [SmolVLA Blog](https://huggingface.co/blog/smolvla) - Introduction and tutorials
+- [SmolVLA Paper](https://huggingface.co/papers/2506.01844) - Technical details
+- [LeRobot Docs](https://huggingface.co/docs/lerobot) - Complete documentation
+- [Dataset Guide](https://huggingface.co/blog/lerobot-datasets) - Best practices
+- [Community Dataset v2](https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v2) - Previous Dataset
+- [Community Dataset v1](https://huggingface.co/datasets/HuggingFaceVLA/community_dataset_v1) - First release
+
+
+*Built with ❤️ by the LeRobot Community and SmolVLA Team*