LeRobot Framework Contributions

Overview

LeRobot is HuggingFace’s framework for robot learning, providing standardized tools for dataset management, policy training, and model deployment. I contribute to this project and maintain comprehensive documentation.

Key Contributions

1. Dataset Management Tools

• Dataset conversion utilities
• Quality checking scripts
• Visualization tools
• HuggingFace Hub integration

2. Training Pipeline Improvements

• Configuration management
• Training scripts for various policies
• Hyperparameter tuning utilities
• Distributed training support

3. Deployment Tools

• Model export utilities
• Inference optimization
• Edge device deployment
• Real-time control scripts

4. Documentation

• Comprehensive guides
• API documentation
• Troubleshooting guides
• Example notebooks

Features

Dataset Format

LeRobot uses a standardized dataset format:

dataset/
├── data/
│   ├── episode_000000.parquet
│   ├── episode_000001.parquet
│   └── ...
├── videos/
│   ├── observation.images.top/
│   │   ├── episode_000000.mp4
│   │   └── ...
│   └── observation.images.wrist/
│       ├── episode_000000.mp4
│       └── ...
├── meta/
│   ├── info.json
│   └── stats.json
└── tasks.parquet

Supported Policies

• ACT: Action Chunking with Transformers
• SmolVLA: Small Vision-Language-Action model
• Diffusion Policy: Diffusion-based policy
• TD-MPC: Temporal Difference Model Predictive Control

Usage Examples

Dataset Conversion

from lerobot.scripts.convert_dataset import convert_dataset

# Convert from custom format to LeRobot format
convert_dataset(
    input_path="/path/to/custom/data",
    output_path="/path/to/lerobot/dataset",
    fps=30
)

Policy Training

# Train ACT policy
python -m lerobot.scripts.train \
    --dataset.repo_id=your-username/your-dataset \
    --policy=act \
    --output_dir=outputs/act_training

Model Evaluation

from lerobot.scripts.evaluate import evaluate_policy

# Evaluate trained policy
results = evaluate_policy(
    policy_path="outputs/act_training/checkpoints/005000",
    dataset_repo_id="your-username/your-dataset",
    num_episodes=10
)

Configuration

Training Configuration

# training_config.yaml
dataset:
  repo_id: "your-username/your-dataset"
  root: "/path/to/local/dataset"

policy:
  type: "act"
  chunk_size: 10
  n_action_steps: 10
  
training:
  batch_size: 8
  num_steps: 10000
  lr: 1e-4
  save_freq: 1000

Integration with SO-101

The LeRobot framework integrates seamlessly with the SO-101 robot system:

1. Data Collection: SO-101 records data in LeRobot format
2. Training: LeRobot trains policies on collected data
3. Deployment: Trained policies are deployed back to SO-101

Troubleshooting

Common Issues

1. Dataset loading errors

   # Check dataset structure
   from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
   dataset = LeRobotDataset("your-username/your-dataset")
   print(f"Number of episodes: {dataset.num_episodes}")
   

2. Training convergence issues
   • Reduce learning rate
   • Increase batch size
   • Check data quality
   • Verify policy configuration
3. Deployment errors
   • Verify model compatibility
   • Check input/output shapes
   • Test with dummy data first

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the Apache 2.0 License.

Acknowledgments

• HuggingFace Team for the LeRobot framework
• LeRobot Community for contributions and feedback
• Dian Team for testing and validation

For more information, visit the GitHub repository.