EmbodyHub

EmbodyHub is a powerful embodied intelligence application development and management framework. It provides a unified interface for developing, testing, and deploying embodied intelligence systems, simplifying the process of integrating various AI models, sensors, and actuators into a unified application.

Features

• Unified Embodied Intelligence Development API
• Modular Architecture for Easy Extension
• Built-in Support for Common AI Models
• Comprehensive Testing Framework
• Performance Optimization Tools
• Real-time Monitoring and Analytics
• Multi-agent Coordination Support
• Adaptive Learning Capabilities
• Extensive Sensor and Actuator Integration

Project Architecture

embodyhub/
├── core/                 # Core Components
│   ├── agent.py         # Agent Implementation
│   ├── environment.py   # Environment Interface
│   ├── adapter.py       # Base Adapter
│   ├── data_manager.py  # Data Manager
│   ├── optimizer.py     # Performance Optimizer
│   ├── profiler.py      # System Profiler
│   └── monitor.py       # Real-time Monitor
├── adapters/            # Framework Adapters
│   ├── pytorch_adapter.py
│   ├── openvla_adapter.py
│   └── octo_adapter.py
└── api/                 # API Interface
    ├── behavior.py      # Behavior Analysis
    └── monitor.py       # Monitoring Interface

Core Components

• Agent: Advanced agent implementation supporting multiple behavior strategies and learning algorithms
• Environment: Abstract environment interface for embodied intelligence tasks, providing standardized interaction methods
• Adapter: Integration interface for different AI frameworks, supporting PyTorch, OpenVLA, etc.
• DataManager: Handles multimodal data streams, supporting data collection, processing, and persistence
• Optimizer: Provides performance optimization strategies and automatic tuning capabilities
• Profiler: System profiling tools for performance analysis and bottleneck detection
• Monitor: Real-time monitoring and analytics for system performance and agent behavior

Advanced Features

Multi-agent Coordination

EmbodyHub provides built-in support for multi-agent systems, enabling:

• Distributed task execution
• Agent communication and synchronization
• Resource sharing and conflict resolution
• Collective learning and adaptation

Adaptive Learning

• Online learning and adaptation
• Transfer learning capabilities
• Experience replay and imitation learning
• Meta-learning support

Performance Optimization

• Automatic hyperparameter tuning
• Resource usage optimization
• Load balancing and scaling
• Memory management

Installation

pip install embodyhub

Quick Start

Basic Example

import torch
import torch.nn as nn
from embodyhub.core.environment import Environment
from embodyhub.adapters.pytorch_adapter import PyTorchAdapter

# Define Policy Network
class PolicyNetwork(nn.Module):
    def __init__(self, input_size: int, output_size: int):
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(input_size, 64),
            nn.ReLU(),
            nn.Linear(64, output_size),
            nn.Softmax(dim=-1)
        )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.network(x)

# Create adapter and register model
adapter = PyTorchAdapter()
model = PolicyNetwork(input_size=4, output_size=2)
adapter.register_model('policy', model)

Advanced Usage

from embodyhub.core.agent import Agent
from embodyhub.core.data_manager import DataManager
from embodyhub.core.optimizer import Optimizer

# Create agent with optimization
agent = Agent(
    adapter=adapter,
    data_manager=DataManager(),
    optimizer=Optimizer(),
    config={
        'learning_rate': 0.001,
        'gamma': 0.99,
        'batch_size': 64,
        'memory_size': 10000
    }
)

# Enable performance monitoring
agent.enable_monitoring()

# Train agent with optimization
agent.train(
    env,
    episodes=1000,
    optimize=True,
    eval_interval=100
)

Example Projects

We provide multiple example projects demonstrating how to use EmbodyHub to build different types of embodied intelligence applications:

• PyTorch Integration Example
• Multimodal Data Processing
• OpenVLA Integration
• Performance Optimization
• Multi-agent Coordination
• Adaptive Learning
• Real-time Monitoring

API Documentation

For complete API documentation, visit: API Reference Guide

Integration Guide

To learn how to integrate your embodied intelligence project with EmbodyHub, please refer to: Integration Guide

Contribution Guidelines

We welcome community contributions! Please read our contribution guidelines before submitting a Pull Request.

Development Environment Setup

1. Clone repository

git clone https://github.com/yourusername/embodyhub.git
cd embodyhub

2. Install dependencies

pip install -r requirements.txt

3. Run tests

python -m pytest tests/

Code Standards

• Follow PEP 8 coding standards
• Write unit tests for all new features
• Provide clear commit messages
• Keep documentation up-to-date
• Use type hints and docstrings
• Follow SOLID principles
• Implement proper error handling

Pull Request Process

1. Create a feature branch from develop
2. Write tests for new features
3. Update documentation
4. Submit PR with clear description
5. Pass CI/CD checks
6. Get code review approval

License

This project is licensed under the MIT License - see the LICENSE file for details

Contact

For questions and suggestions, please raise an Issue in the GitHub repository.

Acknowledgments

Thanks to all developers who have contributed to EmbodyHub!

Citing EmbodyHub

If you use EmbodyHub in your research, please cite:

@software{embodyhub2025,
  title = {EmbodyHub: A Framework for Embodied Intelligence Development},
  author = {EmbodyHub Contributors},
  year = {2025},
  url = {https://github.com/embodyhub/EmbodyHub}
}