integration_guide.md

EmbodyHub Integration Guide

Quick Start

1. Environment Setup

# Install EmbodyHub
pip install embodyhub

# Install dependencies
pip install -r requirements.txt

2. Project Structure

Organize your embodied agent project according to the following structure:

project/
├── config/
│   └── agent_config.yaml    # Agent configuration file
├── models/                  # Model files directory
├── environments/           # Environment implementation directory
└── main.py                 # Main program entry

3. Implement Required Interfaces

3.1 Environment Interface

from embodyhub.core.environment import Environment

class YourEnvironment(Environment):
    def step(self, action):
        # Implement environment step logic
        pass
        
    def reset(self):
        # Implement environment reset logic
        pass

3.2 Adapter Interface

from embodyhub.core.adapter import Adapter

class YourAdapter(Adapter):
    def register_model(self, name, model):
        # Implement model registration logic
        pass
        
    def predict(self, input_data):
        # Implement prediction logic
        pass

4. Configure Agent

Configure agent parameters in config/agent_config.yaml:

agent:
  name: "your_agent"
  type: "your_agent_type"
  model:
    name: "your_model"
    path: "models/your_model.pt"
  adapter:
    type: "your_adapter"
    config: {}

5. Integration with Main Program

from embodyhub.core.agent import Agent
from embodyhub.core.agent_config import AgentConfig

# Load configuration
config = AgentConfig.from_yaml('config/agent_config.yaml')

# Create agent
agent = Agent(config)

# Run agent
while True:
    observation = environment.get_observation()
    action = agent.act(observation)
    environment.step(action)

Advanced Features

1. Multimodal Data Processing

from embodyhub.core.data_manager import DataManager

data_manager = DataManager()

# Add data stream
data_manager.add_stream(
    name="camera",
    config={"type": "image", "format": "rgb"}
)

# Process data
data_manager.process_data(your_data)

2. Performance Optimization

from embodyhub.core.performance_optimizer import PerformanceOptimizer

optimizer = PerformanceOptimizer()

# Optimize model
optimized_model = optimizer.optimize(your_model)

# Monitor performance
optimizer.monitor_performance()

3. Multi-Agent Coordination

from embodyhub.core.multi_agent_coordination import Coordinator

coordinator = Coordinator()

# Add agents
coordinator.add_agent(agent1)
coordinator.add_agent(agent2)

# Start coordination
coordinator.start()

Best Practices

1. Modular Design

   • Decouple components like environment, model, and adapter
   • Use configuration files for parameter management
   • Implement clear interface definitions

2. Error Handling

   • Implement proper exception handling
   • Add logging
   • Conduct unit testing

3. Performance Optimization

   • Use performance profiling tools
   • Optimize data processing pipeline
   • Use caching appropriately

Common Issues

1. How to handle custom environments? Inherit from the Environment class and implement required methods.

2. How to integrate existing models? Use appropriate adapters or implement new ones.

3. How to optimize performance? Use built-in performance optimization tools and follow best practices.

Debugging and Testing

1. Logging Configuration

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

2. Unit Testing

import unittest

class TestYourAgent(unittest.TestCase):
    def test_agent_behavior(self):
        # Implement test cases
        pass

3. Performance Testing

from embodyhub.core.profiler import Profiler

profiler = Profiler()
profiler.start()
# Run code
profiler.stop()
profiler.report()