Train Robotic Manipulation Policies with LeRobot and Isaac Lab

                      
                     
"""
Production implementation for training robotic manipulation policies using LeRobot and Isaac Lab.
Provides secure, scalable operations for learning and fine-tuning robotic behaviors.
"""
from typing import Dict, Any, List
import os
import logging
import asyncio
import aiohttp
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker, Session

# Logger configuration for monitoring
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment settings."""
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: int = int(os.getenv('RETRY_DELAY', 1))

# Database connection pooling
engine = create_engine(Config.database_url, pool_size=5, max_overflow=10)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'manipulation_id' not in data:
        raise ValueError('Missing manipulation_id')  # Essential field
    if not isinstance(data['parameters'], dict):
        raise ValueError('Parameters must be a dictionary')  # Type check
    return True  # Validation successful

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from a given API endpoint.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        JSON response from the API
    Raises:
        Exception: If fetching fails
    """
    async with aiohttp.ClientSession() as session:
        async with session.get(endpoint) as response:
            response.raise_for_status()  # Raise error for bad responses
            return await response.json()

async def save_to_db(session: Session, data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        session: SQLAlchemy session
        data: Data to save
    """
    try:
        session.execute(text("INSERT INTO manipulations (id, parameters) VALUES (:id, :params)"),
                       {'id': data['manipulation_id'], 'params': str(data['parameters'])})
        session.commit()  # Commit transaction
    except Exception as e:
        logger.error(f'Error saving to DB: {e}')  # Log error
        session.rollback()  # Rollback transaction on error
        raise

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of robotic manipulation requests.
    
    Args:
        data: List of requests to process
    """
    async with SessionLocal() as session:
        for item in data:
            try:
                await validate_input(item)  # Validate each item
                sanitized = await sanitize_fields(item)  # Sanitize input
                await save_to_db(session, sanitized)  # Save to DB
                logger.info(f'Successfully processed: {sanitized}')  # Log success
            except ValueError as ve:
                logger.warning(f'Validation error: {ve}')  # Warn on validation issues
            except Exception as e:
                logger.error(f'Processing error: {e}')  # Log unexpected errors

async def call_api_and_process(endpoint: str) -> None:
    """Main workflow for calling API and processing results.
    
    Args:
        endpoint: The API endpoint to call
    """
    try:
        data = await fetch_data(endpoint)  # Fetch data from API
        await process_batch(data['results'])  # Process results
    except Exception as e:
        logger.error(f'Error in workflow: {e}')  # Log errors

if __name__ == '__main__':
    # Example usage with asyncio event loop
    endpoint = os.getenv('DATA_API_URL')
    asyncio.run(call_api_and_process(endpoint))