Deploy Reinforcement Learning Policies to Factory Arms with LeRobot and MoveIt 2

"""
Production implementation for deploying reinforcement learning policies to factory arms using LeRobot and MoveIt 2.
Provides secure, scalable operations with logging and error handling.
"""
from typing import Dict, Any, List
import os
import logging
import time
from contextlib import contextmanager

# Set up logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    le_robot_url: str = os.getenv('LE_ROBOT_URL')
    moveit_service_url: str = os.getenv('MOVEIT_SERVICE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 5))

@contextmanager
def connection_pool():
    """Context manager for managing connection pooling.
    
    Yields:
        Connection object
    """
    conn = create_connection()  # Assuming a function to create a connection
    try:
        yield conn  # Yielding connection to be used
    finally:
        conn.close()  # Ensuring connection is closed

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for required fields.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'policy' not in data:
        raise ValueError('Missing policy in input data')
    if 'robot_id' not in data:
        raise ValueError('Missing robot_id in input data')
    return True  # Input is valid

async def fetch_data(robot_id: str) -> Dict[str, Any]:
    """Fetch data for the specified robot.
    
    Args:
        robot_id: Identifier for the robot
    Returns:
        Data fetched from the service
    Raises:
        RuntimeError: If fetching data fails
    """
    try:
        response = call_api(f'{Config.le_robot_url}/robots/{robot_id}')  # Mock API call
        return response.json()
    except Exception as e:
        logger.error(f'Error fetching data for robot {robot_id}: {e}')
        raise RuntimeError('Failed to fetch data')

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save the transformed data to the database.
    
    Args:
        data: Data to save
    Raises:
        RuntimeError: If saving data fails
    """
    try:
        with connection_pool() as conn:
            conn.execute('INSERT INTO policies (data) VALUES (?)', (data,))  # Example SQL
    except Exception as e:
        logger.error(f'Error saving data to database: {e}')
        raise RuntimeError('Failed to save data')

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform the input records to a desired format.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    # Transform the data structure or values as needed
    transformed_data = {'policy': data['policy'], 'robot_id': data['robot_id']}
    return transformed_data

async def process_batch(data_list: List[Dict[str, Any]]) -> None:
    """Process a batch of input data.
    
    Args:
        data_list: List of input data to process
    Raises:
        RuntimeError: If processing fails
    """
    for data in data_list:
        await validate_input(data)  # Validate each input
        transformed_data = await transform_records(data)  # Transform the data
        await save_to_db(transformed_data)  # Save transformed data to DB

async def deploy_policy(data: Dict[str, Any]) -> None:
    """Deploy a reinforcement learning policy to a factory arm.
    
    Args:
        data: Input data containing policy and robot_id
    Raises:
        RuntimeError: If deployment fails
    """
    try:
        await validate_input(data)  # Validate input
        robot_data = await fetch_data(data['robot_id'])  # Fetch robot data
        transformed_data = await transform_records(data)  # Transform data
        await save_to_db(transformed_data)  # Save data
        logger.info(f'Successfully deployed policy {data["policy"]} for robot {data["robot_id"]}')
    except Exception as e:
        logger.error(f'Failed to deploy policy: {e}')
        raise RuntimeError('Deployment failed')  # Raise error for handling in main block

if __name__ == '__main__':
    # Example usage
    sample_data = {'policy': 'example_policy', 'robot_id': 'robot_1'}
    try:
        await deploy_policy(sample_data)  # Deploy the sample policy
    except RuntimeError as e:
        logger.error(f'Error in deployment: {e}')  # Log deployment error