Generate Dexterous Manipulation Demonstrations for Factory Arms with cuRobo and Isaac Lab

"""
Production implementation for generating dexterous manipulation demonstrations for factory arms using cuRobo and Isaac Lab.
Provides secure, scalable operations with proper logging and error handling.
"""
from typing import Dict, Any, List
import os
import logging
import time
import random
import requests

# Setup logging with INFO level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

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 'task_id' not in data:
        logger.error('Validation failed: Missing task_id')
        raise ValueError('Missing task_id')
    return True

def fetch_data(api_url: str, params: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch data from the API.
    
    Args:
        api_url: URL of the API endpoint
        params: Parameters for the API request
    Returns:
        JSON response from the API
    Raises:
        Exception: If the request fails
    """
    try:
        response = requests.get(api_url, params=params)
        response.raise_for_status()
        return response.json()
    except requests.HTTPError as e:
        logger.error(f'HTTP error occurred: {e}')
        raise
    except Exception as e:
        logger.error(f'An error occurred: {e}')
        raise

def normalize_data(raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize raw data into a usable format.
    
    Args:
        raw_data: List of raw data entries
    Returns:
        Normalized data entries
    """
    return [{'id': entry['id'], 'value': entry['value']} for entry in raw_data if 'id' in entry]

def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data entries.
    
    Args:
        data: List of data entries to process
    Returns:
        Processed data entries
    """
    processed = []
    for entry in data:
        # Simulate some processing logic
        processed.append({'id': entry['id'], 'processed_value': entry['value'] * random.uniform(0.8, 1.2)})
    return processed

def save_to_db(processed_data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        processed_data: List of processed data entries
    Raises:
        Exception: If saving fails
    """
    # Placeholder for database saving logic
    logger.info(f'Saving {len(processed_data)} entries to the database.')
    # Simulating a save with a possible failure
    if random.choice([True, False]):
        raise Exception('Simulated database save failure')

def handle_errors(func):
    """Decorator to handle errors and log them.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            return None
    return wrapper

class DexterousManipulator:
    """Main orchestrator for dexterous manipulation demonstrations."""
    def __init__(self) -> None:
        self.config = Config()
        self.api_url = self.config.api_url

    @handle_errors
    def run(self, task_id: str) -> None:
        """Run the manipulation task.
        
        Args:
            task_id: Unique identifier for the task
        """
        logger.info(f'Starting task: {task_id}')
        raw_data = fetch_data(self.api_url, {'task_id': task_id})
        validated_data = validate_input({'task_id': task_id})
        if validated_data:
            normalized_data = normalize_data(raw_data)
            processed_data = process_batch(normalized_data)
            save_to_db(processed_data)
        logger.info(f'Task {task_id} completed successfully.')  

if __name__ == '__main__':
    # Example usage
    manipulator = DexterousManipulator()
    manipulator.run('12345')