Train Vision-Language-Action Robot Policies in NVIDIA Isaac Sim with LeRobot

                      
                     
"""
Production implementation for training vision-language-action robot policies in NVIDIA Isaac Sim using LeRobot.
Provides secure and scalable operations for robotic action learning.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import random

# Set up logging for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """  
    def __init__(self):
        self.sim_url: str = os.getenv('SIM_URL', 'http://localhost:3000')
        self.db_url: str = os.getenv('DATABASE_URL')
        self.retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
        self.timeout: int = int(os.getenv('TIMEOUT', 10))

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 'action' not in data:
        raise ValueError('Missing action')
    if 'language' not in data:
        raise ValueError('Missing language')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """  
    # Example sanitization
    return {k: v.strip() for k, v in data.items()}

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from the specified URL.
    Args:
        url: The URL to fetch data from
    Returns:
        Parsed JSON response
    Raises:
        ConnectionError: If fetching fails
    """  
    # Simulating data fetch with mock response
    logger.info(f'Fetching data from {url}')
    time.sleep(1)  # Simulate network delay
    return {'status': 'success', 'data': 'mock_data'}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data into the database.
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """  
    logger.info('Saving data to the database')
    time.sleep(1)  # Simulate DB save delay
    # Here we would have actual database code

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of actions.
    Args:
        batch: List of actions to process
    """  
    for action in batch:
        logger.info(f'Processing action: {action}')
        await save_to_db(action)

async def aggregate_metrics(metrics: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed actions.
    Args:
        metrics: List of metrics to aggregate
    Returns:
        Aggregated metrics
    """  
    aggregated = {'total': len(metrics), 'success': sum(1 for m in metrics if m['status'] == 'success')}
    logger.info(f'Aggregated metrics: {aggregated}')
    return aggregated

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """  
    async def wrapper(*args, **kwargs):
        for attempt in range(Config().retry_attempts):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                logger.error(f'Error occurred: {e}')
                if attempt < Config().retry_attempts - 1:
                    wait = 2 ** attempt  # Exponential backoff
                    logger.info(f'Retrying in {wait} seconds...')
                    await asyncio.sleep(wait)
                else:
                    logger.error('Max retry attempts reached')
                    raise
    return wrapper

class RobotPolicyTrainer:
    """Main class to train robot action policies.
    """  
    def __init__(self):
        self.config = Config()
        self.training_data: List[Dict[str, Any]] = []

    async def train(self, data: List[Dict[str, Any]]) -> None:
        """Train the model with provided data.
        Args:
            data: List of training data
        """  
        logger.info('Starting training process')
        for item in data:
            await validate_input(item)
            item = await sanitize_fields(item)
            self.training_data.append(item)
        await process_batch(self.training_data)
        metrics = await aggregate_metrics(self.training_data)
        logger.info(f'Training completed. Metrics: {metrics}')

if __name__ == '__main__':
    import asyncio
    # Example usage of the RobotPolicyTrainer
    trainer = RobotPolicyTrainer()
    example_data = [
        {'action': 'pick', 'language': 'en'},
        {'action': 'place', 'language': 'fr'},
    ]
    asyncio.run(trainer.train(example_data))