Train Robotic Pick-and-Place Policies from Demonstrations with LeRobot and robosuite

                      
                     
"""
Production implementation for training robotic pick-and-place policies from demonstrations using LeRobot and robosuite.
Provides secure, scalable operations for robotic training.
"""

from typing import Dict, Any, List
import os
import logging
import time
import random
import numpy as np
from contextlib import contextmanager

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

# Configuration class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    max_retries: int = 5
    backoff_factor: float = 1.0

@contextmanager
def database_connection():
    """
    Context manager for database connection pooling.
    
    Yields:
        Connection object
    """
    try:
        conn = connect_to_database(Config.database_url)
        yield conn  # Provide the connection to the block
    finally:
        conn.close()  # Ensure connection is closed

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for pick-and-place training.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'demonstrations' not in data:
        raise ValueError('Missing demonstrations data')
    if not isinstance(data['demonstrations'], list):
        raise ValueError('Demonstrations must be a list')
    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
    """
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch training data from the database.
    
    Returns:
        List of demonstration records
    """ 
    with database_connection() as conn:
        records = conn.execute('SELECT * FROM demonstrations').fetchall()
    return records

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records to training format.
    
    Args:
        records: List of raw demonstration records
    Returns:
        Transformed records ready for training
    """
    return [{'input': record['input'], 'output': record['output']} for record in records]

async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records for training.
    
    Args:
        records: Batch of records to process
    """
    for record in records:
        # Simulate processing each record
        logger.info(f'Processing record: {record}')
        time.sleep(0.1)  # Simulate time-consuming operation

async def aggregate_metrics(metrics: List[float]) -> float:
    """Aggregate training metrics.
    
    Args:
        metrics: List of individual metrics
    Returns:
        Average metric value
    """
    return sum(metrics) / len(metrics) if metrics else 0.0

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    """
    with database_connection() as conn:
        conn.execute('INSERT INTO trained_models VALUES (:model)', {'model': data})

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Async function to decorate
    """
    async def wrapper(*args, **kwargs):
        for attempt in range(Config.max_retries):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                logger.error(f'Error occurred: {e}')
                time.sleep(Config.backoff_factor * (2 ** attempt))  # Exponential backoff
        raise RuntimeError('Max retries exceeded')
    return wrapper

class RoboticTrainer:
    """Main orchestrator for robotic training.
    
    Attributes:
        demonstrations: List of demonstration data
    """
    def __init__(self, demonstrations: List[Dict[str, Any]]) -> None:
        self.demonstrations = demonstrations

    async def train(self) -> None:
        """Main training workflow.
        
        Raises:
            RuntimeError: If training fails
        """
        try:
            await validate_input({'demonstrations': self.demonstrations})
            sanitized_data = await sanitize_fields(self.demonstrations)
            transformed_data = await transform_records(sanitized_data)
            await process_batch(transformed_data)
            # Simulate metrics aggregation
            metrics = [random.random() for _ in range(5)]  # Mock metrics
            avg_metric = await aggregate_metrics(metrics)
            await save_to_db({'average_metric': avg_metric})
            logger.info('Training completed successfully.')
        except Exception as e:
            logger.error(f'Training failed: {e}')
            raise RuntimeError('Training process failed')

if __name__ == '__main__':
    # Example usage
    demo_data = [{'input': [1, 2], 'output': [2, 3]}]  # Mock demonstration data
    trainer = RoboticTrainer(demo_data)
    import asyncio
    asyncio.run(trainer.train())