Train Dexterous Assembly Policies with Isaac GR00T N1.7 and robosuite

"""
Production implementation for training dexterous assembly policies with Isaac GR00T N1.7 and robosuite.
Provides secure, scalable operations for robotic assembly training.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import numpy as np
import robosuite as suite
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///local.db')  # Default to local SQLite
    robosuite_env: str = os.getenv('ROBO_SUITE_ENV', 'Panda')  # Default environment

# Create SQLAlchemy engine and session
engine = create_engine(Config.database_url)
Session = sessionmaker(bind=engine)

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' not in data or 'num_steps' not in data:
        raise ValueError('Missing required fields: task and num_steps')
    if not isinstance(data['num_steps'], int) or data['num_steps'] <= 0:
        raise ValueError('num_steps must be a positive integer')
    return True

def fetch_data(task: str) -> List[Dict[str, Any]]:
    """Fetch task-specific data from the database.
    
    Args:
        task: The task identifier
    Returns:
        List of task data
    Raises:
        Exception: On database fetch errors
    """
    with Session() as session:
        try:
            result = session.execute(text("SELECT * FROM tasks WHERE task_name = :task"), {'task': task})
            return [dict(row) for row in result]
        except Exception as e:
            logger.error('Database fetch failed: %s', e)
            raise

def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize input data for further processing.
    
    Args:
        data: List of raw data dictionaries
    Returns:
        Normalized data
    """
    normalized = []
    for record in data:
        normalized.append({k: v / 255.0 for k, v in record.items() if isinstance(v, (int, float))})  # Normalize numerical fields
    return normalized

def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data back to the database.
    
    Args:
        data: List of processed data dictionaries
    Raises:
        Exception: On database save errors
    """
    with Session() as session:
        try:
            for record in data:
                session.execute(text("INSERT INTO processed_data (field1, field2) VALUES (:f1, :f2)"), {'f1': record['field1'], 'f2': record['field2']})
            session.commit()
        except Exception as e:
            logger.error('Database save failed: %s', e)
            session.rollback()
            raise

def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for model training.
    
    Args:
        data: Normalized data
    Returns:
        Transformed data for training
    """
    transformed = []
    for record in data:
        transformed.append({'input': record['input'], 'output': record['output']})  # Simple transformation
    return transformed

def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of data for model training.
    
    Args:
        batch: List of data to process
    """
    normalized = normalize_data(batch)
    transformed = transform_records(normalized)
    save_to_db(transformed)

def handle_errors(func):
    """Decorator to handle function errors gracefully.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error('Error in %s: %s', func.__name__, e)
            raise
    return wrapper

class TrainingOrchestrator:
    """Main orchestrator class for training policies.
    """
    def __init__(self, task: str, num_steps: int):
        self.task = task
        self.num_steps = num_steps

    @handle_errors
    def train_policy(self) -> None:
        """Train the dexterous assembly policy.
        
        Raises:
            Exception: On training errors
        """
        logger.info('Starting training for task: %s with %d steps', self.task, self.num_steps)
        data = fetch_data(self.task)
        for step in range(self.num_steps):
            logger.info('Processing step %d', step)
            process_batch(data)
            time.sleep(1)  # Simulate processing time
        logger.info('Training completed for task: %s', self.task)

if __name__ == '__main__':
    # Example usage
    try:
        orchestrator = TrainingOrchestrator(task='assembly_task', num_steps=10)
        orchestrator.train_policy()
    except Exception as e:
        logger.error('Training failed: %s', e)