Train Adaptive Grasping Policies with Stable-Baselines3 and MoveIt 2

"""
Production implementation for training adaptive grasping policies using Stable-Baselines3 and MoveIt 2.
Provides a robust architecture for grasping tasks in robotics.
"""
from typing import Dict, Any, List
import os
import logging
import time
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from moveit_commander import MoveGroupCommander, RobotCommander

# Logger setup for tracking application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to handle environment variables.
    """
    # Environment variables for configuration
    moveit_robot_namespace: str = os.getenv('MOVEIT_ROBOT_NAMESPACE', 'robot_namespace')
    ppo_model_path: str = os.getenv('PPO_MODEL_PATH', 'model.zip')

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

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security risks.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    return {key: str(value) for key, value in data.items() if value is not None}

def setup_moveit() -> MoveGroupCommander:
    """Initialize MoveIt commander for robotic arm control.
    
    Returns:
        MoveGroupCommander: Initialized MoveGroupCommander
    """
    moveit_commander.roscpp_initialize(sys.argv)
    return MoveGroupCommander(Config.moveit_robot_namespace)

def fetch_data() -> Dict[str, Any]:
    """Fetch object position data from a simulated environment.
    
    Returns:
        Dictionary containing object position
    """
    # Mock data for demonstration purposes
    return {'object_position': [0.5, 0.0, 0.2]}

def train_model(env: DummyVecEnv) -> PPO:
    """Train the PPO model for grasping.
    
    Args:
        env: The vectorized environment for training
    Returns:
        Trained PPO model
    """
    model = PPO('MlpPolicy', env, verbose=1)
    model.learn(total_timesteps=10000)  # Training for 10k timesteps
    return model

def process_grasping_task(data: Dict[str, Any], model: PPO, group: MoveGroupCommander) -> None:
    """Process the grasping task using the trained model.
    
    Args:
        data: Input data for grasping
        model: Trained PPO model
        group: MoveGroupCommander for arm control
    """
    # Process object position
    logger.info('Starting grasping task...')
    object_position = data['object_position']
    # Simulate grasping action based on model prediction
    action, _ = model.predict(np.array(object_position))  # Prediction
    group.go(action, wait=True)  # Execute action
    logger.info('Grasping action completed.')

def save_model(model: PPO) -> None:
    """Save the trained model to disk.
    
    Args:
        model: Trained PPO model
    """
    model.save(Config.ppo_model_path)  # Saving model
    logger.info('Model saved to %s', Config.ppo_model_path)

class GraspingOrchestrator:
    """Main orchestrator for grasping tasks.
    """
    def __init__(self) -> None:
        self.group = setup_moveit()  # Initialize MoveIt
        self.env = DummyVecEnv([lambda: self])  # Create dummy environment
        self.model = train_model(self.env)  # Train the model

    def execute_grasping(self) -> None:
        """Execute the full grasping workflow.
        """
        try:
            data = fetch_data()  # Fetch data
            validate_input(data)  # Validate input
            sanitized_data = sanitize_fields(data)  # Sanitize data
            process_grasping_task(sanitized_data, self.model, self.group)  # Process task
            save_model(self.model)  # Save model after execution
        except ValueError as e:
            logger.error('Input validation error: %s', e)  # Log validation errors
        except Exception as e:
            logger.error('An error occurred: %s', e)  # Log general errors
        finally:
            logger.info('Grasping task concluded.')  # Task conclusion

if __name__ == '__main__':
    orchestrator = GraspingOrchestrator()  # Initialize orchestrator
    orchestrator.execute_grasping()  # Execute the grasping workflow