Train Contact-Rich Grasping Policies for Factory Arms with PyBullet and Stable Baselines3

"""
Production implementation for training contact-rich grasping policies
using PyBullet and Stable Baselines3. This code provides a
secure and scalable training pipeline.
"""

from typing import Dict, Any, Tuple
import os
import logging
import numpy as np
import gym
import pybullet as p
from stable_baselines3 import PPO
from stable_baselines3.common.envs import DummyVecEnv

# Logger setup for tracking the execution flow
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    environment: str = os.getenv('ENVIRONMENT', 'CartPole-v1')
    num_timesteps: int = int(os.getenv('NUM_TIMESTEPS', '100000'))

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 'grasp_policy' not in data:
        raise ValueError('Missing grasp_policy in input data')
    return True

def normalize_data(data: np.ndarray) -> np.ndarray:
    """Normalize input data to a range of [0, 1].
    
    Args:
        data: Raw input data
    Returns:
        Normalized data
    """
    return (data - np.min(data)) / (np.max(data) - np.min(data))

def fetch_data() -> Dict[str, Any]:
    """Simulate data fetching from an API or database.
    
    Returns:
        Sample data dictionary
    """
    return {'grasp_policy': 'path/to/policy'}

def save_to_db(data: Dict[str, Any]) -> None:
    """Simulate saving data to a database.
    
    Args:
        data: Data to save
    """
    logger.info('Data saved to database.')  # Simulated save

def process_batch(env: gym.Env, model: PPO, num_steps: int) -> None:
    """Process a batch of training steps.
    
    Args:
        env: The gym environment
        model: The trained PPO model
        num_steps: Number of steps to process
    """
    obs = env.reset()
    for _ in range(num_steps):
        action, _states = model.predict(obs)
        obs, rewards, done, info = env.step(action)
        if done:
            obs = env.reset()  # Reset the environment

class GraspingTrainer:
    """
    Orchestrator class to manage training of grasping policies.
    """
    def __init__(self, config: Config):
        self.config = config
        self.env = DummyVecEnv([lambda: gym.make(config.environment)])
        self.model = PPO('MlpPolicy', self.env, verbose=1)

    def train(self) -> None:
        """Train the grasping policy.
        """
        logger.info('Starting training...')
        self.model.learn(total_timesteps=self.config.num_timesteps)
        logger.info('Training completed.')
        self.model.save('grasping_policy')

    def load_policy(self, path: str) -> None:
        """Load a pre-trained policy.
        
        Args:
            path: Path to the saved policy
        """
        self.model = PPO.load(path)
        logger.info('Policy loaded from {}'.format(path))

if __name__ == '__main__':
    try:
        # Fetch and validate input data
        input_data = fetch_data()
        validate_input(input_data)  # Validate input
        config = Config()  # Load configuration
        trainer = GraspingTrainer(config)  # Create trainer
        trainer.train()  # Start training
    except ValueError as ve:
        logger.error(f'Value error: {ve}')  # Handle validation errors
    except Exception as e:
        logger.error(f'An error occurred: {e}')  # Handle other errors