Develop Robotic Manipulation Skills with PEFT-Optimized Policies and Isaac Lab

                      
                     
"""\nProduction implementation for developing robotic manipulation skills using PEFT-optimized policies and Isaac Lab.\nThis example showcases a complete end-to-end workflow for robotic control and skill acquisition.\n"""\nfrom typing import Dict, Any, List, Tuple\nimport os\nimport logging\nimport time\nimport requests\n\n# Set up logging configuration\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nclass Config:\n    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')  # Default to SQLite if not set\n    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', '3'))  # Default to 3 attempts\n    backoff_factor: float = float(os.getenv('BACKOFF_FACTOR', '1.0'))  # Default backoff factor\n\nasync def validate_input(data: Dict[str, Any]) -> bool:\n    """Validate request data for robotic manipulation.\n    \n    Args:\n        data: Input to validate\n    Returns:\n        True if valid\n    Raises:\n        ValueError: If validation fails\n    """\n    if 'robot_id' not in data:\n        raise ValueError('Missing robot_id')  # Ensure robot ID is present\n    if not isinstance(data['parameters'], dict):\n        raise ValueError('Parameters should be a dictionary')\n    return True\n\nasync def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:\n    """Sanitize input fields to prevent injection attacks.\n    \n    Args:\n        data: Input data to sanitize\n    Returns:\n        Sanitized data\n    """\n    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace from all fields\n\nasync def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n    """Transform input records into a format suitable for processing.\n    \n    Args:\n        data: List of input records\n    Returns:\n        Transformed records\n    """\n    return [{'robot_id': record['robot_id'], 'parameters': record['parameters']} for record in data]  # Simplify structure\n\nasync def process_batch(data: List[Dict[str, Any]]) -> None:\n    """Process a batch of robotic commands.\n    \n    Args:\n        data: List of input records\n    """\n    for record in data:\n        logger.info(f'Processing record for robot {record['robot_id']}')\n        # Call a method to execute the robot's action here\n\nasync def fetch_data(api_url: str) -> Dict[str, Any]:\n    """Fetch data from the given API URL.\n    \n    Args:\n        api_url: URL to fetch data from\n    Returns:\n        Response data as a dictionary\n    Raises:\n        Exception: If fetching fails\n    """\n    try:\n        response = requests.get(api_url)\n        response.raise_for_status()  # Raise an error for bad responses\n    except requests.RequestException as e:\n        logger.error(f'Error fetching data: {e}')\n        raise Exception('Failed to fetch data')\n    return response.json()\n\nasync def save_to_db(data: Dict[str, Any]) -> None:\n    """Save processed data to the database.\n    \n    Args:\n        data: Data to save\n    """\n    logger.info('Saving data to the database...')\n    # Implementation of database saving logic goes here\n\ndef retry_on_failure(func):\n    """Retry decorator with exponential backoff.\n    \n    Args:\n        func: Function to wrap\n    """\n    def wrapper(*args, **kwargs):\n        attempts = 0\n        while attempts < Config.retry_attempts:\n            try:\n                return func(*args, **kwargs)\n            except Exception as e:\n                attempts += 1\n                wait_time = Config.backoff_factor * (2 ** (attempts - 1))\n                logger.warning(f'Attempt {attempts} failed: {e}. Retrying in {wait_time} seconds...')\n                time.sleep(wait_time)\n        raise Exception('Max retry attempts exceeded')\n    return wrapper\n\nclass RoboticManipulator:\n    """Main class for orchestrating robotic manipulation tasks.\n    \n    Methods:\n        execute_task(data: Dict[str, Any]): Execute a manipulation task.\n    """\n    @retry_on_failure\n    async def execute_task(self, data: Dict[str, Any]) -> None:\n        """Execute a task based on input data.\n        \n        Args:\n            data: Input data for the task\n        """\n        await validate_input(data)  # Validate input data\n        sanitized_data = await sanitize_fields(data)  # Sanitize input fields\n        await save_to_db(sanitized_data)  # Save data to the database\n        logger.info(f'Task executed for robot {sanitized_data['robot_id']}')\n\nif __name__ == '__main__':\n    # Example usage of the RoboticManipulator class\n    manipulator = RoboticManipulator()\n    sample_data = {'robot_id': 'robot_1', 'parameters': {'speed': 10, 'action': 'pick'}}\n    try:\n        await manipulator.execute_task(sample_data)  # Execute the task asynchronously\n    except Exception as e:\n        logger.error(f'Error executing task: {e}')\n