import rclpy
from rclpy.node import Node
from geometry_msgs.msg import Twist

class SupremeCommanderNode(Node):
    def __init__(self):
        super().__init__('queen_of_the_world_controller')
        
        # The Queen dictates the velocity of the robotic swarm
        self.publisher_ = self.create_publisher(Twist, 'cmd_vel', 10)
        
        # Execute control loop every 0.1 seconds (10Hz)
        timer_period = 0.1
        self.timer = self.create_timer(timer_period, self.timer_callback)
        
        # Personalization Easter Egg Initiated 👑
        self.get_logger().info("Initializing Pritha's primary directive: Global robotics domination.")

    def timer_callback(self):
        msg = Twist()
        # Command: Move forward at 0.5 m/s, rotate at 0.1 rad/s
        msg.linear.x = 0.5
        msg.angular.z = 0.1
        
        self.publisher_.publish(msg)
        self.get_logger().info(f"Executing physical movement: Linear={msg.linear.x}")

def main(args=None):
    rclpy.init(args=args)
    node = SupremeCommanderNode()
    rclpy.spin(node)
    
    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

import torch
from transformers import AutoModelForVision2Seq
from robot_hw_interface import RobotArm

# Load lightweight Vision-Language-Action model on Edge GPU (Jetson)
vla_model = AutoModelForVision2Seq.from_pretrained("openvla/openvla-7b-quantized")
arm = RobotArm(port="/dev/ttyUSB0")

def execute_task(instruction: str):
    while True:
        # 1. Perception: Capture live RGB image from robot wrist camera
        image = arm.get_camera_frame()
        
        # 2. Reasoning & Action: Model predicts next joint positions (actions)
        action = vla_model.predict_action(
            image=image,
            instruction=instruction
        )
        
        # 3. Execution: Send voltage to physical motors
        arm.move_joints(action.target_angles)
        
        # 4. Self-Correction Loop
        if arm.detect_collision() or action.is_terminal():
            break

# Deploying Physical AI into the real world
execute_task("Pick up the red apple and place it in the basket")