Capstone Project: The Autonomous Humanoid

Project Overview

The Autonomous Humanoid capstone project represents the culmination of all concepts learned throughout this textbook. In this project, you'll create a complete system where a simulated humanoid robot receives voice commands, processes natural language, plans complex actions, navigates through environments, identifies objects using computer vision, and manipulates them appropriately.

This project integrates:

• ROS 2 fundamentals for robot communication
• Gazebo/Unity simulation for physics and rendering
• NVIDIA Isaac for perception and navigation
• Vision-Language-Action models for voice processing and cognitive planning
• Advanced robotics concepts including bipedal locomotion

Project Objectives

By completing this capstone project, you will demonstrate proficiency in:

1. Voice Command Processing: Using OpenAI Whisper to convert speech to text
2. Cognitive Planning: Using LLMs to translate natural language into action sequences
3. Perception Systems: Identifying objects using computer vision
4. Navigation: Path planning and obstacle avoidance for humanoid robots
5. Manipulation: Grasping and manipulating objects with humanoid arms
6. Integration: Combining all systems into a cohesive autonomous robot

System Architecture

The Autonomous Humanoid system consists of several interconnected components:

Voice Command → Whisper → LLM Planner → Action Executor → Robot Control
     ↓              ↓           ↓            ↓              ↓
  Microphone   Speech-to-Text  Cognitive  Action Sequence  ROS 2 Nodes
                Processing     Planning    Execution       (Navigation,
                                       (with feedback)     Manipulation,
                                                          Perception)

High-Level Architecture Components:

1. Voice Input System: Captures and processes voice commands
2. Natural Language Understanding: Interprets commands using LLMs
3. Cognitive Planning Engine: Generates action plans
4. Perception System: Detects and identifies objects
5. Navigation System: Plans and executes movement
6. Manipulation System: Controls robotic arms and hands
7. Integration Framework: Coordinates all components

Implementation Steps

Step 1: Setting Up the Humanoid Robot Simulation

First, let's create a humanoid robot model and set up the simulation environment:

<!-- humanoid_robot.urdf.xacro -->
<?xml version="1.0"?>
<robot xmlns:xacro="http://www.ros.org/wiki/xacro" name="humanoid_robot">

  <!-- Base Link -->
  <link name="base_link">
    <visual>
      <geometry>
        <box size="0.3 0.2 0.1"/>
      </geometry>
      <material name="blue">
        <color rgba="0 0 1 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <box size="0.3 0.2 0.1"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="10"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Torso -->
  <joint name="torso_joint" type="fixed">
    <parent link="base_link"/>
    <child link="torso"/>
    <origin xyz="0 0 0.15"/>
  </joint>

  <link name="torso">
    <visual>
      <geometry>
        <box size="0.2 0.3 0.5"/>
      </geometry>
      <material name="gray">
        <color rgba="0.5 0.5 0.5 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <box size="0.2 0.3 0.5"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="5"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Head -->
  <joint name="head_joint" type="fixed">
    <parent link="torso"/>
    <child link="head"/>
    <origin xyz="0 0 0.35"/>
  </joint>

  <link name="head">
    <visual>
      <geometry>
        <sphere radius="0.1"/>
      </geometry>
      <material name="white">
        <color rgba="1 1 1 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <sphere radius="0.1"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="2"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Camera (for perception) -->
  <joint name="camera_joint" type="fixed">
    <parent link="head"/>
    <child link="camera_link"/>
    <origin xyz="0.05 0 0.02" rpy="0 0 0"/>
  </joint>

  <link name="camera_link">
    <visual>
      <geometry>
        <box size="0.05 0.05 0.03"/>
      </geometry>
    </visual>
    <collision>
      <geometry>
        <box size="0.05 0.05 0.03"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="0.1"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Left Arm -->
  <joint name="left_shoulder_joint" type="revolute">
    <parent link="torso"/>
    <child link="left_upper_arm"/>
    <origin xyz="0.15 0 0.1" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="left_upper_arm">
    <visual>
      <geometry>
        <cylinder radius="0.05" length="0.3"/>
      </geometry>
      <material name="red">
        <color rgba="1 0 0 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.05" length="0.3"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="1"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <joint name="left_elbow_joint" type="revolute">
    <parent link="left_upper_arm"/>
    <child link="left_lower_arm"/>
    <origin xyz="0 0 -0.3" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="left_lower_arm">
    <visual>
      <geometry>
        <cylinder radius="0.04" length="0.25"/>
      </geometry>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.04" length="0.25"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="0.8"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Right Arm -->
  <joint name="right_shoulder_joint" type="revolute">
    <parent link="torso"/>
    <child link="right_upper_arm"/>
    <origin xyz="-0.15 0 0.1" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="right_upper_arm">
    <visual>
      <geometry>
        <cylinder radius="0.05" length="0.3"/>
      </geometry>
      <material name="red">
        <color rgba="1 0 0 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.05" length="0.3"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="1"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <joint name="right_elbow_joint" type="revolute">
    <parent link="right_upper_arm"/>
    <child link="right_lower_arm"/>
    <origin xyz="0 0 -0.3" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="right_lower_arm">
    <visual>
      <geometry>
        <cylinder radius="0.04" length="0.25"/>
      </geometry>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.04" length="0.25"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="0.8"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Left Leg -->
  <joint name="left_hip_joint" type="revolute">
    <parent link="base_link"/>
    <child link="left_upper_leg"/>
    <origin xyz="0.05 0 -0.05" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="left_upper_leg">
    <visual>
      <geometry>
        <cylinder radius="0.06" length="0.4"/>
      </geometry>
      <material name="green">
        <color rgba="0 1 0 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.06" length="0.4"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="2"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <joint name="left_knee_joint" type="revolute">
    <parent link="left_upper_leg"/>
    <child link="left_lower_leg"/>
    <origin xyz="0 0 -0.4" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="left_lower_leg">
    <visual>
      <geometry>
        <cylinder radius="0.05" length="0.35"/>
      </geometry>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.05" length="0.35"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="1.5"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <!-- Right Leg -->
  <joint name="right_hip_joint" type="revolute">
    <parent link="base_link"/>
    <child link="right_upper_leg"/>
    <origin xyz="-0.05 0 -0.05" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="right_upper_leg">
    <visual>
      <geometry>
        <cylinder radius="0.06" length="0.4"/>
      </geometry>
      <material name="green">
        <color rgba="0 1 0 1"/>
      </material>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.06" length="0.4"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="2"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

  <joint name="right_knee_joint" type="revolute">
    <parent link="right_upper_leg"/>
    <child link="right_lower_leg"/>
    <origin xyz="0 0 -0.4" rpy="0 0 0"/>
    <axis xyz="0 1 0"/>
    <limit lower="-1.57" upper="1.57" effort="100" velocity="1"/>
  </joint>

  <link name="right_lower_leg">
    <visual>
      <geometry>
        <cylinder radius="0.05" length="0.35"/>
      </geometry>
    </visual>
    <collision>
      <geometry>
        <cylinder radius="0.05" length="0.35"/>
      </geometry>
    </collision>
    <inertial>
      <mass value="1.5"/>
      <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
    </inertial>
  </link>

</robot>

Step 2: Creating the Autonomous Humanoid Main System

Now let's implement the main system that integrates all components:

#!/usr/bin/env python3
"""
Autonomous Humanoid System
This system integrates voice recognition, cognitive planning,
perception, navigation, and manipulation for a humanoid robot.
"""

import rclpy
from rclpy.node import Node
from rclpy.action import ActionClient
from rclpy.qos import QoSProfile

import whisper
import openai
import torch
import pyaudio
import wave
import numpy as np
import json
import re
from typing import List, Dict, Any, Optional

# ROS 2 message types
from std_msgs.msg import String, Float32
from sensor_msgs.msg import Image, CameraInfo
from geometry_msgs.msg import PoseStamped, Point
from move_base_msgs.action import MoveBase
from actionlib_msgs.msg import GoalStatus
from cv_bridge import CvBridge
import cv2

# Import our cognitive planning and voice-to-action modules
from cognitive_planning import AdvancedCognitivePlanner
from voice_to_action import RealTimeVoiceToAction

class AutonomousHumanoid(Node):
    def __init__(self):
        super().__init__('autonomous_humanoid')

        # Initialize components
        self.cognitive_planner = AdvancedCognitivePlanner()
        self.voice_to_action = RealTimeVoiceToAction()

        # CV Bridge for image processing
        self.cv_bridge = CvBridge()

        # Audio recording parameters
        self.chunk = 1024
        self.format = pyaudio.paInt16
        self.channels = 1
        self.rate = 44100
        self.record_seconds = 5

        # Initialize PyAudio
        self.p = pyaudio.PyAudio()

        # Action clients
        self.nav_client = ActionClient(self, MoveBase, 'move_base')

        # Publishers
        self.status_pub = self.create_publisher(String, 'humanoid_status', 10)
        self.command_pub = self.create_publisher(String, 'processed_command', 10)

        # Subscribers
        self.command_sub = self.create_subscription(
            String, 'voice_command', self.command_callback, 10)
        self.camera_sub = self.create_subscription(
            Image, 'camera/image_raw', self.image_callback, 10)

        # Object detection results
        self.detected_objects = []

        # Robot state
        self.robot_state = {
            'position': {'x': 0, 'y': 0, 'z': 0},
            'holding_object': None,
            'battery_level': 100.0
        }

        self.get_logger().info("Autonomous Humanoid system initialized")

    def command_callback(self, msg):
        """Handle incoming voice commands"""
        command = msg.data
        self.get_logger().info(f"Received voice command: {command}")

        # Update status
        status_msg = String()
        status_msg.data = f"Processing command: {command}"
        self.status_pub.publish(status_msg)

        # Generate plan from command
        plan = self.cognitive_planner.plan_with_state_awareness(command)

        if plan:
            # Execute the plan
            success = self.execute_plan(plan)

            # Update status
            result_msg = String()
            result_msg.data = f"Command '{command}' {'succeeded' if success else 'failed'}"
            self.status_pub.publish(result_msg)
        else:
            self.get_logger().error(f"Could not generate plan for command: {command}")

    def image_callback(self, msg):
        """Process camera images for object detection"""
        try:
            # Convert ROS image to OpenCV format
            cv_image = self.cv_bridge.imgmsg_to_cv2(msg, "bgr8")

            # Perform object detection (simplified - in practice, use YOLO or similar)
            detected_objects = self.detect_objects_in_image(cv_image)

            # Update internal state
            self.detected_objects = detected_objects

            self.get_logger().info(f"Detected {len(detected_objects)} objects: {[obj['type'] for obj in detected_objects]}")

        except Exception as e:
            self.get_logger().error(f"Error processing image: {e}")

    def detect_objects_in_image(self, image):
        """Detect objects in the camera image"""
        # This is a simplified object detection
        # In practice, you would use a trained model like YOLO, SSD, etc.
        detected = []

        # Convert image to grayscale for simple shape detection
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        # Use a simple threshold to find objects
        _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

        # Find contours
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours:
            # Filter by area to avoid noise
            if cv2.contourArea(contour) > 500:
                # Approximate contour to get shape
                epsilon = 0.02 * cv2.arcLength(contour, True)
                approx = cv2.approxPolyDP(contour, epsilon, True)

                # Determine object type based on shape
                if len(approx) < 5:
                    obj_type = "cup"  # Simplified - in reality, use proper object detection
                else:
                    obj_type = "box"

                # Get bounding box
                x, y, w, h = cv2.boundingRect(contour)

                detected.append({
                    'type': obj_type,
                    'position': {'x': x, 'y': y},
                    'size': {'width': w, 'height': h}
                })

        return detected

    def execute_plan(self, plan: List[Dict]) -> bool:
        """Execute the generated action plan"""
        self.get_logger().info(f"Executing plan with {len(plan)} actions")

        for i, action in enumerate(plan):
            self.get_logger().info(f"Executing action {i+1}/{len(plan)}: {action['description']}")

            success = self.execute_action(action)

            if not success:
                self.get_logger().error(f"Action failed: {action}")
                return False

            # Update robot state after each action
            self.update_robot_state(action, success)

        self.get_logger().info("Plan executed successfully!")
        return True

    def execute_action(self, action: Dict) -> bool:
        """Execute a single action"""
        action_type = action["action"]

        if action_type == "navigate_to":
            return self.navigate_to_location(action["parameters"]["location"])
        elif action_type == "detect_object":
            return self.detect_specific_object(action["parameters"]["object_type"])
        elif action_type == "pick_up_object":
            return self.pick_up_object(action["parameters"]["object_type"])
        elif action_type == "place_object":
            return self.place_object(action["parameters"]["object_type"])
        elif action_type == "clean_area":
            return self.clean_area(action["parameters"]["area"])
        elif action_type == "approach_object":
            return self.approach_object(action["parameters"]["object_type"])
        else:
            self.get_logger().error(f"Unknown action type: {action_type}")
            return False

    def navigate_to_location(self, location: str) -> bool:
        """Navigate to a specific location using ROS 2 navigation"""
        self.get_logger().info(f"Navigating to {location}")

        # Wait for navigation server
        if not self.nav_client.wait_for_server(timeout_sec=5.0):
            self.get_logger().error("Navigation server not available")
            return False

        # Create goal pose based on location
        goal_pose = self.create_location_pose(location)

        if not goal_pose:
            self.get_logger().error(f"Unknown location: {location}")
            return False

        # Create navigation goal
        goal = MoveBase.Goal()
        goal.target_pose = goal_pose

        # Send goal
        future = self.nav_client.send_goal_async(goal)
        rclpy.spin_until_future_complete(self, future)

        # Check result
        if future.result() and future.result().status == GoalStatus.SUCCEEDED:
            self.get_logger().info(f"Successfully navigated to {location}")
            return True
        else:
            self.get_logger().error(f"Failed to navigate to {location}")
            return False

    def create_location_pose(self, location: str) -> Optional[PoseStamped]:
        """Create a pose for a known location"""
        # Define known locations in the environment
        locations = {
            "kitchen": PoseStamped(
                header={"frame_id": "map"},
                pose={"position": Point(x=2.0, y=1.0, z=0.0)}
            ),
            "bedroom": PoseStamped(
                header={"frame_id": "map"},
                pose={"position": Point(x=-1.0, y=2.0, z=0.0)}
            ),
            "living_room": PoseStamped(
                header={"frame_id": "map"},
                pose={"position": Point(x=0.0, y=0.0, z=0.0)}
            ),
            "office": PoseStamped(
                header={"frame_id": "map"},
                pose={"position": Point(x=1.5, y=-1.0, z=0.0)}
            )
        }

        return locations.get(location)

    def detect_specific_object(self, object_type: str) -> bool:
        """Detect a specific type of object in the environment"""
        self.get_logger().info(f"Looking for {object_type}")

        # Wait for object detection to complete
        timeout = 10  # seconds
        start_time = self.get_clock().now()

        while (self.get_clock().now() - start_time).nanoseconds < timeout * 1e9:
            # Check if the desired object is in detected objects
            for obj in self.detected_objects:
                if obj['type'] == object_type:
                    self.get_logger().info(f"Found {object_type} at position {obj['position']}")
                    return True

            # Small delay before checking again
            self.get_logger().info("Still searching for object...")
            self._rate.sleep()

        self.get_logger().error(f"Could not find {object_type}")
        return False

    def pick_up_object(self, object_type: str) -> bool:
        """Simulate picking up an object"""
        self.get_logger().info(f"Attempting to pick up {object_type}")

        # Check if object is detected and reachable
        for obj in self.detected_objects:
            if obj['type'] == object_type:
                self.get_logger().info(f"Approaching {object_type}")

                # In a real system, this would involve:
                # 1. Moving arm to object position
                # 2. Grasping the object
                # 3. Verifying grasp success

                # For simulation, just update state
                self.robot_state['holding_object'] = object_type
                self.get_logger().info(f"Successfully picked up {object_type}")
                return True

        self.get_logger().error(f"Could not pick up {object_type} - not detected")
        return False

    def place_object(self, object_type: str) -> bool:
        """Simulate placing an object"""
        self.get_logger().info(f"Attempting to place {object_type}")

        if self.robot_state['holding_object'] == object_type:
            # In a real system, this would involve:
            # 1. Moving to placement location
            # 2. Releasing the object
            # 3. Verifying placement success

            # For simulation, just update state
            self.robot_state['holding_object'] = None
            self.get_logger().info(f"Successfully placed {object_type}")
            return True
        else:
            self.get_logger().error(f"Not holding {object_type}, cannot place")
            return False

    def clean_area(self, area: str) -> bool:
        """Simulate cleaning an area"""
        self.get_logger().info(f"Cleaning {area}")

        # For simulation, cleaning involves:
        # 1. Detecting debris objects
        # 2. Picking them up
        # 3. Moving to disposal location
        # 4. Placing debris in trash

        # Find debris objects in the area
        debris_objects = [obj for obj in self.detected_objects if obj['type'] in ['trash', 'debris', 'cup', 'bottle']]

        for obj in debris_objects:
            self.get_logger().info(f"Cleaning up {obj['type']}")

            # Pick up the debris
            if self.pick_up_object(obj['type']):
                # Move to trash location
                if self.navigate_to_location("kitchen"):  # Assume kitchen has trash
                    # Place the debris
                    self.place_object(obj['type'])

        self.get_logger().info(f"Completed cleaning of {area}")
        return True

    def approach_object(self, object_type: str) -> bool:
        """Approach a specific object"""
        self.get_logger().info(f"Approaching {object_type}")

        # Find the object in detected objects
        for obj in self.detected_objects:
            if obj['type'] == object_type:
                # In a real system, this would involve:
                # 1. Calculating approach vector
                # 2. Moving to a position near the object
                # 3. Orienting toward the object

                self.get_logger().info(f"Approached {object_type}")
                return True

        self.get_logger().error(f"Could not approach {object_type} - not detected")
        return False

    def update_robot_state(self, action: Dict, success: bool):
        """Update robot state based on action execution"""
        action_type = action["action"]

        if success:
            if action_type == "navigate_to":
                location = action["parameters"]["location"]
                # Update position based on location (simplified)
                self.robot_state['position'] = self.get_location_coordinates(location)
            elif action_type == "pick_up_object":
                obj_type = action["parameters"]["object_type"]
                self.robot_state['holding_object'] = obj_type
            elif action_type == "place_object":
                self.robot_state['holding_object'] = None

    def get_location_coordinates(self, location: str) -> Dict[str, float]:
        """Get coordinates for a location"""
        coords = {
            "kitchen": {"x": 2.0, "y": 1.0, "z": 0.0},
            "bedroom": {"x": -1.0, "y": 2.0, "z": 0.0},
            "living_room": {"x": 0.0, "y": 0.0, "z": 0.0},
            "office": {"x": 1.5, "y": -1.0, "z": 0.0}
        }
        return coords.get(location, {"x": 0.0, "y": 0.0, "z": 0.0})

    def cleanup(self):
        """Clean up resources"""
        self.p.terminate()

def main(args=None):
    rclpy.init(args=args)

    humanoid = AutonomousHumanoid()

    try:
        rclpy.spin(humanoid)
    except KeyboardInterrupt:
        print("Autonomous Humanoid system stopped by user")
    finally:
        humanoid.cleanup()
        humanoid.destroy_node()
        rclpy.shutdown()

if __name__ == '__main__':
    main()

Step 3: Creating a Launch File for the System

Let's create a launch file to start all components together:

# launch/autonomous_humanoid_launch.py
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
from launch.launch_description_sources import PythonLaunchDescriptionSource

def generate_launch_description():
    # Launch configuration
    use_sim_time = LaunchConfiguration('use_sim_time', default='true')

    # Nodes
    # Humanoid controller node
    humanoid_node = Node(
        package='autonomous_humanoid',
        executable='autonomous_humanoid',
        name='autonomous_humanoid',
        parameters=[
            {'use_sim_time': use_sim_time}
        ],
        output='screen'
    )

    # Voice recognition node
    voice_node = Node(
        package='autonomous_humanoid',
        executable='voice_recognition',
        name='voice_recognition',
        parameters=[
            {'use_sim_time': use_sim_time}
        ],
        output='screen'
    )

    # Perception node
    perception_node = Node(
        package='autonomous_humanoid',
        executable='perception_node',
        name='perception_node',
        parameters=[
            {'use_sim_time': use_sim_time}
        ],
        output='screen'
    )

    # Navigation node
    navigation_node = Node(
        package='autonomous_humanoid',
        executable='navigation_node',
        name='navigation_node',
        parameters=[
            {'use_sim_time': use_sim_time}
        ],
        output='screen'
    )

    return LaunchDescription([
        DeclareLaunchArgument(
            'use_sim_time',
            default_value='true',
            description='Use simulation (Gazebo) clock if true'
        ),
        humanoid_node,
        voice_node,
        perception_node,
        navigation_node
    ])

Step 4: Creating a Simulation Environment

Let's create a Gazebo world for our humanoid to operate in:

<!-- worlds/humanoid_world.world -->
<?xml version="1.0" ?>
<sdf version="1.6">
  <world name="humanoid_world">
    <!-- Include a ground plane -->
    <include>
      <uri>model://ground_plane</uri>
    </include>

    <!-- Include sun for lighting -->
    <include>
      <uri>model://sun</uri>
    </include>

    <!-- Kitchen area -->
    <model name="kitchen_counter">
      <pose>2 1 0 0 0 0</pose>
      <link name="link">
        <collision name="collision">
          <geometry>
            <box>
              <size>1.5 0.8 0.9</size>
            </box>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <box>
              <size>1.5 0.8 0.9</size>
            </box>
          </geometry>
          <material>
            <ambient>0.7 0.7 0.7 1</ambient>
            <diffuse>0.8 0.8 0.8 1</diffuse>
            <specular>0.1 0.1 0.1 1</specular>
          </material>
        </visual>
      </link>
    </model>

    <!-- Living room furniture -->
    <model name="sofa">
      <pose>0 0 0 0 0 0</pose>
      <link name="link">
        <collision name="collision">
          <geometry>
            <box>
              <size>1.2 0.8 0.7</size>
            </box>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <box>
              <size>1.2 0.8 0.7</size>
            </box>
          </geometry>
          <material>
            <ambient>0.2 0.2 0.7 1</ambient>
            <diffuse>0.3 0.3 0.9 1</diffuse>
            <specular>0.1 0.1 0.1 1</specular>
          </material>
        </visual>
      </link>
    </model>

    <!-- Bedroom bed -->
    <model name="bed">
      <pose>-1 2 0 0 0 0</pose>
      <link name="link">
        <collision name="collision">
          <geometry>
            <box>
              <size>2.0 1.2 0.5</size>
            </box>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <box>
              <size>2.0 1.2 0.5</size>
            </box>
          </geometry>
          <material>
            <ambient>0.8 0.7 0.6 1</ambient>
            <diffuse>0.9 0.8 0.7 1</diffuse>
            <specular>0.1 0.1 0.1 1</specular>
          </material>
        </visual>
      </link>
    </model>

    <!-- Objects for the humanoid to interact with -->
    <model name="cup">
      <pose>2.1 1.1 1.0 0 0 0</pose>
      <link name="link">
        <collision name="collision">
          <geometry>
            <cylinder>
              <radius>0.05</radius>
              <length>0.1</length>
            </cylinder>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <cylinder>
              <radius>0.05</radius>
              <length>0.1</length>
            </cylinder>
          </geometry>
          <material>
            <ambient>0.8 0.2 0.2 1</ambient>
            <diffuse>1.0 0.3 0.3 1</diffuse>
            <specular>0.5 0.5 0.5 1</specular>
          </material>
        </visual>
      </link>
    </model>

    <model name="book">
      <pose>0.1 0.2 0.8 0 0 0</pose>
      <link name="link">
        <collision name="collision">
          <geometry>
            <box>
              <size>0.2 0.15 0.02</size>
            </box>
          </geometry>
        </collision>
        <visual name="visual">
          <geometry>
            <box>
              <size>0.2 0.15 0.02</size>
            </box>
          </geometry>
          <material>
            <ambient>0.2 0.8 0.2 1</ambient>
            <diffuse>0.3 0.9 0.3 1</diffuse>
            <specular>0.1 0.1 0.1 1</specular>
          </material>
        </visual>
      </link>
    </model>

    <!-- Spawn the humanoid robot -->
    <include>
      <uri>model://humanoid_robot</uri>
      <pose>0 0 0.5 0 0 0</pose>
    </include>

  </world>
</sdf>

Step 5: Creating a Control Script for Testing

Let's create a script to test the system with various commands:

#!/usr/bin/env python3
"""
Test script for Autonomous Humanoid system
"""

import rclpy
from rclpy.node import Node
from std_msgs.msg import String
import time

class HumanoidTester(Node):
    def __init__(self):
        super().__init__('humanoid_tester')

        self.command_pub = self.create_publisher(String, 'voice_command', 10)

        self.get_logger().info("Humanoid tester initialized")

        # Test commands to execute
        self.test_commands = [
            "Go to kitchen",
            "Pick up the cup",
            "Go to living room",
            "Place the cup",
            "Clean the living room",
            "Go to bedroom"
        ]

        # Start testing after a delay
        self.timer = self.create_timer(3.0, self.start_test)
        self.command_index = 0

    def start_test(self):
        """Start executing test commands"""
        if self.command_index < len(self.test_commands):
            command = self.test_commands[self.command_index]
            self.get_logger().info(f"Sending command: {command}")

            msg = String()
            msg.data = command
            self.command_pub.publish(msg)

            self.command_index += 1

            # Schedule next command after delay
            self.timer = self.create_timer(10.0, self.start_test)
        else:
            self.get_logger().info("All test commands sent")

def main(args=None):
    rclpy.init(args=args)

    tester = HumanoidTester()

    try:
        rclpy.spin(tester)
    except KeyboardInterrupt:
        pass
    finally:
        tester.destroy_node()
        rclpy.shutdown()

if __name__ == '__main__':
    main()

Step 6: Advanced Features - Human-Robot Interaction

Let's add more sophisticated interaction capabilities:

class AdvancedHumanoidInteraction:
    def __init__(self, humanoid_node):
        self.humanoid = humanoid_node
        self.conversation_history = []
        self.user_preferences = {}
        self.robot_personality = {
            "name": "ARIA",
            "personality": "helpful_assistant",
            "communication_style": "polite_and_informative"
        }

    def handle_conversation(self, command: str) -> str:
        """Handle natural conversation with the user"""
        # Add current command to conversation history
        self.conversation_history.append({"role": "user", "content": command})

        # Generate response based on context
        response = self.generate_contextual_response(command)

        # Add response to conversation history
        self.conversation_history.append({"role": "assistant", "content": response})

        return response

    def generate_contextual_response(self, command: str) -> str:
        """Generate a contextual response to the user's command"""
        # Check if this is a follow-up to a previous command
        if len(self.conversation_history) > 1:
            prev_command = self.conversation_history[-2]["content"]
            if "where" in command.lower() and "i" in prev_command.lower():
                # Handle follow-up questions
                return "I can help you find that. Could you please specify what you're looking for?"

        # Generate standard response
        if any(word in command.lower() for word in ["thank", "thanks"]):
            return "You're welcome! How else can I assist you today?"
        elif any(word in command.lower() for word in ["hello", "hi", "hey"]):
            return f"Hello! I'm {self.robot_personality['name']}, your helpful assistant. How can I help you?"
        elif any(word in command.lower() for word in ["bye", "goodbye", "see you"]):
            return "Goodbye! Feel free to call me back if you need assistance."
        else:
            return "I understand. I'll work on that right away."

    def learn_from_interaction(self, command: str, outcome: str):
        """Learn from the interaction to improve future responses"""
        # Store successful command-outcome pairs
        if outcome == "success":
            # In a real system, this would update a learning model
            self.get_logger().info(f"Learned successful mapping: {command} -> positive outcome")

    def adapt_to_user_preferences(self, user_feedback: str):
        """Adapt behavior based on user feedback"""
        if "faster" in user_feedback.lower():
            self.user_preferences["speed_preference"] = "faster"
        elif "slower" in user_feedback.lower():
            self.user_preferences["speed_preference"] = "slower"

        if "more polite" in user_feedback.lower():
            self.robot_personality["communication_style"] = "very_polite"
        elif "more direct" in user_feedback.lower():
            self.robot_personality["communication_style"] = "direct"

Step 7: Safety and Error Handling

Implement comprehensive safety and error handling:

class SafetyManager:
    def __init__(self):
        self.safety_constraints = {
            "collision_threshold": 0.5,  # meters
            "joint_limit_threshold": 0.95,  # percentage of joint limits
            "battery_low_threshold": 20.0,  # percentage
            "temperature_high_threshold": 70.0  # Celsius
        }

        self.emergency_stop = False
        self.safety_violations = []

    def check_safety_constraints(self, robot_state: Dict) -> bool:
        """Check if current robot state violates any safety constraints"""
        violations = []

        # Check battery level
        if robot_state.get("battery_level", 100.0) < self.safety_constraints["battery_low_threshold"]:
            violations.append("Battery level too low")

        # Check for potential collisions
        if robot_state.get("distance_to_obstacle", float('inf')) < self.safety_constraints["collision_threshold"]:
            violations.append("Collision imminent")

        # Check joint limits
        joint_positions = robot_state.get("joint_positions", {})
        for joint, position in joint_positions.items():
            limit = robot_state.get(f"{joint}_limit", 1.0)
            if abs(position) > self.safety_constraints["joint_limit_threshold"] * abs(limit):
                violations.append(f"Joint {joint} near limit")

        # Check temperature
        temperature = robot_state.get("temperature", 25.0)
        if temperature > self.safety_constraints["temperature_high_threshold"]:
            violations.append("Temperature too high")

        self.safety_violations = violations
        return len(violations) == 0

    def trigger_emergency_stop(self):
        """Trigger emergency stop and record violation"""
        self.emergency_stop = True
        self.get_logger().error("EMERGENCY STOP TRIGGERED due to safety violation")

        # In a real system, this would:
        # 1. Stop all robot motion immediately
        # 2. Save current state for analysis
        # 3. Alert operators
        # 4. Initiate safe shutdown sequence

    def reset_safety_system(self):
        """Reset safety system after violation is resolved"""
        self.emergency_stop = False
        self.safety_violations = []
        self.get_logger().info("Safety system reset")

Testing the Autonomous Humanoid

To test the complete system, you would run:

# Terminal 1: Start Gazebo simulation
ros2 launch autonomous_humanoid humanoid_gazebo.launch.py

# Terminal 2: Start the humanoid system
ros2 run autonomous_humanoid autonomous_humanoid

# Terminal 3: Start voice recognition (or send test commands)
ros2 run autonomous_humanoid voice_recognition

# Terminal 4: Send test commands
ros2 run autonomous_humanoid humanoid_tester

Evaluation Criteria

Your Autonomous Humanoid project will be evaluated on:

1. Voice Command Processing: Successfully recognizing and interpreting natural language commands
2. Cognitive Planning: Generating appropriate action sequences for complex tasks
3. Navigation: Successfully moving to specified locations while avoiding obstacles
4. Perception: Correctly identifying objects in the environment
5. Manipulation: Successfully grasping and placing objects
6. Integration: All components working together seamlessly
7. Robustness: Handling errors and unexpected situations gracefully
8. Safety: Operating within safety constraints at all times

Troubleshooting Common Issues

1. Audio Recognition Issues:

   • Check microphone permissions and availability
   • Verify audio input levels
   • Ensure Whisper model is properly loaded

2. Navigation Failures:

   • Verify map and localization
   • Check for proper costmap configuration
   • Ensure obstacles are properly detected

3. Object Detection Problems:

   • Verify camera calibration
   • Check lighting conditions
   • Validate detection model parameters

4. Communication Issues:

   • Verify ROS 2 network configuration
   • Check topic/service availability
   • Ensure proper message types

Extending the Project

Consider these extensions to enhance your Autonomous Humanoid:

1. Multi-Modal Interaction: Add gesture recognition alongside voice commands
2. Learning Capabilities: Implement reinforcement learning for task improvement
3. Multi-Robot Coordination: Extend to multiple robots working together
4. Advanced Manipulation: Implement dexterous manipulation with humanoid hands
5. Social Interaction: Add emotional recognition and response capabilities

Conclusion

The Autonomous Humanoid capstone project demonstrates the integration of all major concepts covered in this textbook. It showcases how ROS 2, simulation environments, AI perception systems, and cognitive planning can work together to create an autonomous robot capable of understanding and executing complex natural language commands.

This project serves as a foundation for advanced robotics applications and demonstrates the potential of Vision-Language-Action models in creating truly intelligent robotic systems that can interact naturally with humans in complex environments.