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Capstone Project: The Autonomous Humanoid

This capstone project brings together all concepts learned throughout the course to create an autonomous humanoid robot capable of receiving voice commands, planning paths, navigating obstacles, identifying objects using computer vision, and manipulating them. This project demonstrates the integration of ROS 2, Gazebo simulation, NVIDIA Isaac, and Vision-Language-Action (VLA) systems.

Project Overview​

The Autonomous Humanoid integrates multiple AI and robotics technologies to create a system that can:

  • Receive and understand natural language voice commands
  • Plan navigation paths to reach specified locations
  • Navigate through environments with obstacles
  • Identify objects using computer vision
  • Manipulate objects based on the understood command

Learning Objectives​

  • Integrate all four modules into a unified system
  • Implement end-to-end voice-to-action pipeline
  • Demonstrate multimodal perception and planning
  • Deploy cognitive planning systems on humanoid robot platforms

Architecture Overview​

[Audio Input] → [Whisper] → [LLM] → [Task Planner] → [Navigation] → [Manipulation]
     ↑                                              ↓
[Human Command] ← [Human-Robot Interface] ← [ROS 2 Control]

System Components Integration​

1. Voice Command Processing​

import rospy
from std_msgs.msg import String
from audio_common_msgs.msg import AudioData
import whisper
import openai

class VoiceCommandProcessor:
    def __init__(self):
        # Initialize Whisper model
        self.whisper_model = whisper.load_model("base")
        
        # Initialize ROS node
        rospy.init_node('voice_command_processor')
        
        # Setup subscribers and publishers
        self.audio_sub = rospy.Subscriber('/audio_input', AudioData, self.audio_callback)
        self.command_pub = rospy.Publisher('/nlp_command', String, queue_size=10)
        
    def audio_callback(self, audio_msg):
        # Convert audio to text using Whisper
        audio_array = self.convert_audio_to_array(audio_msg)
        result = self.whisper_model.transcribe(audio_array)
        transcription = result["text"]
        
        # Send to LLM for semantic understanding
        nlp_command = self.process_with_llm(transcription)
        
        # Publish processed command
        self.command_pub.publish(String(data=nlp_command))
    
    def process_with_llm(self, transcription):
        # Use LLM to extract semantic intent
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "You are a command interpreter for a humanoid robot. Convert natural language commands into structured robot commands. Output format: {'action': 'action_type', 'target': 'object_or_location', 'details': {...}}"},
                {"role": "user", "content": f"Command: '{transcription}'"}
            ]
        )
        return response.choices[0].message.content

2. Path Planning for Bipedal Humanoid Movement​

from nav_msgs.msg import OccupancyGrid, Path
from geometry_msgs.msg import PoseStamped
from nav2_msgs.action import NavigateToPose
import rclpy
from rclpy.action import ActionClient

class HumanoidPathPlanner:
    def __init__(self):
        # Initialize ROS 2 components
        self.navigator = ActionClient(self, NavigateToPose, 'navigate_to_pose')
        
    def plan_path(self, start_pose, goal_pose):
        # Account for humanoid-specific constraints
        humanoid_constraints = {
            'max_step_height': 0.1,  # Humanoid maximum step height
            'max_step_length': 0.3,  # Humanoid maximum step length
            'foot_separation': 0.2,  # Distance between feet
            'turning_radius': 0.4    # Minimum turning radius
        }
        
        # Plan path considering bipedal constraints
        path = self.nav2_path_planner.plan_path_with_constraints(
            start_pose, 
            goal_pose, 
            humanoid_constraints
        )
        
        return path

3. Computer Vision for Object Identification​

import cv2
import torch
from yolov5 import detect
from sensor_msgs.msg import Image
from cv_bridge import CvBridge

class HumanoidVisionSystem:
    def __init__(self):
        # Load YOLO model for object detection
        self.yolo_model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
        self.cv_bridge = CvBridge()
        
    def detect_objects(self, image_msg):
        # Convert ROS image to OpenCV format
        cv_image = self.cv_bridge.imgmsg_to_cv2(image_msg, desired_encoding='bgr8')
        
        # Run object detection
        results = self.yolo_model(cv_image)
        
        # Extract relevant objects
        detections = []
        for detection in results.xyxy[0]:
            x1, y1, x2, y2, conf, cls = detection
            if conf > 0.5:  # Confidence threshold
                object_info = {
                    'class': self.yolo_model.names[int(cls)],
                    'confidence': conf,
                    'bbox': [float(x1), float(y1), float(x2), float(y2)],
                    'center': [(x1+x2)/2, (y1+y2)/2]
                }
                detections.append(object_info)
        
        return detections

4. Manipulation System​

from control_msgs.msg import JointTrajectoryControllerState
from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint
import numpy as np

class HumanoidManipulator:
    def __init__(self):
        # Initialize manipulator controllers
        self.arm_controller = rospy.Publisher('/arm_controller/command', JointTrajectory, queue_size=1)
        self.gripper_controller = rospy.Publisher('/gripper_controller/command', JointTrajectory, queue_size=1)
        
    def grasp_object(self, object_pose, approach_angle=0.0):
        # Plan approach trajectory
        approach_traj = self.plan_approach_trajectory(object_pose, approach_angle)
        
        # Execute approach
        self.arm_controller.publish(approach_traj)
        
        # Close gripper
        grip_traj = self.create_gripper_close_trajectory()
        self.gripper_controller.publish(grip_traj)
        
        # Lift object
        lift_traj = self.plan_lift_trajectory()
        self.arm_controller.publish(lift_traj)
        
    def place_object(self, target_pose):
        # Move to target position
        move_traj = self.plan_move_trajectory(target_pose)
        self.arm_controller.publish(move_traj)
        
        # Open gripper to release
        release_traj = self.create_gripper_open_trajectory()
        self.gripper_controller.publish(release_traj)

Integration Pipeline​

1. Voice Command to Action Sequence​

class AutonomousHumanoidSystem:
    def __init__(self):
        self.voice_processor = VoiceCommandProcessor()
        self.path_planner = HumanoidPathPlanner()
        self.vision_system = HumanoidVisionSystem()
        self.manipulator = HumanoidManipulator()
        self.ros_interface = ROS2Interface()
        
    def execute_voice_command(self, command):
        # Step 1: Parse the voice command
        structured_command = self.voice_processor.process_with_llm(command)
        
        # Step 2: Identify required actions
        if structured_command.action == "fetch_and_place":
            self.execute_fetch_and_place(structured_command)
        elif structured_command.action == "navigate_and_identify":
            self.execute_navigation(structured_command)
        # Add more action types as needed
        
    def execute_fetch_and_place(self, command):
        # 1. Find the object to fetch
        target_object = self.find_target_object(command.target)
        
        # 2. Navigate to the object
        self.navigate_to_object(target_object.location)
        
        # 3. Identify and grasp the object
        self.grasp_object(target_object)
        
        # 4. Navigate to destination
        self.navigate_to_destination(command.destination)
        
        # 5. Place the object
        self.place_object(command.destination)
    
    def find_target_object(self, object_description):
        # Use vision system to locate the described object
        # Return object location and properties
        pass
        
    def navigate_to_object(self, object_location):
        # Plan and execute navigation to object
        current_pose = self.ros_interface.get_current_pose()
        path = self.path_planner.plan_path(current_pose, object_location)
        self.ros_interface.execute_path(path)
        
    def navigate_to_destination(self, destination):
        # Plan and execute navigation to destination
        current_pose = self.ros_interface.get_current_pose()
        path = self.path_planner.plan_path(current_pose, destination)
        self.ros_interface.execute_path(path)

Simulation Environment​

Setting up the Gazebo Environment​

<!-- Example world file for humanoid simulation -->
<sdf version='1.7'>
  <world name='humanoid_world'>
    <!-- Add room environment -->
    <include>
      <uri>model://room_with_furniture</uri>
      <pose>0 0 0 0 0 0</pose>
    </include>
    
    <!-- Humanoid robot -->
    <include>
      <uri>model://humanoid_robot</uri>
      <pose>0 0 0.8 0 0 0</pose>
    </include>
    
    <!-- Objects to manipulate -->
    <include>
      <uri>model://object_to_clean</uri>
      <pose>-1 1 0.8 0 0 0</pose>
    </include>
  </world>
</sdf>

Isaac Sim Integration​

class IsaacSimIntegration:
    def __init__(self):
        # Initialize Isaac Sim
        self.gym = gymapi.acquire_gym()
        self.sim = self.gym.create_sim(0, 0, gymapi.SIM_PHYSX)
        
    def setup_humanoid_env(self):
        # Create humanoid robot in Isaac Sim
        # Configure sensors (camera, IMU, etc.)
        # Set up physics properties for bipedal locomotion
        pass
        
    def run_pegasus_locomotion(self, humanoid_actor):
        # Implement humanoid locomotion
        # Balance control for bipedal movement
        # Footstep planning
        pass

Implementation Workflow​

Phase 1: Individual Component Testing​

  1. Test voice recognition and LLM command interpretation
  2. Test path planning with humanoid constraints
  3. Test object detection and identification
  4. Test basic manipulation movements

Phase 2: Component Integration​

  1. Integrate voice processing with command planning
  2. Connect navigation system with object detection
  3. Link manipulation system to visual feedback

Phase 3: End-to-End Testing​

  1. Test complete voice command to execution sequence
  2. Handle failure cases and error recovery
  3. Optimize performance for real-time operation

Evaluation Criteria​

Functional Requirements​

  • Successfully receive and interpret voice commands
  • Navigate through environments with obstacles
  • Identify and manipulate requested objects
  • Complete tasks with minimal human intervention

Performance Metrics​

  • Command Understanding Accuracy: Percentage of commands correctly interpreted
  • Navigation Success Rate: Percentage of successful navigations to targets
  • Object Manipulation Success: Percentage of successful grasps and placements
  • System Response Time: Average time from command to action initiation

Troubleshooting Common Issues​

1. Voice Recognition Problems​

  • Ensure proper microphone setup and audio quality
  • Adjust Whisper model parameters for environment
  • Implement voice activity detection

2. Navigation Failures​

  • Verify map accuracy and obstacle detection
  • Adjust humanoid-specific navigation parameters
  • Implement fallback navigation strategies

3. Object Manipulation Issues​

  • Calibrate vision system to robotic arm
  • Implement grasp pose estimation
  • Add tactile feedback for grasp confirmation

Deployment Considerations​

Simulation to Real Robot Transfer​

  • Validate simulation accuracy against real-world behavior
  • Account for sim-to-real domain differences
  • Implement adaptive control for real-world variations

Safety Measures​

  • Implement emergency stop mechanisms
  • Ensure robot operates within safe joint limits
  • Add collision avoidance during manipulation

Conclusion​

The Autonomous Humanoid capstone project demonstrates the integration of all course modules:

  • Module 1: ROS 2 for robot control and communication
  • Module 2: Gazebo simulation and Unity visualization for testing
  • Module 3: NVIDIA Isaac for advanced perception and navigation
  • Module 4: VLA systems for cognitive planning and voice interaction

This project showcases how embodied intelligence bridges the gap between digital AI and physical robotic systems, creating robots that can understand and act on natural human commands in real-world environments.