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Capstone Project: The Autonomous Humanoid

Overview​

The capstone project integrates everything you've learned into a complete autonomous humanoid robot system. Your robot will receive voice commands, plan actions, navigate environments, identify objects, and manipulate them—all while interacting naturally with humans.

Project Objective​

Create a simulated humanoid robot that can:

  1. Understand natural language voice commands
  2. Plan task sequences using LLM reasoning
  3. Navigate environments while avoiding obstacles
  4. Perceive objects and scene understanding using vision
  5. Manipulate objects with grasping and placement
  6. Interact naturally with humans using speech and gestures

System Architecture​

┌─────────────────────────────────────────────────────────┐
│                   User Voice Command                     │
└─────────────────┬───────────────────────────────────────┘
                  │
         ┌────────▼────────┐
         │  Speech-to-Text │  (OpenAI Whisper)
         │   (Whisper)     │
         └────────┬────────┘
                  │
         ┌────────▼────────┐
         │   LLM Planner   │  (GPT-4 / Claude)
         │  Task Reasoning │
         └────────┬────────┘
                  │
         ┌────────▼────────┐
         │ Action Executor │
         └────┬───┬───┬────┘
              │   │   │
    ┌─────────┘   │   └─────────┐
    │             │             │
┌───▼────┐   ┌───▼────┐   ┌───▼────┐
│Navigate│   │Perceive│   │Manipul.│
│ Nav2 + │   │ Isaac  │   │MoveIt2 │
│ VSLAM  │   │  ROS   │   │  IK    │
└───┬────┘   └───┬────┘   └───┬────┘
    │             │             │
    └─────────┬───┴─────────────┘
              │
         ┌────▼─────┐
         │   ROS 2  │
         │Middleware│
         └────┬─────┘
              │
         ┌────▼─────┐
         │  Isaac   │
         │   Sim    │
         └──────────┘

Project Phases​

Phase 1: Environment Setup (Week 1)​

Tasks​

  1. Set up Isaac Sim environment

    # Create warehouse environment
    from omni.isaac.kit import SimulationApp
    simulation_app = SimulationApp({"headless": False})

    from omni.isaac.core import World
    world = World(stage_units_in_meters=1.0)

    # Load warehouse scene
    from omni.isaac.core.utils.stage import add_reference_to_stage
    add_reference_to_stage(
        usd_path="/Isaac/Environments/Simple_Warehouse/warehouse.usd",
        prim_path="/World/Warehouse"
    )

  2. Load humanoid robot

    from omni.isaac.core.articulations import Articulation

    robot = world.scene.add(
        Articulation(
            prim_path="/World/Humanoid",
            name="my_humanoid",
            position=[0, 0, 1.0]
        )
    )

  3. Place objects in scene

    # Add objects: boxes, tools, containers
    from omni.isaac.core.objects import DynamicCuboid

    objects = []
    for i in range(5):
        obj = world.scene.add(
            DynamicCuboid(
                prim_path=f"/World/Object_{i}",
                name=f"object_{i}",
                position=[i * 0.5, 2.0, 0.5],
                size=0.1,
                color=np.random.rand(3)
            )
        )
        objects.append(obj)

Phase 2: Core Systems (Week 2-3)​

2.1 Vision & Perception​

import rclpy
from sensor_msgs.msg import Image, CameraInfo
from vision_msgs.msg import Detection2DArray

class PerceptionNode(Node):
    def __init__(self):
        super().__init__('perception_node')

        # Subscribe to camera
        self.rgb_sub = self.create_subscription(
            Image, '/camera/rgb', self.rgb_callback, 10
        )
        self.depth_sub = self.create_subscription(
            Image, '/camera/depth', self.depth_callback, 10
        )

        # Publish detections
        self.detection_pub = self.create_publisher(
            Detection2DArray, '/detections', 10
        )

        # Load detection model
        self.detector = load_object_detector()

    def rgb_callback(self, msg):
        # Convert ROS image to numpy
        image = self.ros_to_cv2(msg)

        # Run object detection
        detections = self.detector.detect(image)

        # Publish detections
        detection_msg = self.create_detection_msg(detections)
        self.detection_pub.publish(detection_msg)

    def identify_object(self, detection, rgb, depth):
        """Identify object and compute 3D pose"""

        # Extract object region
        bbox = detection.bbox
        object_rgb = rgb[bbox.y:bbox.y+bbox.h, bbox.x:bbox.x+bbox.w]
        object_depth = depth[bbox.y:bbox.y+bbox.h, bbox.x:bbox.x+bbox.w]

        # Compute 3D position
        position_3d = self.depth_to_3d(
            bbox.center_x, bbox.center_y,
            np.median(object_depth)
        )

        return {
            'class': detection.class_name,
            'position': position_3d,
            'confidence': detection.confidence
        }

2.2 Navigation​

class NavigationController(Node):
    def __init__(self):
        super().__init__('navigation_controller')

        # Nav2 simple commander
        self.navigator = BasicNavigator()

        # Subscribe to goal poses
        self.goal_sub = self.create_subscription(
            PoseStamped, '/goal_pose', self.goal_callback, 10
        )

    def navigate_to(self, target_position):
        """Navigate to target position"""

        goal_pose = PoseStamped()
        goal_pose.header.frame_id = 'map'
        goal_pose.pose.position.x = target_position[0]
        goal_pose.pose.position.y = target_position[1]
        goal_pose.pose.orientation.w = 1.0

        # Send goal to Nav2
        self.navigator.goToPose(goal_pose)

        # Wait for completion
        while not self.navigator.isTaskComplete():
            feedback = self.navigator.getFeedback()
            self.get_logger().info(
                f"Distance remaining: {feedback.distance_remaining:.2f}m"
            )

        result = self.navigator.getResult()
        if result == TaskResult.SUCCEEDED:
            self.get_logger().info("Navigation succeeded!")
        else:
            self.get_logger().error("Navigation failed!")

2.3 Manipulation​

class ManipulationController(Node):
    def __init__(self):
        super().__init__('manipulation_controller')

        # MoveIt2 interface
        self.moveit = MoveGroupInterface("arm", "robot_description")

    def pick_object(self, object_pose):
        """Pick up object at given pose"""

        # 1. Move to pre-grasp pose
        pre_grasp = object_pose.copy()
        pre_grasp.position.z += 0.1  # 10cm above object

        self.moveit.set_pose_target(pre_grasp)
        self.moveit.go()

        # 2. Open gripper
        self.set_gripper(open=True)

        # 3. Move to grasp pose
        self.moveit.set_pose_target(object_pose)
        self.moveit.go()

        # 4. Close gripper
        self.set_gripper(open=False)

        # 5. Lift object
        lift_pose = object_pose.copy()
        lift_pose.position.z += 0.15
        self.moveit.set_pose_target(lift_pose)
        self.moveit.go()

    def place_object(self, target_pose):
        """Place held object at target pose"""

        # 1. Move to pre-place pose
        pre_place = target_pose.copy()
        pre_place.position.z += 0.1

        self.moveit.set_pose_target(pre_place)
        self.moveit.go()

        # 2. Move to place pose
        self.moveit.set_pose_target(target_pose)
        self.moveit.go()

        # 3. Open gripper
        self.set_gripper(open=True)

        # 4. Retract
        self.moveit.set_pose_target(pre_place)
        self.moveit.go()

Phase 3: VLA Integration (Week 4)​

class VLAController(Node):
    """Vision-Language-Action Controller"""

    def __init__(self):
        super().__init__('vla_controller')

        # Components
        self.whisper = whisper.load_model("base")
        self.llm_client = anthropic.Client()
        self.perception = PerceptionNode()
        self.navigation = NavigationController()
        self.manipulation = ManipulationController()

        # Semantic map
        self.semantic_map = SemanticMap()

    def process_voice_command(self, audio_file):
        """Full pipeline: voice → action"""

        # 1. Speech-to-Text
        text = self.whisper.transcribe(audio_file)["text"]
        self.get_logger().info(f"Command: {text}")

        # 2. LLM Planning
        plan = self.llm_plan(text)
        self.get_logger().info(f"Plan: {plan}")

        # 3. Execute plan
        self.execute_plan(plan)

    def llm_plan(self, command):
        """Use LLM to plan action sequence"""

        # Get current scene information
        scene_description = self.get_scene_description()

        prompt = f"""
        You are controlling a humanoid robot in a warehouse.

        Current scene:
        {scene_description}

        User command: "{command}"

        Available actions:
        - navigate_to(location_name)
        - find_object(description)
        - pick_object(object_id)
        - place_object(location)
        - speak(text)

        Generate a JSON action sequence to accomplish the command.
        """

        response = self.llm_client.messages.create(
            model="claude-3-5-sonnet-20241022",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=1024
        )

        plan = json.loads(response.content[0].text)
        return plan

    def execute_plan(self, plan):
        """Execute planned action sequence"""

        for action in plan['actions']:
            action_type = action['type']
            params = action['params']

            if action_type == 'navigate_to':
                location = self.semantic_map.get_location(params['location'])
                self.navigation.navigate_to(location)

            elif action_type == 'find_object':
                obj = self.perception.find_object(params['description'])
                if obj:
                    self.get_logger().info(f"Found {obj['class']} at {obj['position']}")

            elif action_type == 'pick_object':
                obj_pose = self.perception.get_object_pose(params['object_id'])
                self.manipulation.pick_object(obj_pose)

            elif action_type == 'place_object':
                target = self.semantic_map.get_location(params['location'])
                self.manipulation.place_object(target)

            elif action_type == 'speak':
                self.speak(params['text'])

            # Wait for action to complete
            time.sleep(1.0)

Example Task Scenarios​

Scenario 1: "Bring me the red tool"​

1. Speech recognition: "Bring me the red tool"
2. LLM planning:
   - find_object("red tool")
   - navigate_to(object_location)
   - pick_object(object_id)
   - navigate_to("user")
   - place_object("user_hand")
   - speak("Here is the red tool")
3. Execution with vision, navigation, manipulation

Scenario 2: "Clean up the workspace"​

1. Speech recognition: "Clean up the workspace"
2. LLM planning:
   - navigate_to("workspace")
   - find_objects("clutter, trash, misplaced items")
   - for each object:
       - pick_object(object)
       - determine_destination(object_type)
       - navigate_to(destination)
       - place_object(destination)
   - speak("Workspace is clean")
3. Execution loop for multiple objects

Scenario 3: "Show me where the fire extinguisher is"​

1. Speech recognition: "Show me where the fire extinguisher is"
2. LLM planning:
   - find_object("fire extinguisher")
   - navigate_to(fire_extinguisher_location)
   - gesture("point" toward extinguisher)
   - speak("The fire extinguisher is here")
3. Execution with navigation and gestures

Deliverables​

Required Components​

  1. Demonstration Video (5-10 minutes)

    • Show all major capabilities
    • Include voice command examples
    • Show both successes and failure recovery

  2. Technical Report

    • System architecture diagram
    • Component descriptions
    • Algorithm choices and rationale
    • Performance evaluation
    • Challenges and solutions

  3. Source Code

    • Well-commented ROS 2 packages
    • Launch files for full system
    • README with setup instructions

  4. Live Demonstration

    • Real-time execution in Isaac Sim
    • Handle 3-5 different commands
    • Q&A session

Evaluation Criteria​

ComponentWeightCriteria
Voice Understanding15%Accuracy, robustness to variations
Task Planning20%Reasoning quality, plan correctness
Navigation15%Collision-free, efficiency
Perception15%Object detection accuracy, 3D pose estimation
Manipulation15%Grasp success rate, placement precision
Integration10%System coherence, failure recovery
HRI10%Naturalness, responsiveness, safety

Tips for Success​

  1. Start Simple: Begin with single actions, then combine
  2. Test Incrementally: Verify each component before integration
  3. Handle Failures: Implement error recovery for common failures
  4. Use Simulation: Test extensively in simulation before any real robot
  5. Document: Keep notes on challenges and solutions
  6. Iterate: Refine based on testing results

Advanced Extensions (Optional)​

  • Multi-robot coordination: Multiple robots working together
  • Learning from demonstration: Teach new tasks by showing
  • Dynamic environments: Handle moving objects and people
  • Long-horizon tasks: Complex multi-step tasks spanning minutes
  • Sim-to-real: Deploy to actual hardware

Resources​

Final Words​

This capstone project represents the culmination of your journey through Physical AI and Humanoid Robotics. You've learned the foundational technologies—ROS 2, simulation, perception, planning, manipulation—and now you're integrating them into a complete intelligent system.

The future of robotics lies in systems that can understand human intent, reason about their environment, and act autonomously yet safely. Your capstone robot embodies this vision.

Good luck, and enjoy building the future of robotics!

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