Chapter 4: Capstone Project - The Autonomous Humanoid
4.1 Project Overview
The capstone project integrates all components into a complete autonomous humanoid robot system capable of:
- Understanding spoken commands
- Planning complex multi-step tasks
- Navigating dynamic environments with obstacles
- Perceiving objects using computer vision
- Manipulating objects with arms and hands
4.2 System Architecture
┌────────────────────────────────────────────────────────┐
│ HUMANOID ROBOT │
├────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────────────────────────────────┐ │
│ │ Voice Command Interface │ │
│ │ (Whisper ASR + Command Parser) │ │
│ └─────────────────────┬──────────────────────────┘ │
│ │ Command │
│ ┌─────────────────────↓──────────────────────────┐ │
│ │ Cognitive Planning Layer │ │
│ │ (LLM: GPT-4 / Claude) │ │
│ │ Task decomposition → Action sequence │ │
│ └─────────────────────┬──────────────────────────┘ │
│ │ Plan │
│ ┌─────────────────────↓──────────────────────────┐ │
│ │ Perception and Navigation Layer │ │
│ │ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ VSLAM/Odom │ │ Nav2 Path │ │ │
│ │ │ │ │ Planning │ │ │
│ │ └─────────────┘ └─────────────┘ │ │
│ │ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Object │ │ Depth/ │ │ │
│ │ │ Detection │ │ Segmentation│ │ │
│ │ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────┬──────────────────────────┘ │
│ │ State update │
│ ┌─────────────────────↓──────────────────────────┐ │
│ │ Motion Control Layer │ │
│ │ ┌──────────────┐ ┌──────────────┐ │ │
│ │ │ Locomotion │ │ Manipulation │ │ │
│ │ │ (Footsteps) │ │ (Arms/Hands) │ │ │
│ │ └──────────────┘ └──────────────┘ │ │
│ └──────────────────────────────────────────────┘ │
│ │ Actuator commands │
│ ┌─────────────────────↓──────────────────────────┐ │
│ │ Hardware Interface │ │
│ │ (Joint Controllers, Gripper, Sensors) │ │
│ └──────────────────────────────────────────────┘ │
└────────────────────────────────────────────────────────┘
4.3 Capstone Project Implementation
4.3.1 Main Application Loop
"""
Autonomous Humanoid Robot - Capstone Project
Demonstrates: Voice → Plan → Navigate → Detect → Manipulate
"""
import rclpy
from rclpy.node import Node
from rclpy.executors import MultiThreadedExecutor
import json
import time
class AutonomousHumanoid(Node):
"""Main capstone system coordinator"""
def __init__(self):
super().__init__('autonomous_humanoid')
# Initialize components
self.voice_node = VoiceCommandNode()
self.planner = CognitivePlanner()
self.executor = PlanExecutor()
self.vision_system = VisionSystem()
self.navigator = NavigationController()
self.manipulator = ManipulationController()
# Create multithreaded executor for concurrent operation
self.executor_threads = MultiThreadedExecutor(num_threads=4)
# State tracking
self.robot_state = {
'position': [0.0, 0.0],
'orientation': 0.0,
'battery': 100.0,
'gripper_state': 'open',
'task_status': 'idle',
'current_plan': []
}
self.get_logger().info("🤖 Autonomous Humanoid Robot Initialized")
self.get_logger().info("Waiting for voice commands... Say 'Clean the room' or 'Pick up the book'")
def run(self):
"""Main execution loop"""
while rclpy.ok():
try:
# Check for voice commands
if self.voice_node.has_new_command():
command = self.voice_node.get_command()
self.get_logger().info(f"🎤 Received: {command}")
# Process command
self.process_task_command(command)
# Update robot state periodically
self.update_robot_state()
time.sleep(0.1)
except Exception as e:
self.get_logger().error(f"Error in main loop: {e}")
def process_task_command(self, command: str):
"""
Complete pipeline: Parse → Plan → Execute
"""
self.robot_state['task_status'] = 'planning'
# Step 1: Generate plan using LLM
self.get_logger().info("🧠 Generating cognitive plan...")
plan = self.planner.plan_task(command)
if not plan:
self.get_logger().error("Failed to generate plan")
self.robot_state['task_status'] = 'failed'
return
self.robot_state['current_plan'] = plan
# Print plan for debugging
self.get_logger().info(f"📋 Generated plan with {len(plan)} steps:")
for i, step in enumerate(plan, 1):
self.get_logger().info(
f" {i}. {step['action']}({json.dumps(step['parameters'])})"
)
# Step 2: Execute plan
self.robot_state['task_status'] = 'executing'
self.get_logger().info("⚡ Executing plan...")
success = self.execute_full_plan(plan)
if success:
self.get_logger().info("✅ Task completed successfully!")
self.robot_state['task_status'] = 'idle'
else:
self.get_logger().error("❌ Task execution failed")
self.robot_state['task_status'] = 'failed'
def execute_full_plan(self, plan: List[Dict]) -> bool:
"""Execute complete plan with all subsystems"""
for step_idx, step in enumerate(plan):
action = step['action']
params = step['parameters']
self.get_logger().info(
f"Step {step_idx + 1}: {action} {params}"
)
try:
if action == 'navigate':
success = self.navigate_to_location(params['destination'])
elif action == 'look_for':
success = self.detect_object(params['target'])
elif action == 'pick_up':
success = self.pick_up_object(params['object'])
elif action == 'place':
success = self.place_object(
params['object'],
params['location']
)
elif action == 'open':
success = self.open_door(params['target'])
elif action == 'close':
success = self.close_door(params['target'])
else:
self.get_logger().warn(f"Unknown action: {action}")
success = False
if not success:
self.get_logger().warn(f"Step {step_idx + 1} failed, attempting recovery...")
# Could implement recovery here
return False
except Exception as e:
self.get_logger().error(f"Exception in step {step_idx}: {e}")
return False
return True
def navigate_to_location(self, location: str) -> bool:
"""Navigate to named location"""
self.get_logger().info(f"🚶 Navigating to {location}...")
# Get coordinates
coords = self.navigator.location_to_coordinates(location)
if coords is None:
self.get_logger().error(f"Unknown location: {location}")
return False
# Start navigation
success = self.navigator.navigate_to(coords)
if success:
self.robot_state['position'] = list(coords)
self.get_logger().info(f"✓ Reached {location}")
return success
def detect_object(self, object_name: str) -> bool:
"""Use computer vision to detect object"""
self.get_logger().info(f"👁️ Looking for {object_name}...")
# Get camera frame
frame = self.vision_system.get_frame()
# Run object detection
detections = self.vision_system.detect_objects(frame)
# Search for target object
for detection in detections:
if self.vision_system.match_label(detection['label'], object_name):
self.get_logger().info(f"✓ Found {object_name}")
return True
self.get_logger().warn(f"✗ {object_name} not found in view")
return False
def pick_up_object(self, obj: str) -> bool:
"""Pick up object"""
self.get_logger().info(f"🤲 Picking up {obj}...")
# Use vision to locate object
pose = self.vision_system.get_object_pose(obj)
if pose is None:
self.get_logger().warn(f"Cannot locate {obj}")
return False
# Move arm to object
success = self.manipulator.reach_to_pose(pose)
if not success:
return False
# Close gripper
success = self.manipulator.gripper_close()
if success:
self.robot_state['gripper_state'] = 'closed'
self.get_logger().info(f"✓ Picked up {obj}")
return success
def place_object(self, obj: str, location: str) -> bool:
"""Place object at location"""
self.get_logger().info(f"📦 Placing {obj} at {location}...")
# Get placement coordinates
coords = self.navigator.location_to_coordinates(location)
if coords is None:
return False
# Navigate to location
success = self.navigate_to_location(location)
if not success:
return False
# Compute arm pose for placement
placement_pose = self.manipulator.compute_placement_pose(coords)
# Move arm to placement location
success = self.manipulator.reach_to_pose(placement_pose)
if not success:
return False
# Open gripper
success = self.manipulator.gripper_open()
if success:
self.robot_state['gripper_state'] = 'open'
self.get_logger().info(f"✓ Placed {obj} at {location}")
return success
def open_door(self, door: str) -> bool:
"""Open door or drawer"""
self.get_logger().info(f"🚪 Opening {door}...")
# Locate door handle
handle_pose = self.vision_system.get_handle_pose(door)
if handle_pose is None:
return False
# Execute opening motion
success = self.manipulator.execute_open_motion(handle_pose)
return success
def close_door(self, door: str) -> bool:
"""Close door or drawer"""
self.get_logger().info(f"🚪 Closing {door}...")
# Execute closing motion
success = self.manipulator.execute_close_motion(door)
return success
def update_robot_state(self):
"""Periodically update robot state"""
# Update odometry
odometry = self.navigator.get_odometry()
if odometry:
self.robot_state['position'] = [odometry.x, odometry.y]
self.robot_state['orientation'] = odometry.theta
# Update battery
battery_percent = self.get_battery_level()
self.robot_state['battery'] = battery_percent
if battery_percent < 20:
self.get_logger().warn("⚠️ Battery low")
4.3.2 Testing Scenarios
class CapstoneTestSuite:
"""Test scenarios for capstone project"""
TEST_SCENARIOS = [
{
'name': 'Simple Navigation',
'command': 'Go to the kitchen',
'expected_steps': ['navigate']
},
{
'name': 'Object Detection',
'command': 'Find the book',
'expected_steps': ['look_for']
},
{
'name': 'Pick and Place',
'command': 'Pick up the cup and place it on the table',
'expected_steps': ['look_for', 'pick_up', 'navigate', 'place']
},
{
'name': 'Complex Task',
'command': 'Clean the room',
'expected_steps': ['navigate', 'look_for', 'pick_up', 'place']
},
{
'name': 'Interactive Task',
'command': 'Open the door and go outside',
'expected_steps': ['open', 'navigate']
}
]
@staticmethod
def run_all_tests():
"""Execute all test scenarios"""
humanoid = AutonomousHumanoid()
results = []
for scenario in CapstoneTestSuite.TEST_SCENARIOS:
result = CapstoneTestSuite.run_test(humanoid, scenario)
results.append(result)
# Print result
status = "✅ PASS" if result['success'] else "❌ FAIL"
print(f"{status}: {scenario['name']}")
# Summary
passed = sum(1 for r in results if r['success'])
total = len(results)
print(f"\n📊 Results: {passed}/{total} scenarios passed")
return results
@staticmethod
def run_test(humanoid: AutonomousHumanoid, scenario: Dict) -> Dict:
"""Run single test scenario"""
start_time = time.time()
try:
# Execute scenario
humanoid.process_task_command(scenario['command'])
elapsed = time.time() - start_time
return {
'scenario': scenario['name'],
'success': True,
'time': elapsed,
'error': None
}
except Exception as e:
return {
'scenario': scenario['name'],
'success': False,
'time': time.time() - start_time,
'error': str(e)
}
4.4 Performance Metrics and Evaluation
| Metric | Target | Measurement |
|---|---|---|
| Plan Generation Time | < 2 seconds | LLM inference latency |
| Command Recognition Accuracy | > 95% | Whisper WER (Word Error Rate) |
| Plan Execution Success Rate | > 90% | Number of successful completions |
| Navigation Accuracy | < 10 cm | VSLAM pose error |
| Object Detection Accuracy | > 85% | mAP (mean Average Precision) |
| End-to-End Task Time | < 5 min | Total time from command to completion |
| System Responsiveness | < 500 ms | Latency from command to action start |
4.5 Deployment Checklist
## Pre-Deployment Verification
- [ ] Voice recognition working with acceptable WER
- [ ] LLM planning generating valid task sequences
- [ ] Navigation system reliable in test environment
- [ ] Object detection accurate for target objects
- [ ] Manipulation safe and reliable
- [ ] Safety stops functioning
- [ ] Real-time performance (30+ Hz perception)
- [ ] Battery management and charging working
- [ ] Logging and monitoring active
- [ ] Human-robot interaction protocols established
- [ ] Emergency procedures tested
- [ ] Documentation complete
## Safety Requirements
- Emergency stop button accessible
- Robot never approaches humans closer than safety distance
- Gripper force limited to safe levels
- Navigation avoids collision with obstacles
- Continuous monitoring of system health
Integration Summary
The Vision-Language-Action (VLA) framework represents the culmination of modern AI and robotics:
Voice Understanding → Cognitive Planning → Perception & Navigation → Manipulation
This pipeline enables humanoid robots to engage in natural conversation and autonomously execute complex tasks in real-world environments.
Key Innovations in This Module
- Multimodal AI: Combining speech, language, and vision systems
- Reasoning Under Uncertainty: LLMs handle ambiguity in commands
- Adaptive Execution: Dynamic replanning based on environmental feedback
- Human-Robot Collaboration: Intuitive interfaces for non-technical users
- End-to-End Learning: Sim-to-real transfer validated through capstone project
References and Further Reading
- OpenAI Whisper: Robust Speech Recognition via Large-Scale Weak Supervision
- GPT-4 Technical Report and API Documentation
- ROS 2 Action Servers and Clients Guide
- VSLAM and Visual Localization State-of-the-art
- Humanoid Robot Control and Gait Planning
- Human-Robot Interaction Best Practices
- Safety Standards for Collaborative Robots (ISO/TS 15066)
Capstone Project Submission Guidelines
Deliverables:
- Source code (GitHub repository)
- System architecture documentation
- Test results and performance metrics
- Video demonstrations (minimum 5 scenarios)
- Final report (5-10 pages)
Evaluation Criteria:
- Functionality (Plan generation, execution reliability)
- Innovation (Novel use of LLMs or perception systems)
- Robustness (Error handling, recovery mechanisms)
- Code quality (Documentation, modularity)
- Performance (Speed, accuracy metrics)