Capstone: Autonomous Humanoid Pipeline Project
Learning Objectives
By the end of this capstone project, you will be able to:
- Integrate all course components into a complete autonomous humanoid system
- Implement speech-to-action pipeline with natural language understanding
- Combine navigation, perception, and manipulation capabilities
- Deploy and evaluate the complete system in simulation and real-world scenarios
Project Overview
This capstone project brings together all the concepts learned throughout the course to create an autonomous humanoid robot pipeline. The system integrates speech recognition, natural language understanding, path planning, navigation, computer vision, perception, and manipulation capabilities into a unified system.
Project Requirements
- Speech Interface: Natural language processing for robot commands
- Planning & Navigation: Path planning and navigation through environments
- Perception System: Object detection and recognition for manipulation
- Manipulation: Robotic arm control for object interaction
- Integration: All components working together seamlessly
- Evaluation: Performance metrics and validation of the complete system
System Architecture
High-Level Architecture
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Speech & │ │ Planning & │ │ Perception & │
│ Language │ │ Navigation │ │ Manipulation │
│ Processing │───▶│ System │───▶│ System │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ Central Orchestration │
│ (Behavior Trees/FSM) │
└─────────────────────────────────────────────────────────────────┘
▲ ▲ ▲
│ │ │
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ ROS 2 │ │ Isaac Sim │ │ Hardware │
│ Infrastructure│ │ Interface │ │ Interface │
└─────────────────┘ └─────────────────┘ └─────────────────┘
Component Integration
The system is built using a modular architecture where each component can be developed and tested independently:
- Speech Processing Module: Handles voice input, speech recognition, and natural language understanding
- Planning Module: Performs path planning, task planning, and motion planning
- Perception Module: Processes visual information, detects objects, and estimates poses
- Manipulation Module: Controls robotic arms and grippers for object interaction
- Orchestration Layer: Coordinates all modules and manages system state
Speech-to-Action Pipeline
Natural Language Processing
Implement a natural language understanding system that can interpret human commands:
# speech_to_action.py
import speech_recognition as sr
import openai
import json
import re
from typing import Dict, List, Tuple
class NaturalLanguageProcessor:
def __init__(self, api_key=None):
self.recognizer = sr.Recognizer()
self.microphone = sr.Microphone()
# Use OpenAI API if available, otherwise use local NLP
if api_key:
openai.api_key = api_key
self.use_openai = True
else:
self.use_openai = False
# Fallback to local processing (simplified)
# Define action vocabulary
self.action_keywords = {
'move': ['go to', 'move to', 'navigate to', 'walk to', 'travel to'],
'pick': ['pick up', 'grasp', 'take', 'lift', 'get'],
'place': ['put down', 'place', 'drop', 'set down', 'place at'],
'detect': ['find', 'locate', 'identify', 'search for', 'spot'],
'follow': ['follow', 'track', 'escort', 'accompany', 'lead'],
'greet': ['hello', 'hi', 'greet', 'say hi', 'wave to']
}
def speech_to_text(self, audio_file=None):
"""Convert speech to text"""
if audio_file:
with sr.AudioFile(audio_file) as source:
audio = self.recognizer.record(source)
else:
print("Listening...")
with self.microphone as source:
self.recognizer.adjust_for_ambient_noise(source)
audio = self.recognizer.listen(source)
print("Processing...")
try:
text = self.recognizer.recognize_google(audio)
print(f"Recognized: {text}")
return text
except sr.UnknownValueError:
print("Could not understand audio")
return None
except sr.RequestError as e:
print(f"Error with speech recognition service: {e}")
return None
def parse_command(self, text):
"""Parse natural language command into structured action"""
if not text:
return None
# Convert to lowercase for easier processing
text_lower = text.lower()
# Extract action type
action_type = None
for action, keywords in self.action_keywords.items():
for keyword in keywords:
if keyword in text_lower:
action_type = action
break
if action_type:
break
if not action_type:
# Use OpenAI for more complex parsing if available
if self.use_openai:
return self.parse_with_openai(text)
else:
return self.simple_parse(text_lower)
# Extract entities (objects, locations, etc.)
entities = self.extract_entities(text_lower, action_type)
return {
'action': action_type,
'entities': entities,
'original_text': text
}
def extract_entities(self, text, action_type):
"""Extract relevant entities from the command"""
entities = {}
# Common patterns for extracting locations, objects, etc.
location_patterns = [
r'in the (\w+) room',
r'to the (\w+) (?:room|area|location)',
r'at the (\w+) (?:table|desk|counter)',
r'near the (\w+)'
]
object_patterns = [
r'(?:the |a |an )(\w+ (?:box|cup|book|ball|toy|object))',
r'(?:the |a |an )(\w+ \w+ (?:box|cup|book|ball|toy|object))',
r'(\w+ \w+)',
r'(\w+)'
]
# Extract locations
for pattern in location_patterns:
matches = re.findall(pattern, text)
if matches:
entities['location'] = matches[0]
break
# Extract objects
for pattern in object_patterns:
matches = re.findall(pattern, text)
for match in matches:
# Filter out action words
if match not in [word for sublist in self.action_keywords.values() for word in sublist]:
entities['object'] = match
break
return entities
def simple_parse(self, text):
"""Simple command parsing for basic actions"""
# This is a simplified version - in practice, you'd use more sophisticated NLP
command_map = {
'move to kitchen': {'action': 'move', 'entities': {'location': 'kitchen'}},
'pick up red cup': {'action': 'pick', 'entities': {'object': 'red cup'}},
'place cup on table': {'action': 'place', 'entities': {'object': 'cup', 'location': 'table'}},
'find blue ball': {'action': 'detect', 'entities': {'object': 'blue ball'}},
'follow me': {'action': 'follow', 'entities': {}},
'hello robot': {'action': 'greet', 'entities': {}}
}
for key, value in command_map.items():
if key in text:
return value
return {'action': 'unknown', 'entities': {}, 'original_text': text}
def parse_with_openai(self, text):
"""Use OpenAI for advanced command parsing"""
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{
"role": "system",
"content": """You are a command parser for a humanoid robot. Parse the user's command into a structured format.
Respond with a JSON object containing 'action' (move, pick, place, detect, follow, greet) and 'entities' (object, location).
Example: {'action': 'pick', 'entities': {'object': 'red cup', 'location': 'kitchen'}}"""
},
{
"role": "user",
"content": text
}
],
temperature=0.1
)
result = json.loads(response.choices[0].message.content)
result['original_text'] = text
return result
except Exception as e:
print(f"Error with OpenAI parsing: {e}")
return self.simple_parse(text)
class SpeechToActionPipeline:
def __init__(self, nlp_processor):
self.nlp = nlp_processor
self.command_queue = []
def process_audio_command(self, audio_file=None):
"""Process audio command and convert to robot action"""
# Convert speech to text
text = self.nlp.speech_to_text(audio_file)
if not text:
return None
# Parse the command
parsed_command = self.nlp.parse_command(text)
if not parsed_command:
return None
# Add to command queue
self.command_queue.append(parsed_command)
return parsed_command
def get_next_command(self):
"""Get the next command from the queue"""
if self.command_queue:
return self.command_queue.pop(0)
return None
def execute_command(self, command, robot_interface):
"""Execute a parsed command using the robot interface"""
if command['action'] == 'move':
location = command['entities'].get('location', 'default')
robot_interface.move_to_location(location)
elif command['action'] == 'pick':
obj = command['entities'].get('object', 'default')
robot_interface.pick_object(obj)
elif command['action'] == 'place':
obj = command['entities'].get('object', 'default')
location = command['entities'].get('location', 'default')
robot_interface.place_object(obj, location)
elif command['action'] == 'detect':
obj = command['entities'].get('object', 'default')
robot_interface.detect_object(obj)
elif command['action'] == 'follow':
robot_interface.follow_person()
elif command['action'] == 'greet':
robot_interface.greet_person()
else:
print(f"Unknown action: {command['action']}")
return False
return True
# Example usage
def example_usage():
# Initialize the processor
nlp = NaturalLanguageProcessor(api_key=None) # Use local processing for example
pipeline = SpeechToActionPipeline(nlp)
# Simulate a command
test_command = "Please go to the kitchen and pick up the red cup"
parsed = nlp.parse_command(test_command)
print(f"Parsed command: {parsed}")
if __name__ == "__main__":
example_usage()
Planning and Navigation System
Path Planning and Navigation
Implement the planning and navigation system:
# planning_navigation.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
import heapq
from typing import List, Tuple, Optional
import math
class OccupancyGrid:
def __init__(self, width: int, height: int, resolution: float = 1.0):
self.width = width
self.height = height
self.resolution = resolution # meters per grid cell
self.grid = np.zeros((height, width), dtype=np.int8) # 0: free, 1: occupied, -1: unknown
def world_to_grid(self, x: float, y: float) -> Tuple[int, int]:
"""Convert world coordinates to grid coordinates"""
grid_x = int((x / self.resolution) + (self.width / 2))
grid_y = int((y / self.resolution) + (self.height / 2))
return min(max(grid_x, 0), self.width - 1), min(max(grid_y, 0), self.height - 1)
def grid_to_world(self, grid_x: int, grid_y: int) -> Tuple[float, float]:
"""Convert grid coordinates to world coordinates"""
x = (grid_x - (self.width / 2)) * self.resolution
y = (grid_y - (self.height / 2)) * self.resolution
return x, y
def is_free(self, x: int, y: int) -> bool:
"""Check if a grid cell is free"""
if 0 <= x < self.width and 0 <= y < self.height:
return self.grid[y, x] == 0
return False
def add_obstacle(self, x: int, y: int):
"""Add an obstacle to the grid"""
if 0 <= x < self.width and 0 <= y < self.height:
self.grid[y, x] = 1
def update_from_sensor_data(self, sensor_data):
"""Update grid based on sensor data (simplified)"""
# In practice, this would integrate LIDAR, camera, or other sensor data
# For this example, we'll just mark some cells as occupied
for i in range(5):
for j in range(5):
self.add_obstacle(10 + i, 10 + j)
class AStarPlanner:
def __init__(self, occupancy_grid: OccupancyGrid):
self.grid = occupancy_grid
self.directions = [
(-1, -1), (-1, 0), (-1, 1),
(0, -1), (0, 1),
(1, -1), (1, 0), (1, 1)
]
self.direction_costs = [
math.sqrt(2), 1, math.sqrt(2),
1, 1,
math.sqrt(2), 1, math.sqrt(2)
]
def heuristic(self, pos1: Tuple[int, int], pos2: Tuple[int, int]) -> float:
"""Heuristic function for A* (Euclidean distance)"""
return math.sqrt((pos1[0] - pos2[0])**2 + (pos1[1] - pos2[1])**2)
def plan_path(self, start: Tuple[int, int], goal: Tuple[int, int]) -> Optional[List[Tuple[int, int]]]:
"""Plan path using A* algorithm"""
if not self.grid.is_free(start[0], start[1]) or not self.grid.is_free(goal[0], goal[1]):
return None
# Priority queue: (f_score, g_score, position)
open_set = [(0, 0, start)]
closed_set = set()
# For path reconstruction
came_from = {}
# Cost dictionaries
g_score = {start: 0}
f_score = {start: self.heuristic(start, goal)}
while open_set:
current_f, current_g, current = heapq.heappop(open_set)
if current == goal:
# Reconstruct path
path = [current]
while current in came_from:
current = came_from[current]
path.append(current)
path.reverse()
return path
closed_set.add(current)
# Check neighbors
for i, direction in enumerate(self.directions):
neighbor = (current[0] + direction[0], current[1] + direction[1])
# Skip if out of bounds or occupied
if (neighbor[0] < 0 or neighbor[0] >= self.grid.width or
neighbor[1] < 0 or neighbor[1] >= self.grid.height or
neighbor in closed_set or not self.grid.is_free(neighbor[0], neighbor[1])):
continue
tentative_g = current_g + self.direction_costs[i]
if neighbor not in g_score or tentative_g < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g
f_score[neighbor] = tentative_g + self.heuristic(neighbor, goal)
heapq.heappush(open_set, (f_score[neighbor], tentative_g, neighbor))
return None # No path found
class NavigationSystem:
def __init__(self):
self.occupancy_grid = OccupancyGrid(100, 100, 0.1) # 10m x 10m grid with 10cm resolution
self.planner = AStarPlanner(self.occupancy_grid)
self.current_position = (0, 0) # Grid coordinates
self.path = []
self.path_index = 0
def update_map(self, sensor_data):
"""Update the occupancy grid with new sensor data"""
self.occupancy_grid.update_from_sensor_data(sensor_data)
def plan_to_location(self, goal_x: float, goal_y: float) -> bool:
"""Plan a path to a world coordinate location"""
# Convert world coordinates to grid coordinates
start_grid = self.occupancy_grid.world_to_grid(
self.current_position[0], self.current_position[1]
)
goal_grid = self.occupancy_grid.world_to_grid(goal_x, goal_y)
# Plan path
path = self.planner.plan_path(start_grid, goal_grid)
if path:
self.path = path
self.path_index = 0
return True
else:
print(f"No path found from {start_grid} to {goal_grid}")
return False
def get_next_waypoint(self) -> Optional[Tuple[float, float]]:
"""Get the next waypoint in the path"""
if self.path_index < len(self.path):
grid_pos = self.path[self.path_index]
world_pos = self.occupancy_grid.grid_to_world(grid_pos[0], grid_pos[1])
return world_pos
return None
def reached_waypoint(self):
"""Indicate that the current waypoint has been reached"""
self.path_index += 1
def is_path_complete(self) -> bool:
"""Check if the path has been completely traversed"""
return self.path_index >= len(self.path)
def move_to_location(self, location_name: str):
"""Navigate to a named location"""
# In a real system, this would map location names to coordinates
# For this example, we'll use predefined locations
location_map = {
'kitchen': (2.0, 1.0),
'living_room': (-1.0, 0.0),
'bedroom': (0.0, -2.0),
'office': (1.5, -1.5),
'default': (0.0, 0.0)
}
target_pos = location_map.get(location_name, location_map['default'])
print(f"Navigating to {location_name} at ({target_pos[0]:.1f}, {target_pos[1]:.1f})")
if self.plan_to_location(target_pos[0], target_pos[1]):
print("Path planning successful, executing navigation...")
# In a real system, this would execute the navigation
# For this example, we'll just simulate reaching the destination
self.current_position = target_pos
print(f"Reached {location_name}")
else:
print(f"Failed to plan path to {location_name}")
# Example usage
def example_navigation():
nav_system = NavigationSystem()
# Update map with some obstacles
sensor_data = {} # In practice, this would come from sensors
nav_system.update_map(sensor_data)
# Navigate to kitchen
nav_system.move_to_location('kitchen')
# Navigate to living room
nav_system.move_to_location('living_room')
if __name__ == "__main__":
example_navigation()
Perception and Manipulation System
Object Detection and Manipulation
Implement the perception and manipulation components:
# perception_manipulation.py
import cv2
import numpy as np
import open3d as o3d
from typing import List, Dict, Tuple, Optional
import math
class ObjectDetector:
def __init__(self):
# For this example, we'll use a simple color-based detector
# In practice, you'd use YOLO, Detectron2, or similar
self.known_objects = {
'red_cube': ([0, 100, 100], [10, 255, 255]), # HSV ranges
'green_cube': ([40, 50, 50], [80, 255, 255]),
'blue_cube': ([100, 50, 50], [130, 255, 255]),
'cup': ([0, 0, 100], [180, 50, 255]), # Dark colors
}
def detect_objects(self, image: np.ndarray) -> List[Dict]:
"""Detect objects in the image"""
results = []
# Convert to HSV for color-based detection
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
for obj_name, (lower, upper) in self.known_objects.items():
# Create mask for this object color
mask = cv2.inRange(hsv, np.array(lower), np.array(upper))
# Find contours
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
# Filter by size to avoid tiny detections
area = cv2.contourArea(contour)
if area > 500: # Minimum area threshold
# Get bounding box
x, y, w, h = cv2.boundingRect(contour)
# Calculate center
center_x = x + w // 2
center_y = y + h // 2
# Calculate 3D position (simplified - assumes known camera parameters)
# In practice, you'd use stereo vision or depth information
distance = self.estimate_distance(w, h) # Simplified distance estimation
obj_info = {
'name': obj_name,
'bbox': (x, y, w, h),
'center': (center_x, center_y),
'distance': distance,
'confidence': min(0.95, area / 10000) # Confidence based on size
}
results.append(obj_info)
return results
def estimate_distance(self, width: int, height: int) -> float:
"""Estimate distance based on object size in pixels (simplified)"""
# This is a very simplified approach
# In practice, you'd use calibrated camera parameters and known object sizes
focal_length = 500 # pixels (typical for many cameras)
real_width = 0.1 # meters (assumed real width of object)
# Distance = (real_width * focal_length) / pixel_width
distance = (real_width * focal_length) / max(width, height)
return max(0.1, distance) # Minimum distance of 0.1m
class PoseEstimator:
def __init__(self):
# In practice, you'd use a more sophisticated pose estimation method
# like DenseFusion, PVNet, or similar
pass
def estimate_pose(self, object_mask: np.ndarray, object_name: str) -> Dict:
"""Estimate 6D pose of object"""
# Simplified pose estimation
# Calculate center of mass for position
y_coords, x_coords = np.where(object_mask > 0)
if len(x_coords) > 0 and len(y_coords) > 0:
center_x = np.mean(x_coords)
center_y = np.mean(y_coords)
# Estimate orientation (simplified)
# In practice, you'd use more sophisticated methods
orientation = {
'roll': 0.0,
'pitch': 0.0,
'yaw': 0.0
}
position = {
'x': center_x,
'y': center_y,
'z': 0.5 # Assumed height
}
return {
'position': position,
'orientation': orientation,
'confidence': 0.8
}
return None
class ManipulationPlanner:
def __init__(self):
self.reachable_workspace = {
'x_range': (-0.5, 0.5),
'y_range': (-0.5, 0.5),
'z_range': (0.1, 0.8)
}
def plan_grasp(self, object_pose: Dict) -> Optional[Dict]:
"""Plan a grasp for the given object"""
obj_pos = object_pose['position']
# Check if object is in reachable workspace
if (self.reachable_workspace['x_range'][0] <= obj_pos['x'] <= self.reachable_workspace['x_range'][1] and
self.reachable_workspace['y_range'][0] <= obj_pos['y'] <= self.reachable_workspace['y_range'][1] and
self.reachable_workspace['z_range'][0] <= obj_pos['z'] <= self.reachable_workspace['z_range'][1]):
# Plan approach and grasp
grasp_plan = {
'approach_pose': {
'x': obj_pos['x'],
'y': obj_pos['y'],
'z': obj_pos['z'] + 0.1, # 10cm above object
'orientation': object_pose['orientation']
},
'grasp_pose': {
'x': obj_pos['x'],
'y': obj_pos['y'],
'z': obj_pos['z'] + 0.02, # Just above object
'orientation': object_pose['orientation']
},
'gripper_width': 0.05, # 5cm gripper width
'grasp_type': 'top_down' # Approach from above
}
return grasp_plan
return None
def plan_placement(self, target_location: Tuple[float, float, float]) -> Dict:
"""Plan placement at target location"""
placement_plan = {
'approach_pose': {
'x': target_location[0],
'y': target_location[1],
'z': target_location[2] + 0.1, # 10cm above target
'orientation': {'roll': 0, 'pitch': 0, 'yaw': 0}
},
'placement_pose': {
'x': target_location[0],
'y': target_location[1],
'z': target_location[2] + 0.02, # Just above surface
'orientation': {'roll': 0, 'pitch': 0, 'yaw': 0}
},
'release_action': 'open_gripper'
}
return placement_plan
class PerceptionManipulationSystem:
def __init__(self):
self.detector = ObjectDetector()
self.pose_estimator = PoseEstimator()
self.manipulator = ManipulationPlanner()
self.detected_objects = []
def process_scene(self, image: np.ndarray) -> List[Dict]:
"""Process scene and detect objects"""
self.detected_objects = self.detector.detect_objects(image)
# For each detected object, estimate pose
for obj in self.detected_objects:
# Create a simple mask for pose estimation
mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
x, y, w, h = obj['bbox']
mask[y:y+h, x:x+w] = 255
pose = self.pose_estimator.estimate_pose(mask, obj['name'])
if pose:
obj['pose'] = pose
return self.detected_objects
def find_object_by_name(self, name: str) -> Optional[Dict]:
"""Find an object by name in the detected objects"""
for obj in self.detected_objects:
if name.lower() in obj['name'].lower():
return obj
return None
def pick_object(self, object_name: str) -> bool:
"""Plan and execute picking of an object"""
obj = self.find_object_by_name(object_name)
if not obj:
print(f"Object '{object_name}' not found")
return False
print(f"Planning to pick {obj['name']} at position {obj['pose']['position']}")
# Plan grasp
grasp_plan = self.manipulator.plan_grasp(obj['pose'])
if not grasp_plan:
print(f"Cannot grasp {obj['name']} - out of reach")
return False
print(f"Grasp plan created: {grasp_plan}")
# In a real system, this would execute the grasp
print(f"Successfully picked {obj['name']}")
return True
def place_object(self, object_name: str, location: Tuple[float, float, float]) -> bool:
"""Plan and execute placing of an object at location"""
print(f"Planning to place {object_name} at {location}")
# Plan placement
placement_plan = self.manipulator.plan_placement(location)
print(f"Placement plan created: {placement_plan}")
# In a real system, this would execute the placement
print(f"Successfully placed {object_name}")
return True
def detect_object(self, object_name: str) -> bool:
"""Detect and report an object"""
obj = self.find_object_by_name(object_name)
if obj:
print(f"Found {obj['name']} at position {obj['pose']['position']} "
f"with confidence {obj['confidence']:.2f}")
return True
else:
print(f"Could not find {object_name}")
return False
# Example usage
def example_perception_manipulation():
pms = PerceptionManipulationSystem()
# Simulate an image (in practice, this would come from camera)
dummy_image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
# Add some colored rectangles to simulate objects
cv2.rectangle(dummy_image, (100, 100), (150, 150), (0, 0, 255), -1) # Red
cv2.rectangle(dummy_image, (200, 200), (250, 250), (0, 255, 0), -1) # Green
cv2.rectangle(dummy_image, (300, 300), (350, 350), (255, 0, 0), -1) # Blue
# Process the scene
objects = pms.process_scene(dummy_image)
print(f"Detected {len(objects)} objects:")
for obj in objects:
print(f" - {obj['name']} at {obj['pose']['position'] if 'pose' in obj else 'unknown'}")
# Test object detection
pms.detect_object('red')
# Test picking
pms.pick_object('red_cube')
if __name__ == "__main__":
example_perception_manipulation()
System Integration
Main Orchestration System
Combine all components into the main system:
# main_system.py
import threading
import time
from queue import Queue
from speech_to_action import NaturalLanguageProcessor, SpeechToActionPipeline
from planning_navigation import NavigationSystem
from perception_manipulation import PerceptionManipulationSystem
from typing import Dict, Any, Callable
class RobotInterface:
"""Interface to interact with the physical or simulated robot"""
def __init__(self):
self.current_location = (0.0, 0.0)
self.battery_level = 100.0
self.status = "idle"
def move_to_location(self, location_name: str):
"""Move robot to specified location"""
print(f"Moving robot to {location_name}")
# In a real system, this would send commands to the robot
time.sleep(2) # Simulate movement time
self.status = f"at_{location_name}"
print(f"Robot arrived at {location_name}")
def pick_object(self, object_name: str):
"""Pick up an object"""
print(f"Picking up {object_name}")
# In a real system, this would control the manipulator
time.sleep(1) # Simulate action time
print(f"Successfully picked up {object_name}")
def place_object(self, object_name: str, location: str):
"""Place an object at location"""
print(f"Placing {object_name} at {location}")
# In a real system, this would control the manipulator
time.sleep(1) # Simulate action time
print(f"Successfully placed {object_name} at {location}")
def detect_object(self, object_name: str):
"""Detect an object in the environment"""
print(f"Looking for {object_name}")
# In a real system, this would use perception system
time.sleep(0.5) # Simulate processing time
print(f"Detected {object_name}")
def follow_person(self):
"""Follow a person"""
print("Following person")
# In a real system, this would use tracking algorithms
self.status = "following"
def greet_person(self):
"""Greet a person"""
print("Hello! How can I help you?")
# In a real system, this might trigger speech synthesis
time.sleep(1)
print("Greeting completed")
class AutonomousHumanoidSystem:
def __init__(self):
# Initialize all subsystems
self.nlp_processor = NaturalLanguageProcessor()
self.speech_pipeline = SpeechToActionPipeline(self.nlp_processor)
self.navigation_system = NavigationSystem()
self.perception_manipulation = PerceptionManipulationSystem()
self.robot_interface = RobotInterface()
# Threading components
self.command_queue = Queue()
self.running = False
self.main_thread = None
def start_system(self):
"""Start the autonomous system"""
self.running = True
self.main_thread = threading.Thread(target=self.main_loop)
self.main_thread.start()
print("Autonomous Humanoid System started")
def stop_system(self):
"""Stop the autonomous system"""
self.running = False
if self.main_thread:
self.main_thread.join()
print("Autonomous Humanoid System stopped")
def main_loop(self):
"""Main system loop"""
while self.running:
# Check for new commands
if not self.command_queue.empty():
command = self.command_queue.get()
self.execute_command(command)
# Update systems periodically
self.update_systems()
time.sleep(0.1) # Small delay to prevent busy waiting
def update_systems(self):
"""Update all subsystems"""
# In a real system, this would update sensor data, maps, etc.
pass
def execute_command(self, command: Dict[str, Any]):
"""Execute a parsed command"""
print(f"Executing command: {command}")
action = command.get('action')
entities = command.get('entities', {})
if action == 'move':
location = entities.get('location', 'default')
self.robot_interface.move_to_location(location)
elif action == 'pick':
obj = entities.get('object', 'default')
# First, detect the object in the current environment
self.perception_manipulation.detect_object(obj)
# Then pick it up
self.robot_interface.pick_object(obj)
elif action == 'place':
obj = entities.get('object', 'default')
location = entities.get('location', 'default')
self.robot_interface.place_object(obj, location)
elif action == 'detect':
obj = entities.get('object', 'default')
self.robot_interface.detect_object(obj)
elif action == 'follow':
self.robot_interface.follow_person()
elif action == 'greet':
self.robot_interface.greet_person()
else:
print(f"Unknown action: {action}")
def process_audio_command(self, audio_file=None):
"""Process an audio command and add to execution queue"""
parsed_command = self.speech_pipeline.process_audio_command(audio_file)
if parsed_command:
self.command_queue.put(parsed_command)
print(f"Command added to queue: {parsed_command}")
else:
print("Could not parse the command")
def process_text_command(self, text: str):
"""Process a text command and add to execution queue"""
parsed_command = self.nlp_processor.parse_command(text)
if parsed_command:
self.command_queue.put(parsed_command)
print(f"Command added to queue: {parsed_command}")
else:
print("Could not parse the command")
def get_system_status(self) -> Dict[str, Any]:
"""Get current system status"""
return {
'robot_status': self.robot_interface.status,
'battery_level': self.robot_interface.battery_level,
'command_queue_size': self.command_queue.qsize(),
'current_location': self.robot_interface.current_location,
'running': self.running
}
# Example usage and demonstration
def demo_autonomous_system():
"""Demonstrate the autonomous humanoid system"""
print("Initializing Autonomous Humanoid System...")
system = AutonomousHumanoidSystem()
try:
# Start the system
system.start_system()
# Give system time to initialize
time.sleep(1)
# Process some example commands
print("\n--- Demo: Processing example commands ---")
# Command 1: Move to kitchen
print("\nCommand: Move to kitchen")
system.process_text_command("Please go to the kitchen")
time.sleep(3) # Wait for execution
# Command 2: Find a red object
print("\nCommand: Find red object")
system.process_text_command("Look for the red cube")
time.sleep(2)
# Command 3: Pick up an object
print("\nCommand: Pick up object")
system.process_text_command("Pick up the red cube")
time.sleep(2)
# Command 4: Place object
print("\nCommand: Place object at table")
system.process_text_command("Place the cube on the table")
time.sleep(2)
# Command 5: Greet
print("\nCommand: Greet person")
system.process_text_command("Say hello to me")
time.sleep(2)
# Check system status
print("\n--- System Status ---")
status = system.get_system_status()
for key, value in status.items():
print(f"{key}: {value}")
# Wait a bit more before stopping
time.sleep(1)
finally:
# Always stop the system
system.stop_system()
def interactive_demo():
"""Interactive demo where user can input commands"""
print("Starting Interactive Autonomous Humanoid System Demo")
print("Type commands and press Enter (type 'quit' to exit)")
print("Examples: 'go to kitchen', 'pick up red cube', 'find blue ball'")
system = AutonomousHumanoidSystem()
system.start_system()
try:
while True:
user_input = input("\nEnter command: ").strip()
if user_input.lower() in ['quit', 'exit', 'stop']:
break
if user_input:
system.process_text_command(user_input)
# Wait a bit to see the result
time.sleep(0.5)
# Show status occasionally
if system.command_queue.empty():
status = system.get_system_status()
print(f"Status: {status['robot_status']}")
finally:
system.stop_system()
if __name__ == "__main__":
print("Autonomous Humanoid Pipeline Project")
print("=====================================")
# Run the demonstration
demo_autonomous_system()
# Uncomment the next line for interactive demo
# interactive_demo()
Platform Setup and Deployment
Digital Twin Workstation Setup
# setup_digital_twin_workstation.sh
#!/bin/bash
echo "Setting up Digital Twin Workstation for Autonomous Humanoid System..."
# Update system
sudo apt update && sudo apt upgrade -y
# Install ROS 2 Humble
sudo apt install software-properties-common
sudo add-apt-repository universe
sudo apt update && sudo apt install curl -y
curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(. /etc/os-release && echo $UBUNTU_CODENAME) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null
sudo apt update
sudo apt install ros-humble-desktop-full
source /opt/ros/humble/setup.bash
# Install Python dependencies
pip3 install --upgrade pip
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip3 install openai speechrecognition opencv-python open3d numpy scipy matplotlib
pip3 install rclpy
# Install NVIDIA Isaac Sim (if available)
# This requires NVIDIA Omniverse account and GPU
echo "Installing NVIDIA Isaac Sim dependencies..."
sudo apt install nvidia-driver-535-server # or appropriate driver version
sudo apt install cuda-toolkit-12-3
# Install Gazebo Garden
sudo apt install gazebo libgazebo-dev
# Install additional tools
sudo apt install git cmake build-essential python3-colcon-common-extensions python3-rosdep python3-vcstool
# Create workspace
mkdir -p ~/isaac_ws/src
cd ~/isaac_ws
# Build workspace
source /opt/ros/humble/setup.bash
colcon build --symlink-install
echo "Digital Twin Workstation setup complete!"
echo "Please source the ROS environment:"
echo "source /opt/ros/humble/setup.bash"
echo "source ~/isaac_ws/install/setup.bash"
Edge Kit Setup
# setup_edge_kit.sh
#!/bin/bash
echo "Setting up Physical AI Edge Kit for Autonomous Humanoid System..."
# Update system
sudo apt update && sudo apt upgrade -y
# Install essential packages for edge computing
sudo apt install build-essential cmake git python3-dev python3-pip
pip3 install --upgrade pip
# Install robotics libraries
sudo apt install ros-humble-ros-base ros-humble-navigation2 ros-humble-nav2-bringup
sudo apt install ros-humble-vision-opencv ros-humble-cv-bridge ros-humble-image-transport
sudo apt install ros-humble-moveit ros-humble-moveit-visual-tools
# Install AI/ML libraries optimized for edge
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip3 install tensorflow==2.13.0 # CPU version for edge
pip3 install opencv-python-headless open3d numpy scipy
# Install perception libraries
pip3 install ultralytics # YOLOv8 for object detection
pip3 install mediapipe # For pose estimation
# Install speech recognition
pip3 install speechrecognition pyaudio
# Install navigation libraries
pip3 install python-qt-binding tf2_ros
# Create robot workspace
mkdir -p ~/edge_robot_ws/src
cd ~/edge_robot_ws
# Source ROS
source /opt/ros/humble/setup.bash
# Build workspace
colcon build --packages-select robot_hardware_interface perception_pipeline navigation_system
echo "Edge Kit setup complete!"
echo "Remember to configure for your specific hardware constraints"
Cloud-Native Environment Setup
# k8s_deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: humanoid-robot-controller
labels:
app: humanoid-controller
spec:
replicas: 1
selector:
matchLabels:
app: humanoid-controller
template:
metadata:
labels:
app: humanoid-controller
spec:
containers:
- name: robot-core
image: robot/controller:latest
ports:
- containerPort: 9090
env:
- name: ROS_DOMAIN_ID
value: "1"
- name: NVIDIA_VISIBLE_DEVICES
value: "all"
resources:
requests:
memory: "2Gi"
cpu: "500m"
nvidia.com/gpu: 1
limits:
memory: "4Gi"
cpu: "1000m"
nvidia.com/gpu: 1
volumeMounts:
- name: robot-data
mountPath: /data
volumes:
- name: robot-data
persistentVolumeClaim:
claimName: robot-storage
---
apiVersion: v1
kind: Service
metadata:
name: humanoid-controller-service
spec:
selector:
app: humanoid-controller
ports:
- protocol: TCP
port: 9090
targetPort: 9090
type: LoadBalancer
Evaluation and Metrics
Performance Evaluation
# evaluation_metrics.py
import time
import numpy as np
from typing import Dict, List, Tuple
class SystemEvaluator:
def __init__(self):
self.metrics = {
'response_time': [],
'task_success_rate': [],
'navigation_accuracy': [],
'object_detection_accuracy': [],
'manipulation_success_rate': [],
'system_uptime': [],
'energy_efficiency': []
}
def measure_response_time(self, system_func, *args, **kwargs) -> float:
"""Measure response time for a system function"""
start_time = time.time()
result = system_func(*args, **kwargs)
end_time = time.time()
response_time = end_time - start_time
self.metrics['response_time'].append(response_time)
return response_time, result
def evaluate_navigation(self, nav_system, start_pos: Tuple, goal_pos: Tuple) -> Dict:
"""Evaluate navigation system performance"""
start_time = time.time()
# Plan and execute navigation
success = nav_system.plan_to_location(goal_pos[0], goal_pos[1])
end_time = time.time()
execution_time = end_time - start_time
# Calculate metrics
distance_traveled = self.calculate_path_length(nav_system.path)
straight_line_distance = self.euclidean_distance(start_pos, goal_pos)
efficiency_ratio = straight_line_distance / distance_traveled if distance_traveled > 0 else 0
navigation_metrics = {
'success': success,
'execution_time': execution_time,
'path_efficiency': efficiency_ratio,
'distance_traveled': distance_traveled,
'straight_line_distance': straight_line_distance
}
self.metrics['navigation_accuracy'].append(navigation_metrics)
return navigation_metrics
def evaluate_object_detection(self, detector, test_images: List[np.ndarray], ground_truth: List[Dict]) -> Dict:
"""Evaluate object detection performance"""
total_precision = 0
total_recall = 0
total_f1 = 0
for img, gt in zip(test_images, ground_truth):
detected = detector.detect_objects(img)
# Calculate precision, recall, F1 for this image
tp, fp, fn = self.calculate_detection_stats(detected, gt)
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
total_precision += precision
total_recall += recall
total_f1 += f1
avg_precision = total_precision / len(test_images)
avg_recall = total_recall / len(test_images)
avg_f1 = total_f1 / len(test_images)
detection_metrics = {
'average_precision': avg_precision,
'average_recall': avg_recall,
'average_f1': avg_f1,
'total_images': len(test_images)
}
self.metrics['object_detection_accuracy'].append(detection_metrics)
return detection_metrics
def evaluate_task_completion(self, system, test_tasks: List[Dict]) -> Dict:
"""Evaluate overall task completion success rate"""
successful_tasks = 0
total_tasks = len(test_tasks)
for task in test_tasks:
success = self.execute_and_evaluate_task(system, task)
if success:
successful_tasks += 1
success_rate = successful_tasks / total_tasks if total_tasks > 0 else 0
task_metrics = {
'successful_tasks': successful_tasks,
'total_tasks': total_tasks,
'success_rate': success_rate
}
self.metrics['task_success_rate'].append(success_rate)
return task_metrics
def calculate_detection_stats(self, detected: List[Dict], ground_truth: List[Dict]) -> Tuple[int, int, int]:
"""Calculate true positives, false positives, false negatives"""
# This is a simplified version - in practice, you'd use IoU matching
tp = min(len(detected), len(ground_truth)) # Simplified
fp = max(0, len(detected) - len(ground_truth))
fn = max(0, len(ground_truth) - len(detected))
return tp, fp, fn
def calculate_path_length(self, path: List[Tuple[int, int]]) -> float:
"""Calculate total length of a path"""
if len(path) < 2:
return 0
total_length = 0
for i in range(1, len(path)):
dx = path[i][0] - path[i-1][0]
dy = path[i][1] - path[i-1][1]
total_length += np.sqrt(dx*dx + dy*dy)
return total_length
def euclidean_distance(self, pos1: Tuple, pos2: Tuple) -> float:
"""Calculate Euclidean distance between two points"""
return np.sqrt((pos1[0] - pos2[0])**2 + (pos1[1] - pos2[1])**2)
def execute_and_evaluate_task(self, system, task: Dict) -> bool:
"""Execute a task and evaluate its success"""
# In a real system, this would execute the task and check for success
# For this example, we'll simulate success/failure
time.sleep(0.1) # Simulate task execution time
return np.random.random() > 0.2 # 80% success rate for simulation
def generate_evaluation_report(self) -> str:
"""Generate a comprehensive evaluation report"""
report = []
report.append("=== Autonomous Humanoid System Evaluation Report ===\n")
# Response time metrics
if self.metrics['response_time']:
avg_response = np.mean(self.metrics['response_time'])
report.append(f"Average Response Time: {avg_response:.3f}s")
report.append(f"Response Time Std Dev: {np.std(self.metrics['response_time']):.3f}s\n")
# Task success rate
if self.metrics['task_success_rate']:
avg_success_rate = np.mean(self.metrics['task_success_rate'])
report.append(f"Average Task Success Rate: {avg_success_rate:.2%}\n")
# Navigation metrics
if self.metrics['navigation_accuracy']:
nav_results = self.metrics['navigation_accuracy'][-1] # Latest result
report.append(f"Navigation Success: {nav_results['success']}")
report.append(f"Navigation Execution Time: {nav_results['execution_time']:.3f}s")
report.append(f"Path Efficiency: {nav_results['path_efficiency']:.2%}\n")
# Object detection metrics
if self.metrics['object_detection_accuracy']:
det_results = self.metrics['object_detection_accuracy'][-1] # Latest result
report.append(f"Detection Precision: {det_results['average_precision']:.2%}")
report.append(f"Detection Recall: {det_results['average_recall']:.2%}")
report.append(f"Detection F1 Score: {det_results['average_f1']:.2%}\n")
# System statistics
total_evaluations = sum(len(v) for v in self.metrics.values())
report.append(f"Total Evaluations Performed: {total_evaluations}")
return "\n".join(report)
# Example usage
def run_evaluation():
evaluator = SystemEvaluator()
# Simulate some evaluations
# In practice, you would connect to your actual system components
print(evaluator.generate_evaluation_report())
if __name__ == "__main__":
run_evaluation()
Assessment Rubric
Capstone Project Evaluation Criteria
| Component | Weight | Criteria |
|---|---|---|
| System Integration | 25% | Successful integration of speech, planning, navigation, perception, and manipulation |
| Speech Interface | 15% | Natural language understanding and command execution |
| Navigation System | 15% | Path planning and execution accuracy |
| Perception System | 15% | Object detection and pose estimation quality |
| Manipulation System | 15% | Grasping and placement success rate |
| System Performance | 10% | Response time, reliability, and efficiency |
| Documentation | 5% | Clear documentation and code quality |
Grading Scale
- A (90-100%): All components fully functional with high performance, exceptional integration
- B (80-89%): All components functional with good performance, minor integration issues
- C (70-79%): Most components functional with acceptable performance, noticeable integration gaps
- D (60-69%): Basic functionality present but with significant performance or integration issues
- F (<60%): Major components missing or non-functional
Resources and Further Reading
- NVIDIA Isaac Documentation
- ROS 2 Navigation Stack
- OpenAI API Documentation
- Computer Vision Models for Robotics
- Humanoid Robot Control Systems
Conclusion
This capstone project demonstrates the integration of multiple complex systems into a unified autonomous humanoid pipeline. Successfully completing this project shows proficiency in:
- Multi-modal AI integration (speech, vision, planning)
- Real-time system orchestration
- Hardware-software co-design considerations
- Performance optimization across system components
- End-to-end system validation and evaluation
The skills developed in this project form the foundation for advanced robotics research and development in the field of physical AI and humanoid robotics.