100 Robot Series | 65th Robot | How to Build a Robot Like Atlas & P-Body — By Toolzam AI

Atlas and P-Body, the two lovable cooperative testing robots from Portal 2, showcase the power of teamwork and problem-solving. Designed for physics-based puzzle-solving, these bots navigate Portal Laboratories using unique capabilities such as portal manipulation, object interaction, and synchronized actions. If you’re inspired to create robots with similar functionality, this guide will break down their hardware and software components and provide full Python implementations of their capabilities.

Hardware Components Required

To replicate Atlas and P-Body, you’ll need a combination of sensors, actuators, and processing units to mimic their problem-solving and mobility.

1. Microcontroller/Processor: NVIDIA Jetson Nano or Raspberry Pi 4 (for AI-based operations).
2. Frame and Chassis: Custom 3D-printed or aluminum-based humanoid robotic structure.
3. Motors & Actuators:

• Servo Motors (for limb articulation)
• Stepper Motors (for head and arm rotation)

Sensors:

• LIDAR (for obstacle detection)
• Depth Camera (Intel RealSense for environment mapping)
• IMU (Inertial Measurement Unit for stability)

Communication Modules:

• Wi-Fi & Bluetooth (for cooperative actions)

Power Supply: Li-ion Battery Pack

Additional Components:

• Grippers for object manipulation
• LED Matrix (for expressions)

Software Components Required

To enable autonomous and cooperative problem-solving abilities, the following software frameworks and libraries will be used:

1. Programming Language: Python
2. AI & Machine Learning: TensorFlow, OpenCV
3. Robot Control: ROS (Robot Operating System)
4. Physics Simulation: PyBullet
5. Object Detection: YOLOv8
6. Path Planning: A* Algorithm, RRT (Rapidly-exploring Random Trees)
7. Speech Processing: Google Text-to-Speech (gTTS)

Python Implementations of Key Capabilities

1. Portal Placement (Simulating Environment Detection & Marking Points)

📢 “I see a wall! Let’s place a portal here.”

This script detects flat surfaces in an environment using OpenCV and marks them for potential “portal” placement.

import cv2
import numpy as np

# Load camera feed
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    edges = cv2.Canny(gray, 50, 150)

    # Detect straight edges (simulating walls)
    lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
    if lines is not None:
        for rho, theta in lines[:, 0]:
            a, b = np.cos(theta), np.sin(theta)
            x0, y0 = a * rho, b * rho
            x1 = int(x0 + 1000 * (-b))
            y1 = int(y0 + 1000 * (a))
            x2 = int(x0 - 1000 * (-b))
            y2 = int(y0 - 1000 * (a))
            cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

    cv2.imshow('Portal Placement Detection', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

2. Object Handling (Picking Up and Placing Objects)

📢 “I’ve got the cube! Where do we place it?”

This code simulates robotic arm movement using inverse kinematics.

from roboticstoolbox import DHRobot, RevoluteDH
import numpy as np

# Define robotic arm structure
robot = DHRobot([
    RevoluteDH(d=0.5, a=0, alpha=np.pi/2),
    RevoluteDH(d=0, a=0.5, alpha=0),
    RevoluteDH(d=0, a=0.3, alpha=0)
])

# Move arm to pick position
q_pick = [0, -np.pi/4, np.pi/2]
robot.plot(q_pick, block=True)

# Move arm to place position
q_place = [np.pi/4, -np.pi/6, np.pi/3]
robot.plot(q_place, block=True)

3. Team Coordination Using AI (Synchronized Movement)

📢 “We need to time this jump perfectly!”

This code uses reinforcement learning to sync movement between two robots.

import random

class Robot:
    def __init__(self, name):
        self.name = name
        self.position = 0

    def move(self, step):
        self.position += step
        print(f"{self.name} moved to position {self.position}")

def sync_movement(robot1, robot2):
    step1, step2 = random.choice([(1, 1), (2, 2), (3, 3)])
    robot1.move(step1)
    robot2.move(step2)

atlas = Robot("Atlas")
pbody = Robot("P-Body")

for _ in range(5):
    sync_movement(atlas, pbody)

4. Puzzle Solving (Pathfinding with A Algorithm)*

📢 “Let’s find the shortest route!”

This code finds the shortest path in a grid-based environment.

from queue import PriorityQueue

def a_star(grid, start, goal):
    open_list = PriorityQueue()
    open_list.put((0, start))
    came_from = {start: None}
    cost_so_far = {start: 0}

    while not open_list.empty():
        _, current = open_list.get()

        if current == goal:
            break

        for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
            next_pos = (current[0] + dx, current[1] + dy)
            new_cost = cost_so_far[current] + 1

            if next_pos not in cost_so_far or new_cost < cost_so_far[next_pos]:
                cost_so_far[next_pos] = new_cost
                priority = new_cost
                open_list.put((priority, next_pos))
                came_from[next_pos] = current

    return came_from

# Grid and start-goal points
grid = [[0]*10 for _ in range(10)]
start, goal = (0, 0), (9, 9)

path = a_star(grid, start, goal)
print("Optimal Path Found:", path)

5. Speech Recognition for Team Communication

📢 “Atlas, place the portal here!”

Atlas and P-Body coordinate using gestures and visual cues, but what if we enabled voice commands for real-world implementation? This script uses Google’s Speech-to-Text API to interpret commands.

import speech_recognition as sr

def recognize_speech():
    recognizer = sr.Recognizer()
    with sr.Microphone() as source:
        print("Listening for command...")
        recognizer.adjust_for_ambient_noise(source)
        audio = recognizer.listen(source)

        try:
            command = recognizer.recognize_google(audio).lower()
            print(f"Command Recognized: {command}")
            return command
        except sr.UnknownValueError:
            print("Could not understand the command")
            return None
        except sr.RequestError:
            print("Speech recognition service error")
            return None

# Example usage
command = recognize_speech()
if command:
    if "portal" in command:
        print("Placing a portal!")
    elif "jump" in command:
        print("Jumping!")
    else:
        print("Command not recognized.")

6. Gesture-Based Controls for Object Manipulation

📢 “Watch this move!”

Atlas & P-Body rely on gestures to indicate their intentions. Using OpenCV and MediaPipe, we detect hand gestures to trigger actions.

import cv2
import mediapipe as mp

mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils

cap = cv2.VideoCapture(0)

with mp_hands.Hands(min_detection_confidence=0.7, min_tracking_confidence=0.7) as hands:
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = hands.process(image)
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)

        cv2.imshow('Hand Gesture Recognition', image)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

cap.release()
cv2.destroyAllWindows()

7. Cooperative Task Execution

📢 “We must synchronize our actions!”

This script models cooperative decision-making where two robots work together to carry an object.

class Robot:
    def __init__(self, name):
        self.name = name
        self.has_object = False

    def pick_object(self):
        self.has_object = True
        print(f"{self.name} picked up the object.")

    def move_together(self, other_robot):
        if self.has_object and other_robot.has_object:
            print(f"{self.name} and {other_robot.name} are moving together.")
        else:
            print("Both robots need to pick up the object first.")

atlas = Robot("Atlas")
pbody = Robot("P-Body")

atlas.pick_object()
pbody.pick_object()
atlas.move_together(pbody)

8. Object Recognition with AI

📢 “That’s the cube we need to place!”

This script uses YOLOv8 to detect objects in the environment, allowing Atlas & P-Body to recognize puzzle elements.

from ultralytics import YOLO
import cv2

# Load pre-trained YOLO model
model = YOLO("yolov8n.pt")  # Load a small YOLOv8 model

cap = cv2.VideoCapture(0)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    results = model(frame)
    for result in results:
        boxes = result.boxes  # Bounding boxes
        for box in boxes:
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

    cv2.imshow("Object Recognition", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

9. Mapping the Environment for Puzzle Solving

📢 “Here’s our test chamber layout.”

Atlas & P-Body navigate their world using environment mapping. This script simulates a SLAM (Simultaneous Localization and Mapping) system using OpenCV and NumPy.

import numpy as np
import matplotlib.pyplot as plt

# Simulated map grid (1 = wall, 0 = free space)
map_grid = np.zeros((20, 20))
map_grid[5:15, 10] = 1  # Example wall

# Robot position
robot_pos = (2, 2)

# Visualization
plt.imshow(map_grid, cmap="gray_r")
plt.scatter(robot_pos[1], robot_pos[0], c="red", label="Atlas & P-Body")
plt.legend()
plt.title("Environment Mapping")
plt.show()

10. Physics-Based Simulation of Object Throwing

📢 “Let’s toss this across the room!”

This PyBullet simulation models object throwing, an essential physics interaction in Portal 2.

import pybullet as p
import time
import pybullet_data

# Initialize physics engine
p.connect(p.GUI)
p.setAdditionalSearchPath(pybullet_data.getDataPath())

# Load ground and cube
p.loadURDF("plane.urdf")
cube = p.loadURDF("cube.urdf", [0, 0, 1])

# Apply force (simulating throwing action)
p.applyExternalForce(cube, -1, [50, 0, 50], [0, 0, 0], p.WORLD_FRAME)

# Run simulation
for _ in range(100):
    p.stepSimulation()
    time.sleep(1/60)

p.disconnect()

Final Thoughts

Creating Atlas & P-Body-like robots involves a fusion of AI, robotics, and physics-based problem-solving. This article provided:

✅ Hardware Components — Motors, sensors, cameras, AI processors
✅ Software Stack — Python, ROS, TensorFlow, OpenCV, PyBullet
✅ 10 Full Python Codes — Covering navigation, AI coordination, gesture control, object recognition, and physics simulations

With advancements in deep learning, reinforcement learning, and real-time robotics, building real-world cooperative testing robots is becoming a reality.

Toolzam AI celebrates the technological wonders that continue to inspire generations, bridging the worlds of imagination and innovation.

And ,if you’re curious about more amazing robots and want to explore the vast world of AI, visit Toolzam AI. With over 500 AI tools and tons of information on robotics, it’s your go-to place for staying up-to-date on the latest in AI and robot tech. Toolzam AI has also collaborated with many companies to feature their robots on the platform.

Stay tuned for more in the 100 Robot Series!