Shadman Azad
Professor Bubrow
ENGL 21007
14 May, 2025
Roombas: How AI Keeps Your Home Clean
History of the Roomba
1990-2002: Invention & Launch
iRobot was founded by MIT roboticists (Colin Angle, Helen Greiner, Rodney Brooks). The company originally focused on developing robots for military use before launching the first Roomba (2002), a robot vacuum that followed a random cleaning path selling over 1 million units.
2004–2015: Smarter Navigation
Roomba Discovery (2004) introduced lighthouse barriers, while the Roomba 980 (2015) launched vSLAM mapping for better cleaning.
2018–Present: AI & Smart Home Integration
Roomba i7+ (2018) added Smart Mapping, and the j7+ (2021) introduced AI obstacle avoidance (ex: avoiding cords, pet waste, etc.).
Key Figures
Colin Angle (CEO): Led iRobot’s shift to consumer robots.
Joe Jones: Developed the Roomba’s randomized cleaning algorithm.
Chris Jones (CTO): Innovated the use of AI navigation in modern Roombas.
How Do Roombas Work?
Mapping and Path Optimization
Newer Roombas, like the Roomba i7+, j7+ and s9+, use AI and computer vision to recognize objects like cords, pet waste, and furniture.
They learn from past experiences, using reinforcement learning to improve their cleaning paths.
Obstacle Avoidance
The Roomba j7+ uses PrecisionVision Navigation, which uses a front-facing camera and AI object recognition to avoid obstacles before hitting them.
User Feedback Integration
Users can help the Roomba learn faster by labeling objects in the app, like a shoe, in order to avoid it in future cleanings.
Key Definitions
Infrared Sensor – A sensor that uses infrared light to detect nearby objects and walls, helping the Roomba avoid bumping into things.
Cliff Sensor – Detects drop-offs (like stairs) by sensing changes in height, so the Roomba doesn’t fall.
Bump Sensor – A physical sensor that tells the Roomba it has hit something, triggering it to change direction.
Optical Encoder – A device on the wheels that tracks distance traveled, helping the Roomba understand how far it’s moved.
Behavior-Based Programming – A programming style where the robot reacts to its environment using simple rules (e.g., turn when you hit something).
Smart Mapping (on advanced models) – A feature that allows the Roomba to remember room layouts and clean specific areas on command.
What is Machine Learning?
AI (Artificial Intelligence): Is able to perform tasks that use human intelligence like problem solving, decision making, and speech recognition.
Machine Learning (ML): A type of AI that helps systems to learn from data and improve over time without reprogramming. It uses algorithms to identify patterns and make predictions.
Deep Learning (DL): A type of ML using neural networks with many layers to solve complex problems like image and speech recognition.
Types of Learning
Supervised Learning: Trains on labeled data to predict outcomes.
Unsupervised Learning: Finds patterns in unlabeled data.
Reinforcement Learning: Learns by receiving feedback (rewards/penalties) from interactions.
How does the Roomba use Machine Learning?
Navigation and Mapping: Roombas with advanced features, like the i7+ or s9+, use machine learning algorithms to map and learn the layout of a home. They analyze room shapes, obstacles, and furniture placement to clean efficiently and adapt to changes in the environment.
Path Planning: Using ML, Roombas can improve their path-planning strategies. They learn the best routes to avoid re-cleaning areas or missing spots and gradually improve their cleaning efficiency as they gather data over time.
Dirt Detection: Roombas use sensors and machine learning models to detect dirtier areas (ex: high-traffic zones) and focus extra cleaning power there. This helps prioritize cleaning in areas that need it the most.
Obstacle Recognition: Some models use vision-based systems that use machine learning to better recognize obstacles like furniture or cables, helping the Roomba navigate without getting stuck.
FAQs
How does AI improve Roomba’s cleaning?
Roombas use machine learning to analyze floor layouts, remember obstacles, and optimize cleaning paths over time. Advanced models (like the j7+) even recognize objects like shoes or cords to avoid them.
Can Roombas learn my home’s floor plan?
Yes! Models with vSLAM navigation (ex: i7+, j7+, s9+) create and save smart maps in the iRobot app, allowing for only room cleaning and zones to avoid.
Do Roombas get smarter with use?
Somewhat. They adapt by remembering high-traffic areas and adjusting suction power for carpets vs. hard floors. However, they don’t “learn” like a human. The improvements come from pre-programmed algorithms.
The Way of the Future
Roombas demonstrate that machine learning can effectively automate real household tasks which sets the way for future domestic robots beyond just vacuuming.
Their AI navigation systems that handle unpredictable home layouts and obstacles are becoming the standard for service robots in hospitals, hotels and public spaces.
Their ability to continuously improve through use by remembering room layouts and cleaning patterns, Roombas showcase how consumer devices can actually get smarter with use through machine learning.
The AI and sensor technologies developed for Roombas are now being adapted for more sophisticated service robots in eldercare, retail and other industries.
References
IntechOpen. (n.d.). Mobile robots.
https://mts.intechopen.com/storage/books/3751/authors_book/authors_book.pdf
iRobot. (n.d.). Create® 3 robot hardware overview. iRobot Education.
https://iroboteducation.github.io/create3_docs/hw/overview
Hobbs, T. (2017, June 9). Inventor of Roomba, a southwest Missouri native, to release new robot. Springfield News-Leader.
The Zebra. (n.d.). How Roomba works.
https://www.thezebra.com/resources/home/how-roomba-works
iRobot Thailand. (n.d.). About iRobot.
https://irobotthailand.com/en/for-the-home/about-irobot
Alpaydin, E. (2020). A summary of artificial intelligence, machine learning, and deep learning [Figure]. ResearchGate.
