For the first time ever, Roombas learned to predict cleaning needs and act automatically, eliminating floorcare maintenance from users' daily routines.
Freeing Roomba Users From Floorcare with Automated Cleaning
PROJECT OVERVIEW
Building an experience around cleanliness predictions unlocked the ability for Roombas to automatically decide which rooms needed to be cleaned and how best to clean them. This introduced the ability for users to set it and forget it — leaving their Roombas to automatically decide if rooms needed to be cleaned, and prioritize them based on cleanliness.
This feature is rooted deeply in user feedback, and was a huge step towards a truly autonomous, goal-driven robot rather than an appliance that waits to be told what to do.
I led the design effort on a team of product management, research, and engineering leaders, as well as UI designers, and a copywriter. I drove discovery, rapid prototyping, and testing through production release.
STATUS
Shipped, March 2024
CONTRIBUTION
Principal Product Designer
PRODUCT
iRobot Home (Mobile App, iOS & Android)
AUDIENCE
Robot Vacuum Cleaner Users, Premium Tier
SKILLS
UX Strategy & Design
Early Concepts & Storyboarding
User Research & Interviews
Rapid Prototyping & Testing
CONTEXT
iRobot users purchase Roombas to take cleaning off their to-do list and simplify the path to a clean home.
We knew from prior research that people tend to think about where they want to clean before how or what tool they’ll use: "I need to clean the kitchen" versus "I’m going to run my Roomba today."
The original iRobot app started as a single page, centered around Roomba control.
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While our products handle the physical act of cleaning, this model left the user to decide where, when, and how to clean manually.​
As more of our new products came equipped with mapping capabilities and intelligent features, we saw an opportunity to leverage that intelligence to automate cleaning decisions for our users.
KEY INSIGHTS
Early discovery work uncovered insights about how we can better meet user expectations.
01
Match Users' Approach to Cleaning
Align with how users think about cleaning: where first, how second.
02
Offload the Mental “To-do” List
Minimize to-do lists, don’t just replace household tasks with device management.
03
Create a Frictionless Experience
Alert users when actions are required, don’t require users to check in with the app.
04
Build a Partnership with Users
Collaborate with users to achieve cleaning goals.
We decided to create an intelligent system, centered around spaces in the home, automating where, when and how to clean.
I created storyboards to architect these ideas & tested them with users to see what concepts resonated most.
We walked through 6 storyboards in a series of user interviews. We asked users to rank the stories they found most advanced, most helpful and most exciting, and also asked them to share any thoughts, feelings and reactions to the stories.​
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People were generally excited to see their robots working autonomously in the home, and were comfortable collaborating with them.
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Despite a strong interest and trust in our intelligence, most users still wanted an option to override the robot when needed
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Device integration was a nice to have, but did not spark as much excitement as we had guessed it might
With those ideas in mind, I created a series of loose wires to iterate on these broader concepts.
Wires quickly turned into hundreds of rapid low-fi prototypes focused on exploring different ways to present cleanliness insights in a useful way.
We tested each phase of these designs iteratively with users.
Here’s a progression of how the experience evolved over time.
We tested various heatmaps, color schemes, scoring systems - from granular numerical scores to emojis. We tested options that surfaced the why behind the prediction - our back end can identify a closed door, as well as obstacle types.
Our testing showed that the map visualization of cleanliness was intuitive for users to understand. While most users believed in our intelligence, most still wanted the ability to override our predictions and create their own routines, confirming the delicate balance between automation and control.
Ultimately we went with a simple, easy to scan visualization of dirtiness by room paired with the ability to clean them directly.
SOLUTION
We delivered a user-centered experience that matches how people actually think about cleaning while leveraging our intelligence to automate cleaning routines.
INTRODUCING: DIRT DETECTIVE​
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Cleanliness predictions, powered by the Dirt Detective algorithm, introduced the ability for Roombas to automatically decide if rooms needed to be cleaned, and prioritize them based on cleanliness.
Cleaning, Simplified
Users can monitor their home's cleanliness in real-time through the new My Home tab. From there, they can instantly clean dirty rooms or create intelligent recurring schedules to automatically maintain what needs attention — removing floorcare from their to-do list entirely and keeping floors consistently clean.
Tailored to Your Home
By asking users about secondary factors in their home in Home Settings, we could train our intelligence to more accurately reflect ground truth.
WHAT'S NEXT?
As we moved into 2025 we continued focusing our efforts on bringing users a space-centered, intelligent app purpose built to simplify their path to a clean home.
Smart Cleaning Settings
In Q1 2025, we released Smart Cleaning Settings to automatically choose options like vacuum suction level, mopping strength and number of passes based on predicted cleanliness of the space. These settings could be paired with "Clean the Dirty Rooms", or could be applied to any custom cleaning routine.
Smart Notifications
Situations come up where we need users’ help to ensure we can keep their homes clean.
We have designed a out-of-app notifications strategy to provide clear and actionable requests for assistance when needed, further building a partnership with our users.
(No release date set.)
CLOSING NOTES
This project highlighted the power of lock-step collaboration between design, engineering and robotics – exploring what is possible, ensuring the experience works as expected and refining the API to best support user needs and expectations. By centering around user behavior and feedback, we were able to build a simple yet powerful system, while setting the stage for future innovation.
With every new release, we learn more about the delicate balance between control and automation, and continue working together to bring our users the support they need to achieve a cleaner more comfortable living space.