Freeing Roomba Users From Floorcare
For the first time, Roombas predicted cleaning needs and handled the details — deciding where and how to clean without user input. This work shifted floorcare from manual control to a goal-driven, “set it and forget it” experience.
Status
Shipped 2024
Contribution
Principal Product Designer
Product
iRobot Home (Mobile App, iOS & Android)
Audience
Roomba Users, Premium Tier
Skills
UX Strategy & Design
Early Concepts & Storyboarding
User Research & Interviews
Rapid Prototyping & Testing
PROJECT OVERVIEW
Building an experience around cleanliness predictions enabled Roombas to automatically determine which rooms needed attention and how best to clean them, without requiring user input.
Rooted deeply in user feedback, this feature represented a major step toward a truly autonomous, goal-driven robot, rather than an appliance that waits to be told what to do. It shipped in Q1 2024 as iRobot’s first intelligent, predictive cleaning feature and marked a significant departure from the classic robot-centric app experience.
I led the design effort across product management, research, and engineering, driving discovery, rapid prototyping, and testing through production release.
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.
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.
FOUNDATIONAL EXPLORATION
Early in the project, I created six storyboards exploring how an intelligent robot could work with users, from fully autonomous cleaning as part of smart home ecosystems to collaborative partners that suggest, but wait for approval.
In interviews, I asked users to rank the storyboards. Most were excited about autonomous robots, but most still wanted some level control. I was surprised at the lack of interest shown around integration with other devices and more complex autonomy. This provided some clarity: design for collaborative intelligence, but keep humans in the loop.
I moved to loose sketches to explore how this back and forth could work.
When I tested these designs with users, they were overwhelmed.
The visualization challenge: Users looking at heatmaps and numerical scores wanted to know which rooms to clean, not to parse through data. The precise information didn't help them make decisions - it got in the way. They had to work through complexity to find the simple answer they needed: which rooms need attention?
The interaction challenge: The visualization showed which rooms were dirty, but there was a disconnect between that and what "Smart Clean" would actually do. I experimented with ways to bridge the gap: a "See Plan" button, previews that auto-played upon pressing the recommendation. Users needed both visibility and control: even users who would ultimately accept our recommendations wanted to see the plan and have a chance to edit it before starting.
Both challenges had the same root cause: I was designing to showcase our intelligence, not to help users act on it.
DESIGNING INTELLIGENCE USERS TRUST
These early explorations left two parallel design challenges: How should we visualize cleanliness data? And how should we present intelligent recommendations?
I was excited to showcase our intelligence. I brainstormed with a designer on my team and we created some early designs that leaned into complexity: detailed heatmaps showing dirt distribution, numerical scores (even emojis!) for each room, explanations of the robot's reasoning.
For recommendations, I explored variations from suggestion to prominent defaults.
It felt impressive, and like we were giving users transparency into our technology and control over how it worked.
I stripped away everything users didn't need, explicitly highlighting the main insight, which rooms need to be cleaned, placed above the map.
For the visualization: Numerical scores became a simple three-tier color system from dirty (dark green) to clean (light green). Heatmaps and detailed explanations disappeared entirely, showing users their home's cleanliness at-a-glance.
For the interaction: I designed the system to suggest, not decide. The Dirt Detective algorithm predicts which rooms need cleaning and presents it as a recommendation: "Clean Dirty Rooms" using language that connects the plan directly to the map. Users can review and edit, not just accept an automatic action.
THE APPROACH THAT WORKED
BRINGING IT ALL TOGETHER
We delivered a user-centered experience that matches how people actually think about cleaning while leveraging our intelligence to automate routines. Cleanliness predictions, powered by the Dirt Detective algorithm, introduced the ability for Roombas to automatically decide which rooms needed cleaning and prioritize them based on predicted cleanliness.
Users monitor their home's cleanliness through the My Home tab. Rooms are color-coded by dirt level, making it immediately scannable. They can clean recommended rooms with one tap, or create recurring schedules that automatically maintain their home.
Cleaning, Simplified
By asking users about factors that affect cleanliness - like pets, kids, or high-traffic areas, we trained the algorithm to more accurately predict cleaning needs for each specific home.
This improved prediction accuracy over time while keeping the interface simple: users answer a few questions once, and the robot gets smarter about their home.
Tailored to Your Home
REFLECTIONS
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Early concepts created alignment
Testing storyboards and lightweight concepts before building screens helped product, engineering, and robotics align around a shared direction early. It gave users something concrete to react to and created momentum grounded in real behavior rather than opinion.
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Trust came from simplicity, not visibility into intelligence
Early designs surfaced too much of the underlying complexity. What resonated was not seeing how the system worked, but feeling confident in what to do next. Simplifying the experience down to clear, actionable guidance significantly reduced mental effort.
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Partnership mattered more than full automation
Users welcomed intelligent recommendations as long as they stayed in control of when and how to act on them. Designing Dirt Detective as a partner — informative, optional, and transparent — helped establish trust that later enabled more advanced experiences.
WHAT'S NEXT?
The introduction of LiDAR-based robots in 2025 marked a hardware inflection point that forced a full digital reset. Supporting new capabilities meant rebuilding a new Roomba Home app from the ground up, rather than continuing to layer intelligence onto the original experience.
Dirt Detective became the intelligence engine behind recommendations in the new app, reinforcing a core insight: people trusted intelligent cleaning when it felt transparent, optional, and grounded in their home. Early expressions of that intelligence, including cleaning recommendations and later Smart Clean Settings, began in the original app, but came to life in Roomba Home.
Roomba Home centers on maps, routines, and repeatable habits, allowing guidance to show up directly within routine creation — visible when helpful and easy to ignore when not.
See how this foundational work ultimately comes to life in Roomba Home’s Routine Builder.