
A strong value comparison when mopping and low-maintenance dock ownership are central to the decision.
Open robot profile →Hard-floor homes often benefit more from mopping quality, edge reach and repeated low-effort cleaning than from maximum carpet-focused suction. The right choice depends on whether you mainly want dust collection or a robot that genuinely reduces manual mopping too.

Separate vacuum pickup from mopping performance; a robot can be strong at one and average at the other.
Edge and corner reach matters because hard-floor dust is visible along skirting and kitchen edges.
Hot-water washing, drying and detergent dosing can reduce mop maintenance, but they increase dock complexity.
Small-object avoidance is valuable in kitchens and family rooms where clutter can interrupt cleaning runs.
These are starting points, not a universal ranking. The personalised matcher can promote a different robot when your home gives different requirements more weight.Starting points only — Home Fit can change the order for your home.

A strong value comparison when mopping and low-maintenance dock ownership are central to the decision.
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A premium all-in-one candidate for shoppers prioritising automated hard-floor maintenance.
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Worth comparing where mopping technology and everyday maintenance matter more than buying the most expensive flagship.
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A premium mixed-floor option if your hard-floor home also demands strong navigation, low furniture access or threshold handling.
Open robot profile →For routine maintenance they can dramatically reduce manual work. Sticky spills, grout, heavily soiled areas and edges may still need occasional manual cleaning.
Both can work well. The important question is the complete system: pressure, water management, edge reach, pad or roller cleaning in the dock, and real-world evidence.
A robot can gain capabilities and still become a worse buy for a particular household if those capabilities cost money without solving that household's problems. RightRobot keeps evidence, Home Fit and current shopping routes as separate layers so the recommendation can explain both fit and value.