
A premium all-round candidate when navigation, low-profile access, thresholds and broad automation all matter.
Open robot profile →Large homes stress navigation, battery strategy and maintenance more than small flats do. The robot needs to cover a lot of floor reliably, recover from interruptions and ideally reduce the amount of bin, mop and dock attention required between runs.

Battery endurance matters, but intelligent recharge-and-resume behaviour can matter just as much.
A large self-empty or full-service dock is more valuable when cleaning volume is high.
Thresholds and transitions can split a large floorplan into inaccessible zones if the chassis cannot cross them.
No mainstream robot vacuum climbs a normal staircase, so multi-storey homes still need the robot moved between floors unless you own more than one.
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 premium all-round candidate when navigation, low-profile access, thresholds and broad automation all matter.
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A strong high-automation alternative to compare for larger mixed-use homes.
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A premium MOVA option worth considering when large-home automation and navigation are central requirements.
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A high-end large-home comparison point with an emphasis on navigation, threshold capability and dock automation.
Open robot profile →It can usually save multiple maps, but it cannot climb a normal staircase. You still need to carry it between floors or use more than one robot.
Runtime claims are useful context, but actual coverage depends on power mode, floor type, mopping and route efficiency. Recharge-and-resume can make a shorter quoted runtime less limiting.
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.