Airports, hospitals and large stores spend a great deal on people whose main job is telling someone where to go. It is the most-attempted service robotics application and the one with the worst track record.
The difficulty is not navigation. It is that the robot is judged socially: it has to handle being surrounded, blocked, tested and talked to by people with no training and no patience, and a machine that is merely adequate at this reads as broken.
Requirements are stated with the reason they exist. A requirement without a reason is a preference, and preferences are how a shortlist quietly becomes a recommendation.
People do not behave like obstacles; they step in front of the robot to see what it does.
In an airport that is a real multilingual requirement, not a demo in English.
The people most helped by this are the ones a badly placed touchscreen excludes.
A robot that needs a minder has moved the staffing cost rather than removed it.
It records the public in a public space. That is a compliance question before it is a product question.
These are not weaknesses to trade off against a high score. Any one of them ends the conversation, which is why they sit outside the scoring entirely.
Shipping robots in our catalogue whose class matches this job, listed by published RV Score so you can scan them — not a recommendation. No single dimension dominates a job this ill-defined, so we do not pretend a lens applies.
Order follows the published RV Score. Requirements and disqualifiers above are editorial; they do not remove robots from this list. How the score works
Compare candidatesAttempted for longer than anything else here and abandoned more often. Units get bought for the opening, photographed, and quietly parked within a year — not because they fail, but because being slightly worse than a signpost is a worse outcome than having no robot at all. Where it holds up, the robot has one narrow errand rather than a conversational role. Our catalogue now lists service platforms that can appear on this shortlist; treat candidate fits as starting points, not proof the venue problem is solved.
