Companion-Robot Self-Audit: A Decision Tree Before You Buy or Expand the Setup becomes much easier to judge when the household writes down the job first and the technology second. This article focuses on the conditions that change the answer: who uses it, where it lives, how it fails, what must be maintained and which claims need current evidence.

Branch 1: Can you name the primary role?

If no, stop. Do not buy a general promise of companionship. If yes, write one measurable outcome such as easier remote calls or fewer missed low-risk reminders. That outcome becomes the filter for every later feature decision.

Branch 2: Does the role require mobility?

If the task works from one fixed location, compare a stationary device before paying for navigation. If mobility is necessary, test thresholds, rugs, pets, narrow routes and docking. The cost is not only money; it is maps, maintenance and new failure modes.

Branch 3: Does the sensing match the role?

If the product requires cameras, microphones or cloud history beyond what the job needs, either restrict those features or choose another product. If sensing is proportionate, verify understandable indicators, mute controls, retention and account access.

Branch 4: Can a wrong AI answer create a serious consequence?

If yes, keep that decision with a human or authoritative source and limit automation. If no, define an easy correction path. This branch separates entertainment or low-risk assistance from medical, financial, emergency and other high-impact domains.

Branch 5: What happens without internet or the owner phone?

If the household loses all useful functions or support paths, decide whether that dependency is acceptable. Preserve human contact methods and physical controls. If local behavior remains useful, document exactly what survives and how the user knows the robot’s state.

Branch 6: Who maintains it after month three?

If nobody owns charging-area upkeep, updates, account recovery, map changes and privacy review, the setup is not operationally complete. Assign responsibility or reduce the robot’s role until the maintenance burden matches household capacity.

Branch 7: Is there a clean exit?

If subscriptions, proprietary accessories or data lock-in make exit difficult, include that cost in the purchase decision. Keep enough account and model information to remove the robot, delete data where supported and restore a simple human routine.

Working decision notebook

Primary Role

For primary role, anchor the check in a home with cameras, microphones, movement and cloud services. Observe one normal use, then one ordinary failure; note which part of rooms, docking, updates and support life becomes harder and who has to intervene. Mark each check pass, fail or unknown; an unknown is a real finding, not an invitation to guess. Keep the result with the model or setup notes so a future review of companion robots starts from evidence rather than memory.

Mobility Need

Treat mobility need as an operating question inside privacy, navigation, account roles and human fallback. Ask another household member to repeat the task without coaching and record where privacy, navigation, account roles and human fallback introduces hesitation, work or ambiguity. Mark each check pass, fail or unknown; an unknown is a real finding, not an invitation to guess. If the test cannot be repeated, the conclusion is too fragile to support a long-term companion robots decision.

Sensing Proportionality

Put sensing proportionality on paper before changing anything in daily companionship without granting unsafe authority. Write down the expected behavior, reproduce it once, and then remove one convenience so the dependency on rooms, docking, updates and support life becomes visible. Mark each check pass, fail or unknown; an unknown is a real finding, not an invitation to guess. A passing result should be explainable in plain language by somebody who did not configure the system.

Ai Consequence

Use AI consequence to challenge the comfortable assumption around rooms, docking, updates and support life. Follow the process from the user’s first action through recovery, including any hidden work created by privacy, navigation, account roles and human fallback. Mark each check pass, fail or unknown; an unknown is a real finding, not an invitation to guess. If the answer depends on a vendor service, record the date and the exact support assumption that makes it true.

Offline Dependency

Review offline dependency from the perspective of someone managing a home with cameras, microphones, movement and cloud services. Separate what the manual promises from what the household actually sees, especially where rooms, docking, updates and support life can change the result. Mark each check pass, fail or unknown; an unknown is a real finding, not an invitation to guess. Define a stop point so repeated trial and error does not turn a routine issue into a larger safety, privacy or access problem.

Second-pass stress test

Primary Role: retest

Use primary role to challenge the comfortable assumption around a home with cameras, microphones, movement and cloud services. Observe one normal use, then one ordinary failure; note which part of rooms, docking, updates and support life becomes harder and who has to intervene. Mark each check pass, fail or unknown; an unknown is a real finding, not an invitation to guess. Keep the result with the model or setup notes so a future review of companion robots starts from evidence rather than memory. Recheck the same point after a small household change; the difference between the two observations is often more informative than either snapshot alone.

Final acceptance test

Use Companion-Robot Self-Audit: A Decision Tree Before You Buy or Expand the Setup as a decision record rather than a shopping conclusion. On the first page, write the current assumptions for primary role, mobility need and sensing proportionality; on the second, record the evidence for AI consequence and offline dependency. Keep observed facts separate from vendor promises and from household preferences. During the first weeks of use, note the exceptions that consume the most attention. A recurring exception is not 'just user error' until the routine, interface and environment have been checked. If the same person struggles at the same step, redesign the step. If the same service dependency causes uncertainty, document the offline or human alternative. If a physical condition creates resistance or hazard, stop trying to solve it with software. The best final configuration is the one whose trade-offs are visible enough that another household member could explain why it was chosen and when it should be reconsidered. Keep the record with the model or configuration details rather than in a private chat thread. On the next review, start by checking what changed since this test instead of repeating every assumption from scratch. That makes maintenance faster and helps the household distinguish a real new risk from a familiar condition that has already been tested.

Questions before committing

What causes an immediate stop in the tree?

No defined role, unacceptable privacy burden, unsafe authority or no workable fallback for a high-consequence use.

Is mobility always an upgrade?

No. It is valuable only when the role requires room-to-room presence enough to justify navigation complexity.

How should AI errors be handled?

Limit authority according to consequence and preserve a human correction/escalation route.

Who owns maintenance?

A named household adult or operator; 'someone will handle it' is not a complete plan.

What is a good exit plan?

One that preserves human function, allows account/data cleanup where supported and does not leave the home dependent on a dead service.

Boundary note

For Companion-Robot Self-Audit: A Decision Tree Before You Buy or Expand the Setup, this is consumer technology guidance, not medical, caregiving, emergency-response, legal or privacy advice and not an AI/product certification. Do not make a companion robot the sole path for high-consequence care or emergency help unless the exact service is designed and supported for that role. Review the provider’s current privacy, security, update and support terms; this 安全审计 keeps an independent human fallback as a hard boundary.

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