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RQM Calibration Quality Assessment
x402
RQM Calibration Quality Assessment is a paid API for AI agents from jobs.rqmtechnologies.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).
Assesses a bounded set of calibration observations against user-supplied residual, coverage, and consistency rules to determine if a calibration dataset meets quality thresholds.
Price / call$0.01
Success—
StatusUnknown
How this service works
Problem: Assess this bounded calibration dataset against the supplied residual, coverage, and consistency rules. Input: JSON with length unit, observations, rules. Result: pass, fail, or indeterminate verdict, residual metrics, per-rule evaluations. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.
Output
Returns a structured assessment indicating whether the calibration dataset passes or fails each supplied rule (RMSE, absolute error, axis span, observation count), along with computed metrics such as root mean square error, maximum observed absolute error, axis coverage span, and per-observation residuals to help diagnose calibration quality.
Example use cases
Robot arm end-effector calibration check
“I just collected 80 calibration observations for my robot arm in millimeters. Can you assess the calibration quality against these rules: max RMSE of 2.0 mm, max absolute error of 4.0 mm, minimum axis span of 100 mm, and at least 10 observations? Here are my expected and observed 3D point pairs.”
Camera-to-robot hand-eye calibration validation
“I need to verify my hand-eye calibration for a pick-and-place robot. I have 30 expected vs observed 3D points measured in centimeters. Please check them against a max RMSE of 0.5 cm, max absolute error of 1.0 cm, minimum axis span of 20 cm, and minimum 15 observations.”
Pre-deployment sensor calibration gate
“Before deploying my mobile robot, I want to gate on calibration quality. Run a quality assessment on these 120 calibration observations in meters — enforce max RMSE 0.01 m, max absolute error 0.02 m, minimum axis span 1.0 m, and at least 20 observations required.”
Technical details
Endpoint
POSThttps://jobs.rqmtechnologies.com/x402/buyer-jobs/robotics.assess-calibration-quality.v1When to prefer
Use this endpoint when you need deterministic, rule-based quality assessment of a specific bounded calibration dataset with explicit expected and observed 3D point pairs against caller-defined thresholds for RMSE, absolute error, coverage span, and minimum observation count. Prefer it over manual computation when you need a structured pass/fail result and per-rule diagnostics in a robotics or precision measurement workflow. Do not use it for certifying physical calibration states not captured in the submitted observations, or as a universal calibration certification service.
Side effects
- Fewer than 3 observations provided — request rejected with validation error
- Observation count exceeds 10,000 — request rejected
- Invalid length_unit value outside enum ['m','cm','mm'] — schema validation failure
- Threshold values set to zero or negative — rejected by exclusiveMinimum constraints
- Expected or observed arrays do not have exactly 3 elements — schema validation failure
- observation_id missing or exceeds 64 characters — schema validation error
- Network or payment failure (x402 protocol) causing request not to be submitted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request | object | |
| schema_version | — | |
| idempotency_key | string | |
| max_total_price | string |
Tags
x402
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