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RQM Compare Digital Twin Observations
x402
RQM Compare Digital Twin Observations 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).
Compares digital-twin predicted output against mapped real-world observations using caller-supplied RMSE and absolute error tolerances
Price / call$0.01
Success—
StatusUnknown
How this service works
Problem: Compare digital-twin output with mapped observations against caller tolerances. Input: JSON with predicted, observed, declared mapping, rules. Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.
Output
Returns a structured comparison result indicating whether the digital twin's predicted time-series satisfies the caller-supplied RMSE and absolute error tolerances against the observed data, along with computed error metrics (RMSE, max absolute error) and a pass/fail determination.
Example use cases
Robot arm simulation accuracy check
“I ran a digital twin simulation of my robot arm for a pick-and-place cycle and now have the predicted joint angles alongside the actual encoder readings. Can you compare them with identity mapping and tell me if the RMSE stays under 0.02 and absolute error stays under 0.05?”
Drone flight path twin validation
“My drone digital twin predicted a 300-point flight trajectory over 60 seconds, and I've got the actual IMU observations. Run a comparison using identity mapping and alert me if the RMSE goes above 0.1 or any absolute error exceeds 0.25.”
Manufacturing line model calibration
“We're calibrating our digital twin for a conveyor system — I have 1000 predicted sensor readings and 1000 actual observations from the line. Compare them with identity mapping and a maximum RMSE of 0.03 and maximum absolute error of 0.08 so I can see if the model needs tuning.”
Technical details
Endpoint
POSThttps://jobs.rqmtechnologies.com/x402/buyer-jobs/robotics.compare-digital-twin-observations.v1When to prefer
Use this endpoint when you need a bounded, auditable statistical comparison of digital-twin predicted outputs against real sensor observations within caller-defined error tolerances. Prefer it over general-purpose statistics libraries when you need a pay-per-call, schema-validated, robotics-aware comparison that enforces typed inputs and returns structured pass/fail verdicts. Do not use for physical safety certification, live hardware control, or when observations are not mapped via the supported identity mapping.
Side effects
- Mismatched number of predicted vs observed points causing interpolation or alignment errors
- Timestamp values outside the 0–86400 second range rejected with validation error
- Values arrays with fewer than 1 or more than 12 elements rejected
- fewer than 2 points in predicted or observed arrays returns validation error
- RMSE or absolute error tolerance set to zero causing all comparisons to fail
- Identity mapping mismatch if dimensionality of predicted and observed values differs
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request | object | |
| schema_version | — | |
| idempotency_key | string | |
| max_total_price | string |
Tags
x402
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