[ 01 / SIGHTLINE ]
Sensing feasibility.
Estimates whether the proposed viewpoint and sensing conditions meet an explicit inspection requirement—visibility, resolution, or defect detectability.
ACTIVE, TASK-AWARE CAPABILITY INTELLIGENCEBUILT FOR THE PHYSICAL WORLD
AUTONOMOUS INSPECTION. RECONSIDERED.
Know what's
possible.
Before you act.
Reaching an asset isn't the same as inspecting it.
Fovra helps autonomous machines predict task success, resolve critical uncertainty, and act on better evidence.
+ LESS UNCERTAINTY. FEWER UNUSABLE INSPECTIONS.
FOVRA / INTELLIGENCE LAYERV.0.1
ACTIVE MISSION CONTROLSIMULATION
SPATIAL CAPABILITY FIELDX 042.8 Y 018.6
A01A02A03A04A05A06
UNCERTAINTY DETECTED ASSET_04
FEASIBLE ROUTE ACTIVE PROBE6 ASSETS / 2 ROUTES
Z└── X
GP POSTERIOR · TASK FEASIBILITYμ 0.87 ± 0.06
T − 30sOBSERVATION ACQUIREDNOW
00:12LIMITMAPTask-conditioned surface initializedOK
00:15SIGHTLINEAsset 04 · observability below thresholdWARN
00:18SCOUTEvaluating decision-relevant observationsRUN
00:21SCOUTAlternative viewpoint selected · ΔU > 0PROBE
00:24LIMITMAPPosterior capability estimate updatedOK
00:27PLANNERRoute B selected · evidence criteria metOK
SIMULATED TELEMETRYOBSERVE. UNDERSTAND. ADAPT.
THE MISSION ISN'T THE ROUTE.
It's the evidence you bring back.
REACH + OBSERVE + VERIFY01 / SYSTEM ARCHITECTUREThree modules.
Three modules.
One intelligent feedback loop.
From physical possibility to mission-ready evidence.
A task-aware intelligence layer—not another controller.
[ 02 / LIMITMAP ]
Spatial capability.
Stores geospatial, machine- and task-conditioned feasibility estimates and uncertainty. Compares routes and operating conditions.
[ 03 / SCOUT ]
Active inquiry.
Chooses a constrained extra observation or low-risk test when its anticipated value to the task decision exceeds its cost.
CONTINUOUS CAPABILITY UPDATEPREDICT → IDENTIFY UNCERTAINTY → OBSERVE → UPDATE → REPLAN
02 / THE MISSION, REIMAGINEDFrom uncertainty
From uncertainty
to an informed decision.
One robot. Six assets. Two possible routes.
Follow the six-beat demonstration storyboard.
MISSION REPLAY / 01ILLUSTRATIVE SCENARIO
++
TASK REQUIREMENTS DEFINED
One mission. Six accepted observations.
- 6 named inspection assets
- 1 simulated wheeled robot
- 2 candidate routes
- Explicit evidence acceptance criteria
PROOF REQUIRED
Task requirements are measurable and visible.
03 / SCIENTIFIC VALIDATION
Evidence over assumption.
Designed to be tested against strong baselines.
Not a staged success. A reproducible advantage.
| REF. | COMPARATOR | OBSERVATION POLICY | VALIDATION QUESTION |
|---|---|---|---|
| B0 | Static thresholds | Engineering checklists | Do learned models add value beyond simple rules? |
| B1 | Passive Sightline + LimitMap | No extra observation | Is collecting additional evidence useful? |
| B2 | Random safe observation | Random permitted probe | Does targeted selection beat observing more? |
| B3 | Generic information-gain | Entropy / Fisher information | Does mission-decision value beat uncertainty reduction? |
| B4 | Closest reproducible prior art | Active system-ID / traversability | Does the method improve on strong published baselines? |
| FV | Fovra full system | Task-aware active inquiry | Isolate module contributions through system ablations. |
PRIMARY METRIC
Completed inspection missions meeting required evidence criteria / total attempted missions.
EXPERIMENTAL RIGOR
Held-out scenarios. Multiple random seeds. Confidence intervals. Constraint-violation logs.
RESEARCH STATUS / PRE-MVP
Proposed validation protocol. Performance and real-world safety are not yet established.
THE NEXT LAYER OF AUTONOMYDon't just move.
Don't just move.
Know you can deliver.
Exploring task-aware capability intelligence for autonomous inspection.