PHYSICAL AI RELIABILITY & SAFETY OPERATIONS
Visual perception & policy checks
Follow a video input through scene interpretation, policy evaluation, alerts and evidence review. Robot footage is the example input.
LIVE VISUAL OPERATIONS
Priority camera
SYNCHRONIZED SIGNALS
Device telemetry
Waiting for recording linkage.
EVIDENCE STREAM
Correlated activity
- Command view readyChoose a feed to start grounded analysis.system
OPERATIONS INVESTIGATOR
Ask across video, rules, logs & telemetry
Queries use bounded stored pipeline evidence; no fabricated fleet context.OBSERVED DEVICES
Pipeline device sample
Public labels hide device identifiers. Observed does not mean online.
PLATFORM RELIABILITY
Evidence processing health
CURRENT DECISION CONTEXT
Detection establishes facts. Policy decides response.
- Model profile
- Backend profile loads when started
- Analysis type
- Temporal interaction classification
- Latest interpretation
- Waiting for scene interpretation
- Evidence scope
- One tenant · one immutable pipeline identity
VISUAL PERCEPTION EXAMPLE
About this demonstration
This interactive example connects to the public visual-perception service. Real deployed models analyze recorded robot feeds, and every result is tied to evidence: an analysis response is returned only after an immutable receipt has been accepted.
Case 01 is a recorded humanoid-robot incident and Case 02 is a staged industrial work-cell recording. The separate Live MoQ transport control opens a subscriber on an isolated synthetic test channel; it shows media only while an approved publisher is publishing to that channel.
The system reports bounded visual classifications and never claims to determine intent. Safety matches require human review and are not functional-safety proof. The facility list, fleet size, map, device-health table and illustrative telemetry are simulated product context, and no robot is actuated.