Monitoring, Alerting & Maintenance

System and hardware health

• CPU, GPU, memory, storage, thermal, sensor status, network state, battery, and hardware-specific health signals.
• Detect degradation and instability before they become mission failures or safety problems.

Inference and autonomy metrics

These are the metrics that connect deployment health to learning quality.

• model latency
• frame drop rate
• policy confidence or uncertainty signals
• intervention frequency
• recovery behavior
• task completion rate

Task and operational performance

• throughput
• success and failure by scenario
• cycle time
• downtime reason
• customer or site-specific KPIs

Rollout and cohort metrics

• canary vs baseline performance
• site-specific regression patterns
• hardware revision differences
• release line performance over time

Real-time fleet view

• Fleet-wide online/offline state and current release distribution.
• Alerting for severe failures, anomalous drift, or intervention spikes.
• Useful for live operations teams and early rollout observation.

Robot and cohort drilldown

• Inspect one robot, one site, one fleet segment, or one release cohort in detail.
• Compare behavior across rollout cohorts instead of relying only on fleet-wide averages.
• Critical for understanding regressions that affect only one environment.

Incident evidence

• Bundle telemetry slices, deployment context, operator notes, and replay links into compact incident packets.
• Make post-mortem and cross-team diagnosis efficient.
• Support promotion of incidents into curation and evaluation pipelines later.
• Retain those packets according to region-, customer-, and regulatory policy when operational evidence is sensitive.

Threshold-based alerting

• latency ceilings
• thermal limits
• intervention spikes
• availability and uptime drops
• success-rate regressions

Anomaly detection

• Detect deviations from baseline cohort behavior.
• Useful when absolute thresholds are not enough or when environment-specific drift matters.

Workflow integration

• Open incident records automatically.
• Trigger maintenance triage when hardware health crosses policy thresholds.
• Pause rollout or recommend rollback when canary cohorts fail validation.

Release feedback

• Post-deploy behavior validates or invalidates the release evidence that justified the rollout.
• Canary regressions become part of the release record.

Data feedback

• Novel failure conditions, intervention-heavy episodes, or environment drift can be promoted into review and curation queues.
• High-value incidents can seed benchmark and replay packs.

Training feedback

This is the compounding loop that makes deployment improve future models rather than only deliver them.

• Teams can compare not just which model won in evaluation, but which model actually reduced intervention and failure in production.

Inputs

• **Robot agents** and runtime services emit telemetry through the Platform API.
• **Fleet Manager** provides target identity and segmentation.
• **Deployment and Update** provide release and cohort context.

Outputs

• **Maintenance System** receives diagnostic triggers and service context.
• **Evaluation & Release** receives post-deploy evidence and new regression scenarios.
• **Data Explorer** can display incidents and replay-aligned operational evidence.
• **Human-in-the-Loop Operations** can use telemetry to escalate and contextualize intervention.