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Humanoid · Industrial · Autonomous

Insurance forautonomous systems

Humanoid robotics · Industrial robotics · Autonomous machines · AI-driven systems. When a machine leaves the safety cage and enters a shared human environment, the risk profile changes category, from equipment damage to bodily injury, product liability and continuous operational exposure. 1B underwrites the fleet, not the unit.
Risk class
Robotics Insurance
Focus
Humanoid · Industrial · Autonomous
Related products
NOVA
Operating model
Verify → Monitor → Prevent → Recover → Insure
Autonomous system · test cellIDLE
OPERATING ENVELOPE · PERCEPTION SWEEP · PROXIMITY
The gap

Why the traditional market struggles

Industrial robotics has decades of actuarial history behind fixed, caged, deterministic machines. Embodied autonomy has almost none. A humanoid operating alongside people generates a liability surface that combines motor risk, product liability, employers' liability and software failure in a single event, and the fleet learns, so yesterday's loss data describes a system that no longer exists.

Exposure surface

What we structurearound.

The risk surfaces that shape the programme. Cover is structured per counterparty, this is the map, not the wording.

Bodily injury in shared environments

Third-party and employee injury where machines and people occupy the same space.

Product liability

Claims against the manufacturer arising from design, software or component failure in the field.

Fleet-wide software defect

Systemic exposure where a single update propagates a fault across an entire deployed population.

Property and business interruption

Damage to the machine, to surrounding assets, and interruption of the process it serves.

Perception and navigation failure

Sensor degradation, edge-case misclassification and control loss in unstructured environments.

Cyber-physical compromise

Remote interference with control systems, teleoperation channels or fleet management infrastructure.

What changes when the operator leaves the loop

Human-operatedAutonomous
DecisionA person, in the momentA model, against a policy written earlier
FailureHuman error, one machine at a timeBehaviour under an input nobody anticipated
EvidenceAn incident report, after the eventTelemetry, continuously, before and after
Unit of lossThe machine that failedEvery machine running the same build
ResponsibilityOperator and employerDesign, software, integration and deployment
How 1B operates here

The loop, appliedto this class.

The same five stages, calibrated to the specific evidence this risk class produces.
  1. Verify

    Assessment of safety architecture, operational design domain, certification posture and failure modes.

  2. Monitor

    Telemetry-informed view of fleet hours, intervention rate, incident class and software version spread.

  3. Prevent

    Deployment envelope conditions, staged rollout requirements and intervention thresholds.

  4. Recover

    Coordinated response across injury, investigation, recall exposure and fleet grounding decisions.

  5. Insure

    Programme structured across the manufacturer, the operator and the deployment site.

Continuous signals

What stays under observation once the risk is bound.

  • Operational design domain
  • Fleet hours and utilisation
  • Intervention and disengagement rate
  • Software version distribution
  • Proximity to human operators
  • Certification and standards posture
  • Field incident classification
Built for
  • Humanoid robotics developers
  • Industrial automation manufacturers
  • Logistics and warehouse operators
  • Autonomous machinery fleets
  • Robotics-as-a-service platforms
Robotics Insurance

This risk isalready live.The insuranceshould be too.