Decision intelligence for robotics and physical AI governs what machines decide on their own and what they escalate: it fuses robot telemetry with plant, warehouse and safety context, recommends actions like reallocating tasks or pulling a robot for service, and enforces an autonomy envelope so robots act alone only inside limits a human owner has approved.
Why physical AI needs a decision layer
Foundation models for robotics, cheaper sensors and better simulation are putting autonomous mobile robots, collaborative arms, drones, inspection crawlers and humanoids into plants and warehouses at scale. Each machine makes thousands of micro-decisions — path, grasp, speed — inside its controller. But the consequential decisions sit a level above: which robot takes which job, when to pull one for service, whether to stop a line, when a finding on a tank wall justifies taking it out of service. Those decisions cross systems, cost real money and carry safety risk. That is decision intelligence territory.
Decisions decision intelligence governs in robotics
| Decision | Signal | Context it needs | Authority | Outcome measured |
|---|---|---|---|---|
| Reallocate missions across the fleet | Robot degraded, blocked or low battery | Mission queue, WMS priorities, charger availability | Automated in envelope | |
| Pull a robot for service | Drive current, vibration or error-rate drift | Wear models, spares, mission load, service bay | Automated in envelope; supervisor informed | |
| Stop or slow a cell | Near-miss, sensor disagreement, human proximity | Safety PLC state, zone occupancy, production plan | Safety system acts; DI escalates | |
| Act on an inspection finding | Crawler or drone detects wall loss, crack, leak | Asset history, thresholds, production impact | Human — integrity engineer | |
| Accept a new task type | Request outside trained skills | Skill library, simulation results, risk class | Human approval; shadow first | |
| Rebalance fleet size | Sustained backlog or idle time | Demand forecast, shift plan, lease costs | Augmented |
The autonomy envelope
An autonomy envelope is the set of conditions under which a robot or fleet may decide alone: speed, zones, value at stake, confidence, reversibility. Inside it the machine acts and logs; outside it the decision escalates to a named person with context. Hard physical safety — guarding, speed and separation limits — stays in certified safety systems below the AI layer, aligned with standards such as ISO 10218 for industrial robots, ISO/TS 15066 for collaborative operation and ISO 3691-4 for driverless industrial trucks.
The physical AI decision loop
↺ Every outcome updates the wear models and the envelope.
See it live
The homepage includes a live plant-floor simulation with five machines — two AMRs, a robot arm, an inspection crawler and a cycle-count drone. Select any machine to see its telemetry, the recommended decision and whether it sits inside or outside its autonomy envelope. The crawler’s tank finding continues on the oil and gas storage-tank twin.
What to measure
| KPI | Why it matters |
|---|---|
| Fleet availability | Robots ready for work vs total fleet |
| Mean time between interventions | How often humans must step in |
| Mission success rate | Completed without retry or rescue |
| Escalations inside SLA | Humans answered the robot in time |
| Unplanned stoppages avoided | Service pulled forward before failure |
| Safety events | Near-misses and envelope breaches (target: zero) |
Where to start
- Pick one fleet decisionMission reallocation or service timing are the fastest wins.
- Instrument the envelopeWrite down what the fleet may decide alone today, and what it must escalate.
- Shadow the recommendationsCompare DI recommendations with supervisor decisions for two to four weeks.
- Grant autonomy by evidenceExpand the envelope only where outcomes and calibration justify it.
- Robots decide paths; decision intelligence decides missions, service and escalation.
- Autonomy envelopes make robot independence explicit and revocable.
- Hard safety stays in certified systems below the AI layer.