Premium robotics composite: drone inspection incidents plus robotic-arm safety workflows.
Customer proof command center
Premium Operator Control Panel
Operator control panel for robotics field-failure evidence: ROS 2 .db3 bag inspection, Gazebo or Isaac Sim replay, BehaviorTree.CPP patch candidates, ISO 10218 safety-case review, and supervised fleet rollout decisions.
Risk score31 after replay
Evidence ready82 percent coverage
Decision recommendationHold for supervised canary
Robotics Evidence Visual
A premium robotics evidence view: drone incidents, robotic-arm safety workflows, patch evidence, and simulation confidence presented as one coherent workstation story.
Live proof modules
Field failure
Machine AMR-17 reported obstacle instability after a field update.
Open
ROS 2 .db3 bag
LiDAR, odometry, behavior tree state, and sensor trace packet attached.
Locked
Gazebo replay
Simulation scenario generated for the autonomous-machine failure path.
SIM
Behavior tree patch
Generated XML diff adds recovery behavior and rollout hold.
Draft
Safety case
ISO 10218 / IEC 61508 reviewer approval required before deployment.
Queue
Fleet rollout
Regression ledger updated; canary release is gated.
Held
Board impact model
What one avoided robot failure is worth.
3PL warehouse example from the SimPatch evidence package: 50 AGVs, one critical software failure per month, current 3-week MTTR, target 3-day MTTR for supported behavior-tree/navigation failures.
Incident cost now$62.4k$50.4k downtime + $12k engineering labor.
SimPatch target cost$8.4k$7.2k downtime + $1.2k review labor.
Savings per incident$54kMore than the $40k annual fleet license.
SLA exposure cut86%504 grounded hours reduced to 72 hours.
Legal / insurance value: every patch decision carries incident facts, scenario packet, simulation result, safety case, human-review gate, residual-risk note, and fleet rollout hold. That is the proof trail a VP Engineering, Fleet Ops lead, risk team, or insurer can understand.
Field-failure patch demo
Workstation produces BehaviorTree.CPP patch code.
Evidence workflow coverage: ROS 2 bag context, sensor trace, scenario reproduction, candidate patch, simulation verification, safety case, human review, and fleet history.
Incident Packet
Machine: AMR-17
Failure: reflective pallet depth-camera misclassification and E-stop.
ROS 2 bag: historical_reflective_pallet.db3
Topics: /tf, /odom, /scan, /behavior_tree/status, /cmd_vel
Simulation: waiting
Patch status: waiting
Safety review: waiting
Generated Patch Code
Patch-code demo is ready. Press Start Demo to watch SimPatch generate the BehaviorTree.CPP recovery patch once.
Physical AI World Model Lab
Safety-governed scenario packets for robot failures.
This workstation extends SimPatch from patch demo to physical-AI safety and patch-governance lab: scenario packets, synthetic edge cases, action prediction, Patch Confidence Score, sim-fidelity scoring, compatibility matrix, policy gate, and evidence packet export.
Governance Packet
Scenario: waiting
Policy gate: waiting
Synthetic cases: waiting
Sim fidelity: waiting
Patch confidence: waiting
Compatibility: waiting
Human gate: waiting
World Lab Output
World Lab demo is ready. Press Start Demo to watch SimPatch build the scenario packet and safety-governance evidence once.
World-model compatibility layer
SimPatch prepares scenario packets and adapter contracts for Gazebo, Isaac Sim, Omniverse, and future physical-AI model workflows. The customer value is vendor-neutral governance: reusable incident evidence, compatibility status, and human-review gates across simulator and world-model environments.
Transparent workstation
Every incident action is visible, timestamped, charted, and exportable.
The workstation records incident intake, ROS bag metadata parsing, patch generation, simulation scoring, safety review, exports, Physical AI lab actions, internal monitoring checks, alarms, and checkpoints into a traceable evidence ledger.
Trace events--
Incidents--
Patches--
Alarms--
Incident windows
Incident type mix
Severity and machine mix
Severity
Machine class
Patch, review, and alarm state
Patch status
Review verdicts
Alarm state
Patch replay verification outcomes
Each bar is one generated BehaviorTree.CPP patch replay. Green clears the 0.72 human-review gate; amber stays in patch iteration. This is safety-review evidence, not automatic fleet rollout.
This chart shows whether generated patches are moving failures toward human-reviewable safety evidence or staying in patch-iteration work.
Recent incidents
Trace ledger
| Time | Action | Status | Incident | Patch | Output hash |
|---|---|---|---|---|---|
| Refresh the workstation to load trace events. | |||||
Internal monitoring and alarms
No alarm data loaded.Run monitor or refresh workstation.
Email-ready report package
Email sending is disabled until credentials are configured. The workstation can prepare the subject, body, evidence links, trace exports, and alarm exports for operator review.
Verification queue
| Asset | Patch candidate | Safety case | Status |
|---|---|---|---|
| Warehouse AMR | Sensor fusion guard | Reviewer approval pending | Evidence ready |
| Autonomous yard truck | Brake debounce patch | Simulation scenario passed | Fleet rollout gated |
| Inspection robot arm | Localization fallback | Regression ledger updated | SLA protected |
Incident Queue / Diff Viewer / Simulation Replay
| Screen | Whitepaper requirement | POC surface |
|---|---|---|
| Incident Queue | List uploaded failures with Processing, SIM-VERIFIED, or Failed state. | AMR-17 reflective pallet E-stop from ROS 2 .db3 bag is open for simulation replay. |
| Diff Viewer | Show original BehaviorTree.CPP XML vs AI-patched XML. | Patch adds ReflectivePalletRecovery with FollowPath safety_margin_m=0.45 and BackUpAndSpin. |
| Simulation Replay | Show Gazebo pass/fail status and telemetry graph. | Replay watches /tf and /odom; safety violation example is distance to obstacle = 0.1m, limit is 0.3m. |
| Safety Case Export | Downloadable PDF safety case with failure mode, root cause, patch, simulation pass/fail, and residual risk. | The POC exports a text safety case; PDF generation remains a production hardening item. |
Financial Model / Production Architecture
| Area | Whitepaper anchor | Status |
|---|---|---|
| 3PL ROI | 50 AGVs, $62,400 current incident cost, $8,400 SimPatch target, $54,000 savings. | Shown in landing and brochure; dashboard board packet carries ROI and cost of failure. |
| Production frontend | React + TypeScript + TailwindCSS, Dashboard, Incident Queue, Log Viewer, Safety Case Review. | Static POC dashboard; production frontend scaffold remains a later build item. |
| Production backend | FastAPI, rosbags parsing, PostgreSQL, S3, Kubernetes, GPU Gazebo/Isaac Sim jobs, LangChain or LlamaIndex, OAuth2/OIDC. | Flask/SQLite POC with endpoints; production architecture is documented, not complete. |
| Future math | Monte Carlo Simulation, Expected Loss Modeling, Formal Verification. | Roadmap items; not claimed as implemented. |
What the customer is buying
SimPatch gives robotics and autonomous-machine teams a proof workstation for field failures: ingest the ROS 2 incident packet, recreate the failure path, generate a BehaviorTree.CPP patch candidate, verify it in replay, package the safety case, and hold fleet rollout until a human reviewer approves the evidence.
Operational pain
Manual failure reproduction can keep a robot or fleet segment grounded for 2-4 weeks while engineers parse logs, rebuild scenarios, and document safety evidence.
2-4 wk
Buyer result
The target workflow compresses field-failure-to-reviewable patch evidence to 2-4 days while preserving traceability for engineering, safety, and operations leaders.
2-4 days
Business case
The pilot offer is anchored to a $5,000 incident package and a $40,000/year fleet license, supported by downtime, SLA, labor, and legal-exposure evidence.
$40k
Responsible promise: SimPatch supports robotics engineers instead of replacing them. It produces reviewable patch evidence, safety documentation, and rollout gates; human approval remains mandatory before deployment.