Synthetic01
Cluster summary
Related deployment events grouped by failure signature, affected task, and available evidence.
Deployment failure intelligence for sim-to-real robotics
Turn real robot failures into simulator corrections.
Sim2Real helps robotics teams investigating manipulation and perception failures in warehouses and factories turn deployment telemetry into prioritized clusters, reviewable recommendations, and a tighter validation loop.
Best fit: teams with a deployed or field-testable robot, repeatable task outcomes, and an owner for simulator or training changes.
Not for: replacing a simulator, outsourcing labeling, generic observability, or teams without a path to validate a recommended change.
Failure pattern
Needs review
Object tilt + reflective glare
Grouped events37
Primary signalWrist camera
Task impactMissed grasp
Illustrative recommendation
Open the annotated sample
Add controlled object-pose variance and specular-light sweeps, then replay the grasp policy against the same task boundary.
Input boundaryJSON API + scoped telemetry
Core outputFailure clusters + evidence
Decision supportSimulation or training next step
Access modelEngineering-led pilot
Product evidence
Review the artifact, not a promise.
The examples below are synthetic and illustrative. They show the structure of the decision workflow without presenting fictional customer results as proof.
Illustrative02
Recommended correction
A reviewable simulation or training change tied back to the observed evidence and task boundary.
Illustrative03
Validation checkpoint
A before-and-after view of the affected deployment task, without claiming causality the evidence cannot support.
Synthetic demo
Case 01
Warehouse manipulation arm: glare + object tilt
Problem: Missed grasps under reflective glare and pose variance.
Failure cluster: Object tilt + reflective glare (37 grouped events).
Recommendation: Add controlled pose variance and specular-light sweeps, then replay the grasp policy against the same task boundary.
Validation: A before-and-after comparison on the same pick boundary across subsequent deployment runs, reviewed by the engineer who owns the change.
Synthetic demo. This data is fabricated. It shows how Sim2Real structures a failure cluster — it is not a customer result.
Why teams get stuck
Deployment evidence arrives faster than teams can turn it into simulator work.
Logs, videos, task outcomes, and operator notes often live in separate tools. Engineers can see that performance changed, but the path from field symptom to testable simulator correction remains manual.
Clutter variance
→ grasp retries → reduced throughput
Reflective glare
→ missed picks → higher operator intervention
Pose uncertainty
→ policy hesitation → cycle-time slowdown
Failure triage becomes anecdotal
The loudest incident wins attention instead of the most repeatable pattern.
Simulation updates lose traceability
Teams cannot clearly connect a new scene, parameter range, or training run to the deployment evidence that motivated it.
Rollout decisions stay ambiguous
Without a shared validation checkpoint, it is difficult to know whether a change improved the targeted failure mode.
Technical workflow
A reviewable loop from deployment event to next test.
Each step exposes its input, output, user effort, and operating cadence so buyers can evaluate the workflow before committing to a pilot.
Target time to first reviewable failure cluster: 1–2 weeks after the telemetry boundary and task definition are agreed.
-
01
Define the task boundary
Choose one deployment task and the failure evidence worth investigating.
-
02
Ingest deployment evidence
Send structured events through the JSON API with the context available for that task.
-
03
Review failure clusters
Inspect grouped patterns, supporting evidence, affected deployments, and open questions.
-
04
Validate the correction
Apply an agreed simulator or training change, then compare the next run against the same failure pattern.
What you send
Enough context to explain one deployment task.
- Structured task outcomes and timestamps
- Available camera, force-torque, IMU, or controller context
- Robot, software, scene, and deployment identifiers
- Operator labels or incident notes when available
Setup: JSON API mapping scoped during pilot intake. Owner: a robotics or platform engineer who understands the event boundary.
What you get back
A prioritized path to the next simulation test.
- Grouped failure patterns with supporting evidence
- Affected deployments, robots, and task context
- Reviewable simulation or training recommendations
- A validation record for the next field run
Cadence: aligned to the team’s review loop. Owner: the engineer responsible for approving and testing the change.
Robot deploymentTelemetry APIFailure clustersSimulator correctionValidation run
Capability truth
Know what is available, pilot-supported, or custom-scoped.
Authenticated workspace for deployment records, failure clusters, evidence review, recommendations, and validation history. Exact production architecture is qualified before purchase.
Available nowTelemetry intake and workspace review
Authenticated per-user workspace surfaces, API-key telemetry ingestion, deployment records, failure trends, notes, and recommendations.
Pilot-supportedFailure-cluster investigation
Scope one deployment task, review grouped evidence, and connect an agreed failure pattern to a candidate simulation or training change.
Custom scopeSimulator and fleet integration
MuJoCo and Isaac Sim exports are pilot-supported with hands-on engineering support during a pilot. ROS/ROS2, Omniverse, and custom stacks are scoped per engagement against the team’s telemetry boundary and deployment workflow.
Use cases
Start with one expensive, repeatable failure pattern.
Failure patternMissed grasps under pose, clutter, or lighting variance
WorkflowCluster task failures, review evidence, vary the matching simulation conditions
KPI to monitorTask success within the same pick boundary
Failure patternFalse rejects or misses under surface and illumination changes
WorkflowGroup visual conditions, review examples, extend scene or data variation
KPI to monitorError mix on the same inspection class
Failure patternLocalization or planning degradation in changed environments
WorkflowCluster context shifts, identify common conditions, replay targeted scenarios
KPI to monitorMission completion within the same route class
Why not manual?
Keep engineering judgment. Remove the evidence scavenger hunt.
| Decision step | Manual workflow | Sim2Real workflow |
|---|---|---|
| Collect evidence | Search logs, videos, tickets, and notes separately | Use one scoped event boundary and linked deployment context |
| Find repeatable patterns | Rely on ad hoc queries and team memory | Review grouped failure clusters and supporting events |
| Choose a simulator change | Translate symptoms into scene or training changes by hand | Review a recommendation tied to the observed pattern |
| Validate the result | Compare runs with inconsistent documentation | Record the targeted failure, change, and next deployment outcome |
Security and procurement
Scope the data boundary before telemetry moves.
Sim2Real uses authenticated per-user workspace access, API-key telemetry endpoints, server-side route guards, and browser security headers in the current application.
Review the privacy policyQualified during pilot intake
- Fields, files, and sensor references included in the telemetry schema
- Retention, deletion, and operational review requirements
- Deployment environment and integration responsibilities
- People authorized to review workspace outputs
Operator
Developer312, a subsidiary of NIGHT LITE USA LLC
Qualified pilots are handled as direct engineering engagements: define the event boundary, inspect the artifact, and agree on a validation plan before broader rollout claims are made.
Certifications, team-level isolation, and custom retention are not implied by this page; requirements are documented during commercial scoping. Sim2Real is currently cloud-hosted: VPC, on-premise, regional, and air-gapped deployments are not standard offerings and are evaluated individually for qualified enterprise engagements. Evaluation does not guarantee availability.
Pilot Optimizer
$499/month for up to 3 robots.
Use the published pilot tier when one team needs to investigate a bounded deployment problem and can participate in the validation loop.
- One defined task and failure pattern
- Up to 3 robots in the pilot scope
- An available telemetry or event boundary
- An engineering owner for the next test
Enterprise Transfer starts at $2,500/month for broader fleet and integration scope, subject to qualification.
Discuss enterprise scopePilot fit
Apply now or wait?
Apply now if
- A robot can produce real task evidence.
- The failure is costly or blocks rollout.
- Your team can test a proposed change.
Wait if
- The task definition is still changing.
- No deployment evidence exists yet.
- No one owns simulator or training updates.
Qualified pilots include direct engineering discussion about the event boundary and validation plan.
Request pilot assessmentFAQ
Technical and commercial questions to settle before a pilot.
What telemetry does Sim2Real need?
A pilot can start with structured task outcomes and the sensor or controller context already available. The exact schema is scoped around the failure pattern, not a universal data checklist.
Can telemetry be processed live or offline?
The current API can accept events as they are produced or from a controlled batch process. The pilot defines the cadence that matches the team’s validation workflow.
Which simulators and robotics stacks are supported?
MuJoCo and Isaac Sim exports are pilot-supported today. ROS/ROS2, Omniverse, and custom stacks are scoped per engagement; compatibility depends on the event schema and where the team can apply a recommended change.
Do we need perfectly labeled failures?
No. Operator labels and incident notes can help, but a pilot can begin from task outcomes and available context. Engineers still review whether a cluster is meaningful.
How do you validate a recommendation?
The team reviews the evidence, applies the proposed simulation or training change, and compares the next run against the same task and failure pattern.
Can we send images or video?
Media handling is scoped explicitly before transfer. Start with references or metadata unless the pilot agreement defines the media boundary, retention, and authorized reviewers.
What does a successful first 30 days look like?
A useful first month produces an agreed telemetry boundary, at least one reviewable failure cluster, and a documented validation loop for the next change.
Does Sim2Real replace our simulator or MLOps stack?
No. It connects deployment evidence to decisions in the simulator, training, and rollout workflow your team already operates.
How are security and retention requirements handled?
Authentication and route protections exist in the current application. Data fields, retention, deletion, hosting, and access requirements are documented during pilot qualification.
How do billing and cancellation work?
Published plans use the secure billing workflow described on the pricing and account pages. Plan changes and cancellation follow the applicable subscription terms.
Bring one failure pattern
Leave with a scoped validation path.
Share your robot count, deployment stage, simulator, telemetry, and target timeline. We will use the assessment to determine whether a pilot is technically useful.