Twin Fidelity
OpenUSD environments with calibrated physics, materials, lighting, and sensor models.
Platform features
The complete pipeline for certifying Physical AI.
From physics-accurate twins to signed certificates and field conformance — every capability you need to answer "is this robot ready?" with evidence.
Capability map
Five layers, one certification pipeline.
Each layer is built on GPU-native foundations and feeds the next — from twin to signed certificate.
Scenario Generation
Domain randomization plus world-model synthesis to cover the long tail of edge cases.
Policy Validation
Massively parallel simulation and reinforcement-learning evaluation across conditions.
Readiness Scoring
A calibrated estimator maps twin-vs-reality deltas to a defensible readiness score.
Evidence & Audit
Immutable trial logs, artifacts, and signatures for compliance and reproducibility.
Field Conformance
Edge runtime monitors deployed robots and streams telemetry back to the twin.
Twin fidelity
A twin faithful enough to certify against.
Photoreal rendering isn't enough. Certisto calibrates the twin to your real hardware and site so the physics, contacts, and sensor noise match reality where it matters.
- OpenUSD scene graph with physics-accurate materials
- Calibrated sensor models — cameras, depth, LiDAR, IMU
- Contact-rich rigid and soft-body dynamics
- Site-specific lighting, clutter, and layout
Twin calibration report
- Contact model
- Matched to bench ±1.8%
- Camera noise
- Calibrated to device
- Latency profile
- Modeled end-to-end
- Fidelity score
- 96.4
Counterfactual scenario miner
Occluded object, low lightFAIL
Slippery surface + payload shiftFAIL
Sensor dropout at handoffFAIL
Human enters workspacePASS
Scenario generation
Find the failures that matter — automatically.
Random sampling wastes compute on cases you already pass. Our miner searches for the rare, high-consequence scenarios that actually change a readiness verdict.
Validation pipeline
From policy artifact to signed verdict.
Ingest the policy
Bring your trained policy or train on-platform with Isaac Lab at parallel scale.
Run the trial matrix
Millions of parallel rollouts across scenarios, seeds, and morphologies on GPU.
Estimate the gap
The calibrated estimator translates twin performance into predicted field performance.
Sign and store
Results are cryptographically signed and written to the Evidence Vault.
The certificate
What's inside a Real-World Readiness Certificate.
| Field | What it means | Example |
|---|---|---|
| Readiness score | Calibrated 0–100 prediction of real-world success | 94.2 / 100 |
| Confidence bounds | Statistical interval on the prediction | ±2.4% |
| Failure modes | Ranked scenarios where the policy is weakest | Low-light grasp, payload shift |
| Operating envelope | Conditions the policy is certified to run in | 0.2–3.0 kg, 5–40 lux |
| Safety violations | Critical events across the trial matrix | 0 / 50,000 |
| Provenance | Twin version, seeds, and signature | CERT-8F2A · signed |
512
GPUs per validation sweep
50k+
Parallel trials per certificate
6
Sensor modalities modeled
±2.4%
Typical confidence bound
Sensor coverage
Model the sensors your robot actually uses.
RGB cameras
Calibrated noise, exposure, and rolling shutter.
Depth & stereo
Range noise and dropout under real conditions.
LiDAR
Beam patterns, reflectivity, and occlusion.
IMU & proprioception
Drift, bias, and latency modeled end to end.
More capabilities
Built for real engineering teams.
API & CLI
Trigger validation, pull certificates, and gate CI with a first-class API and certi CLI.
Policy versioning
Every certificate is pinned to an exact policy and twin version for reproducibility.
Regression tracking
Compare certificates across versions to catch readiness regressions before release.
Review workflows
Approval gates, sign-off roles, and comments for safety and compliance teams.
Elastic GPU
Burst to thousands of GPUs for large sweeps, then scale back down automatically.
Deploy anywhere
Cloud, on-prem, or DGX — with the same certificate format everywhere.
"The certificate anatomy is what sold our safety team — every number traces back to the exact trials that produced it."
Safety Lead, Meridian AMR
Fits your stack
Native to the NVIDIA robotics stack.
Omniverse, Isaac Sim, Isaac Lab, Cosmos, TensorRT, and Jetson — plus your MLOps and data tooling.
OmniverseIsaac SimIsaac LabCosmosTensorRTJetson
See the platform on your robots.
Book a technical walkthrough and we'll certify a sample policy against a twin of your environment.