Deploying autonomous systems in shared human–robot environments is risky, slow, and hazardous. Generate deterministic, physics-based sensor simulation and perfectly aligned synthetic datasets to validate and train robotics perception before real-world deployment.
Perception is what lets an autonomous mobile robot, forklift, or fleet operate safely around people. But the industrial environments these systems run in are the hardest and riskiest places to collect data and test in.
When robots operate alongside people, a missed or late detection is a safety event, not a metric. Perception must be proven before deployment, not in production.
Meeting functional safety requirements such as IEC 61508 SIL 2 means proving that perception performs as specified — and uncontrolled real-world testing makes that evidence hard to produce.
Synchronizing and labeling pixel- and point-accurate ground truth across sensors from real-world capture is slow and error-prone.
Without a deterministic way to re-run identical conditions, a model update can silently degrade performance — and the regression only surfaces in deployment.
Capturing and labeling multi-sensor datasets in live facilities is slow, costly, and disruptive — and every layout change, new site, or robot platform means capturing again.
The situations most likely to cause a perception failure are the least represented in captured real-world datasets.
One simulation platform covers the full perception workflow: validate against repeatable scenarios, analyze where performance breaks, and train on synthetic datasets built for your models.
Camera, LiDAR, radar, and thermal simulated with physical accuracy in a single platform. Your synthetic data reflects how your target sensors actually respond.
One consistent scene produces perfectly aligned, pixel-accurate ground truth for every sensor at once.
Generate scenarios, test suites, and synthetic datasets on demand, at the scale your program needs.
Anyverse scenario is deterministic — it renders identically on every run — so results are comparable across runs and every failure is reproducible.
Build training and augmentation datasets for perception models directly from simulation.
Understand where and why perception holds or breaks — and make design decisions before hardware is committed.
Run perception against large simulated scenario suites while your facility keeps running — no paused picking, no closed aisles, no instrumented test robots.
Deterministic scenarios give you reproducible test results and ground truth tied to the exact conditions that produced them — evidence you can carry into certification and compliance reviews.
Work with the full sensor suite your robot carries in one toolchain, instead of maintaining separate tools per modality.
Bring a new site, layout, or robot platform into validation and training through configuration, not a new on-site data campaign.
Cover human–robot interactions — people crossing paths, bending, reaching, partially occluded — in simulation before robots share the floor with your team.
No manual annotation on synthetic data means your team spends its time improving perception, not labeling and aligning sensor data.
Book a demo to walk through synthetic data and sensor simulation for your sensors, facilities, and perception pipeline.
