SYN → REAL

Unconventional
Robotics

Synthetic training data for autonomous UAV systems.

SYS STATUS: NOMINAL
UPLINK: ACTIVE
ENCRYPTION: AES-256
LIVE
ALT————————————1,247 m
VELOCITY————————————38.4 m/s
GPS STATUS————————————DENIED
HEADING————————————047°
TRACK: LOCKED

We simulate what can't be risked in the real world.

UNIT STATUS————————DESTROYED
CAUSE————————KINETIC IMPACT SIM
SCENARIO————————BATCH_7721 · JUNGLE_RAIN
DATA CAPTURED————————847 FRAMES · 2.3 GB
TRAINING DATA——EXPORTED ✓
↓  KEEP SCROLLING — DRONE REASSEMBLES
● REASSEMBLING UNIT

03 / SYS.METRICS

PLATFORM SCALE · JUNE 2025

01
—
SCENARIOS OVERNIGHT
Per single AWS G5 batch job

DESERT: 23,400 · URBAN: 31,200 · ARCTIC: 18,900 · JUNGLE: 26,500

02
—
PHYSICS TICK RATE
Decoupled from render thread

C++ PHYSICS ENGINE · IMU + AERO MODELS · REAL-TIME

03
—
PROTOTYPES CRASHED
All risk absorbed by simulation

ZERO HARDWARE RISK · FULL SIM-TO-REAL PIPELINE

04
—
DATASET DELIVERY
From brief to ROS2 .bag export

HEADLESS CLOUD BATCH · DOCKER · LABELED + ANNOTATED

Numbers are live — the 100,000 counter increments in real-time as batches complete on our cloud infrastructure. Hover any stat for a breakdown.

AWS G5 INSTANCESROS2 EXPORTDRACO COMPRESSED

THE PIPELINE

FROM SYNTHETIC
TO REAL.

Three steps. Fully automated. Delivered as ROS2 Bags overnight.

01 — ENVIRONMENT GENERATED
02 — SENSOR DATA SIMULATED
03 — DATASET EXPORTED
SYNTHETIC ENVIRONMENT GENERATED
SENSOR DATA SIMULATED
FORMATROS2_BAG + JSONL
FRAMES100,000
SIZE847 GB
STATUSREADY FOR TRAINING ✓
TRAINING DATASET EXPORTED

OPERATIONAL ENVIRONMENTS

EVERY EDGE CASE.
EVERY ENVIRONMENT.

Urban Canyon
ENV_TYPE: URBANTHREAT: HIGH

URBAN CANYON

VISIBILITY: 40% · WIND: 23 m/s GUSTS

GPS: DENIED
WEATHER: FOG + RAIN
Jungle Canopy
ENV_TYPE: JUNGLETHREAT: EXTREME

JUNGLE CANOPY

VISIBILITY: 15% · HUMIDITY: 94%

GPS: SPOOFED
WEATHER: HEAVY RAIN
Arctic Tundra
ENV_TYPE: ARCTICTHREAT: MODERATE

ARCTIC TUNDRA

VISIBILITY: 70% · TEMP: -34°C

GPS: NOMINAL
WEATHER: BLIZZARD
Desert Canyon
ENV_TYPE: DESERTTHREAT: HIGH

DESERT CANYON

VISIBILITY: 90% · WIND: 18 m/s

GPS: JAMMED
WEATHER: DUST STORM

TECHNICAL FOUNDATION

BUILT FOR
ENGINEERS.

Every component in our pipeline was chosen for accuracy, not convenience.

UNREAL ENGINE 5.3
CUSTOM C++ PHYSICS
ROS2 EXPORT
AWS G5 INSTANCES
DOCKER CONTAINERS
DRACO COMPRESSION
BETAFLIGHT PID SIM
JSONL + PROTOBUF
1000Hz TICK RATE
1,000Hz
PHYSICS SIMULATION TICK RATE
Custom C++ rigid-body engine decoupled from the render thread. Betaflight-style PID control loops ensure the virtual drone flies exactly like its physical counterpart.
6
MODULAR SENSOR TYPES SUPPORTED
IMU, Barometer, FPV camera, DJI feed, Analog transmission, GPS — each with configurable noise models, thermal limits, and transmission artifacts.
∞
PROCEDURAL SCENARIO VARIATIONS
Lighting, wind shear, target velocity, weather conditions — all scrambled automatically across every batch. No two training scenarios are identical.

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CONTACT US

Please reach out to us and we will get back to you at the speed of light.