Home / Data services / Synthetic and sim-to-real
Isaac and MuJoCo scene generation, domain randomization, and validated sim-to-real bridging.
Synthetic data is generated inside physics simulators like Isaac Sim and MuJoCo — domain randomization varies lighting, textures, object shapes, and camera poses across thousands of scenes so your policy trains on diversity it could never see in a real lab.
We build the scenes, run the randomization, and validate that sim-trained policies transfer to your hardware before you see a bill for real data.
Why outsource sim data?
Scene authoring and domain randomization tuning is specialized work. We maintain the asset library and sim infrastructure so your ML team stays focused on architectures.
10,000+ scenes generated per day.
5 randomization dimensions per scene.
91% sim-to-real transfer validated.
Where we collect
41+ delivery centers across 12 countries. Every program runs from a Roborax hub near your target time zone.
Asia Pacific
India · Philippines
Americas
USA · Canada · Colombia · Jamaica · El Salvador · Belize
EMEA
UK · Albania · Kosovo · Morocco
Four synthetic outputs designed to close the sim-to-real gap, not widen it.
Domain randomization across textures, lighting, physics, and object placement.
Identical scenes captured in sim and reality for direct gap measurement.
Controlled-variable runs for ablations and curriculum design.
Synthetic generated to match your real-world statistics.
A pipeline that ends with sim-to-real metrics, not just rendered frames.
Build the parameterized scene with your team. Variables and ranges locked.
Domain randomization sweeps across textures, lighting, physics, and asset variants.
Batch generation with quality gates. Failed sims rejected, not shipped.
Sim-to-real metrics against held-out real captures. Transfer rate reported per batch.
NVIDIA Isaac, MuJoCo, Genesis, and custom Blender pipelines.
NVIDIA stack
DeepMind stack
Asset creation
Custom physics
Sim bridge
Your pipeline
FAQ
From the blog
Sim-to-Real Transfer: Why Synthetic Data Alone Falls ShortDomain randomization helps, but real data remains essential.
From the blog
From Imitation Learning to RL: How Your Data Strategy ChangesWhat changes in your data needs as you move from IL to RL.
Tell us the task and the gap. We come back with a templated scene plan and transfer targets.
FROM THE FIELD
Warehouse robot training data programs consistently underperform their lab benchmarks in production. The reason is almost never the model architecture. It is almost always a
Surgical robot training data has requirements that no general-purpose robotics data program is built to meet out of the box. Sub-millimeter precision, HIPAA compliance, and
A robotics data quality assurance pipeline is not a checklist or a review meeting. At production scale, robotics data quality requires automated validation, per-operator metrics,
Robot data annotation is not image labeling with a different name. The temporal structure of robot trajectories, the grounding in physical task semantics, and the
Sim-to-real robot training with synthetic data is one of the most powerful techniques in embodied AI — and one of the most misunderstood. The gap
The embodied AI training data problem is structurally different from the language model data problem. Language models learned from the internet. Embodied AI must learn
Seven services. One synchronized pipeline.
VR and leader-follower robot control logging.
In-person task demos for imitation learning.
RGB-D, LiDAR, force, and tactile streams.
Bounding boxes, segmentation, action labels.
Held-out test sets and success-rate scoring.
Rare scenarios your policy will face in production.
