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
Isaac Sim, Mujoco, PyBullet, Gazebo, and Genesis. We can also work in custom proprietary simulators if you share access.
Through domain randomisation, photorealistic rendering, and blending synthetic data with real-world capture in proportions tuned to your transfer benchmarks. We measure transfer quality and iterate until targets are met.
Yes. We systematically generate failure-inducing scenarios — lighting extremes, occlusion patterns, novel object placements — that are underrepresented in real-world capture and critical for robust policies.
We run transfer benchmarks on a held-out real-world test set and report the delta between sim-trained and real-trained policy performance. You get a quantified quality score with every synthetic batch.
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
For robots operating in the physical world, seeing an object is only the beginning. A robot must also understand how far away it is, where
What a wearable capture rig must carry, why field logistics stall programmes more than engineering does, consent in shared spaces, and quality controls that work.
A robot may have excellent sensors, sophisticated actuators, and a powerful learning model—but if the data used to train that robot is poorly synchronized, the
A robot can see an object without understanding how it feels. A camera can identify a cup, estimate its position, and guide a robotic hand
A mobile robot cannot navigate a warehouse, factory, hospital, or outdoor environment from camera images alone. It needs to understand distance, geometry, obstacles, surfaces, and
Human demonstration and teleoperation produce different action labels with different traps. Plus the third category, intervention data, that most teams never budget for.
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.
