Robotics Simulation Services
Physics-correct environments your team can use for training, validation, and deployment. Start with a bounded pilot and expand into reusable internal infrastructure.
Isaac Sim · MuJoCo · Unreal Engine · NVIDIA Partner · Unreal Silver Partner
Environments, robots, assets, and scenarios that hold up under training and validation. Every deliverable exports to your engine.
Brownfield capture reconstructed into a physics-ready environment. Geometry, materials, and collision tuned so what happens in sim matches the floor.
Input: site scan or CAD. Output: engine-ready environment.
Asked for by integrators and deployment teams.
A representative environment in your domain, parameterized for lighting, layout, and clutter. The starting point for every pilot.
Input: domain brief. Output: environment plus variant set.
Asked for by teams evaluating simulation readiness.
Your embodiment with validated kinematics, joint limits, sensors, and contact behavior. Tuned against real telemetry where available.
Input: URDF, CAD, or spec sheet. Output: sim-ready robot.
Asked for by robotics teams training policies.
Objects converted from CAD or scans into physics assets with mass, friction, and articulation. Delivered in your format.
Input: CAD or scan. Output: USD, MJCF, or UE5 assets.
Asked for by perception and manipulation teams.
Parameterized scenarios with deterministic reset and replay. Rare events on demand, versioned so a result can be reproduced months later.
Input: task definition. Output: scenario library plus configs.
Asked for by validation and safety teams.
Discrete-event models of the line or floor to test flow, staffing, and robot count before hardware arrives.
Input: layout and process data. Output: FlexSim or Simio model.
Asked for by operations and integration leads.
We deliver into your stack, not ours. Environments, robots, and assets ship in the engine your team trains and validates in, with the same scene reproducible across engines when you need to compare.
Formats: USD / USDZ · MJCF · URDF / SDF · UE5 · GLB · Unity
Isaac Sim / Omniverse
MuJoCo
Unreal Engine 5
Unity
FlexSim
Emulate 3D
A render that looks right is not evidence. We publish the checks behind every environment so your team can see where sim diverges from real and by how much.
— Same maneuver run across four engines, compared frame by frame.
— Timestep studies that show drift against dt before you pick a solver setting.
— Contact and friction validated against measured defaults, not engine presets.
Four bounded ways in. Pick the row that describes you.
| Pilot | Pilot Plus | Sim Core | Full Simulation | |
|---|---|---|---|---|
| Start here if | You are a system integrator or a team evaluating simulation readiness. | You are preparing for deployment or supporting rollouts. | You train policies or run validation and want a repeatable internal tool. | You run a mature, enterprise, public-sector, or safety-critical program. |
| Scope | Canonical environment, one robot, one task | Parameterized variations, multiple scenarios and task conditions | Bounded, production-ready environment for continuous internal use | Multi-scenario system with parameterized tasks |
| Scenario variation | – | ● | ● | ● |
| Pipeline integration | – | – | ● | ● |
| Handoff documentation | Basic | Full | Full | Full |
| You leave with | A de-risked approach and a foundation for reuse. Exportable assets and configs. | Confidence in simulation as a deployment tool and a path into Sim Core. | Simulation as a repeatable internal tool with recurring asset access. | Simulation as core technical infrastructure with validation and replay workflows. |
Simulation is one station in a loop that runs from capture to the floor and back.
↺ Real-world data feeds back into capture and simulation
The boundary is fixed by design. FS Studio builds the simulation and reconstruction layers, the ground truth your models stand on. What your team trains inside them stays yours.
US-based simulation engineers. ITAR-capable delivery.
You
FS Studio
FS Studio
Case study
Humanoid policy trained and validated in a reconstructed facility.
A physics validation program · Contact and friction validated in MuJoCo against measured behavior. Name gated.
A humanoid training program · Policy training in Isaac Sim with NVIDIA. Name gated.
Anything not covered, put it in the form below.
The one your team already trains or validates in. If you have not chosen, we recommend Isaac Sim for manipulation and locomotion policies and Unreal for perception-heavy or photoreal work. A pilot can include a second engine for comparison.
Every environment ships with baseline sanity checks: timestep studies, contact and friction validation against defaults, and where real telemetry exists, sim-versus-real comparison on the same maneuver.
You do. FS Studio delivers the reconstruction, simulation, and data layers. Policies trained inside them, and the IP they carry, stay with your team.
For facility digital twins, yes, a short capture visit with a handheld rig. Canonical environments and robot models can be built from CAD, scans, and spec sheets remotely.
One canonical environment in your domain, one robot, one task, physics sanity checks, and exportable assets and configs. Every tier is standalone; nothing locks you into the next one.
Sim Core and Full Simulation include pipeline integration: deterministic reset and replay, scenario versioning, and hooks for your training and evaluation jobs.
Tell us the robot, the engine, and what you need to prove. We reply with a bounded scope, not a sales deck.