FS Studio builds simulation infrastructure for Physical AI. We deliver high-fidelity simulation, digital twins, sim-ready asset pipelines, and synthetic image, video, and animation data that power AI training, testing, and development for robotics, autonomy, and world modeling.
Put simply: we build the environment and the data, and your team trains the policy and keeps the IP.
Industry 4.0, robotics, advanced manufacturing, agriculture and medical, aerospace and automotive, architecture, engineering and construction, and smart retail. Recent work spans robotics manufacturers, logistics and delivery providers, and aerospace programs.
Two things set us apart. First, everything runs on one validated asset foundation, so a single capture of your site flows into twins, simulation, and data with no rebuild. Second, we treat simulation as ongoing infrastructure your team relies on week after week, not a one-off deliverable. Assets become a reusable, versioned library, so each project starts faster than the last.
Engagements are delivered by US-based simulation engineers and data operations, built on NVIDIA Isaac Sim (OpenUSD scene description, PhysX solver) and Unreal Engine. Assets are exported engine-native, so they drop into your Isaac or Unreal workflows without a re-authoring pass.
They are a single pipeline in four stages. You can enter at any point, and everything you build carries forward with no rebuild.
A scaled, validated spatial model of your real facility, accurate to what is actually there. A phone walkthrough or fleet video is enough to start, and no robot is required. Teams use it to plan facility layout changes before moving a rack, check automation readiness before hardware is on site, onboard operators, and align stakeholders around one shared model.
An ingest and validation pipeline that turns whatever you have, CAD (STEP, IGES, native), 3D scans (point clouds, meshes), or a video of a real scene, into assets a physics engine can trust. We verify the three things that quietly break simulations, real-world scale, pivot and origin placement, and collision geometry, then author convex or convex-decomposition colliders and bake in material and contact properties (friction, restitution, mass, and density) so contact resolves correctly under the PhysX solver.
Assets export engine-native to Isaac Sim and Unreal, versioned with metadata for deterministic reuse across projects. The pipeline is proven across thousands of physics-accurate assets and dozens of scenes.
An environment where geometry, contact dynamics, and material response are accurate enough that a policy trained inside it transfers out. In practice that means validated collision meshes, calibrated friction and mass, and a physics step that behaves consistently under contact. Our environments provide deterministic reset and replay (seeded, reproducible episodes), parameterized scenario generation with versioning, and native hooks into your training pipeline, so a failing rollout can be reproduced bit-for-bit rather than guessed at.
Two products, both generated from your own simulation and twin assets rather than a stock library:
Because labels are generated from simulation state rather than hand-annotated, ground truth is exact and free of human labeling error.
Generic synthetic data is a commodity. Data calibrated to your facility and your embodiment is infrastructure. Because it is generated from your validated twin and simulation assets, it reflects your real geometry and your actual robot, and it stays yours to train on.
Most teams start with a bounded Pilot: one canonical environment, one robot, and one clearly scoped task. It gives you a de-risked read on simulation fit and a foundation every later tier reuses. You can also start upstream by converting a single scene through the asset pipeline to see it run on your own data. The best first step is a scoping conversation.
No. A phone walkthrough or fleet video of your site is enough to begin a twin, and the asset pipeline accepts CAD, scans, or plain video. We meet your data where it is.
Yes. From Sim Core up, environments provide deterministic reset and replay, scenario versioning, and hooks into your existing training pipeline, so simulation becomes a repeatable internal tool rather than a black box.
Scope, environment complexity, and tier drive both. Pilots are designed to be fast and low-friction, while larger programs run as ongoing infrastructure with subscription and data products attached. Reach out for a scoped estimate on your environment and use case.
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, your policies and your models, stays yours. Synthetic data generated for you is yours to train on.
We reduce it on the front end with physics-accurate assets and validated, physics-correct environments, and we close the loop on the back end: real-world feedback improves the sim, which improves the next tranche of data. Simulation is the bridge between what a model can imagine and what a robot can do, and the feedback loop is what keeps it honest.
Yes. Captures, assets, and datasets we build for you are treated as your confidential material, and the models you train on them remain your IP. For specific agreements such as NDAs or data-handling requirements, we work to your terms during scoping.
Stay connected: info@fsstudio.com