Simulation environments you can trust.
AI tools and scans generate environments fast.
Robotics teams need environments that behave correctly.
We engineer the difference.
Validated inside NVIDIA Isaac Sim. Collision hulls, contact offsets, and solver stability verified before delivery.
Discuss a pilot“Simulation-ready” does not mean “simulation-trusted”
Environments that render correctly can still fail in simulation. These are observed failure modes, not hypothetical ones.
| Failure Mode | What Breaks |
|---|---|
| Wrong scale (2–10 cm drift) | Planner rejects valid paths. Grasp points miss by enough to fail manipulation tasks. |
| Unstable collision meshes | Objects jitter at rest, explode under contact, or clip through surfaces. PhysX solver diverges. |
| Incorrect friction coefficients | Wheels slip on flat ground. Grasps drop objects that should hold. Locomotion metrics become unreliable. |
| Broken semantic labels | Labels don't match geometry boundaries. Perception models learn incorrect object mappings. |
| Non-deterministic contact behavior | Evaluation results drift 5–15% between identical runs. Debugging becomes impossible. |
These failures silently corrupt training and evaluation. Your policy learns the wrong thing. Your metrics lie. You discover the problem months later in hardware tests.
Simulation speed is no longer the bottleneck. Simulation trust is.
What we do
We take AI-generated, scanned, or manually built environments and make them reliable for robotics simulation. Each fix addresses a specific failure mode.
Scale drift from scanning or AI generation
Geometry and scale verification against reference measurements
Objects match real-world dimensions within ±2 mm
Collision meshes that cause solver instability
Rebuild collision hulls with proper convex decomposition and contact offsets
Objects rest stable, no jitter or explosion under load
Friction values that break locomotion or grasping
Material and friction calibration based on expected contact behavior
Wheels grip, grasps hold, contacts behave predictably
Disorganized USD structure blocking pipeline integration
Restructure into clean OpenUSD scene graphs with proper hierarchy
Environment loads correctly in Isaac Sim, prims are addressable
Semantic labels misaligned with geometry
Semantic and instance labeling matched to mesh boundaries
Perception ground truth is geometrically accurate
Results vary between simulation runs
Determinism testing and solver parameter documentation
Runs are reproducible within documented constraints
What we do not do
Narrow scope keeps validation credible. We do one thing well.
- Simulator engine development (we use Isaac Sim, we don't build it)
- Robot policy or RL training
- Sensor hardware modeling
- Sim-to-real transfer guarantees
- Generic asset libraries or marketplaces
How it works
You provide the environment
USD stage, scan data, CAD export, or AI-generated scene
Any source format accepted
Environment loaded into Isaac Sim for analysis
We audit failure modes
Loaded environment
Scale checks, collision tests, solver stability runs, semantic review
Failure-mode report with severity ranking
We harden the environment
Audit findings
Collision rebuild, contact offset tuning, friction calibration, USD restructuring
Corrected USD stage + solver test logs
You receive the deliverables
Validated environment
Documentation and packaging
USD stage, fix list, known limitations, reproducibility notes
Pilot engagement
A focused engagement to validate our process on your environment before committing to larger scope.
Scope
- One environment or zone (e.g., single room, warehouse aisle)
- Physics-validated (solver stability confirmed)
- Collision-stable (no jitter, clipping, or explosion)
- Semantically consistent (labels match geometry)
- Delivered Isaac-ready (loads and runs without errors)
Deliverables
- Validated USD stage file
- List of fixes performed with before/after notes
- Known limitations document
- Reproducibility notes (solver settings, tested configurations)
Timeline (7–14 days)
Audit + failure-mode report
Fixes + solver testing
Delivery + documentation
Tools and compatibility
Environments are validated in the simulator you will use. We work within the NVIDIA Isaac ecosystem.
Validation examples
Representative outcomes from environment hardening. These are illustrative, not guarantees—results depend on input quality and use case.
Collision hulls 3–5 cm oversized
Rebuilt collision meshes with proper convex decomposition
Planner path rejection reduced from ~40% to <5%
Scale drift of 8% across scene
Corrected scale using reference measurements
Navigation drift eliminated, robot localization stable
Contact instability causing object jitter
Tuned contact offsets and solver iteration counts
Grasp simulation stabilized, consistent pick success
Specific metrics are environment-dependent. We document actual measured improvements for each engagement.
About
We have spent years building 3D environments and debugging why they fail in simulation. Collision meshes that look correct but cause solver instability. Scale errors that only show up when a planner runs. Friction values copied from templates that break locomotion.
Our focus is preventing these failures before they corrupt your training or evaluation. We know what PhysX does with bad geometry, and we fix it at the source.
We sit between environment generation (AI tools, photogrammetry, CAD exports) and the simulation trust your robotics team needs. The tools generate fast. We make the output reliable.
If simulation correctness matters to your team, let's talk.
Describe your environment and what you're trying to simulate. We'll tell you if we can help.