Rendered warehouse and robotics simulation environment
Simulation Data

Give your robot the scenarios the real world won't hand you

Physics-accurate simulation data for manipulation and navigation, with exact ground truth on every frame.

The Constraint

Real-world training has a ceiling

Collection is expensive

  • Physical rigs and setup
  • Manual recording
  • Human operators on site
  • Every retest costs again

Real data is limited

  • Rare events rarely happen on cue
  • Dangerous scenarios can't be staged
  • Environments barely vary day to day

Scaling takes time

A robot cannot physically live through millions of situations before deployment. Every added scenario means more rigs, more hours, more people in the loop.

Simulation lets a robot live through thousands of scenarios before it meets one real one.

Process

How a pilot runs

Robotics engineer at a dark workstation building a simulation environment
01

Your robot, your challenge

A robot, a CAD model, or just a task it needs to learn.

02

Define the requirement

We scope the task, the environment, and the data it actually needs.

03

Build the environment

A physics-accurate digital twin matched to your robot and use case.

04

Generate the variations

Objects, lighting, layouts, and edge cases, run automatically at scale.

05

Validate against reality

Outputs are checked against your real-world benchmarks and refined.

06

Deploy further ahead

Your robot enters the field already trained past what it could see physically.

Starting Points

Built from wherever your robot already is

Whatever stage it's at, there's a path into simulation. We match the method to what you already have.

01

Your Assets

  • Existing CAD models
  • Existing 3D assets
  • Product models
We prepare them for sim
02

AI-Generated Assets

  • Unlimited variations
  • Different layouts
  • Different environments
Best for early concepts
03

Reality to Digital Twin

When products already exist
Industrial robot inside a simulated warehouse environment
Outputs

What we can generate

Vision

Vision Training Data

  • RGB
  • Depth
  • Segmentation
  • Lighting variation
Motion

Robot Motion Data

  • Pick and place
  • Navigation
  • Manipulation
  • Trajectory generation
Sensors

Sensor Data

  • Camera
  • Depth
  • LiDAR
  • Robot sensors
Robustness

Edge Case Testing

  • Failure scenarios
  • Unexpected conditions
  • Safety testing
  • Rare situations
End to End

Bring your robot into simulation

01
Your Robot
02
Robot Model
03
Simulation
04
Testing
05
Optimization
06
Deployment

Example: Pick and Place

If your robot performs pick and place, we simulate the full range of conditions it will meet in the field.

Different objectsDifferent weightsDifferent lightingDifferent positionsDifferent environments
Technology

Built on industry-leading simulation platforms

NVIDIA Omniverse
Isaac Sim
Isaac Lab
Replicator
USD
How We Work

Start with a paid pilot

Every robotics application is different. Instead of a one size platform, we run focused engineering pilots built around your robot, your objectives, and your data requirements.

Start a Simulation Pilot
1

Share your challenge

2

Simulation prototype

3

Generate initial dataset

4

Validate

5

Scale

CUSTOM SIMULATION PROGRAMS

Tell us what your policy needs to see.

Describe the task, the environment, and your hardware. We'll scope a simulation program around your exact requirements and get back to you within 48 hours.