Robotics and machine builders
Synthetic datasets and simulation for perception. Build robots and machines that see reliably across sensors, environments, and edge cases.
Perception is the hard part.
Robots and automated machines depend on vision to localize, grasp, navigate, and avoid obstacles. The control stack is mature. The perception stack is what limits where the system can be deployed.
Real capture is slow, expensive, and skewed. Simulation closes that gap.
What rides on perception.
Perception drives behavior
A robot acts on what it sees. Bad perception means bad motion.
Sensors are diverse
RGB, depth, stereo, lidar. Each sensor and mounting needs its own data.
Safety in shared spaces
Robots that work near people need to recognize people and obstacles reliably.
Environments vary
Warehouses, workshops, and outdoor sites all look different and change over time.
Why perception is hard to build.
The model is rarely the bottleneck. Data and field time are.
Edge cases are everywhere
Reflective surfaces, occlusions, awkward poses. Real capture misses most of them.
3D labels are expensive
Bounding boxes, segmentation masks, and 6D poses are slow and costly to label by hand.
Hardware changes break models
A new camera, a new mount, or a new lens shifts the input distribution.
Field testing is slow
Every iteration on a real robot costs setup, supervision, and repair time.
Three ways it changes the work.
Generate the long tail
Render the rare object poses, lighting, and clutter that your robot will eventually meet.
Train and test in simulation
Mirror the robot, the sensor, and the scene. Iterate without booking a real cell.
Adapt to new hardware fast
Swap the virtual sensor, regenerate, retrain. Keep the same pipeline.
What teams get out of it.
More reliable perception
Coverage of cases that real capture cannot reach.
Faster integration cycles
New robot or new sensor without restarting from zero.
Lower cost per iteration
Most experiments run in software.
Talk to us about your dataset.
Tell us the inspection task and the conditions. We will come back with what is feasible, the timeline, and the cost.