Pose matters as much as class
Knowing it is a box is useless without knowing how it sits.
Perception, grasping and navigation
Robots fail on the objects and situations nobody labelled. We build the perception data that closes that gap.
A robot has to be right about geometry, not just category — where the object is, which way it faces, where it can be gripped. That demands annotation with real spatial rigour, and validation against what the robot did next rather than only against a held-out set.
Knowing it is a box is useless without knowing how it sits.
Synthetic data transfers imperfectly and needs real data to anchor it.
Bags, cables and stacked goods defeat rigid-object assumptions.
A perception error becomes a dropped item or a collision.
Object position and orientation for pick-and-place.
Where an object can actually be gripped, by gripper type.
Cluttered, overlapping scenes labelled instance by instance.
Traversable-area segmentation for mobile robots.
Person detection and proximity labelling for shared workspaces.
Verifying an assembly or pick succeeded.
Tell us what you are building and we will come back with a plan, a sample and a price.