Robotics AI
Teaching Robots to See, Grasp, and Act in the Real World
Robotic AI is highly sensitive when training data treats 6-Degrees of Freedom (DOF) manipulation like a bounding box problem. Nextura's robotics annotation team — ex-manufacturing engineers, mechatronics, and industrial automation specialists — labels the way robots actually perceive and move.
What we deliver here.
Grasp Detection
Object pose estimation, grasp point annotation, pre- and post-contact state labeling, and friction surface classification for pick-and-place manipulation models.
6-DOF Trajectory Annotation
Full 6 degrees-of-freedom trajectory labeling, waypoint definition, and motion constraint annotation for general-purpose robot arm models.
Human-Robot Interaction
HRI safety zone labeling, collaborative workspace annotation, and gesture-to-intent datasets for cobots operating alongside human workers.
Warehouse & Logistics
Conveyor belt item detection, bin-picking scenario generation, deformable object annotation, and clutter scene labeling for logistics automation.
Humanoid Foundation Models
Imitation learning datasets, whole-body motion annotation, and environment interaction labels for next-generation humanoid robot training.
Sensor Integration
Multi-sensor fusion annotation — RGB-D, tactile, force-torque, and proprioceptive signal labeling for embodied AI models.
Results that survive production.
Ready to ship Robotics AI AI that earns its place in production?
Tell us your model, your data gaps, and your deadline. We'll scope a pilot dataset that proves Nextura's quality before you commit to scale.