Consented first-person (POV) video, LiDAR, and scene annotation data that teaches vehicles and robots how the real world works.
Autonomous systems fail on what they haven't seen. We produce the diverse, real-scene data that closes those gaps.
First-person footage of real driving, walking, and task execution, consented and filmed by vetted contributors on their own devices.
3D bounding boxes, object segmentation, and ground-truth labeling for point-cloud perception.
Traffic elements, pedestrian intent, road conditions, and activity segmentation, labeled to your taxonomy.
Perception models for detection, prediction, and planning across real road scenarios.
Egocentric demonstrations of grasping, tool use, and household tasks for imitation learning.
First-person footage of sorting, packing, and delivery workflows for mobile robots.
Xyntriq