The frontier robotics labs have proven it: human egocentric video, properly annotated and paired with language descriptions, directly transfers to robot performance at scale. The bottleneck is no longer hardware or compute. It is this data. We produce it.
Trusted by teams at
NVIDIA, Figure AI, Physical Intelligence, and others have demonstrated that robotics foundation models trained on human demonstration video achieve dramatic performance gains. The constraint is not model architecture or compute. It is the volume, diversity, and annotation quality of human-sourced training data.
Single vendor for all three: video collection, annotation, and language grounding. No coordination overhead between separate providers for each layer.
Egocentric and third-person video of humans performing manipulation tasks, assembly sequences, navigation, and everyday activities. Controlled environments, diverse demographics, scripted task variations.
Hand and body keypoint labeling, bounding boxes, pose sequences, contact point annotation, and object interaction mapping. Frame-level precision with temporal consistency across video sequences.
Natural language descriptions paired with visual actions: trajectory narration, VLA instruction pairs, task intent labels, success/failure classification, and step-by-step procedure descriptions.
Building an internal data operation for robotics training data is expensive, slow to hire for, and hard to scale across task domains. BTA provides an operational alternative with the annotation rigour that frontier models demand.
Labs and companies that need human-sourced training data at volumes their internal teams cannot produce.
Teams training general-purpose manipulation and navigation models that need massive, diverse human demonstration datasets with rich annotation and language grounding.
Companies building autonomous picking, packing, and sorting systems that need object interaction data, grasp annotation, and task completion labeling.
Academic and industry research groups working on embodied AI, sim-to-real transfer, and vision-language-action models that need structured, high-quality human demonstration data.
Tell us about your model architecture, data requirements, and annotation specifications. We will design a data operation scoped to your task domain and timeline.