Dextra captures egocentric video, hand and body pose, depth, and subtask annotations from real workplaces. We deliver the training data that turns foundation models into robots that actually work.
Synthetic datasets look clean but fail in unstructured, real-world environments. Robots trained on simulations stumble when they meet actual objects, lighting, and human workflows.
Third-person camera footage shows what happens in a scene. It doesn't capture how actions are actually performed. Robots need first-person data that matches their own viewpoint.
As regulations tighten under the EU AI Act and emerging US frameworks, robotics companies need consent-tracked, auditable data. Most datasets can't prove where their data came from.
A full pipeline that turns real human actions into structured training data for foundation models.
Workers in factories, warehouses, and healthcare facilities wear collection rigs. Every task is recorded from their egocentric perspective.
Hand/body pose extraction, depth mapping, and subtask segmentation. Every frame labeled with what action is happening and why.
QA pipeline checks annotation quality, removes PII, and generates provenance records. Full chain-of-custody for every dataset.
Robot-trainable datasets in standard formats. Ready to plug into your manipulation, navigation, or foundation model training pipeline.
First-person footage of skilled workers performing real tasks. CNC operation, assembly, packaging, maintenance. The perspective robots actually need.
Dense keypoint tracking for hands, arms, and full body. Every grasp, reach, and manipulation captured in 3D coordinates.
Synchronized depth maps and spatial context. Objects, surfaces, and workspaces mapped with millimeter precision.
Every video segmented into discrete actions: pick, place, rotate, inspect. The semantic layer that turns video into teachable demonstrations.
Dextra is building the data infrastructure that bridges the gap between human skill and machine learning. Real workplaces. Real actions. Real training data.