Accelerating Physical AI.
Automating the impossible.
We capture, train, and deploy Physical AI for work that still depends on human judgment and touch. Your operators’ embodied skill becomes proprietary models and data others cannot easily replicate, so the edge you have on the line today carries into the Physical AI era.
From headset to deployment, we build the stack that turns real operations into trainable Physical AI: capture and scaling laws you can plan around, backed by QC’d ingest, unified schema, and reproducible train snapshots.
Egonu-headset v1
Egocentric, first-person data capture designed for real industrial environments. We record multimodal signals (human motion, interaction, and context) so policies learn from how work is actually done. Tight pilot protocol first, then full-belt capture as diversity grows; optional phone-rig capture where headsets aren’t on every station yet.
Scaling human data → scaling robot performance
More human data leads to better robot policies, with predictable scaling and direct gains in real-world execution when domain-aligned human-robot data on the same task and scene anchors training; then diverse egodata compounds and capacity planning maps to measured rollouts.
Cross-embodiment training
Humans and robots perform identical tasks with matched viewpoints and environments, producing synchronized observation-action trajectories in a shared camera-centered frame with calibrated cross-embodiment normalization. Human stack: Egonu Headset v1, wrist cameras, Manus Gloves, HTC Vive trackers. Robot stack: matched head and wrist cameras, proprioception, end-effector control, and 22-DoF Sharpa hands for dexterous manipulation.
nuhuman stack
in action
Proprietary reconstruction for high-throughput lines: MANO hands, depth, IMU, SLAM, and segmentation, plus process video with dense time-aligned language for VLA lanes, validated into training-ready packs.
Private data flywheel: capture, train, deploy, verified economics.
We help you turn expert labor on your belt into proprietary physical intelligence, tied to your layout, tools, and tacit know-how, so replication stays expensive for everyone else. Expert egodata on your line flows through managed ingest (QC, metadata, reproducible subsets) into reconstruction you can train, then policies on throughput, rejects, and cycle time. We ship one operator-ready program: belt capture, train in your safety envelope, deploy on your gauges, loop production into the next model. Private tenancy, private weights, economics on verified output.
- Full-scene capture. Hands, depth, motion, SLAM, segmentation. Scene and station coverage are a planned axis for environmental generalization. The belt as a living, trainable twin.
- Expert motion, exported. The best operators on your floor become the curriculum for dexterous automation; widening demonstrators targets stylistic and viewpoint robustness.
- Train at frontier speed. Parallel runs, joint human-robot co-training, imitation and VLA lanes with dense time-aligned language, scored on throughput, rejects, and cycle time.
- Closed loop. Your tenancy. Proprietary stack, private data and weights, no communal model soup.