Stealth Mode

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Building the data infrastructure for physical intelligence.

The human world is the training set. We capture high-fidelity human motion and interaction in the real world — and turn it into robot-ready data.

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THE SYSTEM

Modality

Full-body · Hands · Ego

Signal

6-DoF trajectories

Environment

Unconstrained, in the wild

Team

Two Stanford graduates

The problem

Language models had the internet. Robots have almost nothing.

Physical intelligence is bottlenecked by data, not architecture. Robot learning needs enormous quantities of high-quality real-world interaction — captured with human dexterity, at human scale, in unconstrained environments.
Text corpora

Trillions of tokens

Scraped, cheap, effectively unlimited.

Robot data

Orders of magnitude short

Teleoperation is slow, costly and lab-bound.

Human motion

Continuous, everywhere

Every hand, every day — currently uncaptured.

Our system

Wearable hardware that turns a person into a capture rig.

A coordinated set of worn sensors records motion and interaction while the wearer simply goes about real tasks.
Head-mounted
Egocentric vision and head pose. What the hands do, seen the way a robot will see it.
Finger articulation and hand pose at fidelity sufficient for dexterous manipulation.
Arms, legs and torso. Lightweight sensing that disappears into ordinary clothing.
No studio, no markers, no volume. Data collection wherever work already happens.
From humans to robots

Human movement → structured motion data → robot training.

01. Human

A person performs an ordinary real-world task.

02. Tracked skeleton

Joints, fingers and 6-DoF trajectories are resolved in real time.

03. Structured data

Motion becomes aligned, machine-readable interaction sequences.

04. Robot

Sequences train policies for humanoids and manipulation systems.

Data fidelity

Resolution where robot learning actually breaks.

Coarse body tracking is not enough. Manipulation lives in the fingers, in contact, and in the fine structure of everyday movement.

Fingers

Per-joint articulation

Hands

Full pose + contact events

Arms

Reach and orientation

Torso

Posture and load transfer

Legs

Locomotion and stance

Head

Gaze-aligned egocentric view

Trajectories

Continuous 6-DoF paths

Interaction

Human–object dynamics

Distributed collection

One rig is a demo.
A network is a dataset.

The system is designed so that many wearers, in many environments, can contribute to a single growing corpus of real-world interaction.