01 · Controlled
POD capture
Structured tasks in a repeatable environment with one egocentric camera and zero to four exocentric views, configured around the buyer's measurement needs.
Physical AI · Custom dataset capture
Datasets built to your spec. Not found after the fact.
Diffraction designs and operates human-demonstration capture for robotics and physical AI. Start with one egocentric stream, add up to four exocentric cameras and wrist IMUs when the spec calls for them, then scale through a trained operator network.
The POD
One ego view. Zero to four exo views. The hardware follows the question.
Off-the-shelf
Dedicated community
IMU
Optional wrist motion
POD + network
Controlled or distributed
3 schemas
RLDS · LeRobot · GR00T
Choose the environment
One spec. Three ways to collect.
The task decides whether control, environmental diversity, or embodiment access matters most. Collection can use one mode or combine them.
02 · Distributed
Network capture
Activate trained people across real homes, workshops, and everyday environments when diversity matters more than a single controlled room.
03 · Developing
Robot-arm demonstrations
We are building a network of operators with access to arms so future projects can collect teleoperated demonstrations for ACT and related policy-training workflows.
The capture stack
Instrument only what earns its place.
More hardware is not automatically better data. We choose each view and sensor based on the signal the model actually needs.
Views
1 HMD + 0–4 fixed exo
Use the ego stream alone or add the room coverage the task needs. Camera count follows the spec rather than a fixed bundle.
Motion
Optional wrist IMUs
Add wrist-worn inertial measurements when the project needs motion channels beyond visual hand and body tracking.
Sync & calibration
Declared per session
Flash/audio sync and calibrated cameras are supported today; higher-fidelity hardware sync and machine-vision rigs can be scoped as the capture stack expands.
Hardware
Spec-driven
iPhone/Record3D and standard video ingest exist now. Additional ego devices, depth sensors, IMUs, and robot embodiments are integrated when the signal justifies them.
Inside the output
Pixels are only the first layer.
The processing stack can add geometry, hands, bodies, objects, contact, action, and quality signals. Every delivery distinguishes measured channels from estimates so buyers know what they can trust.
Spec to delivery
A dataset you can trace and rerun.
The brief stays attached to the work from operator instructions through QA and the final dataset card.
- 01
Write the capture spec
Define tasks, environments, viewpoints, hardware, participant profile, signals, acceptance thresholds, and delivery schema with your team.
- 02
Choose the capture mode
Use the Columbus POD, activate distributed operators, or combine controlled and in-the-wild collection for the coverage the policy needs.
- 03
Train and capture
Operators receive task protocols, equipment guidance, consent terms, and quality criteria before a session begins.
- 04
Process and score
The pipeline preserves source media, enriches available signals, distinguishes measured from estimated channels, and scores the asset against the brief.
- 05
Deliver with context
Ship the agreed format with manifests, dataset cards, licensing, provenance, known limitations, and a clear record of what was—and was not—measured.
What ships
Data plus the context needed to use it.
Video + synchronized views
Preserved source media and normalized camera streams, organized by session and task.
Signals with provenance
Hand/body pose, detections, tracks, contact, camera pose, depth, IMU, and action channels where the capture and brief support them.
Training-ready exports
WebDataset, RLDS, and LeRobot/GR00T-compatible layouts scoped to the buyer's training stack.
Dataset card + QA
Acceptance results, measured-versus-estimated declarations, licensing, limitations, and signed asset provenance.
ACT policy training · Building now
From human demonstrations to real-arm policies.
The software loop already exports LeRobot-compatible episodes and can train and evaluate ACT policies. The next scale layer is physical access: operators who own arms, teleoperate tasks, and create diverse demonstrations outside a single centralized lab.
This track is in buildout, not a claim of fleet-scale availability today. We are recruiting arm owners and scoping pilot collections with teams that want to help define the protocol.
Discuss an ACT pilot
Commission a dataset
Bring the question. We'll design the capture around it.
Tell us the task, embodiment, environments, viewpoints, signals, and format you need—or start with the model failure you are trying to fix.