ROBOT LEARNING, CONNECTED.

Human skill in,
robot skill out.

Wearable capture, robot workstations and a data engine
that turn human demonstrations into robot training data.

University labs / Corporate R&D / Data capture teams

FROM HUMAN TO ROBOT
Ego headset and FMG armbands in use
Human demonstrationEgo · UMI · Tactile · FMG
DATA
ENGINE
Atlas R1 dual-arm platform
Robot executionAtlas · Training · Evaluation
CaptureBuild datasetsLearn and validate

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THREE WAYS TO START

Start where you are, not from scratch.

Capture a single task, stand up a robot lab or run multimodal research. Buy devices on their own, or get a complete system built around your goal.

THE LEARNING LOOP

Every manipulation
feeds the next round of learning.

Devices to data, models back to tasks.
Follow a single recording through the full research loop.

HUMAN → DATA → ROBOT

Failure attribution → targeted re-capture → next data version

01/ 04

First, record what happened.

Capture egocentric video, demonstrations and contact signals in one shared task context. Pick Ego, UMI, gloves, armbands or real-robot capture to fit the scene.

Task definition / device records / raw data
Explore the Data Engine

Each stage is configured and validated per project.

BUILT FOR DATA OPERATIONS

More than a lab setup.
Designed for capture at scale.

Enterprises don't need more video. They need data that fits the task definition, passes QA, stays traceable and comes with clear usage rights.

See the data delivery process
01

One protocol, every site

Tasks, scene variables, devices and calibration are specified the same way at every capture site.

02

Accept only valid data

Define what counts as a valid episode, and keep rejection reasons, spot-check records and batch reports.

03

Own your data

Versioned datasets with field dictionaries and access control, not scattered folders.

DATA → POLICY → EVALUATION

TRAIN ON YOUR TERMS

Compute sized to the experiment,
models evaluated on real tasks.

Capture workstations, on-robot inference and cloud training are planned separately. Size compute to your policy, data scale and deployment constraints, or plug into your existing environment.

A CLEAR PATH TO DELIVERY

Define success before you build.

From pilot to full delivery, every step has a clear deliverable and a sign-off.

01

Define the task

Confirm the research question, existing equipment, data rights, timeline and acceptance criteria.

02

Validate with a pilot

Validate equipment, synchronization, interfaces and baselines on a small batch or a single task.

03

Configure and deploy

Freeze the hardware and software scope, then deploy, capture or process data in batches.

04

Accept and iterate

Deliver the configuration, data and reports, then use failure records to plan the next round.

LET’S BUILD YOUR NEXT EXPERIMENT

Contact us.
Every partnership starts with a task.

Whether you're choosing devices, setting up a lab or running enterprise capture, start by telling us your goals and current setup.

YOUR RESEARCH BRIEF

Tell us about your project.

Name, contact details and project needs are required. Everything else is optional.

Your submission is only emailed to the PhysenAI team to answer this inquiry and is never published. Please don't include passwords, trade secrets or sensitive personal information.

Your inquiry goes straight to the PhysenAI team. If sending fails, you can copy a summary and reach us another way.