02 / PHYSICAL AI DATA

Teach machines from the physical world.

We coordinate real-world task capture for physical AI and robotics — from participant and device readiness to structured collection, annotation and quality review.

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IMMERSIVE DATA STORYTeach machines from the physical world.Human expertise · Structured data · Quality at scale

PHYSICAL AI CAPABILITIES

Data from the places where physical work happens.

We coordinate first-person video and real-world task data across residential, commercial and industrial settings, with task instructions and quality checks designed for each project.

01 / EGOCENTRIC

Egocentric video

Head-mounted, first-person recordings of task-based activity in real environments.

02 / ROBOTICS

Robotics & manipulation

Task demonstrations, object interaction, teleoperation and perception-oriented data.

03 / SENSORS

Spatial & sensor data

Where required by the project: synchronized video, motion and spatial information, annotation and validation.

FROM TASK TO TRAINING DATA

Capture with intent. Review with care.

The task, device, recording perspective and acceptance criteria are defined before contributors begin.

Scope the task

Define activity, environment, duration, device and output requirements.

Recruit & verify

Match contributors and locations; check eligibility and device readiness.

Train & pilot

Explain mounting, framing, task flow, safety and submission standards.

Capture & monitor

Coordinate sessions, monitor delivery and address field issues.

QA & package

Review footage and metadata, flag defects and prepare approved outputs.

ENVIRONMENTS

Residential to industrial — built around the task.

Examples of environments include household activities, commercial kitchens, warehouses, manufacturing, agriculture, retail and robotics workspaces.

Everyday environments

Residential tasks, home organization, cooking, cleaning and object interaction.

ResidentialHousehold tasksDaily activities

Commercial & industrial

Warehouses, kitchens, factories, agriculture, retail, logistics and operational workflows.

WarehousingManufacturingAgriculture

PHYSICAL AI DATA CAPABILITIES

Teach intelligent systems from real-world activity.

We support data programs that help AI systems perceive environments, interpret human activity and learn how tasks unfold in homes, workplaces and industrial settings.

01 / FIRST-PERSON DATA

Egocentric data collection

Head-mounted or body-worn camera recordings of task-based activities, with clear capture instructions, participant onboarding and environment checks.

02 / ROBOTICS

Robotics data collection

Human demonstrations, robot interaction sessions and task recordings for manipulation, navigation, object handling and embodied AI research.

03 / LABELING

Egocentric data annotation

Action segmentation, activity labels, hand-object interaction, object state, task steps, temporal events and scene context.

04 / PERCEPTION

Visual perception annotation

Object detection, classification, tracking, bounding boxes, polygons, semantic segmentation, scene labeling and spatial relationships.

05 / MOTION & INTERACTION

Human action understanding

Pose and movement labels, gesture recognition, hand activity, object interaction, action sequences and task completion states.

06 / VALIDATION

Multimodal QA & evaluation

Review of video, image and available sensor-linked data for completeness, temporal consistency, annotation accuracy and technical acceptance criteria.

ADDITIONAL SPECIALIST WORKFLOWS

More than recording: structured data for embodied intelligence.

Task and activity segmentation

Break continuous recordings into meaningful steps, actions, transitions and task phases.

Object and scene understanding

Label objects, locations, scene elements, object states and relationships within an environment.

Hand-object interaction

Describe grasping, releasing, manipulating, placing, carrying and other interactions based on project guidelines.

Spatial and sensor-linked data support

Where available and in scope, coordinate synchronized video and sensor-data checks, metadata validation and sequence integrity review.

Environment and task diversity

Plan collection across residential, retail, hospitality, warehouse, manufacturing, agriculture, construction and other approved environments.

Dataset curation

Organize recordings, labels, metadata, issue logs and approved outputs into delivery structures defined by the client.

FROM HUMAN DEMONSTRATION TO MACHINE LEARNING

Capture what happens. Label why it matters.

Physical AI datasets can connect first-person views, task context and structured labels. Each program is designed around the model's intended learning objective and the client's recording, privacy, safety and quality requirements.

ObserveEnvironment

PerformHuman task

AnnotateActions

ValidateQuality

NEXT STEP

Let’s build better AI data together.

Tell us what you are building and where human data can help.

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