Egocentric industrial workflows and real human task execution.
Physical AI Data Collection
Physical AI Data Collection for Robotics and Embodied Systems
Origin Data Lab designs and operates real-world data collection programs around the tasks, environments, interactions, viewpoints, sensors, metadata, and quality criteria required by Physical AI and robotics teams.
Real Capture Context
Physical AI data must preserve the task, environment, and interaction.
Real-world collection is designed around operational workflows, human behavior, task-centered viewpoints, spatial context, and the sensor signals required by the target system.
Stereo, wearable, and multimodal human-centered capture.
Spatial and manipulation-oriented data for robotics and embodied systems.
What It Requires
Physical AI needs data grounded in real tasks, environments, and interactions.
Training and evaluating systems that act in the physical world requires more than generic video. Collection design must account for the target task, operator behavior, viewpoint, object interaction, motion, sensors, synchronization, environmental diversity, and downstream delivery schema.
01
Task-Aligned Human Demonstration
Capture real human work, hand-object interaction, tool use, bimanual workflows, sequential tasks, and environment-specific behavior.
02
Multimodal Capture Architecture
Configure video, IMU, audio, timestamps, device context, stereo, wearable, spatial, or robotics-linked signals according to project needs.
03
Engineering-Ready Delivery
Produce structured metadata, quality records, manifests, documentation, traceability, and delivery packages aligned to the buyer's pipeline.
Capture Modalities
The model requirement defines the capture system.
Origin Data Lab is not limited to one camera or one modality. Capture architecture is configured around the required signal, environment, hardware, synchronization, and acceptance criteria.
01
Egocentric Human Demonstration
First-person human activity, hand-object interaction, tool use, bimanual work, and task-centered context.
02
Stereo & Multi-View
Project-configurable synchronized left-right or multi-camera capture for spatial perception and viewpoint diversity.
03
Wearable & IMU
Project-defined wearable sensing, motion signals, device IMU, and task-aligned sensor streams.
04
RGB-D & Spatial
Depth-aware and spatial capture can be integrated when project hardware and target model requirements call for it.
05
Robotics & Teleoperation
Customer-provided robotics systems can be incorporated for observation, state, action, trajectory, instruction, or robot-linked sensor data.
06
Video, Audio & Metadata
Standard video, audio, timestamps, GPS where applicable, device information, task context, and structured metadata.
Real-World Environments
Data collection built around the environments your system must understand.
Programs can be scoped around the operational environments, participants, workflows, objects, tools, and regional conditions relevant to the target model.
Industrial & Manufacturing
Assembly, maintenance, production handling, tools, deformable materials, and repetitive industrial workflows.
Logistics & Warehousing
Picking, sorting, scanning, packing, movement, inventory interaction, and operational logistics tasks.
Household & Service
Cleaning, housekeeping, organization, object handling, preparation, and service-oriented tasks.
Food & Skilled Work
Preparation, processing, cutting, wrapping, handling, inspection, and other skilled manual workflows.
Commercial Environments
Retail, hospitality, facilities, kitchens, service spaces, and real operational workplaces.
Mobility & Outdoor
Roads, pedestrian environments, traffic, transport, outdoor work, and perception-oriented field collection.
Production Workflow
From model requirement to validated field data.
Each project begins with the missing condition or target capability, not with a fixed dataset catalog.
01
Define Requirements
Identify target tasks, environments, participants, viewpoints, sensors, metadata, quality rules, and delivery requirements.
02
Configure Capture
Build the practical field protocol, capture architecture, instructions, validation rules, and collection workflow.
03
Pilot & Validate
Run a focused batch to verify technical feasibility, field execution, metadata integrity, and acceptance criteria.
04
Scale Production
Expand validated collection by scenario, participant, environment, geography, volume, or delivery cadence.
05
Quality Control
Review technical compliance, metadata completeness, source traceability, exceptions, and recapture requirements.
06
Engineering Delivery
Deliver structured media, sensor data, metadata, manifests, QA records, schemas, and documentation.
Why Origin Data Lab
We build the data program around the model gap.
Instead of forcing a project into a fixed dataset catalog, we translate the missing capability into a practical capture, field-production, validation, and delivery architecture.
01
Project-Defined Capture
Tasks, environments, viewpoints, participant profiles, hardware, sensors, metadata, and acceptance criteria are defined around the target model requirement.
02
Managed Field Production
Collection protocols, operator instructions, technical checks, QC, exception handling, recapture, provenance, and delivery are managed as one production workflow.
03
Engineering-Ready Delivery
Structured metadata, sensor context, validation records, exception handling, manifests, and project-defined delivery schemas are prepared for downstream model and robotics workflows.
Regional Capability
Real-world collection in South Korea with scalable field operations in Asia.
South Korea can support high-value industrial, service, food-production, skilled-work, household, and technology-centered environments. Additional production locations can be evaluated according to project scope, operational feasibility, and required volume.
South Korea
Industrial Workflows
Skilled Human Tasks
Food Production
Service Environments
Asia Field Operations
FAQ
Physical AI Data Collection FAQ
What types of Physical AI data can you collect?
Programs can include egocentric human demonstration, standard video, stereo or multi-view capture, wearable sensing, IMU, audio, metadata, RGB-D or spatial capture, and robotics-linked data where supported by the project hardware.
Can the capture system be customized for our model?
Yes. Capture architecture can be configured around the task, environment, viewpoint, sensor configuration, synchronization, participant profile, metadata schema, and acceptance criteria.
Can you run a pilot before production?
Yes. A focused pilot can validate capture feasibility, field instructions, sensor integrity, metadata, quality rules, and delivery format before scaling.
Can you collect Physical AI data in South Korea?
Yes. Programs can be developed for approved industrial, service, household, skilled-work, food-production, technology, and other real-world environments in South Korea.
Specialized Capabilities
Explore specialized Physical AI data programs.
Each program focuses on a different technical or regional requirement, from robotics learning and first-person human demonstration to multimodal sensing and South Korea field collection.
01
Robotics Training Data
Human demonstrations, manipulation, perception, VLA, embodied learning, and real-world robotics workflows.
02
Egocentric Human Demonstration
First-person tasks, hands, tools, objects, skilled work, and sequential human workflows.
03
Multimodal Sensor Data
Video, IMU, audio, timestamps, wearable sensing, spatial signals, synchronization, and structured metadata.
04
South Korea AI Data Collection
Region-specific industrial, skilled-work, service, food-production, household, and technology environments.
Start with the Model Requirement
Tell us what your Physical AI system is missing.
A short description of the target task, environment, viewpoint, sensor requirements, metadata, and expected delivery format is enough to begin an initial feasibility review.