Physical AI & Real-World Human Work
Physical AI systems need more than generic video. We design managed data programs around real human tasks, hand-object interaction, tool use, deformable materials, operational environments, and region-specific workflows.
Programs can be configured for first-person, third-person, video, audio, IMU, sensor metadata, Human GT, and project-defined quality requirements across South Korea, Bangladesh, and additional feasible locations.
Two Ways to Begin
Some programs begin by evaluating a representative capture. Others start with a customer-defined task, environment, viewpoint, sensor configuration, or failure case that requires new field production.
Focused Evaluation
Begin with a representative workflow or scoped pilot to validate viewpoint, task visibility, sensor behavior, metadata structure, and acceptance criteria before scaling production.
Custom Production
Define the target task, environment, geography, participant profile, capture viewpoint, sensors, metadata, annotation, quality gates, and delivery requirements around the model objective.
Physical AI Collection Scenarios
These examples illustrate task-centered programs we can design. Final environments, actions, viewpoints, sensors, participant profiles, repetition rules, and acceptance criteria are defined for each project.
Fine hand-object interaction, component placement, connector handling, inspection, fastening, and repeatable assembly actions for manipulation learning.
Tool selection, cable routing, terminal interaction, fastening, inspection, and coordinated two-handed actions in skilled industrial workflows.
Inspection, disassembly, fastening, part handling, tool use, and sequential maintenance tasks in real service and workshop environments.
Folding, positioning, alignment, fabric manipulation, machine-assisted work, and other deformable-material interactions difficult to represent with rigid objects.
Search, reach, grasp, pick, scan, place, sort, transport, and inventory interactions across structured and semi-structured logistics workflows.
Bedding arrangement, object placement, cleaning, room organization, surface interaction, and other multi-step service and household activities.
Region-specific food production and skilled hand workflows such as kimchi preparation, seafood processing, ingredient handling, cutting, sorting, mixing, and other culturally distinctive tasks that are difficult to source globally.
Real-world mobility remains available for projects requiring mixed traffic, pedestrian interaction, weakly structured road behavior, low-light conditions, and difficult perception cases. It is one capability within a broader real-world production model.
Review Mobility Capability →Related Data Capabilities: Physical AI Data Collection · Robotics Training Data · Egocentric Human Demonstration · Multimodal Sensor Data · South Korea Data Collection
Program Definition
A custom collection program begins with the model problem, not a predefined dataset package. We translate your evaluation gap into a feasible field operation, delivery structure, and quality plan.
Share the environment, failure scenario, model behavior, or coverage limitation your team needs to investigate.
We define the task, environment, participant profile, viewpoint, capture platform, sensors, metadata, annotation, and operational constraints.
Begin with a scoped evaluation, calibration capture, or feasibility pilot before expanding into recurring production.
Let's Start with Your Requirements
Tell us the model task, target workflow, environment, viewpoint, or failure case you need to address. We translate it into a feasible capture, validation, and delivery program.
Feasibility review, pilot design, capture planning, QC definition, and scalable field production available.