Physical AI and Robotics Training Data Jobs: What Contributors Do in Japan and South Korea

What physical AI training data is and why humans produce it

Physical AI refers to AI systems that control robots or other physical machines: autonomous vehicles, humanoid robots, robotic arms in factories, and drones. Unlike language models trained on text, physical AI models learn from demonstrations of physical tasks performed by humans in real-world environments.

The 2026 consensus in robotics research treats real-world human demonstration data and simulation as complementary, not substitutes. Nvidia named demonstration data the critical bottleneck at GTC 2026. Scale AI has logged more than 100,000 hours of in-house collection. Micro1 contracted workers across more than 50 countries to film household chores for robot training. Physical AI data is active commissioning at scale, not theoretical demand.

Types of physical AI data tasks available to contributors

Physical AI data tasks fall into several categories depending on the type of system being trained:

1. Teleoperation: controlling a robot arm or mobile robot through a joystick or handheld device to demonstrate how to perform a task, while sensors log every movement.

2. Household task video: recording first-person or third-person video of yourself performing everyday tasks such as folding laundry, loading a dishwasher, or sorting objects, using a provided or personal camera setup.

3. Object annotation in video: labeling objects, hand positions, trajectories, and task boundaries in recorded physical task video.

4. Grasp and manipulation annotation: marking how a human hand approaches and grips an object, used to train robotic grasping models.

5. Sensor data review: validating force, torque, or depth sensor readings against the corresponding video to flag calibration errors.

Teleoperation and household video tasks are the most widely commissioned in 2026 because they are the data types that simulated environments reproduce least accurately. Force-torque data and real-world lighting variation are specific gaps that simulated training data cannot fill without supplementation from real human contributors.

Japan and South Korea as physical AI data hubs

Japan's robotics industry context makes it an attractive geography for physical AI data projects. Manufacturing companies testing collaborative robots for factory work need demonstration data from operators who understand factory environments. Household robotics companies developing domestic assistants need recordings from domestic environments that reflect Japanese home layouts and household item varieties.

South Korea's national AI strategy includes smart factory deployment and humanoid robotics development as specific verticals. Both create demand for demonstration data from people working in those environments or willing to record household task video at home. Contributors who work in manufacturing, logistics, food service, or healthcare in Japan or Korea may have specific task-knowledge that is commercially valuable for robotics training.

What contributors need to get started

Equipment requirements vary by task type. Video recording tasks typically require a smartphone or webcam with reasonable resolution, adequate lighting, and a space that can be cleared for the demonstration. Teleoperation tasks use specialized hardware supplied or arranged by the project operator, so contributors usually need proximity to a facility rather than their own equipment.

1. Sign up for a Jwuma contributor account and complete the standard onboarding assessment.

2. Indicate that you are available for physical task or video recording work in your contributor profile settings.

3. Review the specific equipment and environment requirements listed for any physical AI project you apply to.

4. Complete a sample recording or annotation submitted for quality review before the main project assignment.

Physical AI projects on Jwuma may be local or remote depending on task type. Video-based household tasks can be completed anywhere with adequate space. Teleoperation projects typically require a local session at a designated facility.

Pay and project structure for physical AI data work

Physical AI data work typically pays on a per-session or per-hour basis rather than per-label, because the deliverable is a time-stamped recording rather than a discrete annotation. Session rates vary with task complexity, equipment requirements, and the scarcity of qualifying contributors for specific environments. Projects requiring specialized domain knowledge, such as industrial or medical settings, pay at higher rates than general household recording tasks.

Jwuma pays contributors weekly. For physical AI projects, payment clears after the recorded session passes technical quality review, which checks for equipment calibration, task completion, and usable footage rather than judgment calls about annotation preference.