What work does physical AI data collection outsourcing involve?
Running the operations that produce real-world robot interaction data. Teams set up and reset collection stations, maintain object and scene libraries, record teleoperated and scripted episodes to protocol, grade every episode for success and quality, add metadata and curate balanced datasets. Your researchers define tasks and own the models and robot software. Live intervention in deployed fleets is on humanoid robot teleoperation and labeling of perception data on robotics training data.
Robot foundation models are trained on large, diverse sets of real interactions: grasps, pushes, pours, folds and assemblies. Collecting them at scale is an operations problem as much as a research one.
Let 6–10 mid-sized Philippine physical-AI data teams demonstrate what they can capture.
Real-world demonstrations recorded with care. Let us show you the industry-leading physical-AI specialists robot learning teams depend on. Shortlist in 48–72 hours, at no cost and with no obligation.
The four task families a physical AI collection team covers.
Most teams start with one or two stations on a defined task set, then add stations, tasks and object diversity.
Set up robots, cameras and scenes to protocol, reset between episodes and keep stations running across shifts.
Maintain catalogs of objects and scene layouts, rotate them on schedule and record which were used in every episode.
Record teleoperated and scripted episodes to task protocols, with variations in placement, lighting and distractors.
Grade success, flag anomalies, add metadata and assemble balanced datasets for training and evaluation.
Physical AI data collection outsourcing in the Philippines
Physical AI data collection outsourcing gives robotics teams a dedicated operation for producing the real-world interaction data that robot foundation models need. Every grasp, push, pour, fold and placement recorded on a real robot, with cameras, joint states and often force readings, becomes a training example. Research teams quickly find that recording enough of these, with enough variety, is a full-time operation that engineers cannot run between other work.
Philippine teams already support robotics companies with teleoperation, annotation and data operations. Collection operations can run at client-owned sites with remote operators, at provider-run collection facilities where hardware is shipped and installed, or in hybrid setups. Shift coverage keeps stations productive for most of the day.
Collection station design and throughput
A collection station combines a robot or teleoperation rig, cameras, a workspace, objects and a reset routine. Station design decides throughput: how fast scenes can be reset, how quickly operators move between episodes and how often hardware needs attention. Teams measure episodes per hour, reset time and downtime, and improve station layouts and routines over time.
Multiple stations running the same protocol let programs scale quickly. See robotics teleoperation for operator skills and manufacturing outsourcing for production-line thinking applied to operations.
Task protocols and variation plans
Each task has a written protocol: the goal, starting conditions, allowed strategies, success criteria and what counts as a failure. Variation plans change object positions, orientations, lighting, distractors and backgrounds systematically, so the dataset covers the conditions the robot will meet. Protocol versions are recorded with every episode, so researchers know exactly how data was collected.
When researchers change protocols, operators are retrained and old and new data are kept separate. See synthetic data generation for complementing real episodes with simulated ones.
Who runs what at a collection station
A collection site has several jobs running at once. This is a typical split between your team and the operations team.
| Task | Your research team | Station lead | Operators | QA reviewers |
|---|---|---|---|---|
| Task protocols and variation plans | A | C | I | C |
| Station setup and resets | I | A | R | — |
| Object and scene libraries | C | A | R | — |
| Episode recording | I | A | R | — |
| Episode grading | A | I | — | R |
| Safety incidents | I | A | R | I |
Object and scene libraries
Models generalize when they see many objects and scenes. Teams maintain catalogs of objects with photos, dimensions, materials and IDs, and scene layouts that can be rebuilt consistently. Libraries rotate on schedules, new objects are added regularly and every episode records which objects and scene were used. Damaged or lost objects are replaced and logged.
Balanced libraries prevent datasets dominated by a few familiar items. See data management for catalog and master data practices.
Teleoperated and scripted episodes
Many datasets combine teleoperated demonstrations, where trained operators control the robot through VR, leader arms or other interfaces, with scripted or autonomous episodes that collect policy rollouts. Operators are trained on each task until their demonstrations meet quality targets, and their performance is tracked. Scripted runs are monitored for safety and stopped when behavior goes wrong.
Operator ergonomics and breaks matter for quality and safety. See humanoid robot teleoperation for teleoperation in deployed fleets.
Episode grading and quality control
Not every recorded episode should be used. Reviewers grade each one for task success, smoothness, protocol compliance, sensor completeness and anomalies such as collisions or dropped objects. Grading rules are calibrated with researchers on sample episodes, and reviewer agreement is measured. Failed episodes are kept and labeled, because some training methods use them.
Quality reports show success rates by task, operator and station. See data labeling for agreement measurement and gold-set methods.
Station safety and incident handling
Robots in collection stations can pinch, strike or drop objects, and teleoperation adds the risk of unexpected motion. Stations follow safety rules: guarded workspaces or speed limits, emergency stops within reach, training before operating and clear procedures for incidents. Every incident and near miss is logged and reviewed.
Safety discipline protects people and hardware and keeps collection running. See our compliance and security guide for workplace controls.
Choosing a physical AI collection partner
When you compare vendors, ask about robotics and teleoperation experience, station operations, throughput on similar tasks, grading calibration, safety practices and how they handle data formats such as RLDS or LeRobot. Ask for a pilot on one task with throughput and quality reports.
Vetted mid-sized Philippine providers usually start physical AI programs with 1 to 4 stations and 5 to 30 people, and report episodes per day, success rate, reset time, grading agreement and incident rate. See the AI, robotics and AVs hub.
Who commissions physical AI data collection.
Robotics teams that need far more real-world interaction episodes than their engineers can record, with consistent setups, quality grading and dataset curation.
Pipeline and QA
Your researchers define tasks, robots and success criteria. The vendor runs stations and operators, records episodes to protocol, grades and tags every episode and delivers curated datasets with collection reports.
Two ways outsourced physical AI data collection loses quality.
Both are fixable before go-live.
Rates by annotator and reviewer role, 2026
Ask each vendor to price against episodes per day, number of stations, task complexity and review depth. Rates confirmed per engagement.
Scope my collection operation →Crowd vendor vs. a vetted physical AI collection team.
Where engineer-run collection and a dedicated collection operation differ.
Who outsources physical AI data collection, and where it breaks down.
Large, diverse manipulation datasets across tasks and embodiments.
Whole-body and household task data.
Task-specific data for picking, kitting and assembly.
Benchmark and open dataset programs.
When physical AI data collection belongs with your own team.
What robotics teams ask before they outsource physical AI data collection.
Straight answers to the questions that decide a physical AI data collection engagement, from the principals who run them.
Physical AI data collection in the AI data stack.
This page covers producing robot interaction datasets. Human first-person capture, perception labeling and live fleet teleoperation have their own pages.
Collection stations, episode recording, QA and curation.
First-person video of people doing tasks.
Egocentric data collection →Perception labeling, keypoints and edge cases.
Robotics training data →Also serving: physical AI data collection sits in our AI, Robotics & AVs hub under AI outsourcing. Related pages: egocentric data collection, robotics training data and humanoid robot teleoperation. For a practical walkthrough, see Robot Demonstration Data: Why Diversity Beats Volume for Robot Foundation Models.
Related specialist pages: document annotation, code annotation and sensor and LiDAR data collection. The wider program is on AI data collection.
