Physical AI Data Collection
Real-world capture programs for physical AI, robots, sensors, environments, and evaluation slices that match deployment, not internet-scale proxies.
Physical AI data collection is the work of capturing real-world sensor, action, and outcome data so embodied systems learn and are evaluated against the conditions they will actually face. Operant designs capture programs for robots, sensors, and environments, teleoperation, multimodal logs, egocentric views, and failure data, matched to your deployment. The result is training and evaluation data your models can trust, not internet-scale proxies that miss your embodiment.
What physical AI data includes
Physical AI spans manipulation, locomotion, navigation, and human interaction. The data that supports it includes teleoperation and demonstration trajectories, synchronized RGB-D, LiDAR, IMU, and force/torque streams, proprioceptive and control signals, and explicitly captured tail events. Each is tied to scene-level metadata so episodes are auditable and reproducible.
Why internet-scale data is not enough
Web-scale video and simulation are powerful for pretraining, but they lack calibrated sensors, action labels, and the embodiment-specific dynamics of contact and timing. A policy trained only on those proxies tends to break on the sim-to-real gap: lighting, wear, contact, and human interference that simulation does not reproduce. Targeted real-world capture closes that gap where it actually lives.
Teleop, egocentric, multimodal, and failure data
- Teleoperation: human-guided demonstrations for imitation learning, via our teleoperation capture service.
- Egocentric: first-person capture of target behaviors in real environments.
- Multimodal: tightly synchronized sensor suites with calibration and drift control.
- Failure data: rare events, near-misses, and recoveries that dominate deployment risk.
Data quality checklist
A defensible physical AI program defines, up front: time-sync tolerances, calibration procedures, metadata schema, diversity and coverage targets, and acceptance criteria. Operant agrees these during scoping and reports against them at handoff.
Sample program design
A representative program scopes target behaviors and environments, runs a two-to-four week pilot to validate sync and metadata, then scales capture with QA checkpoints. See how this maps to verticals like humanoid robotics and broader robotics data collection.
FAQ
Physical AI data is the sensor, action, and outcome data that embodied systems, robots, vehicles, and humanoids, need to learn and be evaluated in the physical world. It includes teleoperation trajectories, synchronized multimodal sensor logs, egocentric capture, and failure or tail events.
Internet-scale data lacks calibrated sensors, action labels, and the embodiment-specific dynamics of contact, force, and timing. It is useful for pretraining representations but does not capture how a specific robot behaves in a specific environment.
A typical program includes scoping, a calibration pilot, scaled capture across target environments and behaviors, QA against agreed quality bars, and delivery of synchronized logs, calibration files, and metadata in your formats.
Scope your capture program
Book a discovery call to align on your stack and data requirements.