Robotics Data Collection for Physical AI
Custom robotics data collection programs, teleoperation, synchronized sensors, edge cases, QA, and pilot-to-production delivery built to your spec.
Robotics data collection is the practice of capturing real-world sensor, action, and outcome data so physical AI models learn and are evaluated against the conditions they actually face. Operant designs and runs custom collection programs, teleoperation, synchronized multi-sensor capture, and edge-case scenarios, built around your robot, your environments, and your evaluation goals. We move you from pilot to production with documented provenance and QA, never a generic catalog download.
Why real-world robotics data is scarce
Most robotics teams can simulate cleanly and pretrain on web-scale video, yet still fail on deployment. The gap is real-world data that matches your embodiment: contact-rich manipulation, calibrated multi-camera views, proprioception, and the long tail of failures your evaluation has to trust. That data is expensive to capture well, hard to synchronize, and almost never available off the shelf in a form that matches your action space.
This is why custom collection exists. Rather than reshaping your problem to fit an existing dataset, a collection program captures exactly the trajectories, sensors, and scenarios your model needs.
Collection methods
Operant supports the methods that map to how physical AI teams actually train and evaluate:
- Teleoperation and demonstration capture for imitation learning and policy fine-tuning.
- Egocentric and human-demonstration capture where a human performs the target behavior.
- On-robot autonomous logging during scripted or policy-driven runs.
- Edge-case and failure capture for the rare events that dominate deployment risk.
Methods are combined per program. A manipulation policy might mix teleoperation for the core skill with targeted edge-case scenarios for recovery behaviors.
Sensor modalities
We capture and time-align the modalities your stack consumes, including RGB-D arrays, LiDAR, IMU, force/torque, audio, and proprioceptive and control streams. Synchronization and calibration are first-class deliverables, not afterthoughts, handled through our multi-sensor synchronization service with documented drift characterization.
QA and provenance
Every program ships with quality gates agreed during scoping: time-sync tolerances, calibration checks, metadata completeness, and diversity targets. You receive QA reports, calibration files, and scene-level metadata so your ML team can audit and reproduce what was collected.
Pilot-to-production workflow
- Scope environments, sensors, behaviors, volume, and acceptance criteria.
- Pilot a short capture to validate calibration, labeling, and integration.
- Scale production collection with QA checkpoints and edge-case coverage.
- Handoff deliverables in your formats with documentation and provenance.
Industry use cases
Robotics data collection looks different across verticals. See how it applies to humanoid robotics, warehouse automation, and autonomous vehicles, or browse capture scenarios such as warehouse pallet pick teleoperation.
FAQ
Robotics data collection is the process of capturing real-world sensor, action, and outcome data from robots or human operators so models can learn and be evaluated against deployment conditions. Operant runs this as a custom program scoped to your robot, sensors, and environments.
Open datasets rarely match your robot embodiment, sensor suite, action space, or environment. They are useful for pretraining but miss the deployment-specific behaviors and failure modes that determine real-world performance.
Pilots typically run two to four weeks to validate calibration, metadata, and pipeline fit. Production programs scale over months based on diversity targets and geographies, with timelines fixed in the statement of work.
Yes. Engagements are structured so your organization owns the captured data and associated rights, with terms documented during scoping.
Scope your capture program
Book a discovery call to align on your stack and data requirements.