How to choose a teleoperation interface for data collection
Pick the teleoperation interface that preserves the action space, contact, and operator skill your policy needs.
Insights
From the field
Practical guidance for teams building real-world robotics data programs.
Robotics Data Collection Glossary
Align buyers, ML engineers, and operators on the terms behind custom real-world robotics capture.
How to compare robotics data collection vendors
Evaluate robotics data collection vendors by deployment fit, synchronization rigor, QA, ownership, and how safely they move from pilot to scale.
How to scope a robotics data collection pilot
Define the behavior, sensors, QA bar, and delivery format before scaling custom robotics capture.
Metrics that prove sim-to-real gap closing
Track deployment KPIs on targeted real slices before declaring sim-to-real victory.
Teleoperation capture best practices for imitation learning
Lock the action space, cameras, episode boundaries, and QA gates before a teleoperation pilot scales.
Building evaluation benchmarks from real-world tails
Benchmarks copied from training distributions hide failures you will see in production.
Privacy and consent for on-site robotics capture
On-site capture needs clear consent, minimization, and transfer controls, not afterthoughts.
Running robotics data collection like a production pipeline
Pilots, QA gates, and operator runbooks turn one-off demos into reliable data supply.
What "good" physical AI data looks like
Episode volume alone does not predict deployment success. Sync, calibration, and tail coverage matter.