Capture system design
Camera, IMU, and sync architecture on your prototype or pilot fleet instead of generic open datasets with mismatched optics.
Hardware Form / AI training data / Robotics
Embodied AI · teleop · field capture
Robotics AI training data
Policies fail when training video comes from a lab bench but deployment sees warehouse glare, cable clutter, and the grippers you actually ship. We align dataset design with your mechanisms, sensors, and work cells.
Robotics training data collection gathers synchronized perception streams, actions, and outcome labels from simulation, teleoperation, and deployed fleets. We sanitize and version them so imitation learning, VLA, and classical perception models train on distributions that match your hardware.
Signals that move embodied models
Egocentric video and language-conditioned trajectories get most of the press. Production robotics still needs camera placement on head or wrist, calibration, time sync, and coverage of the failure modes your reliability team tracks.
| Signal | What it trains | Collection notes |
|---|---|---|
| Egocentric RGB-D | Manipulation, navigation | Matched intrinsics, exposure under motion blur |
| Teleop demonstrations | Imitation, VLA policies | Consistent success/fail labels, object IDs |
| Force / torque | Insertions, assembly | Calibrated sensors, safety bounds in capture |
| Fleet logs | Anomaly detection, retraining triggers | Privacy-safe site metadata, OTA versioning |
Hardware Form connects data programs to physical AI and robotics engineering so capture rigs, mounts, and on-robot compute match what you manufacture.
Teleop & human demo
Structured task scripts, object sets, and quality gates for demonstration volume without label drift.
Sim + real blending
Metadata that records which slices are sim versus field so teams can measure sim-to-real gap with clear accounting.
Sanitize & deliver
PII blur for bystanders, format normalization (ROS bags, MCAP, custom), and versioned drops to your training bucket.
Robotics training data FAQ
What is robotics AI training data?
Synchronized multimodal recordings plus labels that teach robots to perceive, plan, and act: video, depth, proprioception, language commands, and success or failure outcomes.
Can you use open datasets like something from a research lab?
Open sets help baselines but rarely match your gripper kinematics, workspace, or SKU mix. We add targeted capture instead of relying on mismatched public corpora alone.
Do you annotate 3D scenes?
Yes. We provide 6-DoF poses, contact states, and semantic instance masks when your pipeline requires them, with QA sampling on geometric consistency.