Computer Vision Training Data for Robotics

A standard computer vision dataset is usually a large set of labeled still images or short independent clips, built to teach recognition or detection. Robotics computer vision data has to teach a policy what to do next, which means the unit of data is a continuous episode: synchronized video streams, calibrated depth, and hand or action labels, all aligned to one clock so the model can learn timing and contact, not just what an object looks like in one frame.

• Standard CV data: independent labeled images or clips, one camera, no cross-stream sync requirement
• Robotics CV data: continuous episodes with multiple synchronized streams (RGB, depth, hand pose) on a shared hardware clock
• The difference matters because manipulation and navigation policies learn from the relationship between frames, not from a single frame in isolation

Every stream in a robotics capture rig is calibrated and hardware-triggered onto one clock domain, so RGB, depth, and any added sensors line up frame-for-frame. Depth comes from a calibrated stereo pair when the task needs 3D geometry; hand pose ships as per-joint 3D coordinates with a visibility flag; and every episode carries the intrinsics, extrinsics, and any stereo baseline so downstream training code can trust the geometry without re-deriving it.

A labeled still-image set can pretrain a perception backbone — object detection, segmentation, classification — the same way it would for any computer vision task. It falls short the moment the policy has to act on what it sees: recognizing an object is not the same as knowing when to move toward it, how depth changes as a hand approaches, or where a grasp attempt failed and recovered. That continuous, synchronized signal only comes from video captured with the hardware-sync and calibration discipline above.

Failure-case coverage matters here too: policies fail exactly where a clean, best-case image set has no examples — occlusion, low light, and recovery attempts.

The spec names the streams (RGB, depth, hand pose, action segments), the capture setup (egocentric, stereoscopic, or bimanual rig), the environments and skill vocabulary, and the delivery format. Firsthand turns a written skill spec into a custom batch, validates every hour against the sync, calibration, coverage, and consent checks before delivery, and ships on a fast, on-demand turnaround.