This looks like another step toward removing the data bottleneck in robotics.

The bottleneck exists because recording demonstrations is easy, but labeling every grasp, hold, move, and release is slow and expensive.

Perceptron Egocentric dropped a new robotics video annotation system.

You give it raw robot-camera or first-person/egocentric video.

It gives back structured labels that look more like training data: where each small manipulation starts and ends, what the subtask is, what each hand is doing, where the hands are frame by frame, and which hand is left or right.

So,

Input: raw video of a person or robot doing a task.
Output: a machine-readable breakdown of the physical actions inside that video.

Basically, it is a machine that converts robot video into policy-training supervision.

That means timestamps, subtask boundaries, per-hand actions, and left-right hand grounding.

So this will turn large piles of robot or egocentric video into supervision for training robot policies.