Manipulation

Grasp Quality Metric

A grasp quality metric is a scalar function that scores how good a candidate grasp is, classically from analytic mechanics, such as the Ferrari-Canny epsilon metric measuring the largest disturbance wrench resistible with bounded contact forces, or volume-based measures of the grasp wrench space. Modern systems increasingly use learned quality estimates, trained on simulated or empirical grasp outcomes as in Dex-Net, which better reflect uncertainty in perception and actuation.

Why it matters for physical AI

Ranking candidate grasps well is the difference between a picking cell that works at 99 percent and one that jams hourly; the analytic-versus-learned metric evolution mirrors the field's larger empirical turn.