Interaction-based multi-objective quality assessment for task-specific vacuum grasping

Two successful grasps can behave very differently: one may require more suction power, another may create excessive force on the object, and a third may form a more uniform and robust seal.

A simple success/failure label loses this information, even though it is directly relevant to industrial process design.

Grasp quality is broken down into five interpretable components: seal strength, pressure uniformity, suction efficiency, gentle handling (low interaction force), and geometric robustness.

Each component captures a different aspect of how the grasp actually behaved during execution, rather than a single aggregate score.

The framework works in two stages. First, point-cloud geometry generates plausible grasp candidates for a given object.

Second, these candidates are physically executed and evaluated using real pressure, force, pose, gripper orientation, and suction-command data, producing a measured quality label that can re-rank the original candidate set.

The best grasp depends on what the process actually values — robust transport, energy-efficient handling, gentle handling, or precision placement can each favour a different grasp position from the same candidate set.

Weighting the five quality components differently therefore allows the same measured database to serve different task requirements.

The resulting grasp database, built from physically measured quality labels, can be used as training data for a prediction model.

This demonstrates how measured grasp quality could eventually guide a robot toward promising grasp regions without exhaustively testing every position.