Vortex grippers are commonly integrated into automated systems for handling objects with complex geometries in packaging, sorting, and assembling. However, their interaction with compliant objects remains difficult to predict, particularly with respect to the vortex-induced torque transmitted during grasping. This study investigates vortex-induced torque during the grasping of compliant objects and quantifies the coupled effects of supply pressure, tangential nozzle diameter, surface geometry, and curvature. The results show that increasing the tangential nozzle diameter primarily enhances the sensitivity of the generated torque to supply pressure rather than producing a uniformly higher torque. Surface geometry also strongly influences the rotational interaction, with dome-concave surfaces generally producing substantially greater torque than corresponding dome-convex surfaces, whereas the difference between cylindrical concave and convex surfaces is comparatively small. The strongest and most repeatable response was observed for a dome-concave surface exhibiting favorable object-gripper geometric matching. Among the evaluated machine-learning regression methods, Gaussian Process Regression provided the best overall predictive performance under repeated cross-validation, demonstrating that vortex-induced torque can be estimated from combined pneumatic and geometric parameters. These findings provide a basis for predicting and utilizing vortex-induced torque in the dexterous manipulation of soft objects.

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