Towards Dexterous Agri-Food Manipulation: Topology-Dependent Interaction Patterns in a Reconfigurable Multifingered Gripper

Abstract

Robotic agri-food manipulation remains challenging because food items vary substantially in geometry, compliance, mass distribution, and surface properties, while their fragile nature makes grasping sensitive to small pose errors. This work presents a compact simulation-based study of how grasp topology affects robustness and mechanics-level interaction behavior in a reconfigurable four-finger gripper. Using AGX Dynamics, we evaluate three grasp configurations across representative agri-food objects under controlled yaw and planar-offset perturbations. The results show that spherical grasping is most robust to planar misplacement, torque is more perturbation-sensitive than force, and friction demand is governed more by object geometry than by grasp configuration. These findings provide an interpretable basis for robust and damage-aware configuration selection in agri-food manipulation.