Towards Dexterous Agri-Food Manipulation: Topology-Dependent Interaction Patterns in a Reconfigurable Multifingered Gripper
Hongyu Lan, Alessio Caporali, Chengxiao Dong, Gianluca Palli, Claudio Melchiorri
AI summary
Problem
Robotic grasping of fragile, variable agri-food items is highly sensitive to perception and placement errors, yet current research on reconfigurable grippers overlooks how grasp topology influences mechanics-level interaction patterns under perturbation.
Approach
We simulate a reconfigurable four-finger gripper across three grasp topologies and five food objects in AGX Dynamics, systematically applying yaw and planar-offset perturbations to measure robustness and contact mechanics.
Key results
- Spherical grasp topology maximizes robustness to planar placement misalignment
- Object-centered torque is more perturbation-sensitive than contact forces
- Friction demand is driven primarily by object geometry rather than grasp configuration
- Penetration depth and slip margins vary more across objects than across topologies
Why it matters
Provides an interpretable, mechanics-level basis for selecting gripper configurations to minimize damage risk in automated agri-food handling systems.
Abstract
Robotic agri-food manipulation remains chal- lenging 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 manipula- tion.