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Computational design can excite the full potential of soft robotics that hasthe drawbacks of being highly nonlinear from material, structure, and contact.Up to date, enthusiastic research interests have been demonstrated forindividual soft fingers, but the frame design space (how each soft finger isassembled) remains largely unexplored. Computationally design remainschallenging for the finger-based soft gripper to grip across multiplegeometrical-distinct object types successfully. Including the design space forthe gripper frame can bring huge difficulties for conventional optimisationalgorithms and fitness calculation methods due to the exponential growth ofhigh-dimensional design space. This work proposes an automated computationaldesign optimisation framework that generates gripper diversity to individuallygrasp geometrically distinct object types based on a quality-diversityapproach. This work first discusses a significantly large design space (28design parameters) for a finger-based soft gripper, including therarely-explored design space of finger arrangement that is converted to variousconfigurations to arrange individual soft fingers. Then, a contact-based FiniteElement Modelling (FEM) is proposed in SOFA to output high-fidelity graspingdata for fitness evaluation and feature measurements. Finally, diverse gripperdesigns are obtained from the framework while considering features such as thevolume and workspace of grippers. This work bridges the gap of computationallyexploring the vast design space of finger-based soft grippers while graspinglarge geometrically distinct object types with a simple control scheme.

Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM