Fin-Bayes: A Multi-Objective Bayesian Optimization Framework for Soft Robotic Fingers.

Computational design is a critical tool to realize the full potential of Soft Robotics, maximizing their inherent benefits of high performance, flexibility, robustness, and safe interaction. Practically, computational design entails a rapid iterative search process over a parameterized design space, with assessment using (frequently) computational modeling and (more rarely) physical experimentation. Bayesian approaches work well for these expensive-to-analyze systems and can lead to efficient exploration of design space than comparative algorithms. However, such computational design typically entails weaknesses related to a lack of fidelity in assessment, a lack of sufficient iterations, and/or optimizing to a singular objective function. Our work directly addresses these shortcomings. First, we harness a sophisticated nonlinear Finite Element Modeling suite that explicitly considers geometry, material, and contact nonlinearity to perform rapid accurate characterization. We validate this through extensive physical testing using an automated test rig and printed robotic fingers, providing far more experimental data than that reported in the literature. Second, we explore a significantly larger design space than comparative approaches, with more free variables and more opportunity to discover novel, high performance designs. Finally, we use a multiobjective Bayesian optimizer that allows for the identification of promising trade-offs between two critical objectives, compliance and contact force. We test our framework on optimizing Fin Ray grippers, which are ubiquitous throughout research and industry due to their passive compliance and durability. Results demonstrate the benefits of our approach, allowing for the optimization and identification of promising gripper designs within an extensive design space, which are then 3D printed and usable in reality.