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.

中文翻译：

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Fin-Bayes：用于软机器人手指的多目标贝叶斯优化框架。


计算设计是实现 Soft Robotics 全部潜力的关键工具，可最大限度地发挥其高性能、灵活性、稳健性和安全互的固有优势。实际上，计算设计需要在参数化设计空间上进行快速迭代搜索过程，使用（经常）计算建模和（更罕见的）物理实验进行评估。贝叶斯方法适用于这些分析成本高昂的系统，并且与比较算法相比，可以有效地探索设计空间。然而，这种计算设计通常会导致与评估中缺乏保真度、缺乏足够的迭代和/或优化为单一目标函数相关的弱点。我们的工作直接解决了这些缺点。首先，我们利用一个复杂的非线性有限元建模套件，该套件明确考虑了几何、材料和接触非线性，以执行快速、准确的表征。我们通过使用自动化测试台和打印的机器人手指进行广泛的物理测试来验证这一点，提供的实验数据比文献中报道的要多得多。其次，我们探索了比比较方法大得多的设计空间，具有更多的自由变量和更多发现新颖、高性能设计的机会。最后，我们使用多目标贝叶斯优化器，该优化器允许确定两个关键目标（柔度和接触力）之间有希望的权衡。我们测试了优化 Fin Ray 抓手的框架，由于其被动的顺应性和耐用性，Fin Ray 抓手在整个研究和工业中无处不在。 结果证明了我们方法的好处，允许在广泛的设计空间内优化和识别有前途的夹持器设计，然后将其 3D 打印并在现实中使用。