In the automated co-design of soft robots, precisely adapting the material stiffness field to task environments is crucial for unlocking their full physical potential. However, mainstream platforms (e.g., EvoGym) strictly discretize the material dimension, artificially restricting the design space and performance of soft robots. To address this, we propose EvoGymCM (EvoGym with Continuous Materials), a benchmark suite formally establishing continuous material stiffness as a first-class design variable alongside morphology and control. Aligning with real-world material mechanisms, EvoGymCM introduces two settings: (i) EvoGymCM-R (Reactive), motivated by programmable materials with dynamically tunable stiffness; and (ii) EvoGymCM-I (Invariant), motivated by traditional materials with invariant stiffness fields. To tackle the resulting high-dimensional coupling, we formulate two Morphology-Material-Control co-design paradigms: (i) Reactive-Material Co-Design, which learns real-time stiffness tuning policies to guide programmable materials; and (ii) Invariant-Material Co-Design, which jointly optimizes morphology and fixed material fields to guide traditional material fabrication. Systematic experiments across diverse tasks demonstrate that continuous material optimization boosts performance and unlocks synergy across morphology, material, and control.

翻译：在软体机器人自动化协同设计中，精确调整材料刚度场以适应任务环境，对于释放其全部物理潜能至关重要。然而，主流框架（如EvoGym）严格离散化材料维度，人为限制了软体机器人的设计空间和性能。为此，我们提出EvoGymCM（连续材料EvoGym），该基准套件将连续材料刚度正式确立为与形态和控制并列的一等设计变量。为贴合真实材料机理，EvoGymCM引入两种设置：（i）EvoGymCM-R（响应式），基于刚度可动态调节的可编程材料；（ii）EvoGymCM-I（不变式），基于刚度场恒定不变的传统材料。针对由此产生的高维耦合问题，我们构建了两种“形态-材料-控制”协同设计范式：（i）响应式材料协同设计，通过学习实时刚度调整策略来指导可编程材料；（ii）不变式材料协同设计，联合优化形态与固定材料场以指导传统材料制造。跨多样任务的系统性实验表明，连续材料优化能提升性能，并释放形态、材料与控制之间的协同效应。