Task-driven design of soft robots requires models that are physically accurate and computationally efficient, while remaining transferable across actuator designs and task scenarios. However, existing modeling approaches typically face a fundamental trade-off between physical fidelity and computational efficiency, which limits model reuse across design and task variations and constrains scalable task-driven optimization. This paper presents a unified reduced-order finite element method (FEM)-based surrogate modeling pipeline for generalized task-driven soft robot design. High-fidelity FEM simulations characterize actuator behavior at the modular level, from which compact surrogate joint models are constructed for evaluation within a pseudo-rigid body model (PRBM). A meta-model maps actuator design parameters to surrogate representations, enabling rapid instantiation across a parameterized actuator family. The resulting models are embedded into a PRBM-based simulation environment, supporting task-level simulation and optimization under realistic physical constraints. The proposed pipeline is validated through sim-to-real transfer across multiple actuator types, including bellow-type pneumatic actuators and a tendon-driven soft finger, as well as two task-driven design studies: soft gripper co-design via Reinforcement Learning (RL) and 3D actuator shape matching via evolutionary optimization. The results demonstrate high accuracy, efficiency, and reliable reuse, providing a scalable foundation for autonomous task-driven soft robot design.

翻译：任务驱动的软体机器人设计要求模型兼具物理准确性与计算高效性，同时需跨致动器设计与任务场景保持可迁移性。然而，现有建模方法通常面临物理保真度与计算效率之间的根本性权衡，这限制了模型在设计与任务变化场景中的复用以及可扩展的任务驱动优化。本文提出一种统一的降阶有限元代理建模管线，用于通用任务驱动型软体机器人设计。通过高保真有限元仿真在模块层面表征致动器行为，进而构建紧凑的代理关节模型，并嵌入伪刚体模型进行评估。元模型将致动器设计参数映射至代理表征，支持参数化致动器家族的快速实例化。所得模型嵌入基于伪刚体模型的仿真环境，可在真实物理约束下实现任务级仿真与优化。所提管线通过多类致动器的虚实迁移实验验证，包括波纹管型气动致动器与肌腱驱动软体手指，并开展两项任务驱动设计研究：基于强化学习的软体夹爪协同设计与基于进化优化的三维致动器形状匹配。结果表明该方法兼具高精度、高效率与可靠复用性，为自主任务驱动的软体机器人设计提供了可扩展基础。