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 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.
Yao et al. (2026) studied this question.