Generalized Task-Driven Design of Soft Robots via Reduced-Order FEM-based Surrogate Modeling

• URL: http://arxiv.org/abs/2603.19794v1
• Date: Fri, 20 Mar 2026 09:33:09 GMT
• Title: Generalized Task-Driven Design of Soft Robots via Reduced-Order FEM-based Surrogate Modeling
• Abstract summary: This paper presents a unified reduced-order finite element method (FEM)-based surrogate modeling pipeline for task-driven soft robot design.<n>High-fidelity FEM simulations characterize actuator behavior at the modular level, from which compact surrogate joint models are constructed.<n>A meta-model maps actuator design parameters to surrogate representations, enabling rapid instantiation across a parameterized actuator family.
• Score: 8.36237361666539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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