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

2026-03-20

AI Summary

This work addresses the challenge in task-driven soft robot design, where balancing physical fidelity and computational efficiency often hinders model reusability across actuators and tasks. The authors propose a generalizable reduced-order finite element surrogate modeling paradigm that extracts modular actuator behavior from high-fidelity simulations to construct compact proxy joint models embedded within a pseudo-rigid-body framework. This enables efficient task-level simulation and optimization under realistic physical constraints. The approach establishes an end-to-end mapping from actuator parameters to dynamic models, achieving high-fidelity simulation-to-reality transfer across diverse actuators—including pneumatic bellows and tendon-driven soft fingers—and demonstrates successful application in co-design of soft grippers and 3D shape-matching tasks, validating its accuracy, computational efficiency, and scalability across designs and tasks.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• soft robots
• task-driven design
• surrogate modeling
• model reuse
• computational efficiency

Innovation

• reduced-order modeling
• surrogate modeling
• task-driven design
• soft robotics
• finite element method