AquaROM: shape optimization pipeline for soft swimmers using parametric reduced order models

2025-11-02

AI Summary

Shape optimization of soft robots under complex nonlinear forces—such as hydrodynamic loads—is computationally prohibitive when relying on traditional finite element method (FEM)-based simulation and optimization. Method: This paper proposes a tensor-parametrized reduced-order modeling (TP-ROM) framework that constructs efficient, differentiable nonlinear reduced bases *without requiring training data*, and integrates analytical gradient computation to accelerate constrained optimization with nonlinear constraints. The approach unifies nonlinear dynamical modeling, hydrodynamic field coupling, and tensor-structured dimensionality reduction. Contribution/Results: By preserving modeling fidelity while drastically reducing computational overhead, the TP-ROM framework achieves over an order-of-magnitude speedup in shape optimization iterations for soft swimming robots. It establishes a new paradigm for rapid design of soft robotic systems subject to complex actuation and environmental interactions.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The efficient optimization of actuated soft structures, particularly under complex nonlinear forces, remains a critical challenge in advancing robotics. Simulations of nonlinear structures, such as soft-bodied robots modeled using the finite element method (FEM), often demand substantial computational resources, especially during optimization. To address this challenge, we propose a novel optimization algorithm based on a tensorial parametric reduced order model (PROM). Our algorithm leverages dimensionality reduction and solution approximation techniques to facilitate efficient solving of nonlinear constrained optimization problems. The well-structured tensorial approach enables the use of analytical gradients within a specifically chosen reduced order basis (ROB), significantly enhancing computational efficiency. To showcase the performance of our method, we apply it to optimizing soft robotic swimmer shapes. These actuated soft robots experience hydrodynamic forces, subjecting them to both internal and external nonlinear forces, which are incorporated into our optimization process using a data-free ROB for fast and accurate computations. This approach not only reduces computational complexity but also unlocks new opportunities to optimize complex nonlinear systems in soft robotics, paving the way for more efficient design and control.

Problem

• Optimizing soft robotic swimmers under nonlinear forces
• Reducing computational cost in finite element simulations
• Developing efficient parametric reduced order models for optimization

Innovation

• Parametric reduced order model for efficient optimization
• Analytical gradients in reduced order basis enhance computation
• Data-free reduced order basis for nonlinear force handling