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This research advances AI-driven robotics by introducing a differentiable simulation framework for elastic robotic fish, enabling efficient gradient-based optimization of body stiffness distributions. By coupling continuous structural dynamics with hydrodynamic forces without prescribing kinematics, the model facilitates rapid design iteration for soft-bodied actuators. This work bridges computational fluid-structure interaction with machine learning techniques to solve complex physical problems in underwater locomotion.
Sources coveringarXiv, Quantum Computing Report
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Differentiable Dynamics and Fast Simulation of Continuous Elastic Robotic Fish

arXiv2 min read

arXiv:2509.16145v3 Announce Type: replace Abstract: Body flexibility plays a critical role in fish-like swimming, as the spatial distribution of stiffness governs body deformation, hydrodynamic loading, and propulsive performance. Exploiting this mechanism in robotic fish requires dynamic models that capture continuous body elasticity, fluid-structure interaction, and the resulting self-propelled motion. Existing approaches often prescribe body kinematics, approximate the body using discrete rigid or compliant segments, or incur high computational costs that limit their use in design optimization. In this letter, we present a differentiable full-body dynamics model and fast simulation framework for motor-actuated elastic robotic fish based on Hamilton's principle. The proposed formulation represents the robot as a continuously deformable elastic body and couples its structural dynamics with hydrodynamic forces without prescribing body kinematics. The resulting simulator is differentiable with respect to model and design parameters, enabling efficient gradient-based optimization. Numerical convergence studies and experiments with a physical robotic fish validate the proposed framework. Finally, gradient-based optimization of the body stiffness distribution demonstrates its utility for efficient design of elastic robotic fish.

Abstract:Body flexibility plays a critical role in fish-like swimming, as the spatial distribution of stiffness governs body deformation, hydrodynamic loading, and propulsive performance. Exploiting this mechanism in robotic fish requires dynamic models that capture continuous body elasticity, fluid-structure interaction, and the resulting self-propelled motion. Existing approaches often prescribe body kinematics, approximate the body using discrete rigid or compliant segments, or incur high computational costs that limit their use in design optimization. In this letter, we present a differentiable full-body dynamics model and fast simulation framework for motor-actuated elastic robotic fish based on Hamilton's principle. The proposed formulation represents the robot as a continuously deformable elastic body and couples its structural dynamics with hydrodynamic forces without prescribing body kinematics. The resulting simulator is differentiable with respect to model and design parameters, enabling efficient gradient-based optimization. Numerical convergence studies and experiments with a physical robotic fish validate the proposed framework. Finally, gradient-based optimization of the body stiffness distribution demonstrates its utility for efficient design of elastic robotic fish.

Robotics (cs.RO)

arXiv:2509.16145 [cs.RO]

(or arXiv:2509.16145v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2509.16145 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 257 words

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