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Using deep learning models to accelerate the design of soft robots with genetic algorithms

Year: 2025

Type: conference-paper

Abstract: The motion of soft robots is intrinsically linked to their shape. Design of soft robots is then still a challenge, with a very large design space to explore in terms of possible shapes. Generative methods can be of interest, but they require intensive use of robots motion prediction. We assess the interest of using deep learning models to accelerate the synthesis. The case of pneumatically-actuated structures is considered. We show first that a Resnet model can accurately describe the structure motion after learning on a dataset based on finite element simulations. Second, we show that the model accuracy can be maintained during a synthesis, outside the initial dataset, using transfer learning.

Source: HAL (Le Centre pour la Communication Scientifique Directe)

DOI: 10.60643/urai.v2023p68

Authors: Mosser, Lo¨ıc, Barbé, Laurent, Rubbert, Lennart, Renaud, Pierre

Institutions: Centre National de la Recherche Scientifique, Institut National des Sciences Appliquées de Strasbourg, Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie, Université de Strasbourg

Language: English

Topic: Soft Robotics and Applications

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4391916459

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