Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

Michail-Antisthenis Tsompanas; Marco Perez Hernandez; Faisal Abdul-Fattah; Karim Elhakim; Mostafa Ibrahim; Judith Fuentes; Florencia Lezcano; Riccardo Collu; Massimo Barbaro; Stefano Lai; Samuel Sanchez; Andrew Adamatzky

arXiv · 2602.16330

Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

Michail-Antisthenis Tsompanas·Marco Perez Hernandez·Faisal Abdul-Fattah·Karim Elhakim·Mostafa Ibrahim·Judith Fuentes·Florencia Lezcano·Riccardo Collu·Massimo Barbaro·Stefano Lai·Samuel Sanchez·Andrew Adamatzky

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Abstract

Skeletal muscle-based biohybrid actuators have proved to be a promising component in soft robotics, offering efficient movement. However, their intrinsic biological variability and nonlinearity pose significant challenges for controllability and predictability. To address these issues, this study investigates the application of supervised learning, a form of machine learning, to model and predict the behavior of biohybrid machines (BHMs), focusing on a muscle ring anchored on flexible polymer pillars. First, static prediction models (i.e., random forest and neural network regressors) are trained to estimate the maximum exerted force achieved from input variables such as muscle sample, electrical stimulation parameters, and baseline exerted force. Second, a dynamic modeling framework, based on Long Short-Term Memory networks, is developed to serve as a digital twin, replicating the time series of exerted forces observed in response to electrical stimulation. Both modeling approaches demonstrate high predictive accuracy. The best performance of the static models is characterized by R2 of 0.9425, whereas the dynamic model achieves R2 of 0.9956. The static models can enable optimization of muscle actuator performance for targeted applications and required force outcomes, while the dynamic model provides a foundation for developing robustly adaptive control strategies in future biohybrid robotic systems.

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Michail-Antisthenis Tsompanas, Marco Perez Hernandez, Faisal Abdul-Fattah, Karim Elhakim, Mostafa Ibrahim, Judith Fuentes, Florencia Lezcano, Riccardo Collu, Massimo Barbaro, Stefano Lai, Samuel Sanchez, Andrew Adamatzky. 2026-02-18. Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators. https://arxiv.org/abs/2602.16330

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