Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

TL;DR

This paper applies machine learning techniques, including random forests, neural networks, and LSTM networks, to accurately predict the behavior of biohybrid muscle actuators, aiding their control and optimization in soft robotics.

Contribution

It introduces static and dynamic machine learning models as digital twins for biohybrid actuators, improving predictability and control strategies.

Findings

Static models achieve R2 of 0.9425 in force prediction.

Dynamic LSTM model achieves R2 of 0.9956, closely replicating force time series.

Models enable optimization and adaptive control of biohybrid actuators.

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.