Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

2026-02-18

AI Summary

This study addresses the challenge of achieving high-precision control and prediction in skeletal muscle-based biohybrid actuators, which is hindered by inherent biological variability and nonlinear dynamics. To overcome this, the work proposes a novel hybrid modeling approach that integrates static machine learning regression models with a dynamic LSTM-based digital twin. The static component predicts the actuator’s maximum output force, while the dynamic component captures the temporal force response under electrical stimulation. Leveraging random forest, neural network regression, and long short-term memory (LSTM) architectures, the framework achieves an R² of 0.9425 for static prediction and an exceptional R² of 0.9956 for dynamic force trajectory forecasting. These results demonstrate a significant improvement in predictive accuracy, thereby providing a critical foundation for high-performance adaptive control of biohybrid systems.

Technology Category

• Machine Learning: Bio-inspired Learning
• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• biohybrid actuators
• biological variability
• nonlinearity
• predictability
• controllability

Innovation

• biohybrid actuators
• machine learning
• digital twin
• LSTM
• force prediction