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.

翻译：基于骨骼肌的生物混合驱动器已被证明是软体机器人领域中一种极具前景的组件，能够提供高效的运动。然而，其固有的生物变异性和非线性对可控性与可预测性构成了重大挑战。为解决这些问题，本研究探讨了应用监督学习（一种机器学习形式）来建模和预测生物混合机器的行为，重点关注锚定在柔性聚合物柱上的肌肉环。首先，训练静态预测模型（即随机森林和神经网络回归器），以根据肌肉样本、电刺激参数和基线输出力等输入变量来估计达到的最大输出力。其次，开发了一个基于长短期记忆网络的动态建模框架，作为数字孪生体，复现了在电刺激响应中观察到的输出力时间序列。两种建模方法均展现出较高的预测准确性。静态模型的最佳性能表现为R2达到0.9425，而动态模型的R2则达到0.9956。静态模型能够针对目标应用和所需力输出结果优化肌肉驱动器性能，而动态模型则为未来生物混合机器人系统中开发鲁棒的自适应控制策略奠定了基础。