Due to the aforementioned material characteristics, it is difficult to analytically or empirically model soft actuators using traditional methods, thus making it difficult to design controllers. On the other hand, machine learning methods have been used to control soft actuators with reliable results in limited workspaces. A major disadvantage of using machine learning in control, compared to physical models, is the requirement of large number of datasets. For example, when it comes to reinforcement learning, it requires a lot of rollouts to train the algorithms to obtain desired controller policies.

Overall, soft actuators commonly show mechanical hysteresis and functional degradation over time. When soft actuators are employed in robotic applications, reliability is a dominant issue. Soft actuators are made of soft materials; these materials are highly non-linear compared to rigid materials, such as large distribution of elasticity and high dimensionality. This leads to a difficulty to predict an appropriate lifetime of the model [127]. Thus, as a future direction, applying a prognostic method will be useful to estimate the performance and the lifetime of soft actuators for the practical implementations [128, 129]. Since the data-driven approaches are widely spread in prognostics field due to its ability of quick implementations and developments, machine learning will be an applicable tool to predict the time-dependent nonlinear performance of the soft actuators. 

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