## 3 Challenges and Potentials of Soft Robots
### Modeling
Nonetheless, it proves to be an accurate model for control for a lot of tendon-driven manipulators [89; 90; 91; 92]. One group also used a PCC kinematic model of a multi-segment pneumatic actuator to develop a dynamic motion controller [93]. Numerical techniques have also been used for modeling soft robots. The Cosserat rod theory is one of these numerical methods [94; 95], accurately representing the tension, shearing, bending, and torsion of rods. A different numerical approach for modeling soft robots is the finite element method (FEM), which yields more accurate results but at the cost of high computational requirements [96]. The method relies on discretizing the structure into a large number of nodes, called mesh, and iteratively solving the differential equations governing the behavior of these nodes, until the model converges. However, the use of FEM for real-time control is difficult, so control approaches based on real-time FEM have been proposed [97; 98; 99]. An approach combining kinematic modeling using PCC and Denavit-Hartenberg (DHI) parameters with FEM analysis was also used to model a soft pneumatic actuator (SPA) [100]. The problem with the FEM is the high dimensional space of the obtained model. A common solution is to reduce the domain of the model in order to achieve high computational efficiency, without sacrificing accuracy [101; 102]. The reduced-order model can help with the development of low-order controllers and observers based on a linearized model of the system [103; 104]. One group developed a dynamic simulation tool for articulated soft robots based on numerical simulation methods for slender structures [105]. Another method uses genetic algorithms for dynamic parameters estimation of an octopus-inspired robot [106]. Controllers developed from the static kinematic models obtained using the described methods are considered static controllers that discard the underlying dynamics of the system. Developing high-order dynamic models for soft robots is difficult and computationally expensive for controllers. A first-order dynamic modeling approach is proposed to reduce the computational space without affecting the controller's performance [107]. Figure 2: An overview of the different modeling and control techniques used in soft robotics. The model-free methods for modeling soft robots mainly use data-driven machine learning and deep learning techniques to find a mapping between the inputs and outputs of the soft system [108]. Input actuation signals and robot states can be obtained by sensors, either embedded or external visual tracking sensors. The data can then be used with different supervised learning, unsupervised learning, and reinforcement learning techniques to develop models and controllers for soft robots. Examples of commonly used techniques include feedforward neural network (FNN) [109; 110; 111; 112], recurrent neural network (RNN) [113], convolutional neural network (CNN) [114], and echo state network (ESN) [109], based on the reservoir computing framework. FNNs are widely used for modeling soft systems. One approach is the use of an FNN to model the work envelope of an SPA with a variable inclination angle [115]. Another group used FEM-generated training data to learn the kinematic model of a 3D motion SPA using an FNN [111]. An RNN used sensory data from cPDMS resistive sensors and a load cell to predict the deformation and force models of a soft pneumatic finger [113]. A reservoir computing approach with the ESN architecture was used to model the 2D motion of a bioinspired turtle actuated through soft pneumatic flippers [109]. Learning-based techniques also proved to be successful in learning the dynamic models of soft robots. One approach involved using a nonlinear autoregressive exogenous (NARX) model to develop a dynamic model for a soft manipulator, which was used to implement a task space controller [116]. A deep neural network (DNN) model learned the non-linear dynamics for a single degree of freedom inflatable pneumatic robot.