## 3 Challenges and Potentials of Soft Robots
### Modeling  
The modeling and control phase is the most challenging part of building functional soft robotic systems capable of performing complex tasks and intelligently interacting with their own environment. All the well-established modeling techniques for rigid robots cannot be applied to soft robots due to their continuum property and their complex non-linear dynamics inherent from their elastic behavior. As conventional kinematic and dynamic modeling methods are inapplicable, new approaches for modeling and control of soft robots are being developed (Figure 2). These modeling approaches can be separated into two main categories: model-based approaches and model-free data-driven methods. While the former relies on formulating either an exactly accurate or a simplified approximate model of a soft robot, the latter tries to implicitly learn the behavior of the robot using input and output data collected directly from the actual system. The main goal of the modeling process is to map the soft robot's actuation space to the configuration or task space. Since continuum soft robots are infinite-dimensional systems, formulating such models becomes highly difficult. Instead, the modeling methods rely on approximations and assumptions to reduce the system to a finite-dimensional one. The most commonly used simplification for kinematic modeling is the constant curvature (CC) method [84]. It assumes that the soft robot has constant strain along its whole length [85]. The piecewise constant curvature (PCC) method is an extended version of the CC that \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Robot** & **Biomimicry** & **Actuation** & **Swimming** & **Compliance** \\ \hline
[61] & Octobot & Octopus &
\begin{tabular}{c} FEA (Chemical \\ Reaction) \\ \end{tabular} & - & High \\ \hline
[64] &
\begin{tabular}{c} Morphing \\ Underwater \\ Walking Robot \\ \end{tabular} & - & FEA (Hydraulic) & Walking/Crawling & Medium \\ \hline
[67] &
\begin{tabular}{c} Jellyfish-Inspired \\ Soft Robot \\ \end{tabular} & Jellyfish & DEA & Propulsion & High \\ \hline
[69] & Robotic Manta Ray & Manta Ray & IPMC & MPF Undulation & Medium \\ \hline
[75] &
\begin{tabular}{c} Micro Biomimetic \\ Manta Ray \\ \end{tabular} & Manta Ray & SMA & MPF Undulation & Medium \\ \hline
[71] & Starfish Robot & Starfish & SMA Wires & Propulsion & High \\ \hline
[77] &
\begin{tabular}{c} Starfish-Like Soft \\ Robot \\ \end{tabular} & Starfish & SMA & Crawling & High \\ \hline
[78] & RoboSCallop & Scallop & FEA & Jet Propulsion & Medium \\ \hline
[79] & \begin{tabular}{c} Eel-like Robot \\ \end{tabular} & \
\begin{tabular}{c} Leptoccephalus (Eel \\ Larva) \\ \end{tabular} &
\begin{tabular}{c} Fluid Electrode \\ DEA (FEDEA) \\ \end{tabular} & BCF Undulation & High \\ \hline
[80] & \begin{tabular}{c} Morphing Limb \\ Amphibious Turtle \\ Robot \\ \end{tabular} & Turtle/Tortoise & \
\begin{tabular}{c} Variable Stiffness \\ Material- \\ pneumatic \\ Actuators \\ \end{tabular} &
\begin{tabular}{c} Drag-induced \\ Swimming/Walking \\ \end{tabular} & Medium \\ \hline
[81] & \
\begin{tabular}{c} FinRay Robotic \\ Jellyfish \\ \end{tabular} & Jellyfish &
\begin{tabular}{c} FinRay Actuators \\ driven with \\ Servomotors \\ \end{tabular} & Propulsion & Medium \\ \hline
[82] &
\begin{tabular}{c} PATRICK: Brittle \\ Star-Inspired Soft \\ Robot \\ \end{tabular} & Brittle Star & SMA Wires & Crawling & High \\ \hline
[83] & \
\begin{tabular}{c} Soft Underwater \\ Starfish \\ \end{tabular} & Starfish &
\begin{tabular}{c} Servo-driven \\ Tendon Wires \\ \end{tabular} & Propulsion & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. assumes the strain to be piecewise constant, with each segment of the soft structure having a constant strain [86]. A further extension is the variable curvature (VC) [87; 88], which models each section as a CC. The PCC approximation is mainly valid for cable-driven soft manipulators. However, it cannot capture all the complex dynamics of soft structures, as it is a steady-state model.