## 3 Challenges and Potentials of Soft Robots
### Design
#### 3.1.2 Design Optimization  
Even when taking inspiration from nature, designing soft robots with the desired mechanical behaviors that allow them to perform specific tasks presents another challenge. The complexity of such robotic systems, due to their unconventional components from materials to actuation, makes it hard to use currently known design and simulation tools to build soft robots [42; 43]. Optimization techniques have been proposed to help automate the design process, and bridge the gap between simulation, fabrication, and the actual performance of soft robots. The general optimization framework can be summarized as choosing the design behavior to be optimized, such as crawling or grasping; identifying the design variables to be optimized, such as the material and the actuation; and defining the constraints of the system. The optimization process iteratively evaluates the design candidates using analysis tools and searches for the optimal design.  
One approach uses evolutionary optimization algorithms to automate the design and manufacturing of freeform soft robots. This approach uses voxel-based dynamic simulation to evaluate the morphology and locomotion of the robot [44]. Voxels are soft cubic blocks with specific parameters, such as stiffness and Poisson's ratio, that undergo volumetric change when forces are applied to them. Another voxel-based method aims to optimize the morphology to achieve adaptability using the property of criticality, which allows the robot to perform more diverse tasks [45].  
Another conceptual design approach provides a spatial grammar to build soft robots and optimize their design for locomotion and actuation [46]. The spatial grammar generates sub-assemblies of interconnected balls based on a set of defined rules. The generated models are then evaluated and optimized in terms of locomotion abilities.  
Performing design optimization for underwater soft robots is an even more challenging problem, as the effect of the environment on the robot's morphology needs to be taken into account. DiffAqua [47], a computational design pipeline, relies on differentiable simulation to perform gradient-based optimization for the geometry and control of soft underwater swimmers. The benefits of exploiting the morphology of soft robots and optimizing it to simplify the control are further discussed in the upcoming modeling and control sections.  
Fabricating and assessing these designs are also challenging processes due to the traditional manufacturing methods being unsuitable for these unconventional soft material. Additive manufacturing (AM) is one of the impactful technologies that helped enable this process [48; 49]. One approach is to use AM to only fabricate the mold that would be used to pour the soft material in them. A more hybrid approach takes advantage of AM techniques, such as the fused filament fabrication (FFF) method, in addition to molding techniques to fabricate and assemble complex soft robotic systems. The third approach is the total additive manufacturing (TAM) approach. It exploits all the benefits of AM to fabricate soft robots, whether by 3D printing multiple soft parts and assemble them, or manufacturing the complete soft robot as a whole part. Such advance in 3D printing techniques for soft materials increased the ability to produce and test different designs of soft robots and optimize their morphological and material parameters.