## 3 Challenges and Potentials of Soft Robots
### Control  
Models of soft robots developed using the discussed approaches are used to develop kinematic and dynamic control for these complex systems. Model-based controllers rely on models obtained from analytical kinematic methods such as PCC, whereas model-free controllers use data-driven techniques [91]. Different control algorithms are used, depending on the level at which the controller operates. Low-level controllers drive the actuators, whereas mid-level ones are responsible for the kinematic and dynamic control, and high-level control involves advanced trajectory and path planning for tasks such as obstacle avoidance. The main task of soft robots' controllers is to manage the whole-body deformation of the robot; it may also include controlling the exact position and orientation of an end effector in the case of soft manipulators. One approach uses open-loop control for a soft manipulator [118]. The implemented open-loop dynamic controller uses a data-driven model with only mechanical feedback. The swimming eel-like robot [119] was modeled using the geometrically exact beam theory with a torque control algorithm. A different approach uses an energy-shaping approach to develop the control law for a soft continuum manipulator [120]. MPC is another technique that was employed for the control of large-scale soft robots [121]. The MPC algorithm relies on a PCC model alongside a kinematic representation for efficient state prediction. A model reference predictive adaptive control (MRPAC) was also implemented on the same model and showed robustness to model uncertainties. Closed-loop control methods have also been demonstrated for the position control of soft robots [122; 123]. One of the most promising approaches for the model-free control in the robotics field, in general, is reinforcement learning (RL), which has been proven successful for soft robots [124]. RL can be described as an iterative learning process where the agent takes an action, its new state is observed, and a response in the form of a reward function is given to it based on the resulting interaction with its environment. When the agent learns a policy to map appropriate state-action pairs, then the learning is successful. RL can be implemented regardless of whether a model of the system is known. The use of deep reinforcement learning (DRL) and imitation learning algorithms in the development of soft robots hasbeen shown in several experimental examples. DRL methods are now integrated with soft robots in various applications such as biomedical and edible robotics. Some research focused on the deep Q-network (DQN) algorithm, which was used in a soft robotic fish used for underwater exploration [40]. Other common algorithms are deep deterministic policy gradient (DDPG), normalized advantage function (NAF), and advantage actor-critic (A2C). However, problems arise from the differences between the simulation environments where the robots are trained and the real-world environment. The use of generative adversarial network (GAN) is a suggested solution to help perform domain adaptation and narrow the gap between the simulation and real-world environments. Imitation learning is also beneficial when it is difficult to formulate a reward function to train a DRL model. The imitation learning algorithms use demonstrations constructed by an expert agent that are transferred to the soft robot. The most common imitation learning approaches are behavior cloning, inverse reinforcement learning, and generative adversarial imitation learning. The future scope is the combination of both deep reinforcement learning and imitation learning algorithms to benefit from both approaches and train better agents.One example is a soft artificial-muscle-driven robot mimicking cuttlefish actuated by a dielectric elastomer (DE) membrane [66]. The robot uses reinforcement learning to provide the actuation. To simplify the problem, only two actions are considered, with only two voltage amplitudes: 0 and 6.8 kV.