## 3 Challenges and Potentials of Soft Robots
### Control
The robot is trained through trial-and-error interaction with its environment in order to find an optimal policy to maximize its reward function, which is the displacement at each time step. The displacement is monitored using a camera and then fed to the reinforcement learning algorithm. Applying reinforcement learning with soft robots is a costly operation due to their non-linear hyperelastic properties. To overcome this issue, the reinforcement learning approach used to control the Honeycomb Pneu-Nets Soft Robot ignores the specific properties of the materials and the structural characteristics of the robot [125], concentrating only on the geometric model, which simplifies the modeling task. Another challenge to implementing RL is the lack of accurate simulators for soft robots. To overcome this problem, researchers performed the training process on both the simulation as well as the physical hardware to obtain a more realistic control policy that works well with the actual robot. The reinforcement learning framework consists of two parts: formulating the set of representations for the robot's states and actions, and the training process to search for accurate results in the problem space. The physical hardware uses air pumps and valves to achieve the actuation needed and uses the OptiTrack motion capture system for visual sensing to determine the actual state of the robot. Two methods of control were used to execute the learned RL policy: open-loop and closed-loop. The closed-loop method obtains the actual robot state from the sensor as the input to the trained control policy function, while in the open-loop method, the robot's state is obtained from the simulator, leading to some errors. Despite providing a good solution in many cases, RL may be impractical to implement when the reward function cannot be clearly defined. Imitation learning solves this issue by using demonstrations performed by an expert agent. One group used imitation learning to perform motion control and trajectory planning for soft continuum robots [126]. They proposed the learning from demonstration (LfD) approach and implemented it on the Bionic Handling Assistant (BHA) robot, which resembles an elephant trunk. One of the simplest methods to implement LfD is kinesthetic teaching, which is achieved by directly recording demonstrations on the target robot to collect the position and orientation data of the end effector. The problem of using kinesthetic teaching with soft robots arises from the dynamic complexity of non-linear elasticity associated with these soft materials. They proposed an active compliant control to record the demonstrations during kinesthetic teaching, then encoded the recorded data with a task-parametrized Gaussian mixture model (TP-GMM). A similar approach was used with the soft cylindrical robot arm STIFF-FLOP [127]. However, the researchers worked on transferring the movement patterns of an octopus arm to the STIFF-FLOP. The octopus arm movements were obtained from a database of the cartesian position of several octopuses' arms with an average of 100 points along the arm. They exploited several methods for the representation of the octopus reaching motion to help transfer the arm movements to the soft robot. They considered spatiotemporal representation and dynamical movement primitives to allow for a more robust movement transfer. They then used Gaussian mixture regression (GMR) for encoding, and applied a self-refinement algorithm with a weighted reward function according to different tasks. Following a similar thought process to imitation learning, a trending new paradigm called morphological computation aims to learn from living creatures, but on the level of morphology instead of the locomotion level. Morphological computation is a process related to embodied intelligence, where some of the computation needed for actuation and perception is conducted automatically by the body of the living creature instead of sending sensory information to the brain and waiting for a control signal [128].