Robotic grasping has been studied for more than 30 years, but it is still a challenging field. Today, most robotic grippers are rigid, making it hard for them to grasp and handle irregularly shaped objects that are delicate and easily deformed such as a compact disc, an egg, or an empty plastic cup. To tackle this issue, soft robotic hands have been introduced. Despite advantages of soft robotic hands, their applications are still limited to simple pick-and-place tasks. The main reason for this is their lack of sensing capabilities, which leads to the absence of information about the internal state of the hand or the interaction between the hand and the environment. This thesis aims to tackle this issue by integrating appropriate sensors into a soft robotic hand. The information extracted from the sensory readings is then used to develop a control strategy to study the interaction between the hand and objects. Experiments performed on the developed soft hand and controller board showed that the interaction between the hand and objects could be studied by using only sensors integrated into the hand. The final results also showed that this information could be used to successfully control the soft hand in real time to achieve a manipulation task such as grasping deformable planar objects especially thin-shell objects like empty plastic cups
