# Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
## 1 Introduction  
The ability of humans to grasp and manipulate objects with their hands and arms is one of their greatest strengths in navigating their environment. Transferring this extraordinary ability to robotics is a complicated and worthwhile challenge, as it could lend robots the adaptability and generalizability of humans and human hands. In recent years, soft materials have played an essential role in advances in robotics [1; 2]. Taking advantage of softness greatly simplifies the grasping process, as it allows more room for error in the placement of the fingers and offers other modes of grasping, such as suction or adhesion gripping [3; 4]. The compliance of these mechanisms also increases the system's safety when compared to rigid bodies [5]. Soft materials allow robots to approximate the softness of human hands, leading to robust and adaptable robotic systems. However, using soft components as an alternative to rigid bodies presents challenges in both control and sensing. The main difficulty in developing soft robotic systems is the implementation of sensors without interfering with the material's softness and controllingthe inherent nonlinearities of compliant bodies [6]. Due to the flexible nature of compliant structures, these mechanisms are often designed to be underactuated and have high-order degrees of freedom [7]. Underactuated robotic hands effectively decrease the complexity of traditional robotic hands, reducing costs and simplifying control architectures [8; 9]. Soft materials have shown promise for solving many problems associated with robotic gripping. Using flexible components to grasp objects can create a grasp similar to how humans manipulate objects. Strategies have been developed for the force control of rigid robots [10], and many techniques have been investigated for the implementation of soft grippers, ranging from sensing and control to the design of compliant mechanisms, with much of this work focusing on motion and shape control [11; 12]. Regulating the force applied to the target object is crucial to ensure safe and efficient manipulation during the gripping process. Force sensing is a fundamental and difficult challenge in the force control of a robotic gripper because sensors can add unwanted stiffness and change the friction of the gripper. Thus, the main objective of this study was to investigate visual force sensing to control the force applied by a soft robotic gripper. Many studies have been conducted on the design of different methods of soft grasping, such as granular jamming [13; 14], surface adhesion [15], and the use of deformable structures to bend around the applied force [16]. The final method was the focus of this study. Fin ray effect grippers, such as the one used in this study, are popular for adaptive gripping solutions. These biomimetic passive compliment mechanisms are designed to mimic the function of fish fins and can perform soft gripping without additional actuators [17]. Designed by placing connecting bars between two beams attached at the tip, entirely made of soft materials, fin ray grippers can deform around externally applied forces, allowing the gripper to adapt to the object in contact with the finger. Visual force sensing is the focus of significant research in soft robotics. Many of these solutions use complex models to approximate the shape and estimate the torques on the joints, while others relate the images to data gathered offline [18; 19; 20; 21; 22]. The methods of sensing for fin ray effect grippers can be broken into two main streams: embedding or attaching sensors into the body or onto the surface of the finger to measure the gripping force directly, or by using the shape of the gripper and either FEA or mathematical modeling to estimate the force visually. Estimating the shape of the gripper is often done visually, with some work attaching a flex sensor to measure deflections [19].