To make interactions between humans and robots safer, soft robots may offer a solution. The autonomous closed loop control of these robots so far, however, is not accurate enough to perform specific tasks as handovers. The purpose of this paper is to propose a control algorithm that can make the control of soft robots accurate enough for human-to-robot handovers. The main focus within this research was on the state estimation and the Jacobian based control. Due to gravity, the internal system state is not an accurate representation of the actual system behavior. The optimized pose estimation solves this problem. In order to test the proposed algorithm, the complete control architecture has been implemented including the object and end-effector detection. Experiments have shown that the algorithm works with different step sizes within the Jacobian based control,
consistently resulting in a successful handover. A second experiment has shown that the handovers are still successful and faster when a human guides the robot toward the right position. Lastly, a possible use case has been shown.