The aim of this thesis was to make a touch sensor whose main feature is a rubber membrane. Membrane deformation was first simulated in the Unreal Engine platform, and two membrane models were created for this purpose, with a point and grid patterns. The deformation of the membrane formed in contact with the test prismatic body at a certain angle and depth of penetration were analyzed by deep and machine learning methods. The positions of the centroids of the point membrane served as input examples of the multivariate multiple linear regression, while the images from both types of membrane were input examples of a fully connected deep convolutional network with two regression outputs. As for the physical realization of the sensor, the rubber membrane is located on top of the sensor box whose upper half is filled with gel. The gel is separated from the rest of the case by plexiglass below which is a Rasberry Camera V2 and LEDs that illuminate the inside of the sensor. The camera is connected with flat cable to a Rasberry Pi computer on which the Ubuntu operating system is installed, and communication with camera is achieved with the help of the ROS package raspicam_node. Data collection for learning was accomplished using the robotic maipulator Franka. The images represented an input set of examples for learning a fully connected convolutional deep network with two regression outputs: external force and depth of penetration.