Our project focuses on developing a tactile sensor for slip detection and force characterization in minimally invasive robotic surgery. Robotic-assisted surgery is becoming more common, but one major limitation is the lack of tactile feedback available to surgeons. Since the surgeon is controlling the tool remotely, they cannot directly feel tissue interaction, friction changes, or slip. This can make it difficult to know how much force is being applied or whether the surgical tool is losing grip. Existing sensing methods, such as optical sensors, can also be unreliable in surgical environments because wet and reflective surfaces interfere with accurate detection. To address this problem, our team designed a soft tactile sensing system that can be integrated with a robotic surgical gripper. The sensor detects contact force and slip by converting mechanical deformation at the contact surface into measurable electrical signals. When force is applied, the sensor deforms, causing localized changes in electrical behavior across different sensing regions. These signals are read using an Arduino Uno and processed through a computer interface to estimate applied force and identify where force is being applied. The sensor is divided into four regions, allowing the system to detect directional force changes across the contact area. This makes it possible to observe whether pressure is concentrated on one side of the sensor and helps characterize shear, torque, and slip behavior. To calibrate the system, a force gauge is used to apply known forces while sensor outputs are recorded over time. The collected data is then used to create a model that estimates unknown forces in real time.