Human-Robot Interaction (HRI) using biological signals has garnered research interest due to its potential to assist people with motor disabilities, such as in controlling wheelchairs, speech aids, or interacting with a computer. These systems involve acquiring physiological signals generated by the user’s intentions, which are then processed by a computational algorithm. In this study, we developed a method to interact with a mobile manipulator robot in a controlled virtual environment, allowing it to pick up and move objects using eye movements acquired through Electrooculography (EOG) and classified by an eight-class Convolutional Neural Network (CNN) algorithm. A Graphical User Interface (GUI) was designed for interacting with the robot, enabling the user to control various commands through eye movements, with visual feedback provided for the robot’s workspace. The algorithm achieved 92% accuracy in classifying eight eye movements, and during the experiment, the user successfully interacted with the robot and located an object of interest within its workspace in 19 s.