Reinforcement Learning is a discipline that allows the creation of policies and functions that generate control actions for a system given a state. These produce a change in the environment and generate an associated reward. Artificial Intelligence and Deep Learning neural models allow the estimation of these functions to operate in high dimensional domains even in situations where there is a continuous modelling of the system. In this Master's Thesis (TFM) an agent for the control of a UR5e manipulator robot has been developed to perform a reaching task to an object given only visual feedback from the environment. The object is then manipulated by means of classical control techniques. In addition, orientation is included as an objective together with the positioning of the robot at the desired point. For this purpose, the use of dual quaternions is proposed, a tool that allows the encoding of transformations in space in a compact and unified way. The theoretical aspects of the system related to the reinforcement methods and the robotic modelling of the system, together with the design decisions of the agents and the environment, are addressed in this report. In addition, the results obtained in both training and test stages in reaching and grasping tasks with classical control are shown.

This project involves the teleoperation of two UR5e arms. The position/speed control must be implemented, allowing it to be controlled by two Phantom Omni. Work will first have to be carried out on the simulation using rviz and the connection with the Phantoms, to subsequently adjust the controller to the system made up of the two real UR5e arms. The whole implementation will be made to work in ROS as a library.