A Deep Reinforcement Learning Algorithm for Robotic Manipulation Tasks in Simulated Environments

Abstract

Industrial robots are used in a variety of industrial process tasks, and due to the complexity of the environment in which these systems are deployed, more robust and accurate control methods are required. Deep reinforcement learning emerges as a comprehensive approach that directly allows for the mapping of sensor data and the setting of motion actions to the robot. In this work, we propose a robotic system implemented in a semi-photorealistic simulator whose motion control is based on the A2C algorithm in a DRL agent; the task to be performed is to reach a goal within a work area. The evaluation is executed in a simulation scenario where a fixed position of a target is maintained while the agent (robotic manipulator) tries to reach it with the end-effector from an initial position. Finally, the trained agent fulfills the established task; this is demonstrated by the results obtained in the training and evaluation processes, and the reward value increases when the measured distance decreases between the end-effector and the target.

Keywords

• robotics manipulation
• DRL algorithms
• deep learning
• reinforcement learning
• robotic simulator
• CoppeliaSim