ROBOT ARM CONTROL FOR REINFORCEMENT LEARNING BASED TACTILE OBJECT MANIPULATION AND HUMAN-ROBOT OBJECT HANDOVER

Abstract

Industrial manipulators are capable of completing a wide range of tasks. One of these tasks is object manipulation. There exist two ways to perform such tasks. The first is to have a fully defined problem with known kinematic and dynamic models of the manipulator and the objects. This approach has been shown to be successful in a huge number of works. However, it has some limitations connected to cases when we do not have complete knowledge about the robot, object, or the task itself. Thus, another approach is to use machine learning in order to learn to perform a task without knowing its intrinsic parameters. In this work, we research both approaches and show how we can use them in order to perform some dexterous manipulation tasks. The former is illustrated in the example of the human-robot object handover. The latter is shown in a variety of experiments where a robot learns to perform a specific task.