An experimental approach to robotic grasping using reinforcement learning and generic grasping functions

Abstract

In this paper we present an experimental approach to robotic grasping that is based on mapping grasping rules to a generic representation that can then be learned by experiments. Furthermore, grasping rules acquired in this format can then be used on different objects using different grippers. During experimentation, reinforcement learning is used to minimize the number of failed experiments. Results show that the system is able to learn how to grasp various objects while maintaining a small number of experiments.