Learning Graspability of Unknown Objects via Intrinsic Motivation

Abstract. Interacting with unknown objects, and learning and produc-ing effective grasping procedures in particular, are challenging problems for robots. This paper proposes an intrinsically motivated reinforcement learning mechanism for learning to grasp uknown objects. The mech-anism uses frustration to determine when grasping of an object is not possible. The critical threshold of frustration is dynamically regulated by impulsiveness of the robot. Here, the artificial emotions regulate the learning rate according to the current task and performance of the robot. The proposed mechanism is tested in a real world scenario where the robot, using the grasp pairs generated in simulation, has to learn which objects are graspable. The results shows that the robot equipped with frustration and impulsiveness learns faster than the robot with standard action selection strategies providing some evidence that the use of arti-ficial emotions can improve the learning time