Learning to grasp everyday objects using reinforcement-learning with automatic value cut-off

Although grasping of everyday objects has been a research topic over the last decades, it still is a crucial task for service robots. Several methods have been proposed to generate suitable grasps for objects. Many of them are restricted to a certain type of grasp or limited to a fixed number of contacts. In this paper we propose an algorithm based on reinforcement learning, to enable a service robot to grasp every kind of object with as many contacts as needed. The proposed method will be evaluated using a simulation with a three-fingered robotic hand.