Viewpoint Selection without Subject Experiments for Teleoperation of Robot Arm in Reaching Task Using Reinforcement Learning

Abstract

In this study, we proposed a method to evaluate the viewpoint of a robot arm in a reaching movement using reinforcement learning. The optimal viewpoint for operators in teleoperation was studied by conducting a subject experiment. However, in some special situations, such as inside the pedestal of a nuclear plant crushed in a disaster, the lack of environmental information makes it challenging to prepare the subject experiment in advance. In addition, individual differences cannot be eliminated by conducting the subject experiment. In this study, we used reinforcement learning to select viewpoints and found that the world model inspired by the prediction function of the brain exhibited similar performance to that of humans in the reaching motion of a robot arm. This study demonstrated that the world model can evaluate viewpoints using reinforcement learning in the reaching task.