Viewpoint Selection for the Efficient Teleoperation of a Robot Arm Using Reinforcement Learning

In this study, we developed a novel method to determine the optimal viewpoint from which an operator could realize faster and more accurate robot teleoperation using reinforcement learning. The reinforcement learning model was trained using images obtained from several candidate viewpoints from scratch, and the viewpoint at which the model achieved the highest rewards was considered the optimal viewpoint. The target robot, task, and environment were modeled using computer simulations and the candidate viewpoint images were obtained using those simulations. We employed the world model as our reinforcement learning model to maximize rewards in the reaching task of a robot arm. The reward function was designed to encourage the robot arm to reach the target position both quickly and accurately. The experimental results validated the choice of the world model as the reinforcement learning model. Moreover, subject experiments wherein subjects operated a robot arm remotely to reach the target position were conducted. The experiments produced results that strongly aligned with the performance obtained through computer simulations, indicating that the proposed method is capable of selecting the optimal viewpoint without handcrafted design and subject experiments.