Reinforcement learning of shared control for dexterous telemanipulation: Application to a page turning skill

Abstract

The ultimate goal of this study is to develop a method that can accomplish dexterous manipulation of various non-rigid objects by a robotic hand. In this paper, we propose a novel model-free approach using reinforcement learning to learn a shared control policy for dexterous telemanipulation by a human operator. A shared control policy is a probabilistic mapping from the human operator's (master) action and complementary sensor data to the robot (slave) control input for robot actuators. Through the learning process, our method can optimize the shared control policy so that it cooperates to the operator's policy and compensates the lack of sensory information of the operator using complementary sensor data to enhance the dexterity. To validate our method, we adopted a page turning task by telemanipulation and developed an experimental platform with a paper page model and a robot fingertip in simulation. Since the human operator cannot perceive the tactile information of the robot, it may not be as easy as humans do directly. Experimental results suggest that our method is able to learn task-relevant shared control for flexible and enhanced dexterous manipulation by a teleoperated robotic fingertip without tactile feedback to the operator.