Reinforcement learning of interface mapping for interactivity enhancement of robot control in assistive environments

Abstract

The supervisory control of robots is a very demanding application. In the context of robots control in assistive environments, it is important that the robot user is able to give commands to robots in a way that is easy and intuitive. There are several tasks that can be achieved using robots under assistive environments. It is challenging to efficiently control multiple robots / robots with degrees of freedom with a simple/intuitive interface by a single operator. In this proposal, we propose the use of Reinforcement Learning for intuitive interface mapping. Based on interaction with the environments, we can determine the optimal interface mapping through the process of Reinforcement Learning. The novelty of this paper is the use of changing reward functions based on qualitative performance evaluation for the Reinforcement Learning algorithm. In this paper, we show that the use of proposed reward functions can result in optimal/intuitive interface mapping for multiple robots / robots with degrees of freedom control applications.