Human-Robot Interaction (HRI) using Machine Learning (ML): a Survey and Taxonomy

Abstract

Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the humanrobot interaction in a flexible manner. For the sake of reflecting and shaping the interactions between humans and robots, HRI is based on the fusion of the two areas: the people's behaviour and attitudes towards using these robots, as well as the physical, technological, and interactive features of the robots. As the robot has tightly integrated from a set of sensors that collect the data from the environment and send them to the processor which in turn translates the collected data into information that can be used in the robot itself, machine learning (ML) is a well-known research area that focuses on the building of well-stocked knowledge systems by using supervised and unsupervised algorithms. From a conceptual standpoint, this research survey and taxonomy pursue to present an in-depth evaluation and review of the most current state-of-the-art papers that have already been published so far and encompass the use of ML algorithms in the HRI field. Thus, a total of 30 research papers devoted to HRI were examined and analysed to give the most ML algorithms implemented in the field of HRI. Evidently, this study shows that the Neural and Reinforcement learning machine algorithms that are used mostly in the recent studies that have an interest in HRI use a machine learning algorithm with a supervised technique in a physical application. There are many challenges facing HRI using ML algorithms, which reduce the use of other ML algorithms such as deep and SVM learning algorithm. Unfortunately, these challenges limit use in social and mobile applications.