A Review of Human Robot Proxemics Robot Autonomy and Interactive Learning

Robots are increasingly becoming ubiquitous, but are currently limited in their social capabilities. For robots to become ubiquitous in social environments they need to have an understanding of proxemics. Proxemics is a quickly evolving field in robotics that is rooted in anthropology and psychology. Although it initially sought to explain human-human interaction zones, it is now deeply influential in robotics research and its applications in human-robot interaction problems. This paper examines recent developments in human robot proxemics, how it is affected by emerging technologies and key limitations on further research. Introduction Human robot interaction is near a turning point where robots will start to become ubiquitous. Homes are already being filled by different forms of artificial intelligence (AI), such as vacuum cleaners, personal assistants, and toy robots; however they are solely responsive to a person’s whims rather than self-motivated to interact with said person. Additionally, other social spaces such as hotels, stores, and hospitals are beginning to see the how larger more useful robots could improve their functionality. In order for robots to exist in these spaces, they need to be able to function respectfully and safely around humans. This is where proxemics, the study of human spatial behavior, is needed. Robots need to be polite to humans and respect their space, they need not walk through a group of people interacting with each other and they need to be able to adapt to people of different shapes and sizes. By allowing AI to reason about proxemics, robots will be able to navigate social environments more freely, allowing innovation for human robot interaction to flourish in spaces where robots work alongside with humans as industrial robots or care for humans in the form of companion robots [6, 9]. Current research involving human robot proxemics can be divided into two sections, distance focused and navigation focused. Distance focused research extracts empirical values for reaction bubbles based on how humans react under different circumstances, such as standing or sitting [7, 9, 8, 20]. Navigation focused research works on applications and enhancements of robot navigation using the Social Force Model and in combination with Reaction Bubbles [1, 2, 3, 4, 5, 11, 18]. Proxemics seeks to ensure not only safety but also the comfort of humans near the robot. Kruse T. et al in Human-Aware Robot Navigation goes in depth on existing models up 2013 [24]. They divide navigation approaches into sociability, comfort and naturalness and discuss these in depth. In the more recent years, models have been developed to include these navigation with more than one of these approaches in mind [4, 12, 14]. However, Kruse T. et al discusses the comfort is used very loosely and the most problematic. Recent developments in computer vision and machine learning have bolstered machine perception [16, 23, 25, 26]. This has inspired new approaches to human robot proxemics either through deep reinforcement learning [21] or deep supervised learning [14]. Unfortunately, these new approached are limited by the availability of data. The remaining of this paper will discuss proxemics in further detail, current research, how machine learning and cloud computing are opening new doors, and how gathering data sets that illustrate human robot interactions should be an open and cooperative effort in order to propel human-robot interaction forwards. Proxemics Background Origin of Reaction Bubbles Human special behavior is explained by prior work on anthropology and psychology and is now used as the basis of reasoning for human robot proxemics. Edward T Hall is known for coining the term proxemics. He defines proxemics as the study of the interpretation, manipulation, and dynamics of human spatial behavior in co-present social encounters in The Hidden Dimension [18]. Hall also went on to describe reaction bubbles and how people have differently sized bubbles based on who and how they are interacting with other people. Figure 1 below illustrates how there are different psychophysical aspects to each of the zones proposed by Hall. Mead et al. developed a Bayesian network to determine if a person was expressing social queues based on these psychophysical aspects [11]. Figure 1. Mead et al.’s representation of Halls Reaction Bubbles [11]. Other studies have attempted to empirically measure changes on the size of these zones as a result of different circumstances. Mumm J. et al measures how a person’s perception of a robot, and maintained gaze affect how distance they maintain between themselves and the robot [7]. Walters M. et al. measures how human-robot distances are affected by the robots appearance [8], and Dautenhahn K et al examines how the distance is affected by whether a person is sitting or standing up. These studies develop on ways measuring human robot proxemics providing us with usable values while simultaneously revealing their variability. Additionally, they all are based on the assumption that either the speaker or the robot is static, thus introducing the Social Force Model next. Social Force Model In 1995 D. Helbing et al. published Social Force Model for Pedestrian Dynamics [1]. The Social Force Model (SFM) viewed pedestrian crossings and measured how people avoided each other. The study concluded that based on a person’s position and velocity, one could model their behavior in a crowded environment. This model now serves as the basis of several human robot navigation studies as it is a measurable and replicable model [2, 4, 5, 6, 12]. However, the Social Force Model also has shortcomings, as it designed with the primary goal to circumvent people rather than to interact with them; this is useless if the primary goal of the robot is to interact with