A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams

Abstract

As robots and other autonomous agents are increasingly incorporated into complex domains, characterizing interaction within heterogeneous teams that include both humans and machines becomes more necessary. Previous literature has addressed the task of characterizing human-robot interaction from different perspectives and in multiple contexts. However, the numerous factors behind interaction work in conjunction, and the insights gained from one perspective can inadvertently affect another, creating a need for unification of these taxonomies and frameworks within an overarching taxonomy that systematically defines these relationships. In this paper we review existing taxonomies related to human-robot interaction, the behavioral sciences, and social and algorithmic taxonomies, and propose an overarching ontology for the factors from these works. We identify three main components characterizing the structure of an interaction (environment, task, and team), and structure them over two levels: contextual factors and factors driven by local dynamics. Finally, we present an analysis of how these factors affect decisions about levels of robot automation and level of information abstraction in an interaction, and discuss curent gaps in the literature that can motivate future research.

Determining Optimal Control Frequency for Large Numbers of Virtual Agents within an Augmented Reality Application

“…This visualization may need to change, update, or provide some form of response due to the user's action requiring some form of control script. Another important field in AR is bridging the gap between robotics and humans [15], [16]. These applications will employ virtual agents to represent robots or robot-human scenarios and goals.…”