We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous, empirically validated examples of ACT-R/E models.

Recent research in human-robot interaction has investigated the idea of Sliding, or Adjustable, Autonomy, a mode of operation bridging the gap between complete robot autonomy and full teleoperation.This work, by and large, has been in single-agent domains -involving only one human and one robot -and has not examined the issues that arise when moving to multi-agent domains. Here, we discuss the issues involved when adapting Sliding Autonomy concepts to coordinated multi-agent teams. In our system, remote human operators have the ability to join, or leave, the team at will, to assist the autonomous agents with their tasks while not disrupting the team's coordination. We employ user modeling in order to allow agents to request help when appropriate, regardless of whether human operators are actively monitoring their progress. To validate our approach, we present the results of two experiments. The first evaluates the human-multi-robot team's performance under four different collaboration strategies including complete teleoperation, pure autonomy, and two distinct versions of Sliding Autonomy. The second experiment compares a variety of user interface configurations, to investigate how quickly a human operator can attain situational awareness when asked to help. The results of these studies support our belief that by incorporating a remote human operator into multi-agent teams, the team as a whole becomes more robust and efficient.

Because in military situations, as well as for self-driving cars, information must be processed faster than humans can achieve, determination of context computationally, also known as situational assessment, is increasingly important. In this article, we introduce the topic of context, and we discuss what is known about the heretofore intractable research problem on the effects of interdependence, present in the best of human teams; we close by proposing that interdependence must be mastered mathematically to operate human-machine teams efficiently, to advance theory, and to make the machine actions directed by AI explainable to team members and society. The special topic articles in this issue and a subsequent issue of AI Magazine review ongoing mature research and operational programs that address context for human-machine teams.

In order for humans and robots to work effectively together, they need to be able to converse about abilities, goals and achievements. Thus, we are developing an interaction infrastructure called the "Human-Robot Interaction Operating System" (HRI/OS). The HRI/OS provides a structured software framework for building human-robot teams, supports a variety of user interfaces, enables humans and robots to engage in task-oriented dialogue, and facilitates integration of robots through an extensible API.

Teamwork is best achieved when members of the team understand one another. Human–robot collaboration poses a particular challenge to this goal due to the differences between individual team members, both mentally/computationally and physically. One way in which this challenge can be addressed is by developing explicit models of human teammates. Here, we discuss, compare and contrast the many techniques available for modeling human cognition and behavior, and evaluate their benefits and drawbacks in the context of human–robot collaboration.