Successful human-robot (H-R) teams working with direct interaction and close coupling will have relationships that vary, depending on whether or not collaboration is prioritized and how collaboration changes the human mental workload and the team's task performance. Modeling of representative functions can provide predictions of changes in mental workload and the potential impact on team performance. The presented research focuses on modeling and quantifying mental workload for H-R teams in which team members have some individually assigned responsibilities but must also make joint decisions with a teammate. IMPRINT Pro, a discrete event simulation modeling tool created by the U.S. Army Research Laboratory, was used to model human-human (H-H) and H-R teams completing a reconnaissance task in a building. This research evaluated H-H and H-R teams completing the same reconnaissance tasks. Predictions of mental-workload levels from the model and the evaluation results showed that mental workload was lower for the H-R teams. The results for the closely coupled teams were compared to results from a prior evaluation with a master-slave relationship; similar results were found for both evaluations. Mental workload was lower in H-R teams than in H-H teams, but task performance did not differ between the two.

When people interact with intelligent agents, they likely rely upon a wide range of existing knowledge about machines, minds, and intelligence. This knowledge not only guides these interactions, but it can be challenged and potentially changed by interaction experiences. We hypothesized that a key factor mediating conceptual change in response to human-machine interactions is cognitive conflict, or dissonance. In this experiment, we evaluated whether interactions with a robot partner during a realistic medical triage scenario caused increased levels of cognitive dissonance relative to a control condition in which the same task was performed with a human partner. In addition, we evaluated whether heightened levels of dissonance affected concepts about agents. We observed increased cognitive dissonance after the human-robot interaction and found that this dissonance was correlated with a significantly less intentional (e.g., less human-like) view of the intelligence inherent to computers.

The effect of a robotic teammate on a human partner's workload has not been fully quantified. Prior research found that human participants experienced lower workload when working with a robotic partner than when working with a human partner. An evaluation investigated whether a similar trend in workload exists for tasks requiring direct and collaborative interaction between the partners, and joint team decision-making. The subjective results indicate a similar trend to the prior results; participants rated workload lower for the more complex task when partnered with a robot than when partnered with a human.

Clips from an evaluation where participants, each paired with either a human or robot partner, were deployed to search a hallway for suspicious items, in a manner similar to tactics used by first responders handling bomb threats are presented. The teams used natural, verbal communication to collaborate, determine where hazards were located, and which items were suspicious. The video demonstrates that the investigations in both conditions played out in a similar manner and participants were able to complete the investigations successfully with a robot partner; however, sometimes the participants were uncertain how to interact with the robot.

Map-based interfaces have been developed to support collaborative control of unmanned vehicles (i.e., robots). Annotation on the map (or geospatial annotation) has been proposed as an effective way to support team collaboration; however, there is a lack of research focused on the design of geospatial annotation tools to promote usability and task performance. The utility of location reference in geospatial annotations for communication and information sharing is the focus of this article. Two annotation tool designs were developed. The annotation contents were directly anchored on the map in the first design, whereas in the second design annotations were summarized in a separate panel on the interface. Evaluation participants followed instructions from a simulated team leader and assigned unmanned vehicles to different tasks for two simulated scenarios that include searching for victims and collecting hazardous materials samples. The results demonstrate the potential of using geospatial annotations to enrich communication and support map-based unmanned vehicle control. Participants appreciated the direct location reference feature of the first design and had generally shorter response time, but felt that the second design provided better usability and lower task workload. These results suggest that the user experience depends on the manner of obtaining information from the annotation tools, and the integration of the tool with user's task flow and other interface components, such as the map display. The presented results can be used as a basis for designing geospatial annotation tools for team collaboration that better fit user needs and preferences.

Human-Robot peer-based teams are evolving from a far-off possibility into a reality. Human Performance Moderator Functions (HPMFs) can be used to predict human behavior by incorporating the effects of internal and external influences such as fatigue and workload. The applicability of HPMFs to human-robot teams is not proven. The presented research focuses on determining the applicability of workload HPMFs in team tasks for first response mass casualty triage incidents between a Human-Human and a Human-Robot team. A model representing workload for each team was developed using IMPRINT Pro. The results from an empirical evaluation were compared to the model results. While significant differences between the two conditions were not found in all data, there was a general trend that workload in the human-robot condition was slightly lower than the workload experienced in the human-human condition. This trend was predicted by the IMPRINT Pro models. These results are the first to indicate that existing HPMFs can be applied to human-robot peer-based teams.