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Urban Search and Rescue (USAR) missions continue to benefit from the incorporation of human–robot teams (HRTs). USAR environments can be ambiguous, hazardous, and unstable. The integration of robot teammates into USAR missions has enabled human teammates to access areas of uncertainty, including hazardous locations. For HRTs to be effective, it is pertinent to understand the factors that influence team effectiveness, such as having shared goals, mutual understanding, and efficient communication. The purpose of our research is to determine how to (1) better establish human trust, (2) identify useful levels of robot transparency and robot explanations, (3) ensure situation awareness, and (4) encourage a bipartisan role amongst teammates. By implementing robot transparency and robot explanations, we found that the driving factors for effective HRTs rely on robot explanations that are context-driven and are readily available to the human teammate. 

Virtual testbeds are fundamental to the success of research on cognitive work in safety-critical domains. A testbed that can meet researchers' objectives and create a sense of reality for participants positively impacts the research process; they have the potential to allow researchers to address questions not achievable in physical environments. This paper discusses the development of a synthetic task environment (STE) for Urban Search and Rescue (USAR) to advance the boundaries of Human-Robot Teams (HRTs) using Roblox. Virtual testbeds can simulate USAR task environments and HRT interactions. After assessing alternative STE platforms, we discovered Roblox not only met our research capabilities but also would prove invaluable for research teams without substantial coding experience. This paper outlines the design process of creating an STE to meet our research team's objectives. 

Urban Search and Rescue (USAR) missions often involve a need to complete tasks in hazardous environments. In such situations, human-robot teams (HRT) may be essential tools for future USAR missions. Transparency and explanation are two information exchange processes where transparency is real-time information exchange and explanation is not. For effective HRTs, certain levels of transparency and explanation must be met, but how can these modes of team communication be operationalized? During the COVID-19 pandemic, our approach to answering this question involved an iterative design process that factored in our research objectives as inputs and pilot studies with remote participants. Our final research testbed design resulted in converting an in-person task environment to a completely remote study and task environment. Changes to the study environment included: utilizing user-friendly video conferencing tools such as Zoom and a custom-built application for research administration tasks and improved modes of HRT communication that helped us avoid confounding our performance measures. 

Objective This work examines two human–autonomy team (HAT) training approaches that target communication and trust calibration to improve team effectiveness under degraded conditions. Background Human–autonomy teaming presents challenges to teamwork, some of which may be addressed through training. Factors vital to HAT performance include communication and calibrated trust. Method Thirty teams of three, including one confederate acting as an autonomous agent, received either entrainment-based coordination training, trust calibration training, or control training before executing a series of missions operating a simulated remotely piloted aircraft. Automation and autonomy failures simulating degraded conditions were injected during missions, and measures of team communication, trust, and task efficiency were collected. Results Teams receiving coordination training had higher communication anticipation ratios, took photos of targets faster, and overcame more autonomy failures. Although autonomy failures were introduced in all conditions, teams receiving the calibration training reported that their overall trust in the agent was more robust over time. However, they did not perform better than the control condition. Conclusions Training based on entrainment of communications, wherein introduction of timely information exchange through one team member has lasting effects throughout the team, was positively associated with improvements in HAT communications and performance under degraded conditions. Training that emphasized the shortcomings of the autonomous agent appeared to calibrate expectations and maintain trust. Applications Team training that includes an autonomous agent that models effective information exchange may positively impact team communication and coordination. Training that emphasizes the limitations of an autonomous agent may help calibrate trust.