Human-Autonomy Teaming in Underwater Environments

Autonomous underwater vehicles (AUVs) depend on reliable vision in low visibility, yet we lack clear evidence on when specialized detectors (YOLO-World) outperform multimodal vision-language models (Gemini 2.5 Pro.) This gap limits the informed model selection for underwater tasks. To address it, we examine how these two paradigms behave when applied to the same operational task focused on object presence determination. The dataset was generated using a synthetic underwater simulator spanning Low, Medium, and High visibility. Both models are evaluated on accuracy and latency. YOLO-World performs better in Low visibility and runs faster, while Gemini improves in clearer scenes but requires more computation. These findings indicate that model choice should align with visibility and runtime constraints, with detectors suited for real-time use and a multimodal model for offline tasks.

Human-Autonomy Teams (HATs) stand at an intersection of domains that benefit from both teaming principles and technological innovation. HATs have demonstrated technical success in various domains, including emergency response, medicine, and manufacturing. However, we submit that the integration of autonomous teammates should not only be driven by the technological capacity of autonomous systems, but also by the demonstrated need within their intended domains. This study qualitatively explored hazardous-environment teams' functional dynamics through 20 semi-structured interviews to identify a driving need to expand the workforce in these domains and illuminate socio-economic factors that may impact the utilization of autonomous teammates to do so. The results indicate that, beyond the performance benefits of including technology, these teams would benefit from technology supplementing their workforce, a capability unique to the implementation of autonomous technologies as interdependent teammates. Furthermore, this study identified current top-down and bottom-up technology adoption pathways that can be used to streamline HAT formation in these domains. Lastly, cultural inertia to novel methods and economic limitations within these teams serve as barriers to HAT implementation. In consequence, this study submits that hazardous-environment domains should adopt HATs, and that the integration process can be accelerated by presenting the operational benefits of autonomous teammates to both domain leaders and frontline team members.

As artificial intelligence (AI) and robotics have become more capable as technologies, human-AI teams (HATs) have sought to model how humans can interdependently work with AI to accomplish a shared goal. Specifically, HATs seek to leverage autonomy in both digital and physical AI agents to improve the teamwork and outcomes of these teams. In search-and-rescue (SAR) teams, where situations are dynamic and the environments are hazardous, HATs require complex considerations for interdependent work with AI. This qualitative interview study explores insights from 17 SAR professionals to investigate teamwork considerations that can benefit the formation of search-and-rescue human-AI teams (SAR-HATs). Participants’ concerns about time and affective needs suggest that SAR-HATs would benefit significantly from the explicit delineation of search and rescue tasks. HATs can expedite search tasks without forgoing safety by delegating roles that afford parallel completion of information acquisitions, search routines, and safety precautions. Additionally, search tasks can afflict human teammates with psychological trauma, but AI teammates benefit from their insusceptibility to trauma-related stressors. However, in rescue tasks, these capabilities have inverse benefits, wherein humans benefit from their capacity to feel emotion when interacting with the patients, and AI may struggle.