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This paper explores the role of Extended Reality (XR) as a mediation layer in human-robot teaming, emphasizing the importance of situated human control, which allows human collaborators to understand and influence robot actions in real-time contexts. By analyzing scenarios such as robot-assisted nursing and collaborative assembly, the authors identify four key mediation functions that facilitate the interaction between human intent and robot autonomy, as well as the dynamics of shared control. The study culminates in six design dimensions that can enhance the effectiveness and accountability of XR systems in managing robot behavior in dynamic environments.
XR can transform human-robot interaction by enabling real-time, situated control over robot actions, ensuring accountability and adaptability in dynamic settings.
Extended Reality (XR) is increasingly used in human-robot interaction to communicate robot intent, planned motion, reachability, and state. We argue that XR should also be understood as a mediation layer for situated human control in human-robot teaming. Situated human control denotes the human collaborator's ability to understand, shape, authorize, and interrupt robot action within the concrete physical, social, and temporal context in which that action unfolds. We ground this perspective in scenarios from robot-assisted bedside nursing, multi-arm supervisory control, and collaborative assembly under divided attention. Across these scenarios, robot autonomy must remain inspectable and adjustable as people move, goals change, sensing is incomplete, control roles shift, and plans become invalid. We identify four mediation functions connecting human intent and robot autonomy, robot plans and human judgment, levels of shared control, and team roles, handover, and recovery. Building on these functions, we derive six design dimensions: joint action possibilities, socio-physical constraints, uncertainty and plan validity, multimodal control and correction, roles, handover, and accountability, and anticipatory recovery. The paper outlines a research agenda for XR systems that make robot autonomy more actionable and accountable in dynamic shared environments.