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Recently, researchers have initiated a new wave of convergent research in which Mixed Reality visualizations enable new modalities of human-robot communication, including Mixed Reality Deictic Gestures (MRDGs) – the use of visualizations like virtual arms or arrows to serve the same purpose as traditional physical deictic gestures. But while researchers have demonstrated a variety of benefits to these gestures, it is unclear whether the success of these gestures depends on a user’s level and type of cognitive load. We explore this question through an experiment grounded in rich theories of cognitive resources, attention, and multi-tasking, with significant inspiration drawn from Multiple Resource Theory. Our results suggest that MRDGs provide task-oriented benefits regardless of cognitive load, but only when paired with complex language. These results suggest that designers can pair rich referring expressions with MRDGs without fear of cognitively overloading their users. 

We investigate the effectiveness of robot-generated mixed reality gestures. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time, and that robots can pair long referring expressions with mixed reality gestures without cognitively overloading users. 

We present the first experiment analyzing the effectiveness of robot-generated mixed reality gestures using real robotic and mixed reality hardware. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time during visual search tasks, and show that robots can safely pair longer, more natural referring expressions with mixed reality gestures without worrying about cognitively overloading their interlocutors. 

In the field of Human-Robot Interaction, researchers often techniques such as the Wizard-of-Oz paradigms in order to better study narrow scientific questions while carefully controlling robots’ capabilities unrelated to those questions, especially when those other capabilities are not yet easy to automate. However, those techniques often impose limitations on the type of collaborative tasks that can be used, and the perceived realism of those tasks and the task context. In this paper, we discuss how Augmented Reality can be used to address these concerns while increasing researchers’ level of experimental control, and discuss both advantages and disadvantages of this approach 

This paper explores the tradeoffs between different types of mixed reality robotic communication under different levels of user workload. We present the results of a within-subjects experiment in which we systematically and jointly vary robot communication style alongside level and type of cognitive load, and measure subsequent impacts on accuracy, reaction time, and perceived workload and effectiveness. Our preliminary results suggest that although humans may not notice differences, the manner of load a user is under and the type of communication style used by a robot they interact with do in fact interact to determine their task effectiveness