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  1. null (Ed.)
    As collaborative robots become increasingly widespread in manufacturing settings, there is a greater need for tools and interfaces to support operators who integrate, supervise, and troubleshoot these systems. In this paper, we present an application of the Robot Attention Demand (RAD) metric for use in the design of user interfaces to support operators in collaborative manufacturing scenarios. Building on prior work that introduced RAD, we designed and implemented prototype timeline and countdown-timer interfaces to be used within a collaborative assembly-inspection task where an operator is also responsible for a separate sorting task. We performed a user evaluation to investigate the effects of displaying predictive RAD information on operator performance and perceptions of the task. Our results show lower perceived task load and increased usability scores compared to baseline condition without an interface. These findings suggest that predictive RAD should be used by designers and engineers developing operator interfaces for collaborative robot applications in manufacturing. 
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  2. Collaborative robots promise to transform work across many industries and promote “human-robot teaming” as a novel paradigm. However, realizing this promise requires the understanding of how existing tasks, developed for and performed by humans, can be effectively translated into tasks that robots can singularly or human-robot teams can collaboratively perform. In the interest of developing tools that facilitate this process we present Authr, an end-to-end task authoring environment that assists engineers at manufacturing facilities in translating existing manual tasks into plans applicable for human-robot teams and simulates these plans as they would be performed by the human and robot. We evaluated Authr with two user studies, which demonstrate the usability and effectiveness of Authr as an interface and the benefits of assistive task allocation methods for designing complex tasks for human-robot teams. We discuss the implications of these findings for the design of software tools for authoring human-robot collaborative plans. 
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