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  1. ; ( , Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction)
    Authored robotics applications have a diverse set of requirements for their authoring interfaces, being dependent on the underlying architecture of the program, the capability of the programmers and engineers using them, and the capabilities of the robot. Visual programming approaches have long been favored for both novice-level accessibility, and clear graphical representations, but current tools are limited in their customizability and ability to be integrated holistically into larger design interfaces. OpenVP attempts to address this by providing a highly congurable and customizable component library that can be integrated easily into other modern web-based applications. 
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    Free, publicly-accessible full text available March 11, 2025
  2. ; ; ; ( , Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction)
    Robots are ubiquitous in manufacturing settings from small-scale to large-scale. While collaborative robots (cobots) have signicant potential in these settings due to their exibility and ease of use, they can only reach their full potential when properly integrated. Specically, cobots need to be integrated in a manner that properly utilizes their strengths, improves the performance of the manufacturing process, and can be used in concert with human workers. Understanding how to properly integrate cobots into existing manufacturing workows requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. In this work, we propose an approach to collaborating with manufacturers prior to the integration process that involves planning, analysis, development, and presentation of results. This approach ultimately allows manufacturers to make an informed choice about cobot integration within their facilities. We illustrate the application of this approach through a case study with a manufacturing collaborator and discuss insights learned throughout the process. 
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    Free, publicly-accessible full text available March 11, 2025
  3. ; ; ; ; ( , AAAI 2023 Fall Symposium Series)
    We argue for the use of Petri nets as a modeling language for the iterative development process of interactive robotic systems. Petri nets, particularly Timed Colored Petri nets (TCPNs), have the potential to unify various phases of the development process — design, specification, simulation, validation, implementation, and deployment. We additionally discuss future directions for creating a domain-specific variant of TCPNs tailored specifically for HRI systems development. 
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    Free, publicly-accessible full text available October 25, 2024
  4. ; ; ; ; ( , HRI '23: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction)
    Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability. 
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    Full Text Available 
  5. ; ; ; ; ; ( , HRI '22: Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction)
    The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios. 
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  6. ; ; ; ( , UIST '20: Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology)
    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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  7. ; ; ; ; ; ; ( , Human Factors: The Journal of the Human Factors and Ergonomics Society)
    Objective

    Trade-offs between productivity, physical workload (PWL), and mental workload (MWL) were studied when integrating collaborative robots (cobots) into existing manual work by optimizing the allocation of tasks.

     Background

    As cobots become more widely introduced in the workplace and their capabilities greatly improved, there is a need to consider how they can best help their human partners.

     Methods

    A theoretical data-driven analysis was conducted using the O*NET Content Model to evaluate 16 selected jobs for associated work context, skills, and constraints. Associated work activities were ranked by potential for substitution by a cobot. PWL and MWL were estimated using variables from the O*Net database that represent variables for the Strain Index and NASA-TLX. An algorithm was developed to optimize work activity assignment to cobots and human workers according to their most suited abilities.

     Results

    Human workload for some jobs decreased while workload for some jobs increased after cobots were reassigned tasks, and residual human capacity was used to perform job activities designated the most important to increase productivity. The human workload for other jobs remained unchanged.

     Conclusions

    The changes in human workload from the introduction of cobots may not always be beneficial for the human worker unless trade-offs are considered. Application: The framework of this study may be applied to existing jobs to identify the relationship between productivity and worker tolerances that integrate cobots into specific tasks.

     
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  8. ; ; ; ; ; ; ( , Scientific Reports)
    Abstract

    Reduced hippocampal volume is frequently observed in posttraumatic stress disorder (PTSD), but the psychological processes associated with these alterations remain unclear. Given hippocampal involvement in memory and contextual representations of threat, we investigated relationships between retrospectively reported combat exposure, perceived threat, and hippocampal volume in trauma-exposed veterans. T1-weighted anatomical MRI scans were obtained from 56 veterans (4 women, 52 men; 39 with elevated PTSD symptoms, “PTSS” group) and hippocampal volume was estimated using automatic segmentation tools in FreeSurfer. Hippocampal volume was regressed on self-reported perceived threat from the Deployment Risk and Resilience Inventory, and combat exposure from the Combat Exposure Scale. As a secondary analysis, hippocampal volume was regressed on Clinician-Administered PTSD Scale (CAPS) symptoms. In veterans with elevated PTSD symptoms, hippocampal volume was inversely related to perceived threat while deployed while controlling for self-reported combat exposure. Hippocampal volume was also inversely correlated with avoidance/numbing CAPS symptoms. Future research should clarify the temporal milieu of these effects and investigate whether individual differences in hippocampal structure and function contribute to heightened threat appraisal at the time of trauma vs. subsequently elevated appraisals of traumatic events.

     
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