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Theater-based design methods are seeing increased use in social robotics, as embodied roleplay is an ideal method for designing embodied interactions. Yet theater-based design methods are often cast as simply one possible tool; there has been little consideration of the importance of specific improvisational skills for theater-based design; and there has been little consideration of how to train students in theater-based design methods. We argue that improvisation is not just one possible tool of social robot design, but is instead central to social robotics. Leveraging recent theoretical work on Applied Improvisation, we show how improvisational skills represent (1) a set of key capabilities needed for any socially interactive robot, (2) a set of learning objectives for training engineers in social robot design, and (3) a set of methodologies for training those engineers to engage in theater-based design methods. Accordingly, we argue for a reconceptualization of Social Robotics as an Applied Improvisation project; we present, as a speculative pedagogical artifact, a sample syllabus for an envisioned Applied Improvisation driven Social Robotics course that might give students the technical and improvisational skills necessary to be effective robot designers; and we present a case study in which Applied Improvisation methods were simultaneously used (a) by instructors, to rapidly scaffold engineering students’ improvisational skills and (b) by those students, to engage in more effective human-robot interaction design. 

Working memory is an important component of cognition that infuences key cognitive processes, such as language. As such, working memory should play a key role in cognitive models for languagecapable robots. The ways in which working memory bufers are organized within a robot’s architecture can inform processes such as Referring Expression Generation. Thus, it is important to understand how information and resources within working memory may be organized to lead to human-like robotic language. Previous work on the DIARC cognitive architecture described an entitylevel, feature-based working memory framework in which each known entity had its own dedicated working memory bufer. This paper expands on that framework and proposes a new resource management strategy in which sets of entities that belong to the same type share a single working memory bufer.We end the paper with a brief discussion of how this novel strategy compares to the previously implemented entity-level strategy. 

In this work, we present Robots for Social Justice (R4SJ): a framework for an equitable engineering practice of Human-Robot Interaction, grounded in the Engineering for Social Justice (E4SJ) framework for Engineering Education and intended to complement existing frameworks for guiding equitable HRI research. To understand the new insights this framework could provide to the feld of HRI, we analyze the past decade of papers published at the ACM/IEEE International Conference on Human-Robot Interaction, and examine how well current HRI research aligns with the principles espoused in the E4SJ framework. Based on the gaps identifed through this analysis, we make fve concrete recommendations, and highlight key questions that can guide the introspection for engineers, designers, and researchers. We believe these considerations are a necessary step not only to ensure that our engineering education eforts encourage students to engage in equitable and societally benefcial engineering practices (the purpose of E4SJ), but also to ensure that the technical advances we present at conferences like HRI promise true advances to society, and not just to fellow researchers and engineers. 

In this work, we present Robots for Social Justice (R4SJ): a framework for an equitable engineering practice of Human-Robot Interaction, grounded in the Engineering for Social Justice (E4SJ) framework for Engineering Education and intended to complement existing frameworks for guiding equitable HRI research. To understand the new insights this framework could provide to the field of HRI, we analyze the past decade of papers published at the ACM/IEEE International Conference on Human-Robot Interaction, and examine how well current HRI research aligns with the principles espoused in the E4SJ framework. Based on the gaps identified through this analysis, we make five concrete recommendations, and highlight key questions that can guide the introspection for engineers, designers, and researchers. We believe these considerations are a necessary step not only to ensure that our engineering education efforts encourage students to engage in equitable and societally beneficial engineering practices (the purpose of E4SJ), but also to ensure that the technical advances we present at conferences like HRI promise true advances to society, and not just to fellow researchers and engineers. 

The proliferation of Large Language Models (LLMs) presents both a critical design challenge and a remarkable opportunity for the field of Human-Robot Interaction (HRI). While the direct deployment of LLMs on interactive robots may be unsuitable for reasons of ethics, safety, and control, LLMs might nevertheless provide a promising baseline technique for many elements of HRI. Specifically, in this position paper, we argue for the use of LLMs as Scarecrows: ‘brainless,’ straw-man black-box modules integrated into robot architectures for the purpose of quickly enabling full-pipeline solutions, much like the use of “Wizard of Oz” (WoZ) and other human-in-the-loop approaches. We explicitly acknowledge that these Scarecrows, rather than providing a satisfying or scientifically complete solution, incorporate a form of the wisdom of the crowd, and, in at least some cases, will ultimately need to be replaced or supplemented by a robust and theoretically motivated solution. We provide examples of how Scarecrows could be used in language-capable robot architectures as useful placeholders, and suggest initial reporting guidelines for authors, mirroring existing guidelines for the use and reporting of WoZ techniques. 

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in HRI research in recent years. However, the HRI community lacks a set of shared terminology and framework for characterizing aspects of mixed reality interfaces, presenting serious problems for future research. Therefore, it is important to have a common set of terms and concepts that can be used to precisely describe and organize the diverse array of work being done within the field. In this article, we present a novel taxonomic framework for different types of VAM-HRI interfaces, composed of four main categories of virtual design elements (VDEs). We present and justify our taxonomy and explain how its elements have been developed over the past 30 years as well as the current directions VAM-HRI is headed in the coming decade. 

Robots need to be able to communicate with people through natural language. But how should their memory systems be designed to facilitate this communication? 

Because robots are perceived as moral agents, they must behave in accordance with human systems of morality. This responsibility is especially acute for language-capable robots because moral communication is a method for building moral ecosystems. Language capable robots must not only make sure that what they say adheres to moral norms; they must also actively engage in moral communication to regulate and encourage human compliance with those norms. In this work, we describe four experiments (total N =316) across which we systematically evaluate two different moral communication strategies that robots could use to influence human behavior: a norm-based strategy grounded in deontological ethics, and a role-based strategy grounded in role ethics. Specifically, we assess the effectiveness of robots that use these two strategies to encourage human compliance with norms grounded in expectations of behavior associated with certain social roles. Our results suggest two major findings, demonstrating the importance of moral reflection and moral practice for effective moral communication: First, opportunities for reflection on ethical principles may increase the efficacy of robots’ role-based moral language; and second, following robots’ moral language with opportunities for moral practice may facilitate role-based moral cultivation.