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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. 

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. 

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 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. 

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

Significant segments of the HRI literature rely on or promote the ability to reason about human identity characteristics, including age, gender, and cultural background. However, attempting to handle identity characteristics raises a number of critical ethical concerns, especially given the spatiotemporal dynamics of these characteristics. In this paper I question whether human identity characteristics can and should be represented, recognized, or reasoned about by robots, with special attention paid to the construct of race, due to its relative lack of consideration within the HRI community. As I will argue, while there are a number of well-warranted reasons why HRI researchers might want to enable robotic consideration of identity characteristics, these reasons are outweighed by a number of key ontological, perceptual, and deployment-oriented concerns. This argument raises troubling questions as to whether robots should even be able to understand or generate descriptions of people, and how they would do so while avoiding these ethical concerns. Finally, I conclude with a discussion of what this means for the HRI community, in terms of both algorithm and robot design, and speculate as to possible paths forward. 

Working Memory (WM) plays a key role in natural language understanding and generation. To enable a human-like breadth and flexibility of language understanding and generation capabilities, cognitive systems for language-capable robots should feature a human-like WM system in a similarly central role. However, it is still quite unclear how robotic WM should be designed, as a variety of models of human WM have been proposed in cognitive psychology. Moreover, human reliance on WM during language production is sometimes to help the speaker rather than to help hearers. Thus, it is unclear whether different robotic WM systems might harm certain dimensions of interaction for the sake of the robot speaker’s ostensible ease of cognitive processing. In this paper we demonstrate how different models of human WM can be implemented into robot cognitive architectures. Our results suggest that these models can be effective in terms of accuracy, perceived naturalness, and perceived human-likeness.