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1. Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

   Asano, Y.; Litman, D.; Yu, M.; Lobczowski, N. G.; Nokes-Malach, T.; Kovashka, A.; & Walker, E. (January 2022, Proceedings of the SIGdial 2022 Conference)

   Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work. 

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2. Bodystorming Human-Robot Interactions

   https://doi.org/10.1145/3332165.3347957  

   Porfirio, David; Fisher, Evan; Sauppé, Allison; Albarghouthi, Aws; Mutlu, Bilge (October 2019, Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology)

   Designing and implementing human-robot interactions requires numerous skills, from having a rich understanding of social interactions and the capacity to articulate their subtle requirements, to the ability to then program a social robot with the many facets of such a complex interaction. Although designers are best suited to develop and implement these interactions due to their inherent understanding of the context and its requirements, these skills are a barrier to enabling designers to rapidly explore and prototype ideas: it is impractical for designers to also be experts on social interaction behaviors, and the technical challenges associated with programming a social robot are prohibitive. In this work, we introduce Synthé, which allows designers to act out, or bodystorm, multiple demonstrations of an interaction. These demonstrations are automatically captured and translated into prototypes for the design team using program synthesis. We evaluate Synthé in multiple design sessions involving pairs of designers bodystorming interactions and observing the resulting models on a robot. We build on the findings from these sessions to improve the capabilities of Synthé and demonstrate the use of these capabilities in a second design session. 

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3. Human Gaze Following for Human-Robot Interaction

   Saran, Akanksha; Majumdar, Srinjoy; Short, Elaine; Thomaz, Andrea; Niekum, Scott (October 2018, Proceedings of the International Conference on Intelligent Robots and Systems)
4. A robot for overground physical human-robot interaction experiments

   https://doi.org/10.1371/journal.pone.0276980  

   Regmi, Sambad; Burns, Devin; Song, Yun Seong (November 2022, PLOS ONE)

   Ferretti, Gianni (Ed.)

   Many anticipated physical human-robot interaction (pHRI) applications in the near future are overground tasks such as walking assistance. For investigating the biomechanics of human movement during pHRI, this work presents Ophrie, a novel interactive robot dedicated for physical interaction tasks with a human in overground settings. Unique design requirements for pHRI were considered in implementing the one-arm mobile robot, such as the low output impedance and the ability to apply small interaction forces. The robot can measure the human arm stiffness, an important physical quantity that can reveal human biomechanics during overground pHRI, while the human walks alongside the robot. This robot is anticipated to enable novel pHRI experiments and advance our understanding of intuitive and effective overground pHRI. 

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5. Robot-generated Mixed Reality Gestures Improve Human-Robot Interaction

   https://doi.org/10.1007/978-3-030-90525-5_69  

   Tran, Nhan; Grant, Trevor; Phung, Thao; Hirshfield, Leanne; Wickens, Christopher; Williams, Tom (January 2022, International Conference on Social Robotics (Brief Research Reports))

   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. 

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