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1. “Excuse Me, Robot”: Impact of Polite Robot Wakewords on Human-Robot Politeness

   Williams, T.; Grollman, D.; Han, M.; Jackson, B.; Lockshin, J.; Wen, R.; Nahman, Z.; Zhu, Q. (January 2020, International Conference on Social Robotics)

   While the ultimate goal of natural-language based Human-Robot Interaction (HRI) may be free-form, mixed-initiative dialogue,social robots deployed in the near future will likely primarily engage in wakeword-driven interaction, in which users’ commands are prefaced by a wakeword such as “Hey, Robot.” This style of interaction helps to allay user privacy concerns, as the robot’s full speech recognition module need not be employed until the target wakeword is used. Unfortunately, there are a number of concerns in the popular media surrounding this style of interaction, with consumers fearing that it is training users (in particular,children) to be rude towards technology, and by extension, rude towards other humans. In this paper, we present a study that demonstrates how an alternate style of wakeword, i.e., “Excuse me, Robot” may allay this concern, by priming users to phrase commands as Indirect Speech Acts 

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2. 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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3. 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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4. 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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5. Intention aware robot motion planning for safe worker robot collaboration

   https://doi.org/10.1111/mice.13129  

   Liu, Yizhi; Jebelli, Houtan (December 2023, Computer-Aided Civil and Infrastructure Engineering)

   Recent advances in robotics have enabled robots to collaborate with workers in shared, fenceless workplaces in construction and civil engineering, which can improve productivity and address labor shortages. However, this collaboration may lead to collisions between workers and robots. Targeting safe collaboration, this study proposes an intention‐aware motion planning method for robots to avoid collisions. This method involves two novel deep networks that allow robots to anticipate the motions of workers based on inferences about workers' motion intentions. Then, a probabilistic collision‐checking mechanism is developed that enables robots to estimate the collision probability with the motions of workers and generate collision‐free adjustments. The results verify that the method enables robots to predict workers' intended motions 1 s in advance and generate adjustments with a collision probability of less than 5.0% during collaborative masonry tasks. This study facilitates the safe implementation of collaborative robots in construction and civil engineering. 

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