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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 survey of communicating robot learning during human-robot interaction

   https://doi.org/10.1177/02783649241281369  

   Habibian, Soheil; Alvarez_Valdivia, Antonio; Blumenschein, Laura_H; Losey, Dylan_P (October 2024, The International Journal of Robotics Research)

   For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior. 

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3. 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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4. How Do Robot Experts Measure the Success of Social Robot Navigation?

   https://doi.org/10.1145/3610978.3640636  

   Tsoi, Nathan; Romero, Jessica; Vázquez, Marynel (March 2024, HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction)

   We interviewed 8 individuals from industry and academia to better understand how they valued different aspects of social robot navigation. Interviewees were asked to rank the importance of 10 measures commonly used to evaluate social navigation policies. Interviewees were then asked open-ended questions about social navigation, and how they think about evaluating the challenges they face. Our interviews with industry and academic experts in social navigation revealed that avoiding collisions was the only universally important measure. Beyond the safety consideration of avoiding collisions, roboticists have varying priorities regarding social navigation. Given the high priority interviewees placed on safety, we recommend that social navigation approaches should first aim to ensure safety. Once safety is ensured, we recommend that each social navigation algorithm be evaluated using the measures most relevant to the intended application domain. 

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5. A framework of controlled robot language for reliable human-robot collaboration

   https://doi.org/10.1007/978-3-030-90525-5_29  

   Tran, Dang M.; Yan, Fujian; Yihun, Yimesker S.; Tan, Jindong; He, Hongsheng (October 2021, International conference on social robotics)