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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A POMDP-based Robot-Human Trust Model for Human-Robot Collaboration
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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.more » « less
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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 Actsmore » « less
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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.more » « less