Micro-timing of backchannels in human-robot interaction

Inden B, Malisz Z, Wagner P, Wachsmuth I. Micro-timing of backchannels in human-robot interaction. Presented at the Timing in Human-Robot Interaction: Workshop in Conjunction with the 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI2014), Bielefeld, Germany

Introduction to the special issue on “designing the robot body: Critical perspectives on affective embodied interaction”

Designing and evaluating the affectivity of the robot body has become a frontier topic in Human-Robot Interaction (HRI), with previous studies emphasizing the importance of robot embodiment for human-robot communication. In particular, there is growing interest in how the tactile, haptic materiality of the robot influences and mediates users’ affective and emotional states. Indeed, the sheer physicality of robotic systems is a crucial factor in the morphology of the robotic platform, and therefore in the robot's appearance to the user. How do the tactile properties of materials subtly influence user interaction? Why do certain morphologies prompt more empathetic interactions than others? How is nonverbal communication affected through the coordination of movements of the torso, head, and appendages to provide more naturalistic-seeming interaction? What is the role of nonverbal communication in the production of artificial empathy? And how do such factors encourage trust and foster confidence for nonexpert users to interact in the first place? This recognition of machinic corporeality has been of practical interest to designers and engineers working across a range of robot forms and functions. The objective of this special issue is to further this discussion, to consider theoretical, ethical, empirical, and methodological questions related to the design of robotic bodies in the context of affective HRI, and thus foster cross currents among engineering, design, social science, and artistic communities. It originally emerged as a set of conceptual and practical questions from a workshop at the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI’20) in Cambridge, UK, co-organized by two of the editors [3]. The workshop, like so many other events, was canceled because of the restrictions of the COVID-19 pandemic. Consequently, we tried to pursue a longer-term exchange of engineering, design, and conceptual considerations through the publication of this special issue. Building out from the more practically minded exchanges of an in-person workshop, here was an opportunity to invite more wide-ranging contributions to consider questions related to the design of robotic bodies in the context of affective HRI. The issue could thus explore topics bridging embodiment and affect, including touch, materials, and physical form from the points of view of artists, designers, engineers, and social scientists alike

Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education

As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.Comment: Accepted to the Proceedings of the AAAI Conference on Artificial Intelligence (2024

Fluency and embodiment for robots acting with humans

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 225-234).This thesis is concerned with the notion of fluency in human-robot interaction (HRI), exploring cognitive mechanisms for robotic agents that would enable them to overcome the stop-and-go rigidity present in much of HRI to date. We define fluency as the ethereal yet manifest quality existent when two agents perform together at high level of coordination and adaptation, in particular when they are well-accustomed to the task and to each other. Based on mounting psychological and neurological evidence, we argue that one of the keys to this goal is the adaptation of an embodied approach to robot cognition. We show how central ideas from this psychological school are applicable to robot cognition and present a cognitive architecture making use of perceptual symbols, simulation, and perception-action networks. In addition, we demonstrate that anticipation of perceptual input, and in particular of the actions of others, are an important ingredient of fluent joint action. To that end, we show results from an experiment studying the effects of anticipatory action on fluency and teamwork, and use these results to suggest benchmark metrics for fluency. We also show the relationship between anticipatory action and a simulator approach to perception, through a comparative human subject study of an implemented cognitive architecture on the robot AUR, a robotic desk lamp, designed for this thesis. A result of this work is modeling the effect of practice on human-robot joint action, arguing that mechanisms that govern the passage of cognitive capabilities from a deliberate yet slower system to a faster, sub-intentional, and more rigid one, are crucial to fluent joint action in well-rehearsed ensembles. Theatrical acting theory serves as an inspiration for this work, as we argue that lessons from acting method can be applied to human-robot interaction.by Guy Hoffman.Ph.D