Transferability of HRI Research: Potential and Challenges

With advancement of robotics and artificial intelligence, applications for robotics are flourishing. Human-robot interaction (HRI) is an important area of robotics as it allows robots to work closer to humans (with them or for them). One crucial factor for the success of HRI research is transferability, which refers to the ability of research outputs to be adopted by industry and provide benefits to society. In this paper, we explore the potentials and challenges of transferability in HRI research. Firstly, we examine the current state of HRI research and identify various types of contributions that could lead to successful outcomes. Secondly, we discuss the potential benefits for each type of contribution and identify factors that could facilitate industry adoption of HRI research. However, we also recognize that there are several challenges associated with transferability, such as the diversity of well-defined job/skill-sets required from HRI practitioners, the lack of industry-led research, and the lack of standardization in HRI research methods. We discuss these challenges and propose potential solutions to bridge the gap between industry expectations and academic research in HRI.Comment: AAAI Spring Symposium 202

Des Styles pour une Personnalisation de l'Interaction Homme-Robot

Les robots compagnons sont des robots personnels qui ont pour objectif d’accompagner l’utilisateur dans ses activités de la vie quotidienne. Nous nous intéressons pour notre part aux enfants, accompagnés par un robot dans différentes situations : travail scolaire, réconfort, jeu, protection,...L’acceptabilité de ces robots compagnons dans le quotidien est questionnée dans de nombreux travaux ; elle est liée à son apparence physique, son utilité, sa facilité d’utilisation mais aussi à d’autres critères qu’il reste à étudier. Un des défis dans la conception de tels robots est de les doter de réelles compétences sociales de perception, de raisonnement et d’action lors de leurs interactions avec l’utilisateur. La recherche dans le domaine de l’interaction humain- robot se tourne ainsi de plus en plus vers les travaux en Informatique Affective (Affective Computing) pour concevoir des robots personnels plus sociaux, l’émotion étant une dimension centrale dans les interactions.D’autres dimensions comme la confiance, la légitimité ou encore la crédibilité du compagnon sont importantes pour leur acceptabilité. Des travaux ont proposé l’idée d’un compagnon ”polyvalent” capable d’endosser de multiples rôles et de s’adapter en fonction des besoins de l’utilisateur et en fonction du contexte de la situation. La Théorie du Compagnon présentée soulève la question des différences entre individus influençant la qualité de l’interaction et la construction d’une relation entre le compagnon et l’utilisateur. Ce travail sur la personnalisation des robots a pour but de créer de la valeur, en particulier pour des parents souhaitant un robot compagnon pour leur enfant, en accord avec leurs propres attentes

Exploring Data Agency and Autonomous Agents as Embodied Data Visualizations

In the light of recent advances in embodied data visualizations, we aim to shed light on agency in the context of data visualization. To do so, we introduce Data Agency and Data-Agent Interplay as potential terms and research focus. Furthermore, we exemplify the former in the context of human-robot interaction, and identify future challenges and research questions.Comment: 2 pages, 1 figure, Presented as poster at 2023 IEEE Visualization Conference (VIS

Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents

Embodied agents, in the form of virtual agents or social robots, are rapidly becoming more widespread. In human-human interactions, humans use nonverbal behaviours to convey their attitudes, feelings, and intentions. Therefore, this capability is also required for embodied agents in order to enhance the quality and effectiveness of their interactions with humans. In this paper, we propose a novel framework that can generate sequences of joint angles from the speech text and speech audio utterances. Based on a conditional Generative Adversarial Network (GAN), our proposed neural network model learns the relationships between the co-speech gestures and both semantic and acoustic features from the speech input. In order to train our neural network model, we employ a public dataset containing co-speech gestures with corresponding speech audio utterances, which were captured from a single male native English speaker. The results from both objective and subjective evaluations demonstrate the efficacy of our gesture-generation framework for Robots and Embodied Agents.Comment: RO-MAN'23, 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), August 2023, Busan, South Kore

Envisioning social drones in education

Education is one of the major application fields in social Human-Robot Interaction. Several forms of social robots have been explored to engage and assist students in the classroom environment, from full-bodied humanoid robots to tabletop robot companions, but flying robots have been left unexplored in this context. In this paper, we present seven online remote workshops conducted with 20 participants to investigate the application area of Education in the Human-Drone Interaction domain; particularly focusing on what roles a social drone could fulfill in a classroom, how it would interact with students, teachers and its environment, what it could look like, and what would specifically differ from other types of social robots used in education. In the workshops we used online collaboration tools, supported by a sketch artist, to help envision a social drone in a classroom. The results revealed several design implications for the roles and capabilities of a social drone, in addition to promising research directions for the development and design in the novel area of drones in education

Une architecture cognitive et affective orientée interaction

National audienceLes robots trouvent de nouvelles applications dans notre vie de tous les jours et interagissent de plus en plus etroi-tement avec leurs utilisateurs humains. Cependant, malgré une longue tradition de recherche, les architectures cogni-tives existantes restent souvent trop génériques et pas as-sez adaptées aux besoins spécifiques de l'Interaction sociale Humain-Robot, comme la gestion des emotions, du langage, des normes sociales, etc. Dans cet article, nous présentons CAIO, une architecture Cognitive et Affective Orientée Interaction. Elle permet aux robots de raisonner sur les etats mentaux (y compris les emotions) et d'agir physiquement, ´ emotionnellement et verbalement

A Cognitive and Affective Architecture for Social Human-Robot Interaction

International audienceRobots show up frequently in new applications in our daily lives where they interact more and more closely with the human user. Despite a long history of research, existing cognitive architectures are still too generic and hence not tailored enough to meet the specific needs demanded by social HRI. In particular, interaction-oriented architectures require handling emotions, language, social norms, etc, which is quite a handful. In this paper, we present an overview of a Cognitive and Affective Interaction-Oriented Architecture for social human-robot interactions abbreviated CAIO. This architecture is parallel to the BDI (Belief, Desire, Intention) architecture that comes from philosophy of actions by Bratman. CAIO integrates complex emotions and planning techniques. It aims to contribute to cognitive architectures for HRI by enabling the robot to reason on mental states (including emotions) of the interlocutors, and to act physically, emotionally and verbally

Permanent Magnet-Assisted Omnidirectional Ball Drive

We present an omnidirectional ball wheel drive design that utilizes a permanent magnet as the drive roller to generate the contact force. Particularly interesting for novel human-mobile robot interaction scenarios where the users are expected to physically interact with many palm-sized robots, our design combines simplicity, low cost and compactness. We first detail our design and explain its key parameters. Then, we present our implementation and compare it with an omniwheel drive built with identical conditions and similar cost. Finally, we elaborate on the main advantages and drawbacks of our design

The Grenoble System for the Social Touch Challenge at ICMI 2015

International audienceNew technologies and especially robotics is going towards more natural user interfaces. Works have been done in different modality of interaction such as sight (visual computing), and audio (speech and audio recognition) but some other modalities are still less researched. The touch modality is one of the less studied in HRI but could be valuable for naturalistic interaction. However touch signals can vary in semantics. It is therefore necessary to be able to recognize touch gestures in order to make human-robot interaction even more natural.We propose a method to recognize touch gestures. This method was developed on the CoST corpus and then directly applied on the HAART dataset as a participation of the Social Touch Challenge at ICMI 2015.Our touch gesture recognition process is detailed in this article to make it reproducible by other research teams.Besides features set description, we manually filtered the training corpus to produce 2 datasets.For the challenge, we submitted 6 different systems.A Support Vector Machine and a Random Forest classifiers for the HAART dataset.For the CoST dataset, the same classifiers are tested in two conditions: using all or filtered training datasets.As reported by organizers, our systems have the best correct rate in this year's challenge (70.91% on HAART, 61.34% on CoST).Our performances are slightly better that other participants but stay under previous reported state-of-the-art results