Motion Generation during Vocalized Emotional Expressions and Evaluation in Android Robots

Vocalized emotional expressions such as laughter and surprise often occur in natural dialogue interactions and are important factors to be considered in order to achieve smooth robot-mediated communication. Miscommunication may be caused if there is a mismatch between audio and visual modalities, especially in android robots, which have a highly humanlike appearance. In this chapter, motion generation methods are introduced for laughter and vocalized surprise events, based on analysis results of human behaviors during dialogue interactions. The effectiveness of controlling different modalities of the face, head, and upper body (eyebrow raising, eyelid widening/narrowing, lip corner/cheek raising, eye blinking, head motion, and torso motion control) and different motion control levels are evaluated using an android robot. Subjective experiments indicate the importance of each modality in the perception of motion naturalness (humanlikeness) and the degree of emotional expression

Becoming Human with Humanoid

Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry

Having Different Dialog Roles in Telecommunication by Using Two Teleoperated Robots Reduces an Operator’s Guilt

The version of record of this article, first published in International Journal of Social Robotics, is available online at Publisher’s website: https://doi.org/10.1007/s12369-024-01125-1.In recent years, applications of social robots as the operator’s avatar have been widely studied for remote conversation with rich nonverbal information. Having another side-participant robot beside the avatar robot of the operator was found to be effective for providing long-lasting backchannels to the interlocutor. The side-participant robot is also expected to play a role in assisting human participation in multiparty conversations. However, such a focus has not been applied to remote conversations with multiple robots. Here, we propose a multiple-robot telecommunication system with which the operator can use a side-participant robot to assist conversation that is developed by the operator through the main speaker robot to verify its effectiveness. In the laboratory experiment where the subjects were made to feel stressed by being forced to provide rude questions to the interlocutor, the proposed system was shown to reduce guilt and to improve the overall mood of operators. The result encourages the application of a multi robot remote conversation system to allow the user to participate in remote conversations with less anxiety of potential failure in maintaining the conversation

Simultaneous Dialogue Services Using Multiple Semiautonomous Robots in Multiple Locations by a Single Operator: A Field Trial on Souvenir Recommendation

Sakai K., Kawata M., Meneses A., et al. Simultaneous Dialogue Services Using Multiple Semiautonomous Robots in Multiple Locations by a Single Operator: A Field Trial on Souvenir Recommendation. IEEE Robotics and Automation Letters 9, 6280 (2024); https://doi.org/10.1109/LRA.2024.3404752.Recently, teleoperation systems have been developed enabling a single operator to engage with users across multiple locations simultaneously. However, under such systems, a potential challenge exists where the operator, upon switching locations, may need to join ongoing conversations without a complete understanding of their history. Consequently, a seamless transition and the development of high-quality conversations may be impeded. This study directs its attention to the utilization of multiple robots, aiming to create a semiautonomous teleoperation system. This system enables an operator to switch between twin robots at each location as needed, thereby facilitating the provision of higher-quality dialogue services simultaneously. As an initial phase, a field experiment was conducted to assess user satisfaction with recommendations made by the operator using twin robots. Results collected from 391 participants over 13 days revealed heightened user satisfaction when the operator intervened and provided recommendations through multiple robots compared with autonomous recommendations by the robots. These findings contribute to the formulation of a teleoperation system that allows a single operator to deliver multipoint conversational services

Opinion attribution improves motivation to exchange subjective opinions with humanoid robots

In recent years, the development of robots that can engage in non-task-oriented dialogue with people, such as chat, has received increasing attention. This study aims to clarify the factors that improve the user’s willingness to talk with robots in non-task oriented dialogues (e.g., chat). A previous study reported that exchanging subjective opinions makes such dialogue enjoyable and enthusiastic. In some cases, however, the robot’s subjective opinions are not realistic, i.e., the user believes the robot does not have opinions, thus we cannot attribute the opinion to the robot. For example, if a robot says that alcohol tastes good, it may be difficult to imagine the robot having such an opinion. In this case, the user’s motivation to exchange opinions may decrease. In this study, we hypothesize that regardless of the type of robot, opinion attribution affects the user’s motivation to exchange opinions with humanoid robots. We examined the effect by preparing various opinions of two kinds of humanoid robots. The experimental result suggests that not only the users’ interest in the topic but also the attribution of the subjective opinions to them influence their motivation to exchange opinions. Another analysis revealed that the android significantly increased the motivation when they are interested in the topic and do not attribute opinions, while the small robot significantly increased it when not interested and attributed opinions. In situations where there are opinions that cannot be attributed to humanoid robots, the result that androids are more motivating when users have the interests even if opinions are not attributed can indicate the usefulness of androids

Human-Machine Communication: Complete Volume 5. Gender and Human-Machine Communication

This is the complete volume of HMC Volume

A Curious Robot Learner for Interactive Goal-Babbling (Strategically Choosing What, How, When and from Whom to Learn)

Les dé s pour voir des robots opérant dans l environnement de tous les jours des humains et sur unelongue durée soulignent l importance de leur adaptation aux changements qui peuvent être imprévisiblesau moment de leur construction. Ils doivent être capable de savoir quelles parties échantillonner, et quelstypes de compétences il a intérêt à acquérir. Une manière de collecter des données est de décider par soi-même où explorer. Une autre manière est de se référer à un mentor. Nous appelons ces deux manièresde collecter des données des modes d échantillonnage. Le premier mode d échantillonnage correspondà des algorithmes développés dans la littérature pour automatiquement pousser l agent vers des partiesintéressantes de l environnement ou vers des types de compétences utiles. De tels algorithmes sont appelésdes algorithmes de curiosité arti cielle ou motivation intrinsèque. Le deuxième mode correspond au guidagesocial ou l imitation, où un partenaire humain indique où explorer et où ne pas explorer.Nous avons construit une architecture algorithmique intrinsèquement motivée pour apprendre commentproduire par ses actions des e ets et conséquences variées. Il apprend de manière active et en ligne encollectant des données qu il choisit en utilisant plusieurs modes d échantillonnage. Au niveau du metaapprentissage, il apprend de manière active quelle stratégie d échantillonnage est plus e cace pour améliorersa compétence et généraliser à partir de son expérience à un grand éventail d e ets. Par apprentissage parinteraction, il acquiert de multiples compétences de manière structurée, en découvrant par lui-même lesséquences développementale.The challenges posed by robots operating in human environments on a daily basis and in the long-termpoint out the importance of adaptivity to changes which can be unforeseen at design time. The robot mustlearn continuously in an open-ended, non-stationary and high dimensional space. It must be able to knowwhich parts to sample and what kind of skills are interesting to learn. One way is to decide what to exploreby oneself. Another way is to refer to a mentor. We name these two ways of collecting data sampling modes.The rst sampling mode correspond to algorithms developed in the literature in order to autonomously drivethe robot in interesting parts of the environment or useful kinds of skills. Such algorithms are called arti cialcuriosity or intrinsic motivation algorithms. The second sampling mode correspond to social guidance orimitation where the teacher indicates where to explore as well as where not to explore. Starting fromthe study of the relationships between these two concurrent methods, we ended up building an algorithmicarchitecture with a hierarchical learning structure, called Socially Guided Intrinsic Motivation (SGIM).We have built an intrinsically motivated active learner which learns how its actions can produce variedconsequences or outcomes. It actively learns online by sampling data which it chooses by using severalsampling modes. On the meta-level, it actively learns which data collection strategy is most e cient forimproving its competence and generalising from its experience to a wide variety of outcomes. The interactivelearner thus learns multiple tasks in a structured manner, discovering by itself developmental sequences.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics