Visual Attention and Eye Gaze During Multiparty Conversations with Distractions

Our objective is to develop a computational model to predict visual attention behavior for an embodied conversational agent. During interpersonal interaction, gaze provides signal feedback and directs conversation flow. Simultaneously, in a dynamic environment, gaze also directs attention to peripheral movements. An embodied conversational agent should therefore employ social gaze not only for interpersonal interaction but also to possess human attention attributes so that its eyes and facial expression portray and convey appropriate distraction and engagement behaviors

Gaze aversion in conversational settings: An investigation based on mock job interview

We report the results of an empirical study on gaze aversion during dyadic human-to-human conversation in an interview setting. To address various methodological challenges in as- sessing gaze-to-face contact, we followed an approach where the experiment was conducted twice, each time with a different set of interviewees. In one of them the interviewer’s gaze was tracked with an eye tracker, and in the other the interviewee’s gaze was tracked. The gaze sequences obtained in both experiments were analyzed and modeled as Discrete-Time Markov Chains. The results show that the interviewer made more frequent and longer gaze contacts compared to the interviewee. Also, the interviewer made mostly diagonal gaze aversions, whereas the interviewee made sideways aversions (left or right). We discuss the relevance of this research for Human-Robot Interaction, and discuss some future research problems

Look me in the eyes: A survey of eye and gaze animation for virtual agents and artificial systems

International audienceA person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: "The face is the portrait of the mind; the eyes, its informers.". This presents a huge challenge for computer graphics researchers in the generation of artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human-human interactions. This State of the Art Report provides an overview of the efforts made on tackling this challenging task. As with many topics in Computer Graphics, a cross-disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We discuss the movement of the eyeballs, eyelids, and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Further, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye-gaze, during the expression of emotion or during conversation, and how they are synthesised in Computer Graphics and Robotics

In good company? : Perception of movement synchrony of a non-anthropomorphic robot

Copyright: © 2015 Lehmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Recent technological developments like cheap sensors and the decreasing costs of computational power have brought the possibility of robotic home companions within reach. In order to be accepted it is vital for these robots to be able to participate meaningfully in social interactions with their users and to make them feel comfortable during these interactions. In this study we investigated how people respond to a situation where a companion robot is watching its user. Specifically, we tested the effect of robotic behaviours that are synchronised with the actions of a human. We evaluated the effects of these behaviours on the robot’s likeability and perceived intelligence using an online video survey. The robot used was Care-O-bot®3, a non-anthropomorphic robot with a limited range of expressive motions. We found that even minimal, positively synchronised movements during an object-oriented task were interpreted by participants as engagement and created a positive disposition towards the robot. However, even negatively synchronised movements of the robot led to more positive perceptions of the robot, as compared to a robot that does not move at all. The results emphasise a) the powerful role that robot movements in general can have on participants’ perception of the robot, and b) that synchronisation of body movements can be a powerful means to enhance the positive attitude towards a non-anthropomorphic robot.Peer reviewe

A Framework of Personality Cues for Conversational Agents

Conversational agents (CAs)—software systems emulating conversations with humans through natural language—reshape our communication environment. As CAs have been widely used for applications requiring human-like interactions, a key goal in information systems (IS) research and practice is to be able to create CAs that exhibit a particular personality. However, existing research on CA personality is scattered across different fields and researchers and practitioners face difficulty in understanding the current state of the art on the design of CA personality. To address this gap, we systematically analyze existing studies and develop a framework on how to imbue CAs with personality cues and how to organize the underlying range of expressive variation regarding the Big Five personality traits. Our framework contributes to IS research by providing an overview of CA personality cues in verbal and non-verbal language and supports practitioners in designing CAs with a particular personality

Kolaboratif robotlarda güven özelliği: Sanal insan robot etkileşim ortamında, sözsüz ipuçlarının deneysel araştırması

This thesis reports the development of non-verbal HRI (Human-Robot Interaction) behaviors on a robotic manipulator, evaluating the role of trust in collaborative assembly tasks. Towards this end, we developed four non-verbal HRI behaviors, namely gazing, head nodding, tilting, and shaking, on a UR5 robotic manipulator. We used them under different degrees of trust of the user to the robot actions. Specifically, we used a certain head-on neck posture for the cobot using the last three links along with the gripper. The gaze behavior directed the gripper towards the desired point in space, alongside with the head nodding and shaking behaviors. We designed a remote setup to experiment subjects interacting with the cobot remotely via Zoom teleconferencing. In a simple collaborative scenario, the efficacy of these behaviors was assessed in terms of their impact on the formation of trust between the robot and the user and task performance. Nineteen people participated in the experiment with varying ages and genders.Bu tez insan robot arası etkileşimi geliştirmek amacıyla, yardımcı UR5 robotunun manipülatörü ile, bakış ve kafa davranışları yaratmayı ve etkilerini montaj senaryosu altında test etmeyi hedeflemektedir. Bu doğrultuda çeşitli sözlü olmayan robot davranışları UR5 robotu ve Robotiq çene kıskacı kullanılarak geliştirildi, bunlar; yana ve öne kafa sallama, kafa eğme ve bakış davranışıdır. Bu davranışları uygulayabilmek için daha önceden dizayn edilmiş bir robot duruşu kullanıldı ve son üç robot eklemi, çene kıskacı kullanılarak baş-boyun yapısına çevrildi. Bu duruş yapısı ile birlikte çene kıskacı uzayda bir noktaya doğrultularak bakış davranışı yapabilmektedir. Bakış davranışına ek olarak kafa yapısı ile birlikte kafa sallama gibi davranışlarda modellendi, bunun yanında katılımcıların aktif olarak cobot ile birlikte telekonferans programı olan Zoom üzerinden etkileşime geçebileceği özgün bir deney ortamı geliştirildi. Ortak çalışmaya dayalı bir senaryoda bu davranışların güven kazanımı ve performans üzerindeki etkisi test edildi. Farklı yaş ve cinsiyet gruplarından 19 katılımcı ile birlikte deneyler gerçekleştirildi.M.S. - Master of Scienc

Latent-Dynamic Discriminative Models for Continuous Gesture Recognition

Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper we develop a discriminative framework for simultaneous sequence segmentation and labeling which can capture both intrinsic and extrinsic class dynamics. Our approach incorporates hidden state variables which model the sub-structure of a class sequence and learn the dynamics between class labels. Each class label has a disjoint set of associated hidden states, which enables efficient training and inference in our model. We evaluated our method on the task of recognizing human gestures from unsegmented video streams and performed experiments on three different datasets of head and eye gestures. Our results demonstrate that our model for visual gesture recognition outperform models based on Support Vector Machines, Hidden Markov Models, and Conditional Random Fields