Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

Engagement is crucial to designing intelligent systems that can adapt to the characteristics of their users. This paper focuses on automatic analysis and classification of engagement based on humans’ and robot’s personality profiles in a triadic human-human-robot interaction setting. More explicitly, we present a study that involves two participants interacting with a humanoid robot, and investigate how participants’ personalities can be used together with the robot’s personality to predict the engagement state of each participant. The fully automatic system is firstly trained to predict the Big Five personality traits of each participant by extracting individual and interpersonal features from their nonverbal behavioural cues. Secondly, the output of the personality prediction system is used as an input to the engagement classification system. Thirdly, we focus on the concept of “group engagement”, which we define as the collective engagement of the participants with the robot, and analyse the impact of similar and dissimilar personalities on the engagement classification. Our experimental results show that (i) using the automatically predicted personality labels for engagement classification yields an F-measure on par with using the manually annotated personality labels, demonstrating the effectiveness of the automatic personality prediction module proposed; (ii) using the individual and interpersonal features without utilising personality information is not sufficient for engagement classification, instead incorporating the participants’ and robot’s personalities with individual/interpersonal features increases engagement classification performance; and (iii) the best classification performance is achieved when the participants and the robot are extroverted, while the worst results are obtained when all are introverted.This work was performed within the Labex SMART project (ANR-11-LABX-65) supported by French state funds managed by the ANR within the Investissements d’Avenir programme under reference ANR-11-IDEX-0004-02. The work of Oya Celiktutan and Hatice Gunes is also funded by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref.: EP/L00416X/1).This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers via http://dx.doi.org/10.1109/ACCESS.2016.261452

Comparing Social Science and Computer Science Workflow Processes for Studying Group Interactions

In this article, a team of authors from the Geeks and Groupies workshop, in Leiden, the Netherlands, compare prototypical approaches to studying group interaction in social science and computer science disciplines, which we call workflows. To help social and computer science scholars understand and manage these differences, we organize workflow into three major stages: research design, data collection, and analysis. For each stage, we offer a brief overview on how scholars from each discipline work. We then compare those approaches and identify potential synergies and challenges. We conclude our article by discussing potential directions for more integrated and mutually beneficial collaboration that go beyond the producer–consumer model

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction

Human personality has been identified as a predictor of robot acceptance in the human robot interaction (HRI) literature. Despite this, the HRI literature has provided mixed support for this assertion. To better understand the relationship between human personality and robot acceptance, this paper conducts a meta-analysis of 26 studies. Results found a positive relationship between human personality and robot acceptance. However, this relationship varied greatly by the specific personality trait along with the study sample’s age, gender diversity, task, and global region. This meta-analysis also identified gaps in the literature. Namely, additional studies are needed that investigate both the big five personality traits and other personality traits, examine a more diverse age range, and utilize samples from previously unexamined regions of the globe.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/165339/1/Esterwood et al. 2021 (one column).pdfDescription of Esterwood et al. 2021 (one column).pdf : Preprint one column versionSEL

A Review of Personality in Human Robot Interactions

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.Comment: 70 pages, 2 figure

Evaluation of Laban Effort Features based on the Social Attributes and Personality of Domestic Service Robots

Today, it is not uncommon to see robots adopted in various domains and environments. From manufacturing facilities to households, robots take over several roles and tasks. For instance, the adoption of robotic vacuum cleaners has drastically increased in the recent decades. During their interaction with these embodied autonomous agents, humans tend to ascribe certain personality traits to them, even when the robot has a mechanoid appearance and very low degree-of-freedom. As the social capabilities and the persuasiveness of robots increase, design of robots with certain personality traits will become a significant design problem. The current advancements in AI and robotics will led to development of more realistic and persuasive robots in the foreseeable future. For this, it is crucial to understand people’s judgment of the robots’ social attributes since the findings can shape the future of personality and behavior design for social robots. Therefore, using only a simple and mono-functional robotic vacuum cleaner, this study aims to investigate the impact of expressive motions on how people perceive the social attributes and personality of the robot. To investigate this, the framework of Laban Effort Features was modified to fit the needs and constraints of a robotic vacuum cleaner. Expressive motions were designed for a simple cleaning task performed by iRobot’s Create2. The four movement features that have been controlled for robot include path planning behavior, radius of curvature at rotational turns, velocity, and vacuum power. Next, participants were asked to rate the personality and social attributes of the robot under several treatment conditions using a video-based online survey. Participants were recruited through the crowd-sourcing platform, Amazon Mechanical Turk. The results indicated that people’s ratings of personality and social attributes of the robot were influenced by the robot’s movement features. For social attributes, there were two main findings. First, velocity influenced robot’s ratings of warmth and competence. Second, path planning behavior influenced robot’s ratings of competence and discomfort. In terms of robot personality, the results indicated three main findings. First, random path planning behavior was associated with higher Neuroticism ratings. Second, high velocity yielded higher Agreeableness ratings. Third, vacuum power with higher duty cycle yielded higher Agreeableness and Conscientiousness ratings. In conclusion, this study showed the framework of Laban Effort Features can be applied to fit the cleaning task of a domestic service robot, and that the framework’s application makes a difference in how humans perceive the personality and social attributes of the robot. Overall, the findings should be considered in human-robot interaction when incorporating expressive motions and affective behavior into domestic service robots

Imitating Human Responses via a Dual-Process Model Approach

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model

Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

Engagement is crucial to designing intelligent systems that can adapt to the characteristics of their users. This paper focuses on the automatic analysis and classification of engagement based on humans' and robot's personality profiles in a triadic human-human-robot interaction setting. More explicitly, we present a study that involves two participants interacting with a humanoid robot, and investigate how participants' personalities can be used together with the robot's personality to predict the engagement state of each participant. The fully automatic system is first trained to predict the Big Five personality traits of each participant by extracting individual and interpersonal features from their nonverbal behavioural cues. Second, the output of the personality prediction system is used as an input to the engagement classification system. Third, we focus on the concept of 'group engagement', which we define as the collective engagement of the participants with the robot, and analyze the impact of similar and dissimilar personalities on the engagement classification. Our experimental results show that: 1) using the automatically predicted personality labels for engagement classification yields an F-measure on par with using the manually annotated personality labels, demonstrating the effectiveness of the automatic personality prediction module proposed; 2) using the individual and interpersonal features without utilizing personality information is not sufficient for engagement classification, instead incorporating the participants and robots personalities with individual/interpersonal features increases engagement classification performance; and 3) the best classi fication performance is achieved when the participants and the robot are extroverted, while the worst results are obtained when all are introverted