Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Social Robotics
Erschienen in: International Journal of Social Robotics 1/2017

02.06.2016

Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human–Robot Assembly Task

Experiments with the iCub humanoid

verfasst von: Serena Ivaldi, Sebastien Lefort, Jan Peters, Mohamed Chetouani, Joelle Provasi, Elisabetta Zibetti

Erschienen in: International Journal of Social Robotics | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Estimating the engagement is critical for human–robot interaction. Engagement measures typically rely on the dynamics of the social signals exchanged by the partners, especially speech and gaze. However, the dynamics of these signals are likely to be influenced by individual and social factors, such as personality traits, as it is well documented that they critically influence how two humans interact with each other. Here, we assess the influence of two factors, namely extroversion and negative attitude toward robots, on speech and gaze during a cooperative task, where a human must physically manipulate a robot to assemble an object. We evaluate if the score of extroversion and negative attitude towards robots co-variate with the duration and frequency of gaze and speech cues. The experiments were carried out with the humanoid robot iCub and N = 56 adult participants. We found that the more people are extrovert, the more and longer they tend to talk with the robot; and the more people have a negative attitude towards robots, the less they will look at the robot face and the more they will look at the robot hands where the assembly and the contacts occur. Our results confirm and provide evidence that the engagement models classically used in human–robot interaction should take into account attitudes and personality traits.
Vorheriger Artikel Robots in Education and Care of Children with Developmental Disabilities: A Study on Acceptance by Experienced and Future Professionals
Nächster Artikel On the Role of Affective Properties in Hedonic and Discriminant Haptic Systems

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
In social psychology, there is a net distinction between personality traits and attitudes. Here, we use methods from differential psychology rather than social psychology: the distinction between the two is not important, as long as the two factors are two characteristics of the individual that are evaluated at a certain time prior to the interaction. We measured the attitude towards robots with the NARS questionnaire, a test that was created to capture the projected anxiety of the person before its interaction with the robot. We used it to evaluate an individual attitude prior to the direct interaction with the robot (participants filled the NARS questionnaire several days before the experiment—see details about the experimental procedure in Sect. 4.4).
 
2
See for example the press article: “Will workplace robots cost more jobs than they create?” http://​www.​bbc.​com/​news/​technology-27995372.
 
3
We interviewed our participants after the experiments. Some reported that they “do not like robots because they are going to take our jobs”. Some reported to have enjoyed the experiment with the robot and made explicit reference to their expectations being influenced by “the robots of Star Wars”.
 
4
In social psychology, there is a net distinction personality traits and attitudes. Here, we use methods from differential psychology rather than social psychology. We measured the attitude towards robots with the NARS questionnaire, a test that was created to capture the projected anxiety of the person before its interaction with the robot. We used it to evaluate an individual attitude prior to the direct interaction with the robot (participants filled the NARS questionnaire several days before the experiment—see details about the experimental procedure in Sect. 4.4).
 
6
We cannot report the questions, as the questionnaire is not publicly available: we refer the interested reader to the English manual [12] and the official French adaptation that we used [13].
 
7
A recent paper from Dinet and Vivian [16] studied the NARS and validated it on a sample of French population. Their study was published only after our work and experiments. They employed their own translation of the questionnaire, which has some slight differences with ours, mostly due to some nuances of the French language. These do not preserve the original meaning when translated back into English. In their paper there is no mention of a double translation mechanism for validating the French adaptation of the questionnaire.
 
8
This was done as a safety measure. However, nothing happened during the experiments: the experimenter never had to push the safety button, and she never had to stop the physical interaction between the robot and the subject.
 
9
It is a dissemination video from IIT showing the iCub, available on Youtube: http://​youtu.​be/​ZcTwO2dpX8A.
 
10
In the post-experiment interview, we asked the participants if they thought or had the impression that the robot was controlled by someone: all the participants thought that the robot was fully autonomous.
 
11
The demonstration was also part of the safety measures required by the Ethics Committee to approve our protocol.
 
12
The operator could switch the control mode without the need of the verbal command, since he had a direct visibility of the interaction zone in front of the robot through an additional camera that was centered on the workspace in front of the robot (see Fig. 2).
 
13
Utterances are units of speech that begin and end by a pause. To determine the beginning and the end of each utterance, we consider pauses greater than 500ms.
 
14
Correlation is frequently used to study the link between personality and behavior, as discussed in [18], a survey on the link between extroversion and behavior where all the cited studies use correlations to test their hypothesis.
 
15
According to the NEO-PIR, a participant obtaining a score bigger than 137 is considered extrovert, while one with a score below 80 is introvert.
 
16
According to the NARS, a score over 65 is a sign of negative attitude towards robots, while a score below 35 indicates a rather positive attitude towards robots.
 
17
Contact forces are the forces due to the physical interaction between the human and the robot, originated at the contact location where the interaction occurs.
 
Literatur
1.
Ajzen Icek I,Madden TJ (1986) Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control. J Exp Soc Psychol 22(5):453–474
2.
Ajzen Icek I,Madden TJ (1986) Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control. J Exp Soc Psychol 22(5):453–474
3.
Anzalone S, Yoshikawa Y, Ishiguro H, Menegatti E, Pagello E, Sorbello R (2012) Towards partners profiling in human robot interaction contexts. In: Noda I, Ando N, Brugali D, Kuffner J (eds) Simulation, modeling, and programming for autonomous robot, vol 7628., Lecture Notes in Computer ScienceSpringer, Berlin, pp 4–15
4.
Anzalone SM, Boucenna S, Ivaldi S, Chetouani M (2015) Evaluating the engagement with social robots. Int J Soc Robot 7(4):465–478CrossRef
5.
Argyle M (1976) Personality and social behaviour. Blackwell, Oxford
6.
Ba S, Odobez JM (2009) Recognizing visual focus of attention from head pose in natural meetings. IEEE Trans Syst Man Cybernet B 39(1):16–33CrossRef
7.
Beatty M, McCroskey J, Valencic K (2001) The biology of communication: a communibiological perspective. Hampton Press, Cresskill
8.
Berry D, Hansen J (2000) Personality, nonverbal behavior, and interaction quality in female dyads. Pers Soc Psychol Bull 26(3):278–292CrossRef
9.
Boucenna S, Anzalone S, Tilmont E, Cohen D, Chetouani M (2014) Learning of social signatures through imitation game between a robot and a human partner. IEEE Trans Auton Mental Dev 6(3):213–225CrossRef
10.
Castellano G, Pereira A, Leite I, Paiva A, McOwan P W (2009) Detecting user engagement with a robot companion using task and social interaction-based features. In: Proceedings of the 2009 international conference on multimodal interfaces, pp 119–126
11.
Chen T, King CH, Thomaz A, Kemp C (2014) An investigation of responses to robot-initiated touch in a nursing context. Int J Soc Robot 6(1):141–161CrossRef
12.
Costa P, McCrae R (1992) Revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI) professional manual. Psychological Assessment Resources, Odessa
13.
Costa P, McCrae R, Rolland J (1998) NEO-PI-R. Inventaire de Personnalité révisé, Editions du Centre de Psychologie Appliquée, Paris
14.
Dang TH, Tapus A (2014) Towards personality-based assistance in human-machine interaction. In: Proceedings of IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
15.
Dewaele JM, Furnham A (2000) Personality and speech production: a pilot study of second language learners. Pers Individ Differ 28(2):355–365CrossRef
16.
Dinet J, Vivian R (2015) Perception and attitudes towards anthropomorphic robots in france: validation of an assessment scale. Psychologie Francaise 60(1):173–189CrossRef
17.
Eysenck HJ (1981) A model for personality. Springer-Verlag, General features of the model. New YorkCrossRef
18.
France BHL, Heisel AD, Beatty MJ (2004) Is there empirical evidence for a nonverbal profile of extraversion? A meta-analysis and critique of the literature. Commun Monogr 71(1):28–48CrossRef
19.
Fumagalli M, Ivaldi S, Randazzo M, Natale L, Metta G, Sandini G, Nori F (2012) Force feedback exploiting tactile and proximal force/torque sensing. Theory and implementation on the humanoid robot icub. Auton Robots 4:381–398CrossRef
20.
Gaudiello I, Zibetti E, Lefort S, Chetouani M, Ivaldi S (2016) Trust as indicator of robot functional and social acceptance. an experimental study on user conformation to iCub answers. Comput Hum Behav 61:633–655. doi:10.​1016/​j.​chb.​2016.​03.​057
21.
Goffman E (1967) Interaction ritual: essays on face-to-face behavior. Anchor Books, New York
22.
Goldberg L (1990) An alternative description of personality: the big-five factor structure. J Pers Soc Psychol 59:1216–1229CrossRef
23.
Gray K, Wegner D (2012) Feeling robots and human zombies: mind perception and the uncanny valley. Cognition 125(1):125–130CrossRef
24.
Hanninen L, Pastell M (2009) Cowlog: open-source software for coding behaviors from digital video. Behav Res Methods 41(2):472–476CrossRef
25.
Huang CM, Thomaz A (2011) Effects of responding to, initiating and ensuring joint attention in human–robot interaction. In: IEEE RO-MAN, pp 65–71
26.
Iishi R, Shinohara Y, Nakano T, Nishida T (2011) Combining multiple types of eye-gaze information to predict user’s conversatinal engagement. In: 2nd workshop on eye gaze in intelligent human machine interaction, pp 1–8
27.
Ivaldi S, Anzalone S, Rousseau W, Sigaud O, Chetouani M (2014) Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement. Front Neurorobot 8(5):1–23
28.
Ivaldi S, Nguyen SM, Lyubova N, Droniou A, Padois V, Filliat D, Oudeyer PY, Sigaud O (2014) Object learning through active exploration. IEEE Trans Auton Mental Dev 6(1):56–72CrossRef
29.
Le Maitre J, Chetouani M (2013) Self-talk discrimination in human-robot interaction situations for supporting social awareness. Int J Soc Robot 5(2):277–289CrossRef
30.
Lepri B, Subramanian R, Kalimeri K, Staiano J, Pianesi F, Sebe N (2010) Employing social gaze and speaking activity for automatic determination of the extraversion trait. In: International conference on multimodal interfaces and the workshop on machine learning for multimodal interaction, pp 1–7
31.
Mara M, Appel M (2015) Science fiction reduces the eeriness of android robots: a field experiment. Comput Hum Behav 48(1):156–162CrossRef
32.
Mohammadi G, Vinciarelli A (2012) Automatic personality perception: prediction of trait attribution based on prosodic features. IEEE Trans Affect Comput 3(3):273–284CrossRef
33.
Mori M, MacDorman K, Kageki N (2012) The uncanny valley (from the field). IEEE Robot Autom Mag 19(2):98–100CrossRef
34.
Natale L, Nori F, Metta G, Fumagalli M, Ivaldi S, Pattacini U, Randazzo M, Schmitz A, Sandini G (2013) The iCub platform: a tool for studying intrinsically motivated learning. Springer, Berlin
35.
Nomura T, Kanda T, Suzuki T (2006) Experimental investigation into influence of negative attitudes toward robots on human-robot interaction. AI Soc 20(2):138–150CrossRef
36.
Nomura T, Kanda T, Suzuki T, Kato K (2006) Exploratory investigation into influence of negative attitudes toward robots on human-robot interaction. In: Mobile Robots: towards New Applications, Aleksandar Lazinica
37.
Nomura T, Kanda T, Suzuki T, Kato K (2008) Prediction of human behavior in human-robot interaction using psychological scales for anxiety and negative attitudes toward robots. IEEE Trans Robot 24(2):442–451CrossRef
38.
Pianesi F, Mana N, Cappelletti A, Lepri B, Zancanaro M (2008) Multimodal recognition of personality traits in social interactions. Proceedings of the 10th International Conference on Multimodal Interfaces, ICMI ’08. ACM, New York, pp 53–60
39.
Poggi I, D’Errico F (2012) Social signals: a framework in terms of goals and beliefs. Cogn Process 13(2):427–445CrossRef
40.
Rahbar F, Anzalone S, Varni G, Zibetti E, Ivaldi S, Chetouani M (2015) Predicting extraversion from non-verbal features during a face-to-face human-robot interaction. In: International Conference on Social Robotics, pp 1–10
41.
Rauthmann J, Seubert C, Sachse P, Furtner M (2012) Eyes as windows to the soul: gazing behavior is related to personality. J Res Pers 46(2):147–156CrossRef
42.
Rich C, Ponsler B, Holroyd A, Sidner C L (2010) Recognizing engagement in human–robot interaction. In: Proceedings of ACM/IEEE international conference human–robot interaction (HRI), pp 375–382
43.
Sanghvi J, Castellano G, Leite I, Pereira A, McOwan P W, Paiva A (2011) Automatic analysis of affective postures and body motion to detect engagement with a game companion. In: 6th ACM/IEEE International Conference on human–robot interaction (HRI), 2011, pp 305–311
44.
Saygin A, Chaminade T, Ishiguro H, Driver J, Frith C (2012) The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions. Soc Cogn Affect Neurosci 7(4):413–422CrossRef
45.
Scherer K, Scherer U (1981) Speech evaluation in psychiatry. Speech behavior and personality, Grune and Stratton
46.
Sidner C, Kidd C, Lee C, Lesh N (2004) Where to look: a study of human-robot engagement. In: Proceedings 9th International Conference on Intelligent User Interfaces, pp 78–84
47.
Sidner C, Lee C, Kidd C, Lesh N (2005) Explorations in engagement for humans and robots. Artif Intell 1(166):140–164CrossRef
48.
Sidner CL, Lee C, Morency L P, Forlines C (2006) The effect of head-nod recognition in human-robot conversation. In: Proceedings of 1st ACM SIGCHI/SIGART Conference on human–robot interaction, pp 290–296
49.
Stefanov N, Peer A, Buss M (2009) Role determination in human–human interaction. In: 3rd Joint EuroHaptics conference and world haptics, pp 51–56
50.
Takayama L, Pantofaru C (2009) Influences on proxemic behaviors in human–robot interaction. In: Proceedings of IEEE-RAS international conference on intelligent robots and systems
51.
Tapus A, Ţǎpuş C, Matarić MJ (2008) User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intell Serv Robot 1(2):169–183CrossRef
52.
Tapus A, Matarić MJ (2008) Socially assistive robots: the link between personality, empathy, physiological signals, and task performance. In: AAAI spring symposium on emotion, personality, and social behavior, pp 133–140
53.
Vinciarelli A, Mohammadi G (2014) A survey of personality computing. IEEE Trans Affect Comput 5(3):273–291CrossRef
54.
Wilcox R, Nikolaidis S, Shah JA (2012) Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing. In: Robotics: science and systems
55.
Wood W (2000) Attitude change: Persuasion and social influence. Annu Rev Psychol 51:539–570CrossRef
56.
Wu D, Bischof W, Anderson N, Jakobsen T, Kingstone A (2014) The influence of personality on social attention. Pers Individ Differ 60:25–29CrossRef
57.
Yuichi I (1992) Extraversion, introversion, and visual interaction. Percept Mot Skills 74(1):43–50CrossRef
58.
Zen G, Lepri B, Ricci E, Lanz O (2010) Space speaks: towards socially and personality aware visual surveillance. In: Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis, pp 37–42
Metadaten
Titel
Towards Engagement Models that Consider Individual Factors in HRI: On the Relation of Extroversion and Negative Attitude Towards Robots to Gaze and Speech During a Human–Robot Assembly Task
Experiments with the iCub humanoid
verfasst von
Serena Ivaldi
Sebastien Lefort
Jan Peters
Mohamed Chetouani
Joelle Provasi
Elisabetta Zibetti
Publikationsdatum
02.06.2016
Verlag
Springer Netherlands
Erschienen in
International Journal of Social Robotics / Ausgabe 1/2017
Print ISSN: 1875-4791
Elektronische ISSN: 1875-4805
DOI
https://doi.org/10.1007/s12369-016-0357-8

Weitere Artikel der Ausgabe 1/2017

International Journal of Social Robotics 1/2017 Zur Ausgabe

Editorial

Editorial

OriginalPaper

Nonverbal Immediacy as a Characterisation of Social Behaviour for Human–Robot Interaction

OriginalPaper

Interacting with Non-anthropomorphic Robotic Artworks and Interpreting Their Behaviour

OriginalPaper

The Influence of Prior Expectations of a Robot’s Lifelikeness on Users’ Intentions to Treat a Zoomorphic Robot as a Companion

OriginalPaper

Robots in Education and Care of Children with Developmental Disabilities: A Study on Acceptance by Experienced and Future Professionals

OriginalPaper

Teaching Psychomotor Skills to Autistic Children by Employing a Robotic Training Kit: A Pilot Study

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise