Trust in Social HRI Attributes which influence the trust in a robot - - PowerPoint PPT Presentation

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Trust in Social HRI Attributes which influence the trust in a robot - - PowerPoint PPT Presentation

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MIN Faculty Department of Informatics Trust in Social HRI Attributes which influence the trust in a robot Ann-Katrin Thebille University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical

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MIN Faculty Department of Informatics

Trust in Social HRI

Attributes which influence the trust in a robot Ann-Katrin Thebille

University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems

  • 11. December 2017
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Outline

Motivation Fundamentals Attributes Summary

  • 1. Motivation
  • 2. Fundamentals
  • 3. Attributes

Anthropomorphism Matching robot behaviour Adapting proxemics Vocal cues Gaze Gestures

  • 4. Summary
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Why is this topic relevant?

Motivation Fundamentals Attributes Summary

Motivation

Figure: “Buddy” the companion robot [Blu17]

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What is social HRI?

Motivation Fundamentals Attributes Summary

Figure: Human-robot interaction in a social context [SD17]

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Why is trust important?

Motivation Fundamentals Attributes Summary

◮ No trust = robot is not used ◮ Too much trust = robot is misused

Figure: Relation between Capability and Trust [LS04]

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Outline

Motivation Fundamentals Attributes Summary

  • 1. Motivation
  • 2. Fundamentals
  • 3. Attributes

Anthropomorphism Matching robot behaviour Adapting proxemics Vocal cues Gaze Gestures

  • 4. Summary
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What influences Human-Robot Trust?

Motivation Fundamentals Attributes Summary

Figure: Factors which influence trust [Sch13]

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What influences Human-Robot Trust?

Motivation Fundamentals Attributes Summary

Figure: Factors which influence trust [Sch13]

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Outline

Motivation Fundamentals Attributes Summary

  • 1. Motivation
  • 2. Fundamentals
  • 3. Attributes

Anthropomorphism Matching robot behaviour Adapting proxemics Vocal cues Gaze Gestures

  • 4. Summary
  • A. Thebille – Trust in Social HRI

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Anthropomorphism

Motivation Fundamentals Attributes Summary

◮ Humans generally prefer familiar objects/shapes/faces ◮ Humanoid robots are judged as more likeable, intelligent, ... ◮ BUT:

Figure: The uncanny valley [Mor70]

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Matching robot behaviour I

Motivation Fundamentals Attributes Summary

◮ Goetz et al. [GKP03] tested two competing hypotheses ◮ Natural preference of attractive people with positive attitude

(“Positivity hypothesis”)

◮ Appearance and task-type should match (“Matching

hypothesis”)

◮ Study compliance to robot regarding robot behaviour:

Types/ Compliance in seconds Playful robot Serious robot Fun task 218 148 Serious task 95 125

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Matching robot behaviour II

Motivation Fundamentals Attributes Summary

→ Behaviour and appearance influence willingness to comply → Match robot to task to improve trust + Easy to switch from playful to serious behaviour (e.g. change

  • f words)

− General appearance not so easy to adapt − Robot has to be able to understand the tone of a task − Adapting only to the task might not work for all users

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Adapting proxemics I

Motivation Fundamentals Attributes Summary

◮ People adapt distance to interaction partner (0.5 − 3.5m) ◮ Standing too close to someone makes us uncomfortable

→ Robot should adapt distance to increase trust

◮ If robot stands too close, cameras can’t capture all of the

human

Figure: Distance types of proxemics [MM17]

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Adapting proxemics II

Motivation Fundamentals Attributes Summary

◮ Studies found that people stand closer to robots (0.3 − 1.3m)

[HRI16]

◮ Cues for proxemics subtle (Tone of voice, posture, ..)

+ Important aspect of social interaction + Necessary to adapt to increase performance (speech/posture recognition) − Difficult to find balance between social aspects and functionality − Reasons for moving might have to be communicated

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Vocal cues I

Motivation Fundamentals Attributes Summary

Effects of different voice types (human /robot) and gender studied by [EKHR12]

◮ People perceived human-like voice as significantly more likeable ◮ Both genders tend to perceive a voice of their own gender as

more likeable

◮ Males felt significantly closer to a male-voice

→ Adapt voice type to the user

Figure: [Pixabay.com]

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Vocal cues II

Motivation Fundamentals Attributes Summary

Why do so many computer-assistances have a femal voice? "It’s much easier to find a female voice that everyone likes than a male voice that everyone likes” [Gri11] + Human-like voice significantly improves closeness (Trust) + Initial positive reaction towards robot apperance reinforced with voice − Gender of voice has to fit the appearance → Design choice, which can’t be adapted − Only relevant if the communication is performed via speech − Complex speech generation might not sound very human-like yet

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Gaze I

Motivation Fundamentals Attributes Summary

◮ Interaction more fluent, if human can predict what the robot is

doing next

◮ Indicater of intentions = eye gaze ◮ Gaze also shows attentention / distraction ◮ Gaze example

Figure: Reaction to handing over an object [MTG+14]

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Gaze II

Motivation Fundamentals Attributes Summary

◮ High level of mutual gaze = High level of trust ◮ Too much mutual gaze might make the dialogue partner

uncomfortable + Robot looks lifeless without gaze + Smoother interaction with humans − Head and eyes have to be turned, even if not necessary for “seeing” − Level of mutual gaze has to be adapted to user

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Gestures I

Motivation Fundamentals Attributes Summary

◮ Human-like robots are expected to behave human-like ◮ Gesturing is an essential part of communication ◮ Gestures can covey information which speech cannot provide ◮ Study by Salem et. al [SKW+12] to see effects of

(in-)congruent gestures accompanying speech

Figure: Asimo instructing a participant [SKW+12]

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Gestures II

Motivation Fundamentals Attributes Summary

Figure: Results of the study [SKW+12]

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Gestures III

Motivation Fundamentals Attributes Summary

◮ Gesture example ◮ Even non-perfect gestures add trust ◮ Some level of information convayable with only gestures

+ Significantly improves trust + Could be used instead of generating speech + Gestures don’t have to be perfect − Some gestures can’t be performed while handling another task − Adds further problems (e.g. Need for space to perform gestures) − Different gestures for different types of robots necessary

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Example for a gesture generation implementation I

Motivation Fundamentals Attributes Summary

Figure: Generation of gestures [SKW+12]

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Example for a gesture generation implementation II

Motivation Fundamentals Attributes Summary

◮ MURML “provides flexible means of describing gestures [..] and

expressing their relations to accompanying speech” [KKW12]

◮ ACE generates movement according to constraints and the

kinematic body model

◮ Wrist position and orientation are transmitted to the Motion

controller (Task space)

◮ The motion controller solves the IK (Inverser kinematics) ◮ Information about join positions is handed to the real robot ◮ Feedback loop updates the internal model

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Outline

Motivation Fundamentals Attributes Summary

  • 1. Motivation
  • 2. Fundamentals
  • 3. Attributes

Anthropomorphism Matching robot behaviour Adapting proxemics Vocal cues Gaze Gestures

  • 4. Summary
  • A. Thebille – Trust in Social HRI

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Summary

Motivation Fundamentals Attributes Summary

◮ Attributes have to be selected according to area of operation ◮ Always ask: How social does my robot have to be? ◮ Don’t forget: Performance has higher impact on trust ◮ Be aware of the uncanny valley effect

Figure: Sophia [Cam16]

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Thank you for listening! Questions?

Motivation Fundamentals Attributes Summary

Figure: ASIMO signing “I love you” [Hon17]

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Sources

Motivation Fundamentals Attributes Summary

[.2003] The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003. IEEE, 2003 . – ISBN 0–7803–8136–X [.2012] 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 2012 . – ISBN 978–1–4673–4606–1 [Blu17] Blue frog robotics: Buddy. http://www.bluefrogrobotics.com/en/press/. Version: 2017

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[Cam16] Campanella, Emanuela; Global news (Ed.): Meet Sophia, the human-like robot that wants to be your friend and ‘destroy humans’. https://globalnews.ca/news/2888337/ meet-sophia-the-human-like-robot-that-wants-to-be-your-friend- Version: 2016 [Cam17] Cambridge University Press (Ed.): Social Signal Processing. 2017. http://dx.doi.org/10.1017/9781316676202. http://dx.doi.org/10.1017/9781316676202. – ISBN 9781108124997

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[EKHR12] Eyssel, Friederike; Kuchenbrandt, Dieta; Hegel, Frank; Ruiter, Laura de: Activating elicited agent knowledge: How robot and user features shape the perception of social robots. In: 2012 IEEE RO-MAN: The 21st IEEE International Symposium

  • n Robot and Human Interactive Communication, IEEE, 2012. –

ISBN 978–1–4673–4606–1, pp. 851–857 [GKP03] Goetz, J.; Kiesler, S.; Powers, A.: Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003, IEEE, 2003. – ISBN 0–7803–8136–X, pp. 55–60

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[Gri11] Griggs, Brandon: Why computer voices are mostly female. http://edition.cnn.com/2011/10/21/tech/innovation/ female-computer-voices/. Version: 2011 [Hon17] Honda: ASIMO signs "I Love You". http://asimo.honda.com/gallery.aspx. Version: 2017 [HRI16] HRI’16: The Eleventh ACM/IEEE International Conference on Human Robot Interation : March 7-10, 2016, Christchurch, NZ. Piscataway, NJ : IEEE, 2016 http: //ieeexplore.ieee.org/servlet/opac?punumber=7446754. – ISBN 978–1–4673–8370–7

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[IFA12] IFAAMAS (Ed.): Proceedings of the AAMAS 02 workshop on embodied conversational agents - let’s specify and evaluate them. 2012 [KKW12] Kranstedt, A.; Kopp, S.; Wachsmuth, I.: MURML: a multi-modal utterance representation markup language for conversational agents. Version: 2012. https://www.techfak.uni-bielefeld.de/~skopp/download/ aa02b.pdf. In: IFAAMAS (Hrsg.): Proceedings of the AAMAS 02 workshop

  • n embodied conversational agents - let’s specify and evaluate

them. 2012

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[LS04] Lee, John D.; See, Katrina A.: Trust in automation: Designing for appropriate reliance. In: Human factors 46 (2004), No. 1, pp. 50–80. http://dx.doi.org/10.1518/hfes.46.1.50{_}30392. – DOI 10.1518/hfes.46.1.50_30392. – ISSN 0018–7208 [MM17] Mead, Ross; Matarić, Maja J.: Autonomous human–robot proxemics: Socially aware navigation based on interaction potential. In: Autonomous Robots 41 (2017), No. 5, pp. 1189–1201. http://dx.doi.org/10.1007/s10514-016-9572-2. – DOI 10.1007/s10514–016–9572–2. – ISSN 0929–5593 [Mor70] Mori, Masahiro: Energy: "The uncanny valley". vol. 7. 1970

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[MTG+14] Moon, AJung; Troniak, Daniel M.; Gleeson, Brian; Pan, Matthew K.; Zeng, Minhua; Blumer, Benjamin A.; MacLean, Karon; Croft, Elizabeth A.: Meet me where i’m gazing. In: Sagerer, Gerhard (Hrsg.); Imai, Michita (Hrsg.); Belpaeme, Tony (Hrsg.); Thomaz, Andrea (Hrsg.): Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction - HRI ’14. New York, New York, USA : ACM Press, 2014. – ISBN 9781450326582, pp. 334–341 [Sch13] Schaefer, Kristin E.: The Perception and Measurement of Human-Robot Trust. Orlando, Florida, University of Central Florida, Dissertation, 2013. http://etd.fcla.edu/CF/CFE0004931/Schaefer_Kristin_E_ 201308_PhD.pdf

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[SD17] Salem, Maha; Dautenhahn, Kerstin: Social Signal Processing in Social Robotics. Version: 2017. www.cambridge.org/9781107161269. In: Cambridge University Press (Hrsg.): Social Signal Processing Bd. 978-1-107-16126-9.

  • 2017. –

ISBN 9781108124997, 317–328 [SIBT14] Sagerer, Gerhard (Ed.); Imai, Michita (Ed.); Belpaeme, Tony (Ed.); Thomaz, Andrea (Ed.): Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction - HRI ’14. New York, New York, USA : ACM Press, 2014 . – ISBN 9781450326582

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Sources (cont.)

Motivation Fundamentals Attributes Summary

[SKW+12] Salem, Maha; Kopp, Stefan; Wachsmuth, Ipke; Rohlfing, Katharina; Joublin, Frank: Generation and Evaluation of Communicative Robot Gesture. In: International Journal of Social Robotics 4 (2012), No. 2, pp. 201–217. http://dx.doi.org/10.1007/s12369-011-0124-9. – DOI 10.1007/s12369–011–0124–9. – ISSN 1875–4791

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