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Invoking and identifying task-oriented interlocutor confusion in human-robot interaction.

Na Li1, Robert Ross1

  • 1School of Computer Science, Technological University, Dublin, Ireland.

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Summary
This summary is machine-generated.

Researchers studied user confusion during human-robot interactions. They found that multimodal signals like gaze, head pose, and speech analysis can effectively detect user confusion, enabling robots to adapt their responses.

Keywords:
confusion detectionmultimodal modelingsituated dialoguesocial robotuser engagementwizard-of-oz

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Area of Science:

  • Human-Robot Interaction
  • Affective Computing
  • Social Robotics

Background:

  • Effective human-robot interaction (HRI) necessitates understanding user emotional and attitudinal states.
  • Detecting user confusion is crucial for robots to adapt their conversational strategies.

Purpose of the Study:

  • To systematically trigger, interpret, and detect user confusion states during human-robot interaction.
  • To identify multimodal behavioral cues correlated with user confusion.

Main Methods:

  • A Wizard-of-Oz (WoZ) study with a Pepper robot and 81 participants.
  • Collected audio-visual data and participant survey data.
  • Analyzed multimodal signals: eye gaze, head pose, facial emotion, speech, and silence duration.

Main Results:

  • Significant differences in participant behavior were observed across confusion states.
  • Strong correlations were found between induced confusion and self-reported confusion scores.
  • Multimodal signals effectively correlated with user confusion levels.

Conclusions:

  • Observable multimodal features can reliably indicate user confusion in HRI.
  • This research provides a foundation for affect-aware social robots.
  • The study offers a methodology, dataset, and analysis for detecting confusion in HRI.