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Unraveling the thread: understanding and addressing sequential failures in human-robot interaction.

Lucien Tisserand1, Brooke Stephenson1,2, Heike Baldauf-Quilliatre1

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Human robot interaction struggles with real-time conversation flow. Analyzing library interactions reveals issues in sequential understanding, guiding future dialogue system design for better context adaptation.

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Area of Science:

  • Human-Robot Interaction (HRI)
  • Conversation Analysis
  • Natural Language Processing (NLP)

Background:

  • Interaction is a dynamic, real-time process.
  • Participants use relevance and social norms to interpret speech turns.
  • Adapting to changing conversational context is a key challenge in HRI.

Purpose of the Study:

  • Identify issues in sequential flow handling within in-the-wild Human Robot Interactions (HRIs).
  • Analyze HRIs in an open-world university library setting.
  • Guide the design of improved HRI systems for complex situations.

Main Methods:

  • Analysis of a corpus of in-the-wild HRIs.
  • Identification of problems related to inadequate sequential flow handling.
  • Survey of Natural Language Processing (NLP) and machine dialogue management approaches.

Main Results:

  • Inadequate handling of sequential flow is a significant issue in current HRI systems.
  • Real-world library interactions present complex contextual challenges.
  • Specific problems arising from poor sequential understanding were identified.

Conclusions:

  • Insights from analyzing in-the-wild HRIs can inform better system design.
  • Improved handling of conversational context is crucial for effective HRI.
  • Further research in NLP and dialogue management is needed to mitigate identified problems.