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Multi-party open-ended conversation with a social robot.

Giulio Antonio Abbo1, Maria Jose Pinto-Bernal1, Martijn Catrycke1

  • 1IDLab-AIRO, Ghent University - imec, Ghent, Belgium.

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Summary
This summary is machine-generated.

This study introduces a multi-party conversational system for robots, combining multimodal perception and large language models. The system shows promise in maintaining coherent dialogue and accurate speaker recognition, though technical challenges remain for fluid group interactions.

Keywords:
conversational agentfurhathuman-robot interactionlarge language modelmulti-party conversations

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Area of Science:

  • Human-Robot Interaction
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Multi-party open-ended conversation is a significant challenge in human-robot interaction.
  • Robots struggle with speaker recognition, turn allocation, and coherent responses in dynamic dialogue.

Purpose of the Study:

  • To develop and evaluate a multi-party conversational system for social robots.
  • To integrate multimodal perception with large language models for enhanced dialogue capabilities.

Main Methods:

  • The system combines voice direction of arrival, speaker diarisation, and face recognition with a large language model.
  • Implemented on the Furhat robot and evaluated with 30 participants in parallel and group conversation scenarios.

Main Results:

  • The system achieved high addressee accuracy (92.6%) in parallel conversations and strong face recognition (80-94%).
  • Participants reported positive social presence and engagement.
  • Technical issues like audio-based speaker recognition errors and response latency impacted group interaction fluidity.

Conclusions:

  • LLM-based multi-party interaction shows potential but has limitations.
  • Future research should focus on improving multimodal cue integration and responsiveness for social robots.