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Implementation of Engagement Detection for Human-Robot Interaction in Complex Environments.

Sin-Ru Lu1, Jia-Hsun Lo1, Yi-Tian Hong1

  • 1Mechanical Engineering Department, National Taiwan University, Taipei 10617, Taiwan.

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Summary
This summary is machine-generated.

This study introduces a robot cognitive system to improve human-robot interaction (HRI) by detecting human behavior and intentions. The system enhances naturalness and comfort in complex environments, as demonstrated by the Mobi robot.

Keywords:
action recognitioncognitive systemengagementhuman behaviorshuman–robot interaction

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Human-robot interaction (HRI) often lacks naturalness and comfort.
  • Robots struggle to accurately detect human behaviors, intentions, and emotions.

Purpose of the Study:

  • To develop a comprehensive robot cognitive system for complex environments.
  • To enhance the naturalness and comfort of HRI through accurate detection of human cues.
  • To demonstrate the system's efficacy using a novel dual-arm-hand mobile robot, Mobi.

Main Methods:

  • Integrated three models: engagement, intention, and HRI.
  • Engagement model: eye gaze, head pose, action recognition for interaction timing.
  • Intention model: sentiment and emotion analysis for intent inference.
  • HRI model: Google Dialogflow for context-aware responses.

Main Results:

  • The robot cognitive system accurately detects human behaviors, intentions, and emotions.
  • The Mobi robot successfully demonstrated the system's potential in a retail environment.
  • The system addresses eye contact anxiety and improves interaction appropriateness.

Conclusions:

  • The developed robot cognitive system significantly enhances HRI.
  • The system shows promise for improving user experience in real-world applications.
  • Further research can explore advanced HRI in complex, dynamic environments.