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Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
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Audio-Driven Robot Upper-Body Motion Synthesis.

Jan Ondras, Oya Celiktutan, Paul Bremner

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    This study introduces a neural network system that generates human-like upper-body gestures for robots, enhancing human-robot communication. The research explores how different models and speaker traits influence the naturalness of synthesized robotic movements.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Nonverbal cues, including body language, account for a significant portion of communication.
    • Current robotic platforms struggle to generate synchronized speech and body language.
    • Effective human-robot interaction (HRI) necessitates robots mimicking human nonverbal communication.

    Purpose of the Study:

    • To develop a neural network system for generating upper-body gestures synchronized with speech for humanoid robots.
    • To evaluate the impact of generic versus person-specific models on synthesized movement quality.
    • To investigate the correlation between quantitative and qualitative assessments and the influence of speaker personality on generated movements.

    Main Methods:

    • A neural network-based system was developed to process audio input and generate upper-body movements (head, hand, torso) for a humanoid robot (Pepper).
    • The system's performance was evaluated using quantitative and qualitative web surveys with both natural and synthetic speech.
    • Comparisons were made between generic and person-specific neural network models.

    Main Results:

    • The study presents a novel system for generating synchronized upper-body gestures for humanoid robots.
    • Evaluation results indicate differences in synthesized movement quality based on the neural network model type (generic vs. person-specific).
    • The research explores the relationship between objective evaluations, subjective user perceptions, and the effect of speaker personality traits on robotic gestures.

    Conclusions:

    • The developed system demonstrates a viable approach to enhancing human-robot interaction through synchronized nonverbal behaviors.
    • Person-specific models may offer advantages in generating more tailored and naturalistic movements.
    • Understanding the interplay between speech, movement, and speaker characteristics is crucial for advancing social robotics.