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A Generative Model to Embed Human Expressivity into Robot Motions.

Pablo Osorio1,2, Ryusuke Sagawa1,2, Naoko Abe3

  • 1Department of Mechanical Systems Engineering, Faculty of Engineering, Tokyo University of Agriculture and Technology, Koganei Campus, Tokyo 184-8588, Japan.

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This study introduces a novel method for robots to mimic human expressiveness in motion. The model effectively transfers human movement qualities to robot actions, creating diverse and expressive robotic behaviors.

Keywords:
human factorshuman-centered roboticshuman-in-the-loophuman–robot interaction

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Human expressive movements contain rich information crucial for natural human-robot interaction.
  • Current methods often struggle to capture and transfer these nuanced expressive qualities to robot motion.

Purpose of the Study:

  • To develop a data-driven model capable of generating expressive robot motions by learning from human movements.
  • To effectively transfer the underlying expressive features from human motion to robot motion.

Main Methods:

  • A data-driven approach combining variational autoencoders (VAEs) and a generative adversarial network (GAN) framework.
  • Input includes robot task parameters (linear and angular velocities) and human expressive data (body part acceleration and angular velocity).

Main Results:

  • The model successfully recognized and transferred expressive cues from human motion to robot motion.
  • Generated robot movements incorporated expressive qualities derived from human input.
  • The model produced diverse outputs, with motion variability corresponding to different human inputs.

Conclusions:

  • The proposed model effectively bridges the gap between human expressiveness and robot motion generation.
  • This approach enables robots to exhibit more natural and engaging expressive behaviors.
  • The findings open avenues for more intuitive and human-like robot interactions.