Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917206977150976 |
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| author | Wang, Chao Gienger, Michael Zhang, Fan |
| author_facet | Wang, Chao Gienger, Michael Zhang, Fan |
| contents | Expressive behaviors in robots are critical for effectively conveying their emotional states during interactions with humans. In this work, we present a framework that autonomously generates realistic and diverse robotic emotional expressions based on expert human demonstrations captured in Mixed Reality (MR). Our system enables experts to teleoperate a virtual robot from a first-person perspective, capturing their facial expressions, head movements, and upper-body gestures, and mapping these behaviors onto corresponding robotic components including eyes, ears, neck, and arms. Leveraging a flow-matching-based generative process, our model learns to produce coherent and varied behaviors in real-time in response to moving objects, conditioned explicitly on given emotional states. A preliminary test validated the effectiveness of our approach for generating autonomous expressions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality Wang, Chao Gienger, Michael Zhang, Fan Robotics Human-Computer Interaction Expressive behaviors in robots are critical for effectively conveying their emotional states during interactions with humans. In this work, we present a framework that autonomously generates realistic and diverse robotic emotional expressions based on expert human demonstrations captured in Mixed Reality (MR). Our system enables experts to teleoperate a virtual robot from a first-person perspective, capturing their facial expressions, head movements, and upper-body gestures, and mapping these behaviors onto corresponding robotic components including eyes, ears, neck, and arms. Leveraging a flow-matching-based generative process, our model learns to produce coherent and varied behaviors in real-time in response to moving objects, conditioned explicitly on given emotional states. A preliminary test validated the effectiveness of our approach for generating autonomous expressions. |
| title | Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.08999 |