{"ID":2883305,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08999","arxiv_id":"2508.08999","title":"Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality","abstract":"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.","short_abstract":"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 syst...","url_abs":"https://arxiv.org/abs/2508.08999","url_pdf":"https://arxiv.org/pdf/2508.08999v2","authors":"[\"Chao Wang\",\"Michael Gienger\",\"Fan Zhang\"]","published":"2025-08-12T15:10:50Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.HC\"]","methods":"[]","has_code":false}