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.
Abstract (translated)
机器人表达性行为在与人类互动时传达其情感状态方面至关重要。在这项工作中，我们提出了一种框架，该框架能够自主生成基于混合现实（MR）中捕捉到的人类专家演示的逼真且多样化的机器人情绪表情。我们的系统使专家可以从第一人称视角远程操作一个虚拟机器人，并捕获他们的面部表情、头部动作和上半身手势，并将这些行为映射到机器人的相应组件，包括眼睛、耳朵、颈部和手臂。通过基于流匹配的生成过程，我们的模型能够实时学习并根据给定的情绪状态产生连贯且多样的行为以应对移动物体。初步测试验证了我们方法在生成自主表情方面的有效性。
URL
https://arxiv.org/abs/2508.08999
