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.

翻译：机器人的表达行为对于在与人类交互过程中有效传达其情感状态至关重要。本研究提出一种框架，能够基于混合现实（MR）中捕捉的专家人类演示，自主生成真实且多样化的机器人情感表达。我们的系统允许专家以第一人称视角远程操控虚拟机器人，捕捉其面部表情、头部运动和上半身手势，并将这些行为映射到相应的机器人组件（包括眼睛、耳朵、颈部和手臂）。通过利用基于流匹配的生成过程，我们的模型学习在给定情感状态的显式条件下，对运动物体实时产生连贯且多样的行为。初步测试验证了本方法在生成自主表达方面的有效性。