Head movements are crucial for social human-human interaction. They can transmit important cues (e.g., joint attention, speaker detection) that cannot be achieved with verbal interaction alone. This advantage also holds for human-robot interaction. Even though modeling human motions through generative AI models has become an active research area within robotics in recent years, the use of these methods for producing head movements in human-robot interaction remains underexplored. In this work, we employed a generative AI pipeline to produce human-like head movements for a Nao humanoid robot. In addition, we tested the system on a real-time active-speaker tracking task in a group conversation setting. Overall, the results show that the Nao robot successfully imitates human head movements in a natural manner while actively tracking the speakers during the conversation. Code and data from this study are available at https://github.com/dingdingding60/Humanoids2024HRI

翻译：头部运动在社交性人机交互中至关重要。它们能够传递仅凭言语交互无法实现的重要线索（例如共同注意、说话人检测）。这一优势同样适用于人机交互。尽管近年来通过生成式AI模型建模人体运动已成为机器人学中的一个活跃研究领域，但将这些方法用于生成人机交互中的头部运动仍未被充分探索。在本研究中，我们采用了一个生成式AI流程来为Nao人形机器人生成类人的头部运动。此外，我们在群组对话场景下的实时主动说话人跟踪任务中测试了该系统。总体而言，结果表明，Nao机器人在对话过程中能够以自然的方式成功模仿人类头部运动，同时主动跟踪说话者。本研究的代码和数据可在 https://github.com/dingdingding60/Humanoids2024HRI 获取。