Imitation of human motion achieves natural head movements for humanoid robots in an active-speaker detection task
This work addresses the challenge of enhancing human-robot interaction through more natural non-verbal cues, though it is incremental as it applies existing generative AI methods to an underexplored but specific task.
The authors tackled the problem of generating natural head movements for humanoid robots in social interactions by using a generative AI pipeline, resulting in a Nao robot successfully imitating human head movements and actively tracking speakers in group conversations.
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