Imitation of human motion achieves natural head movements for humanoid robots in an active-speaker detection task

TL;DR

This paper presents a generative AI approach for producing natural human-like head movements in a humanoid robot, enhancing active-speaker detection in group conversations.

Contribution

It introduces a novel AI pipeline for realistic head movement imitation in robots, applied to real-time active-speaker tracking during social interactions.

Findings

Robot successfully imitates human head movements naturally.

System effectively tracks speakers in real-time group conversations.

Code and data are publicly available for further research.

Abstract

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