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

arXiv: https://arxiv.org/abs/2407.11915 · HTML: https://mlsanity.com/paper/2407.11915/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Neural probabilistic motor primitives for humanoid control](https://mlsanity.com/paper/1811.11711/) (2018)
- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection](https://mlsanity.com/paper/1901.01342/) (2019)
- [ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation](https://mlsanity.com/paper/2405.02292/) (2024)
- [A Light Weight Model for Active Speaker Detection](https://mlsanity.com/paper/2303.04439/) (2023)
- [Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors](https://mlsanity.com/paper/2203.17138/) (2022)
- [End-to-End Active Speaker Detection](https://mlsanity.com/paper/2203.14250/) (2022)
- [Cross-modal Supervision for Learning Active Speaker Detection in Video](https://mlsanity.com/paper/1603.08907/) (2016)
- [TalkNCE: Improving Active Speaker Detection with Talk-Aware Contrastive Learning](https://mlsanity.com/paper/2309.12306/) (2023)
- [A Real-Time Active Speaker Detection System Integrating an Audio-Visual Signal with a Spatial Querying Mechanism](https://mlsanity.com/paper/2309.08295/) (2023)

## Built on by (most cited first, 1 of 1)

- [TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning](https://mlsanity.com/paper/2605.07943/) (2026)

## Abstract (arXiv)

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