BROWSE
Detail
Conference Paper
Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots
Cited 190 time in 
Share 
Share
- Authors
- Youngwoo Yoon, Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, Jaehong Kim, Geehyuk Lee
- Issue Date
- 2019-05
- Citation
- International Conference on Robotics and Automation (ICRA) 2019, pp.4303-4309
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Co-speech gestures enhance interaction experiences between humans as well as between humans and robots. Most existing robots use rule-based speech-gesture association, but this requires human labor and prior knowledge of experts to be implemented. We present a learning-based co-speech gesture generation that is learned from 52 h of TED talks. The proposed end-to-end neural network model consists of an encoder for speech text understanding and a decoder to generate a sequence of gestures. The model successfully produces various gestures including iconic, metaphoric, deictic, and beat gestures. In a subjective evaluation, participants reported that the gestures were human-like and matched the speech content. We also demonstrate a co-speech gesture with a NAO robot working in real time.
- KSP Keywords
- 2 H, End to End(E2E), Gesture generation, Human labor, Human-like, Learning-based, Nao robot, Real-Time, Rule-based, TED talks, Text understanding