Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots

Autor:	Yoon, Youngwoo, Ko, Woo-Ri, Jang, Minsu, Lee, Jaeyeon, Kim, Jaehong, Lee, Geehyuk
Rok vydání:	2018
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	Co-speech gestures enhance interaction experiences between humans as well as between humans and robots. 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. Comment: 7 pages; video and dataset: https://sites.google.com/view/youngwoo-yoon/projects/co-speech-gesture-generation
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.12541 Zobrazit plný text záznamu View this record from Arxiv