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학술대회 Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots
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저자
윤영우, 고우리, 장민수, 이재연, 김재홍, 이기혁
발행일
201905
출처
International Conference on Robotics and Automation (ICRA) 2019, pp.4303-4309
DOI
https://dx.doi.org/10.1109/ICRA.2019.8793720
협약과제
18HS4800, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
초록
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 제안 키워드
2 H, End to End(E2E), Gesture generation, Human labor, Human-like, Learning-based, Nao robot, Real-Time, Rule-based, TED talks, Text understanding