Deep Gesture Generation for Social Robots Using Type-Specific Libraries

概要

Body language such as conversational gesture is a powerful way to ease communication. Conversational gestures do not only make a speech more lively but also contain semantic meaning that helps to stress important information in the discussion. In the field of robotics, giving conversational agents (humanoid robots or virtual avatars) the ability to properly use gestures is critical, yet remain a task of extraordinary difficulty. This is because given only a text as input, there are many possibilities and ambiguities to generate an appropriate gesture. Different to previous works we propose a new method that explicitly takes into account the gesture types to reduce these ambiguities and generate human-like conversational gestures. Key to our proposed system is a new gesture database built on the TED dataset that allows us to map a word to one of three types of gestures: “Imagistic” gestures, which express the content of the speech, “Beat” gestures, which emphasize words, and “No gestures.” We propose a system that first maps the words in the input text to their corresponding gesture type, generate type-specific gestures and combine the generated gestures into one final smooth gesture. In our comparative experiments, the effectiveness of the proposed method was confirmed in user studies for both avatar and humanoid robot.

論文種別
発表文献
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
中島悠太
中島悠太
准教授

コンピュータビジョン・パターン認識などの研究。ディープニューラルネットワークなどを用いた画像・映像の認識・理解を主に、自然言語処理を援用した応用研究などに従事。