Abstract
The ability to express semantic co-speech gestures in an appropriate manner of the robot is needed for enhancing the interaction between humans and social robots. However, most of the learning-based methods in robot gesture generation are unsatisfactory in expressing the semantic gesture. Many generated gestures are ambiguous, making them difficult to deliver the semantic meanings accurately. In this paper, we proposed a robot gesture generation framework that can effectively improve the semantic gesture expression ability of social robots. In this framework, the semantic words in a sentence are selected and expressed by clear and understandable co-speech gestures with appropriate timing. In order to test the proposed method, we designed an experiment and conducted the user study. The result shows that the performances of the gesture generated by the proposed method are significantly improved compared to the baseline gesture in three evaluation factors: human-likeness, naturalness and easiness to understand.
Supported by ENSTA Paris, Institut Polytechnique de Paris, France and the CSC PhD Scholarship.
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Zhang, H., Yu, C., Tapus, A. (2022). Towards a Framework for Social Robot Co-speech Gesture Generation with Semantic Expression. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13817. Springer, Cham. https://doi.org/10.1007/978-3-031-24667-8_10
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