Abstract
Gestures and speech modalities play potent roles in social learning, especially in educational settings. Enabling artificial learning companions (i.e., humanoid robots) to perform human-like gestures and speech will facilitate interactive social learning in classrooms. In this paper, we present the implementation of human-generated gestures and speech on the Pepper robot to build a robotic teacher. To this end, we transferred a human teacher gesture to a humanoid robot using a web and a kinect cameras and applied a video-based markerless motion capture technology and an observation-based motion mirroring method. To evaluate the retargeting methods, we presented different types of a humanoid robotic teacher to six teachers and collect their impressions on the practical usage of a robotic teacher in the classroom. Our results show that the presented AI-based open-source gesture retargeting technology was found attractive, as it gives the teachers an agency to design and employ the Pepper robot in their classes. Future work entails the evaluation of our solution to the stakeholders (i.e. teachers) for its usability.
This project was primarily funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135.
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Yun, H.S. et al. (2022). AI-Based Open-Source Gesture Retargeting to a Humanoid Teaching Robot. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_51
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