Multimodal Fuzzy Assessment for Robot Behavioral Adaptation in Educational Children-Robot Interaction
Multimodal Interfaces and Machine Learning for Multimodal Interaction(2020)
摘要
ABSTRACTSocial robots' contributions to education are notorious but, in times, limited by the difficulty in their programming by regular teachers. Our framework named R-CASTLE aims to overcome this problem by providing the teachers with an easy way to program their content and the robot's behavior through a graphical interface. However, the robot's behavior adaptation algorithm maybe still not the best intuitive method for teachers' understanding. Fuzzy systems have the advantage of being modeled in a more human-like way than other methods due to their implementation based on linguistic variables and terms. Thus, fuzzy modeling for robot behavior adaptation in educational children-robot interactions is proposed for this framework. The modeling resulted in an adaptation algorithm that considers a multimodal and autonomous assessment of the students' skills: attention, communication, and learning. Furthermore, preliminary experiments were performed considering videos with the robot in a school environment. The adaptation was set to change the content approach difficulty to produce a suitably challenging behavior according to each students' reactions. Results were compared to a Rule-Based adaptive method. The fuzzy modeling showed similar accuracy to the ruled-based method with a suggestion of a more intuitive interpretation of the process.
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关键词
fuzzy systems, adaptive systems, multimodal assessment, children-robot interaction
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