Robot Group Adaptation Gestures Based on Utterance Content and Social Position
Daisuke Katagami*, Ken Ogawa**, and Katsumi Nitta**
*Department of Applied Computer Science, Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan
**Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
We suggest adaptive gestures for robots adapting to groups based on utterance situations and social position and study the differences in type and number of teaching gestures based on the difference in groups in equivalence utterance. Gesture data is collected in which group members are directly taught to a robot through utterance situations and social position to make adaptation rule sets. Such rule sets are built by gestures to work in the group and group utterances to become common knowledge in the group. The degree of gesture adaptation is higher in a group when gestures are generated from a new text as input, confirming goodness of fit for the rule sets of individual groups through experiments.
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