Abstract
A key issue in social intelligence design is the realization of artifacts that can fluently communicate with people. Thus, we proposed a two-layered approach to enhance a robot’s capacity of involvement and engagement. The upper layer flexibly controls social interaction by dynamic Bayesian networks (DBN) representing social interaction patterns. The lower layer improves the robustness of the system by detecting rhythmic and repetitive gestures. We designed a listener robot that can follow and record humans’ explanation on how to assemble and/or disassemble a bicycle. The implementation of this system is described by assembling the key algorithms presented in this paper.
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