Interactive Human Intention Reading by Learning Hierarchical Behavior Knowledge Networks for Human-Robot Interaction

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For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.
Publisher
ELECTRONICS TELECOMMUNICATIONS RESEARCH INST
Issue Date
2016-12
Language
English
Article Type
Article
Citation

ETRI JOURNAL, v.38, no.6, pp.1229 - 1239

ISSN
1225-6463
DOI
10.4218/etrij.16.0116.0106
URI
http://hdl.handle.net/10203/220989
Appears in Collection
EE-Journal Papers(저널논문)
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