Alle Publikationen
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2016
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(2016): Interactive Human Intention Reading by Learning Hierarchical Behavior Knowledge Networks for Human-Robot Interaction. In: ETRI Journal 38 (6), S. 1229-1239. DOI: 10.4218/etrij.16.0116.0106
Abstract: 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.
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(2016): Behavior Hierarchy-Based Affordance Map for Recognition of Human Intention and Its Application to Human–Robot Interaction. In: IEEE Transactions on Human-Machine Systems 46 (5), S. 708-722. DOI: 10.1109/THMS.2016.2558539
Abstract: To prepare for the anticipated age of human-robot symbiosis, robots should be able to interact and cooperate with humans effectively by understanding the meaning and intention of human behavior. In this paper, we define human intention as "desired behavior of the human using objects." To infer the defined human intention, a robot should learn the object affordance along with a behavior hierarchy structure. Thus, in this paper, we propose a behavior hierarchy-based affordance network (BHAN) and a behavior hierarchy-based affordance map (BHAM) to represent the object affordance, behavior hierarchy structure, and object hierarchy structure, simultaneously. Autonomous and interactive BHAN/BHAM learning algorithms are also proposed to make a robot develop the BHAN and BHAM by itself, as well as by interacting with a human. Based on the newly developed BHANs and BHAM, a robot could infer the human intention from information observed in context and from human behavior. The effectiveness of the proposed method was demonstrated through experiments on human-robot interaction with building blocks using a simulated differential wheel robot and a real human-sized humanoid robot.
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