Adapting Robot Behaviors for Providing Services through Observing Human's Attention Responses from Human-Robot Interactions
Date Issued
2015
Date
2015
Author(s)
Chiang, Yi-Shiu
Abstract
Robots that service humans in private places, such as homes or senior centers, must consider humans'' preferences to behave in a socially acceptable manner. Human beings subconsciously adapt their actions to start a conversation according to the historical interaction experiences, but robots often fail to do this and result in disrupting their users. To endow service robots with such socially acceptable ability, this thesis proposes an online human-aware interactive learning framework, under which the robot behaves so as to optimize its service providing behavior while inferring user''s awareness of robot itself. To this purpose, a human-aware Markov decision process (HAMDP) is proposed to model this kind of problem, which requires planning of robot actions and inference on user''s social attention concurrently. For social attention inference model, it is based on a Dynamic Bayesian Network (DBN), which is also employed to infer the possibility of user''s awareness of the robot, extit{i.e.} the robot''s theory of awareness. The correlation between the robot''s theory of awareness, the user''s social attention, and the robot behavior are explored through reinforcement learning. Besides, to let the robot behave more naturally, the mood of the user is estimated by recognizing his/her body gesture based gross affective state. In order to verify the effectiveness of our proposed framework, experiments with real social scenarios have been conducted.
Subjects
Robot Behavior Adaptation
Interactively Learning
Dynamic Bayesian Network
Human-robot Interaction
Robots in Daily Life
Type
thesis
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