Human-aware Interaction Behaviors of an Intelligent Robot in a Dynamic Social Environment
Date Issued
2016
Date
2016
Author(s)
Tseng, Shih-Huan
Abstract
In a situation where a robot initiates interaction with a group of people, questions such as ”where is the people group?” and ”when the robot should approach them?” should be addressed. This thesis develops a new system that enables a robot to determine when it approaches the aforementioned human group and interact with them after identifying what the current social situation is. The system is mainly to fuse depth-related data to track the positions of a group of people, extract social cues of those people by using depth-related data, and a dynamic decision network (DDN) model to provide service in proper time. The main challenge lies in understanding of the social cues of the group and the current underlying social situation concerning the relation between the robot and the group. The social cues are consist of Proxemics and F-formations, whereas the social situations based on social cues are categorized as individual-to-individual, individual-to-robot, robot-to-individual, group-to-robot, robot-to-group, confidential discussion and group discussion. Our system proceeds as follows : once a group of people are detected and the social cues of that target group of people are extracted, the corresponding social situation is appropriately inferred, and in turn the robot decides whether it should initiate interaction with the group based on rules to be specified later. The conducted experimental results demonstrate the properness of the system design and the efficacy of the proposed method in recognizing the social cues among individuals of the group as well as the nature of the social situations concerning the group and the robot. To accomplish more natural and intelligent service provided by a robot, the robot should not only be able to perform the desirable service in accordance with the user intention and the circumstances, but also perceive user feedbacks and adjust service content and/or executions if needed to meet the user''s expectation. In this thesis, the learning scheme with a reasoning strategy is proposed to adapt the dynamic decision network. The robot can analyze a user''s natural speech feedback to adjust its decisions and the current social situation as what the user expects through the strategy. The experimental results show the effectiveness of the proposed learning scheme that enables autonomous adaptation of robot''s tasks to fill the user''s expectation.
Subjects
Human-robot interaction
initiate interaction
human-aware dynamic decision networks
data fusion
social situations
reinforcement learning.
Type
thesis
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